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Search Results (1,170)

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25 pages, 50028 KiB  
Article
Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark
by Antonio Matellon, Eleonora Maset, Alberto Beinat and Domenico Visintini
Remote Sens. 2024, 16(18), 3439; https://doi.org/10.3390/rs16183439 - 16 Sep 2024
Abstract
The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. [...] Read more.
The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems. Full article
18 pages, 2627 KiB  
Article
Numerical Simulation Study on Rotary Air Preheater Considering the Influences of Steam Soot Blowing
by Youfu Chen, Yaou Wang, Bo Chen, Hongda Zhu and Lingling Zhao
Energies 2024, 17(18), 4618; https://doi.org/10.3390/en17184618 - 14 Sep 2024
Viewed by 216
Abstract
The ash deposition is a general problem that needs to be solved effectively for the rotary air preheater of the coal-fired boiler. Taking the rotary air preheater of a 600 MW power station as the object, the mesh model of the flue gas [...] Read more.
The ash deposition is a general problem that needs to be solved effectively for the rotary air preheater of the coal-fired boiler. Taking the rotary air preheater of a 600 MW power station as the object, the mesh model of the flue gas side of the air preheater, considering the influences of steam soot blowing, is established using the Gambit 2.4.6 software. Based on the SIMPLE algorithm, the velocity field and the temperature field in the air preheater under varied working conditions are simulated using the software of Ansys Fluent 2021R1, and the influences of the boiler load, the operation parameters of the steam soot blower, and the running and outage of the soot blower on the flue gas velocity distribution in the depth direction of the corrugated plates, the soot-blowing coverage area, the inlet flue gas velocity, and the inlet flue gas temperature of the corrugated plates are analyzed. Under the base working condition, the flue gas velocity on the axis of the steam nozzle first decreases rapidly with increasing the corrugated plate depth (Z < 1.0 m), and then it decreases slowly with an almost equal slope. The longitudinal flue gas velocity has a positive correlation with the boiler load. The longitudinal flue gas velocity obviously decreases when the boiler load is decreased, and its reduction increases as the corrugated plate depth increases. It is one reason that the ash deposition is prone to occur on the cold end surface of corrugated plates under the condition of low boiler load. The longitudinal flue gas velocity increases with the soot-blowing steam velocity increasing when the corrugated plate depth is less than 1.5 m, but after that, it is almost not affected by the change in soot-blowing steam velocity. The soot-blowing coverage area has a negative correlation with the boiler load but a slight positive correlation with the steam velocity of the soot blower on the whole. The inlet flue gas velocity of the corrugated plates has a positive correlation with the boiler load and the inlet steam velocity of the soot blower. The average inlet flue gas velocity decreases by 21.7% when the boiler load is reduced by 50%. For every 5 m/s variation in the inlet steam velocity, the inlet flue gas velocity changes by about 10–14% whether the steam soot blower is put into operation or not, which has an obvious effect on the inlet gas velocity of the corrugated plates. The inlet flue gas temperature of the corrugated plates is, respectively, positively correlated with the boiler load and the inlet steam temperature of the soot blower. When the boiler load is reduced from 100% BMCR to 50% BMCR, the average inlet flue gas temperature of the corrugated plates is reduced by 44.2 K; however, when the soot-blowing steam temperature varies by 20 K, the average inlet flue gas temperature of the corrugated plates varies by only about 1.8 K. It means that it is difficult to enhance the cold end flue gas temperature of the corrugated plates only by raising the soot-blowing steam temperature at low boiler load. Adding a soot blower using high-temperature steam or hot air at the outlet of the corrugated plates may be an option to solve the ash deposition of the corrugated plates. Full article
(This article belongs to the Section J: Thermal Management)
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<p>Schematic diagram of a geometric model of the air preheater flue gas side.</p>
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<p>Schematic diagram of the nozzle layout of steam soot blower.</p>
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<p>Grid division of the air preheater flue gas side model.</p>
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<p>The fluid temperature and pressure vary with the number of grids at the outlet of the air preheater.</p>
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<p>The flue gas velocity distributions of various planes in the depth direction of the corrugated plates under the base working condition.</p>
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<p>The flue gas velocity variations in the axis direction of Nozzle No.4 under the base working condition.</p>
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<p>The flue gas velocity variations on the axis of Nozzle No.4 along the corrugated plate depth with varying the boiler load at the constant steam velocity.</p>
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<p>The flue gas velocity variations on the axis of Nozzle No.4 along the corrugated plate depth with changing the soot-blowing steam velocity at high load (600 MW).</p>
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<p>The flue gas velocity variations on the axis of Nozzle No.4 along the corrugated plate depth with changing the soot-blowing steam velocity at low load (300 MW).</p>
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15 pages, 1822 KiB  
Review
Association between Estrogen Levels and Temporomandibular Disorders: An Updated Systematic Review
by Grzegorz Zieliński and Beata Pająk-Zielińska
Int. J. Mol. Sci. 2024, 25(18), 9867; https://doi.org/10.3390/ijms25189867 - 12 Sep 2024
Viewed by 300
Abstract
The aim of this systematic review is to evaluate the impact of estrogen levels on the occurrence of temporomandibular disorders (TMDs) in humans. Searches were conducted in the same databases as follows: PubMed, the Cochrane Collaboration database, and the Scopus database. In accordance [...] Read more.
The aim of this systematic review is to evaluate the impact of estrogen levels on the occurrence of temporomandibular disorders (TMDs) in humans. Searches were conducted in the same databases as follows: PubMed, the Cochrane Collaboration database, and the Scopus database. In accordance with the MeSH database and previous work, the following keywords were used: ‘estrogens’ and ‘temporomandibular joint disorders’. Twelve studies were included in the review and were assessed for the quality of evidence. Estrogen levels are associated with pain modulation in the temporomandibular joint and the entire orofacial region. There is insufficient evidence to either confirm or refute the influence of estrogen on the occurrence of TMDs. The study was registered under the identifier: 10.17605/OSF.IO/BC7QF. Full article
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Graphical abstract

Graphical abstract
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<p>Estrone (E1)—chemical structure. Author: Edgar181, Public domain, via Wikimedia Commons.</p>
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<p>Estradiol (E2)—chemical structure. Author: NEUROtiker, Public domain, via Wikimedia Commons.</p>
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<p>Estriol (E3)—chemical structure. Author: No machine-readable author provided. Ayacop assumed (based on copyright claims), Public domain, via Wikimedia Commons.</p>
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<p>PRISMA flow diagram.</p>
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<p>Graphical representation of studies linking estrogen levels to pain levels in the orofacial region. The figure was created based on the findings in <a href="#ijms-25-09867-t004" class="html-table">Table 4</a>.</p>
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<p>Graphical representation of studies linking estrogen levels to the occurrence of TMJ disorders. The figure was created based on the findings in <a href="#ijms-25-09867-t004" class="html-table">Table 4</a>.</p>
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10 pages, 554 KiB  
Review
The Mirror Theory: Parallels between Open Angle and Angle Closure Glaucoma
by Vasile Potop, Christiana Diana Maria Dragosloveanu, Alina Mihaela Ciocâlteu, Miruna Gabriela Burcel, Maria Cristina Marinescu and Dana Margareta Cornelia Dăscălescu
Life 2024, 14(9), 1154; https://doi.org/10.3390/life14091154 - 12 Sep 2024
Viewed by 271
Abstract
Glaucoma is a widespread ophthalmological disease, with a high impact and frequent visual morbidity. While the physiopathology of the two types of primary glaucoma (open angle and angle closure) has been studied, there seems to be little relationship between the two. In this [...] Read more.
Glaucoma is a widespread ophthalmological disease, with a high impact and frequent visual morbidity. While the physiopathology of the two types of primary glaucoma (open angle and angle closure) has been studied, there seems to be little relationship between the two. In this study, we gather clinical and preclinical data to support the idea that the two primary glaucomas are “mirrored” in terms of morphological parameters and disease physiopathology. In short, primary angle closure glaucoma (PACG) is associated with hyperopia and low axial length, and primary open angle glaucoma (POAG) is associated with myopia and high axial length. Moreover, in PACG and in primary angle closure or primary angle closure suspect cases, while there is extensive iridotrabecular contact, the intraocular pressure (IOP) is still maintained in the lower half of the normal range throughout the evolution of the disease, which suggests a baseline trabecular hyperfiltration in PACG. In the opposite case, myopic eyes with open angles and a higher risk of developing POAG often have a baseline IOP in the upper half of the normal range, suggesting a baseline trabecular hypofiltration. As we explore clinical, genetic and animal model data regarding these opposing aspects, we hypothesize the existence of a mirroring relationship between PACG and POAG. Defining the relationship between the two potentially blinding diseases, with a high prevalence worldwide, may aid in understanding the mechanisms better and refining diagnosis and treatment. Thus, our theory has been named the Mirror Theory of Primary Glaucomas. Full article
(This article belongs to the Collection New Diagnostic and Therapeutic Developments in Eye Diseases)
9 pages, 2430 KiB  
Article
T-Cells Rich Classical Hodgkin Lymphoma, a Pathology Diagnostic Pitfall for Nodular Lymphocyte-Predominant Hodgkin Lymphoma; Case Series and Review
by Haneen Al-Maghrabi, Ghadeer Mokhtar and Ahmed Noorsaeed
Lymphatics 2024, 2(3), 168-176; https://doi.org/10.3390/lymphatics2030014 - 12 Sep 2024
Viewed by 300
Abstract
Background: Some cases of classic Hodgkin lymphoma (CHL) display similarities to nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) in terms of architecture, leading to potential challenges in diagnosis. However, these difficulties can be overcome by conducting a thorough set of immunohistochemical examinations. Objective: To [...] Read more.
Background: Some cases of classic Hodgkin lymphoma (CHL) display similarities to nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) in terms of architecture, leading to potential challenges in diagnosis. However, these difficulties can be overcome by conducting a thorough set of immunohistochemical examinations. Objective: To examine cases of T-cell-rich CHL that closely resemble the diagnosis of NLPHL, specifically pattern D, which can pose challenges in accurately determining the diagnosis even after conducting a thorough immunophenotypic assessment. Materials and methods: Histopathology slides of three cases of T-cell-rich CHL were retrieved and thoroughly examined to assess their clinical, immunomorphologic, and molecular features. Results: We present three cases containing cells that resembled lymphocyte predominant and Hodgkin Reed–Sternberg cells, expressing some B-cell antigens and CHL markers but all were lacking Epstein–Barr virus-encoded small RNA. All three cases were found in a background rich in T-cells with focal remaining follicular dendritic cell meshwork in one case. Only one case had few eosinophils while the other two had no background of eosinophils and plasma cells. Two patients presented with stage IIA and B-symptoms presented in one of them. Two patients were treated with four and six cycles of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine), respectively. One patient planned to be treated with four cycles of ABVD plus Rituximab therapy. Conclusions: Some cases of Reed–Sternberg cells can show expression of both B-cell and CHL markers. This overlapping characteristic, which has not been extensively discussed in the existing literature, presents a unique challenge for treatment. Further research into these neoplasms may reveal valuable diagnostic and therapeutic implications. Full article
(This article belongs to the Collection Lymphomas)
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Figure 1
<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number one. (<b>A</b>): Lymph nodes show total effacement of nodal architecture by vague nodules, and no remaining reactive lymphoid follicles detected (H&amp;E; 4×). (<b>B</b>): Reed–Sternberg (RS)-like cells present, no background of eosinophils nor plasma cells (H&amp;E; 40×). T-cells forming a ring around the neoplastic cells are observed (inset). (<b>C</b>): CD3 immunohistochemistry stain of T-cells in the background forming a rosette around the neoplastic cells (40×). (<b>D</b>): Target cells showing weak PAX5 nuclear expression compared to the background small non-neoplastic B-cells (40×).</p>
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<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number two. (<b>A</b>): Lymph nodes show partial nodal effacement of architecture by large vague nodules (H&amp;E; 4×). (<b>B</b>): Focal remaining reactive lymphoid follicles are detected at the periphery of the lymph node (H&amp;E; 40×). (<b>C</b>): CD4 immunohistochemistry stain of T-cells in the background forming a rosette around the target cells (40×). (<b>D</b>): Target cells showing CD30 membranous and Golgi positive expression (40×).</p>
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<p>Histopathology examination by hematoxylin and eosin stain (H&amp;E) and immunohistochemistry studies of case number three. (<b>A</b>): Disturbed normal lymphoid tissue by neoplastic Reed–Sternberg (RS)-like cells (very scattered), the background of eosinophils and rare plasma cells are seen (H&amp;E; 40×). (<b>B</b>): Target cells showing CD30 membranous and Golgi positive expression (10×). (<b>C</b>): Some of the target cells show dim PAX5 nuclear expression compared to the background small non-neoplastic B-cells (arrow) (40×). (<b>D</b>): Target cells showing strong CD20 membranous positive expression in a background rich in T-cells (40×).</p>
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27 pages, 639 KiB  
Systematic Review
Comparing the Effectiveness of Open and Minimally Invasive Approaches in Coronary Artery Bypass Grafting: A Systematic Review
by Arwa Alsharif, Abdulaziz Alsharif, Ghadah Alshamrani, Abdulhameed Abu Alsoud, Rowaida Abdullah, Sarah Aljohani, Hawazen Alahmadi, Samratul Fuadah, Atheer Mohammed and Fatma E. Hassan
Clin. Pract. 2024, 14(5), 1842-1868; https://doi.org/10.3390/clinpract14050147 (registering DOI) - 10 Sep 2024
Viewed by 360
Abstract
Coronary artery bypass grafting (CABG) is an essential operation for patients who have severe coronary artery disease (CAD). Both open and minimally invasive CABG methods are used to treat CAD. This in-depth review looks at the latest research on the effectiveness of open [...] Read more.
Coronary artery bypass grafting (CABG) is an essential operation for patients who have severe coronary artery disease (CAD). Both open and minimally invasive CABG methods are used to treat CAD. This in-depth review looks at the latest research on the effectiveness of open versus minimally invasive CABG. The goal is to develop evidence-based guidelines that will improve surgical outcomes. This systematic review used databases such as PubMed, MEDLINE, and Web of Science for a full electronic search. We adhered to the PRISMA guidelines and registered the results in the PROSPERO. The search method used MeSH phrases and many different study types to find papers. After removing duplicate publications and conducting a screening process, we collaboratively evaluated the full texts to determine their inclusion. We then extracted data, including diagnosis, the total number of patients in the study, clinical recommendations from the studies, surgical complications, angina recurrence, hospital stay duration, and mortality rates. Many studies that investigate open and minimally invasive CABG methods have shown that the type of surgery can have a large effect on how well the patient recovers and how well the surgery works overall. While there are limited data on the possible advantages of minimally invasive CABG, a conclusive comparison with open CABG is still dubious. Additional clinical trials are required to examine a wider spectrum of patient results. Full article
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<p>Detailed PRISMA chart used for this systematic review, outlining the many stages of this study’s selection process.</p>
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26 pages, 13334 KiB  
Article
Generating 3D Models for UAV-Based Detection of Riparian PET Plastic Bottle Waste: Integrating Local Social Media and InstantMesh
by Shijun Pan, Keisuke Yoshida, Daichi Shimoe, Takashi Kojima and Satoshi Nishiyama
Drones 2024, 8(9), 471; https://doi.org/10.3390/drones8090471 - 9 Sep 2024
Viewed by 467
Abstract
In recent years, waste pollution has become a severe threat to riparian environments worldwide. Along with the advancement of deep learning (DL) algorithms (i.e., object detection models), related techniques have become useful for practical applications. This work attempts to develop a data generation [...] Read more.
In recent years, waste pollution has become a severe threat to riparian environments worldwide. Along with the advancement of deep learning (DL) algorithms (i.e., object detection models), related techniques have become useful for practical applications. This work attempts to develop a data generation approach to generate datasets for small target recognition, especially for recognition in remote sensing images. A relevant point is that similarity between data used for model training and data used for testing is crucially important for object detection model performance. Therefore, obtaining training data with high similarity to the monitored objects is a key objective of this study. Currently, Artificial Intelligence Generated Content (AIGC), such as single target objects generated by Luma AI, is a promising data source for DL-based object detection models. However, most of the training data supporting the generated results are not from Japan. Consequently, the generated data are less similar to monitored objects in Japan, having, for example, different label colors, shapes, and designs. For this study, the authors developed a data generation approach by combining social media (Clean-Up Okayama) and single-image-based 3D model generation algorithms (e.g., InstantMesh) to provide a reliable reference for future generations of localized data. The trained YOLOv8 model in this research, obtained from the S2PS (Similar to Practical Situation) AIGC dataset, produced encouraging results (high F1 scores, approximately 0.9) in scenario-controlled UAV-based riparian PET bottle waste identification tasks. The results of this study show the potential of AIGC to supplement or replace real-world data collection and reduce the on-site work load. Full article
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Figure 1
<p>Process of plastics’ entry into the food chain. The lifecycle of plastic waste in aquatic ecosystems includes the following steps: initial river entry; collapse of the oceanic macroplastic to microplastic pollution from the force of the wind/sunshine; bio-magnification through the marine food chain into the human body.</p>
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<p>On-site waste pollution detected in the Hyakken River, Japan.</p>
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<p>Bottle-related models were generated by Luma AI GENIE (website page in the <b>upper panel</b>). Derived from the several results, four bottle-related models (the <b>lower panel</b>) were selected, using different prompts.</p>
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<p>Samples of PET bottle waste derived from txt2img AIGC, on-site, and Luma AI GENIE generations.</p>
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<p>Clean-Up Okayama website (English content in this figure was derived from image-based Google Translate) that includes four main parts: 1. Total number of participants and the amount of waste picked up in Okayama prefecture; 2. Number of waste items from the whole period distributed in the Okayama prefecture, with mapping derived from Google Maps, Alphabet Inc., Mountain View, CA, USA; 3. Comments and field images collected and uploaded by the website users, with obscured user names and profile logos; 4. Chart of waste collection activities in Okayama prefecture, including number of people and waste by date.</p>
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<p>Pick-up sample images from section 3 in <a href="#drones-08-00471-f005" class="html-fig">Figure 5</a>.</p>
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<p>Process of collecting local bottle waste-based objects: 1. Collecting on-site waste; 2. Taking the image; 3. Uploading the image to the website; 4. Generating the 3D model.</p>
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<p>The upper map displays the locations (icons) of the waste-related image capture and upload. The lower satellite shows the Hyakken River area (both are derived from Google Maps; Alphabet Inc.).</p>
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<p>The upper map displays the locations (icons) of the waste-related image capture and upload. The lower satellite shows the Hyakken River area (both are derived from Google Maps; Alphabet Inc.).</p>
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<p>InstantMesh model architecture.</p>
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<p>Process of generating S2PS AIGC using InstantMesh: 1. Inputting the image; 2. Generating the multiple views derived from a single input image; 3. Outputting the GLB/OBJ-formatted S2PS AIGC.</p>
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<p>Samples of the S2PS AIGC.</p>
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<p>Process of generating the automatic rotating bottle videos using the autoRotate function in glTF Viewer.</p>
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<p>Frame images derived from the automatic rotating bottle video.</p>
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<p>Process of generating specific datasets (one resource image): 1. Selecting one specific frame image; 2. Transparency of the black background; 3. Selecting one drone image derived using a 75° camera angle and 2 cm GSD (ground sample distance) resource images; 4. Adjusting the bottle size from Step 2 to match the bottle size of the drone image in Step 3; 5. Generating the new image with the bottle shown in Step 4 and the drone image in Step 3; 6. Preparing data augmentation for the specific dataset, mainly including image direction change and blur.</p>
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<p>Process of generating S2PS AIGC Dataset (multiple resource images): 1. Pre-Processing—selecting the background and object images; 2. Setting the parameters—mainly adjusting the image size between the background and the object; 3. Generating multiple resource images.</p>
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<p>Results of training the model, which include train/valid-based box_loss/cls_loss/dfl_loss, precision/recall, and mAP50/50–95 (one source image, epoch 1000, batch-size 16, patience 50).</p>
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<p>Samples of the batch images used for training: train_batch 0, train_batch 1, and train_batch 2 (one source image, epoch 1000, batch-size 16, patience 50).</p>
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<p>Process of selecting the test image: <b>Left panel</b>, using 50 drone images to reconstruct the 3D model; <b>Right panel</b>, zooming in on the screen closer to the object and outputting the image. The described process was performed with open source photogrammetry software called 3DF Zephyr free version, which can create 3D models from photographs. This process is to select the object which has a similar size using the zoom-in function of the 3D model.</p>
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<p>Relation between inference image size and corresponding confidence value (test 1).</p>
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<p>Results of training the model (one source image, epoch 10,000, batch-size 16, patience 1000).</p>
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<p>Validation results (test 2): <b>Left panel</b>, inference with confidence value; <b>Right panel</b>, true label.</p>
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<p>F1 score derived from the validation results (one source image).</p>
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<p>Object sizes (<b>left panel</b>, generated resource image; <b>middle panel</b>, a 3D-derived test with the same background; <b>right panel</b>, a similar object test with a similar background).</p>
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<p>Results of training the model, which include train/valid-based box_loss/cls_loss/dfl_loss, precision/recall and mAP50/50–95 (one source image, epoch 1000, batch-size 256, patience 1000).</p>
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<p>Samples of batch images used for training: train_batch 0, train_batch 1, train_batch 2, train_batch 19800, train_batch 19801, and train_batch 19802.</p>
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<p>Validation results: <b>Left panel</b>, inference with confidence value; <b>Right panel</b>, true label.</p>
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<p>F1 score derived from the validation results (multiple source images).</p>
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<p>Samples of the 3D waste group generations.</p>
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<p>Samples of failed 3D model generations.</p>
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12 pages, 1853 KiB  
Article
Visualization of the Postoperative Position of the Hydrus® Microstent Using Automatic 360° Gonioscopy
by Julian Alexander Zimmermann, Sarah Kleemann, Jens Julian Storp, Cedric Weich, Ralph-Laurent Merté, Nicole Eter and Viktoria Constanze Brücher
J. Clin. Med. 2024, 13(17), 5333; https://doi.org/10.3390/jcm13175333 - 9 Sep 2024
Viewed by 347
Abstract
Introduction: Glaucoma, one leading cause of irreversible vision loss worldwide, is primarily caused by elevated intraocular pressure (IOP). Recently, minimally invasive glaucoma surgeries (MIGSs) have become popular due to their shorter surgical times, tissue-sparing nature, and faster recovery. One such MIGS, the [...] Read more.
Introduction: Glaucoma, one leading cause of irreversible vision loss worldwide, is primarily caused by elevated intraocular pressure (IOP). Recently, minimally invasive glaucoma surgeries (MIGSs) have become popular due to their shorter surgical times, tissue-sparing nature, and faster recovery. One such MIGS, the Hydrus® nickel–titanium alloy Microstent, helps lower IOP by improving aqueous humor outflow. The NIDEK GS-1 automated 360° gonioscope provides advanced imaging of the chamber angle for evaluation and documentation. The aim of this study was to test automated 360° gonioscopy for the detection of postoperative positional variations after Hydrus® Microstent implantation. This study is the largest to date to evaluate post-op positioning of the Hydrus® Microstent using the NIDEK GS-1. Materials and Methods: This study analyzed postoperative outcomes and stent location in eyes diagnosed with mild to moderate glaucoma that underwent Hydrus® Microstent implantation with or without phacoemulsification. Patients with prior IOP-lowering surgery or vitrectomy were excluded. Analyses of the postoperative Hydrus® Microstent position were based on the evaluation of automated 360° gonioscopy images. Results: Twenty-three eyes were included in the study, and all showed a reduction in IOP and a decrease in antiglaucomatous drop use postoperatively. Postoperative gonoscopic images showed variations in implant position. In all cases, the proximal inlet was clearly visible in the anterior chamber. The degree of protrusion into the anterior chamber was variable. The distal tip of the stent was visible behind the trabecular meshwork in Schlemm’s canal in five cases, in the anterior chamber in one case, and not visible in seven cases. In no case did postoperative alterations in the position of the implant lead to explantation. Conclusions: This study demonstrated that the Hydrus® Microstent can effectively lower IOP even in the presence of postoperative positional variations. Automated 360° gonioscopy was found to be a useful tool to verify and document the postoperative position of the implant. Positional changes did not require device explantation in any of the cases evaluated. Full article
(This article belongs to the Special Issue Clinical Debates in Minimally Invasive Glaucoma Surgery (MIGS))
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<p>Study design.</p>
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<p>Hydrus<sup>®</sup> Microstent visualized with NIDEK GS-1 Gonioscope.</p>
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<p>Visualization of the postoperative position of the proximal and distal tips of the Hydrus<sup>®</sup> Microstent using automated 360° gonioscopy. Only the proximal tip is visible in the anterior chamber (<b>1a</b>). Half of the first window of the stent (viewed from the proximal end with the inlet in the anterior chamber) protrudes into the anterior chamber (<b>1b</b>). The entire first window is in the anterior chamber (<b>1c</b>). At least half of the bridge, called the spine, between the first and second windows is in the anterior chamber (<b>1d</b>). The distal rounded tip is clearly visible behind the trabecular meshwork within Schlemm’s canal (<b>2a</b>). The distal tip protrudes into the anterior chamber (<b>2b</b>). The distal stent is not visible through the trabecular meshwork, suggesting a posterior location (<b>2c</b>).</p>
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<p>Visualization of the postoperative position of the proximal and distal tips of the Hydrus<sup>®</sup> Microstent using automated 360° gonioscopy. Only the proximal tip is visible in the anterior chamber (<b>1a</b>). Half of the first window of the stent (viewed from the proximal end with the inlet in the anterior chamber) protrudes into the anterior chamber (<b>1b</b>). The entire first window is in the anterior chamber (<b>1c</b>). At least half of the bridge, called the spine, between the first and second windows is in the anterior chamber (<b>1d</b>). The distal rounded tip is clearly visible behind the trabecular meshwork within Schlemm’s canal (<b>2a</b>). The distal tip protrudes into the anterior chamber (<b>2b</b>). The distal stent is not visible through the trabecular meshwork, suggesting a posterior location (<b>2c</b>).</p>
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<p>Visualization of two cases (<b>a</b>,<b>b</b>) of peripheral synechiae between the iris and the proximal tip of the Hydrus<sup>®</sup> Microstent.</p>
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14 pages, 1280 KiB  
Article
Dexamethasone Impairs ATP Production and Mitochondrial Performance in Human Trabecular Meshwork Cells
by Shane Kennedy, Clayton Williams, Emily Tsaturian and Joshua T. Morgan
Curr. Issues Mol. Biol. 2024, 46(9), 9867-9880; https://doi.org/10.3390/cimb46090587 - 5 Sep 2024
Viewed by 352
Abstract
Mitochondrial damage occurs in human trabecular meshwork (HTM) cells as a result of normal aging and in open angle glaucoma. Using an HTM cell model, we quantified mitochondrial function and ATP generation rates after dexamethasone (Dex) and TGF-β2 treatments, frequently used as in [...] Read more.
Mitochondrial damage occurs in human trabecular meshwork (HTM) cells as a result of normal aging and in open angle glaucoma. Using an HTM cell model, we quantified mitochondrial function and ATP generation rates after dexamethasone (Dex) and TGF-β2 treatments, frequently used as in vitro models of glaucoma. Primary HTM cells were assayed for metabolic function using a Seahorse XFp Analyzer. We additionally assessed the mitochondrial copy number and the expression of transcripts associated with mitochondrial biogenesis and oxidative stress regulation. Cells treated with Dex, but not TGF-β2, exhibited a significant decrease in total ATP production and ATP from oxidative phosphorylation relative to that of the control. Dex treatment also resulted in significant decreases in maximal respiration, ATP-linked O2 consumption, and non-mitochondrial O2 consumption. We did not observe significant changes in the level of mitochondrial genomes or mRNA transcripts of genes involved in mitochondrial biogenesis and oxidative stress regulation. Decreased mitochondrial performance and ATP production are consistent with the results of prior studies identifying the effects of Dex on multiple cell types, including HTM cells. Our results are also consistent with in vivo evidence of mitochondrial damage in open-angle glaucoma. Overall, these results demonstrate a decrease in mitochondrial performance in Dex-induced glaucomatous models in vitro, meriting further investigation. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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<p>Schematic of Seahorse assays used in this study. In both plots, each square marks a measurement spaced by 5 min. The numbers are representative, not experimental. (<b>A</b>) In the Mito Stress Test, the O<sub>2</sub> consumption rate (OCR) is measured via the pharmacological disruption of the mitochondria to measure specific aspects of mitochondria performance, including maximal respiration induced by the protonophore FCCP. (<b>B</b>) In the ATP Rate Assay, OCR and extracellular acidification rate (ECAR) are both measured, allowing for accurate assessment of both oxidative and glycolytic ATP generation. Additionally, FCCP is not used, as this can disrupt the calculation of the glycolysis, as described in the text.</p>
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<p>Contributions of oxidative phosphorylation and glycolysis to ATP production. Primary HTM cells were assayed using the Seahorse ATP Rate Assay. (<b>A</b>) HTM cells treated with 100 nM Dex for 3 days (magenta) exhibit reduced ATP production compared to that of the control (cyan). Both OxPhos and glycolysis are reduced. ● represent individual experiments; ■ represent means; gray lines represent standard deviation error bars on both axes. (<b>B</b>) When quantified relative to control ATP generation, 100 nM Dex treatment for 3 days results in significant decreases in total and OxPhos ATP generation. Control data (normalized to 100%) is included for visual reference. ● represent individual experiments; mean and standard deviation error bars are represented by gray lines. (<b>C</b>) HTM cells treated with 1 ng/mL TGF-β2 for 3 days (magenta) exhibit similar ATP production rates compared to those of the control (cyan). ● represent individual experiments; ■ represent means; gray lines represent standard deviation. (<b>D</b>) When quantified relative to control ATP generation, 1 ng/mL TGF-β2 treatment for 3 days does not result in significant differences in ATP generation. Control data (normalized to 100%) are included for visual reference. ● represent individual experiments; mean and standard deviation error bars are represented by gray lines. * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>mtDNA levels per cell in response to Dex treatment. HTM cells from four donors were treated with 100 nM Dex for 3 days, and the amount of mtDNA was determined. Control data (normalized to 1) is included for visual reference. ● represent individual experiments; mean and standard deviation error bars are represented by gray lines. There was no significant change in mtDNA levels with Dex treatment.</p>
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<p>Reactive oxygen species and mitochondrial associated mRNA transcript response to Dexamethasone. Primary HTM cells were treated with 100 nM Dex for 3 days and assayed for (<b>A</b>) PGC1A (<span class="html-italic">n</span> = 4), (<b>B</b>) TFAM (<span class="html-italic">n</span> = 5), (<b>C</b>) CATA (<span class="html-italic">n</span> = 5), and (<b>D</b>) SOD2 (<span class="html-italic">n</span> = 5) mRNA levels. Control data (normalized to 1) are included for visual reference. ● represent individual experiments; mean and standard deviation error bars are represented by gray lines. There were no significant changes with Dex treatment.</p>
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13 pages, 4047 KiB  
Article
The Wear Behaviour of a New Eccentric Meshing Reducer with Small Teeth Difference
by Renqiang Yang, Zhengjun Guan, Dongdong Yang, Shuaidong Zou, Haifeng He and Guangjian Wang
Machines 2024, 12(9), 605; https://doi.org/10.3390/machines12090605 - 1 Sep 2024
Viewed by 350
Abstract
Eccentric meshing reducers are widely used in agriculture, industrial robots, and other fields due to their ability to achieve a high reduction ratio within a compact volume. However, the contact wear problem seriously affects the service performance of the eccentric meshing reducer, thereby [...] Read more.
Eccentric meshing reducers are widely used in agriculture, industrial robots, and other fields due to their ability to achieve a high reduction ratio within a compact volume. However, the contact wear problem seriously affects the service performance of the eccentric meshing reducer, thereby limiting their range of applications. To effectively address this issue, this study involved a stress analysis of the contact pairs and a surface wear analysis of a new eccentric meshing reducer. The wear equation for the contact pairs was derived using Archard’s wear theory, incorporating geometric and material parameters from both the reducer gear contact pair and the spline contact pair. In parallel, a wear simulation was conducted by integrating the UMESHMOTION subprogram with ALE adaptive grids. Additionally, the effects of load amplitudes on contact pair stress and surface wear were systematically investigated. It is revealed that the contact pair stress of the reducer gear was higher than that of the spline contact pair. Furthermore, the internal spline exhibited the highest wear rate, followed by the output shaft gear, external spline, and input shaft gear, in that order. This work provides a comprehensive and in-depth understanding of the wear behaviors of general reducers with small teeth differences and offers valuable scientific references for design optimization, fault diagnosis, and maintenance strategy formulation. Full article
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<p>The geometry and components of gear reducer. (<b>a</b>) Assembly drawing of gear reducer; (<b>b</b>) The major components of gear reducer.</p>
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<p>The 3D finite element model of the contact pairs. (<b>a</b>) The 3D model of the contact pairs; (<b>b</b>) The meshing detail around the spline contact area; (<b>c</b>) The meshing detail around the gear contact area.</p>
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<p>The flow chart for the wear simulation for the reducer.</p>
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<p>The von Mises stress contour for the reducer at the engage-in point. (<b>a</b>) The stress contour of the contact pair; (<b>b</b>) The stress contour around the spline contact area; (<b>c</b>) The stress contour around the gear contact area.</p>
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<p>The von Mises stress at different engage points for the gear pair. (<b>a</b>–<b>d</b>) The stress contours for gear pair in the different contact positions.</p>
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<p>The von Mises stress at different engage points for the spline pair. (<b>a</b>–<b>d</b>) The stress contours for spline pair in the different contact positions.</p>
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<p>The root stress for the input and output gears.</p>
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<p>The evolutions of root stress in the width direction for the input and output gears.</p>
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<p>The evolutions of von Mises stress in the tooth profile direction for the input and output gears.</p>
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<p>The evolution of maximum von Mises stress for input gear as the input torque increases.</p>
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<p>The wear depth for the internal spline as the loading cycle increases.</p>
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<p>The evolution of wear for the input gear and the external spline as the loading cycle increases.</p>
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<p>The wear depth for the intput gear under different loading amplitudes.</p>
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<p>The wear depth for the output gear under different loading amplitudes.</p>
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22 pages, 4494 KiB  
Article
Analysis of Inherent Frequencies to Mitigate Liquid Sloshing in Overhead Double-Baffle Damper
by Ashraf Ali, Mohamed Ismail, Madhan Kumar, Daniel Breaz and Kadhavoor R. Karthikeyan
Mathematics 2024, 12(17), 2727; https://doi.org/10.3390/math12172727 - 31 Aug 2024
Viewed by 446
Abstract
A disco-rectangular volume-fraction-of-fluid (VOF) model tank of a prismatic size is considered here for investigating the severe effect of overhead baffles made of three different materials, nylon, polyamide, and polylactic acid. In this work, the overdamped, undamped, and nominal damped motion of baffles [...] Read more.
A disco-rectangular volume-fraction-of-fluid (VOF) model tank of a prismatic size is considered here for investigating the severe effect of overhead baffles made of three different materials, nylon, polyamide, and polylactic acid. In this work, the overdamped, undamped, and nominal damped motion of baffles and their effect are studied. In this research, the behaviour of different material baffles based on the sloshing effect and natural frequency of each baffle excited in damped, undamped, and overdamped cases is studied. VOF modelling is carried out in moving Yeoh model mesh with fluid–structure interaction between the water models for various baffle plates. The results of the water volume distribution and baffle displacement operating between a sloshing time of 0 and 1 s are recorded. Also, a strong investigation is carried out for the water volume suspended on overhead baffles by three different material selections. Full article
(This article belongs to the Special Issue Computational Fluid Dynamics with Applications)
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<p>Geometry of FSI model.</p>
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<p>Free tetrahedral mesh for disco-rectangular tank.</p>
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<p>Mesh convergence test for baffle displacement in x-axis.</p>
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<p>Mesh convergence test for baffle displacement in z-axis.</p>
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<p>(<b>a</b>) Phase field of Liquid 1 (<math display="inline"><semantics> <mrow> <mi>φ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> water volume. (<b>b</b>) Phase field of Liquid 2 (<math display="inline"><semantics> <mrow> <mi>φ</mi> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> air volume.</p>
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<p>Wetted-wall boundaries in a disco-rectangular tank.</p>
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<p>Overhead baffles selected for shell-structured fixed wall constraint.</p>
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<p>Pressure contours of liquid sloshing for various types of baffles.</p>
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<p>Velocity slice contours of liquid sloshing for various types of baffles.</p>
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<p>(<b>a</b>) Effect of nylon baffles and their water volume distribution inside the disco-rectangular tank. (<b>b</b>) Directional displacement of nylon baffles in x, y, and z directions.</p>
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<p>(<b>a</b>) Effect of polyamide baffles and their water volume distribution inside the disco-rectangular tank. (<b>b</b>) Directional displacement of polyamide baffles in x, y, and z directions.</p>
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<p>(<b>a</b>) Effect of PLA baffles and their water volume distribution inside the disco-rectangular tank. (<b>b</b>) Directional displacement of PLA baffles in x, y, and z directions.</p>
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<p>Effect of displacement magnitude of different material overhead baffles vs. liquid-sloshing time.</p>
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<p>Comparison of natural frequency of different baffles under sloshing.</p>
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<p>VOF model isosurface—surface displacement of nylon baffle.</p>
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20 pages, 19252 KiB  
Article
Towards a Direct Consideration of Microstructure Deformation during Dynamic Recrystallisation Simulations with the Use of Coupled Random Cellular Automata—Finite Element Model
by Kacper Pawlikowski, Mateusz Sitko, Konrad Perzyński and Łukasz Madej
Materials 2024, 17(17), 4327; https://doi.org/10.3390/ma17174327 - 31 Aug 2024
Viewed by 423
Abstract
Dynamic recrystallisation (DRX) is one of the fundamental phenomena in materials science, significantly impacting the microstructure and mechanical properties of components subjected to large plastic deformations. Experimental research on that topic carried out for a wide range of new metallic materials is often [...] Read more.
Dynamic recrystallisation (DRX) is one of the fundamental phenomena in materials science, significantly impacting the microstructure and mechanical properties of components subjected to large plastic deformations. Experimental research on that topic carried out for a wide range of new metallic materials is often supported by computational materials science. A direct consideration and detailed understanding of this phenomenon are possible with a class of full-field numerical models based on the cellular automata (CA) method. However, the classical CA approach is based on a regular, fixed computational space and has limitations in capturing large deformations of the computational domain. Therefore, the main goal of the work is to develop and implement an alternative solution to overcome this limitation. The proposed solution is based on coupling the finite element (FE) method with the random cellular automata (RCA) approach. Such a model can directly consider the influence of geometrical changes in microstructure during large plastic deformation on recrystallisation progress. Details of the developed RCA DRX model assumptions and coupling issues with FE mesh are discussed. Particular attention is also paid to increasing model efficiency and robustness studies. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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<p>Different approaches to the CA space deformation modelling: (<b>a</b>) space mapping, (<b>b</b>) geometric changes in cells, (<b>c</b>) CAFE, and (<b>d</b>) RCAFE.</p>
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<p>Concept of the direct problem model definition based on the uniaxial compression (UC) test.</p>
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<p>Inverse analysis flow chart.</p>
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<p>Uniaxial compression experimental setup.</p>
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<p>Final agreement between the measured and calculated load-displacement values after the inverse analysis at (<b>a</b>) 900 °C, (<b>b</b>) 1000 °C, and (<b>c</b>) 1100 °C.</p>
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<p>Stress–strain curves after the inverse analysis at (<b>a</b>) 900 °C, (<b>b</b>)1000 °C, and (<b>c</b>) 1100 °C.</p>
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<p>The time overhead of various neighbour search algorithms.</p>
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<p>Computation time reduction for (<b>a</b>) basic and (<b>b</b>) bucket-based parallel algorithms implemented to increase the omp thread number.</p>
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<p>Computation time reduction for the bucket-based algorithm in the (<b>a</b>) preparatory step and (<b>b</b>) execution step for increasing the omp thread number.</p>
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<p>Examples of results from the developed RCAFE model: (<b>a</b>) material morphology evolution, (<b>b</b>) recrystallisation volume fraction, and (<b>c</b>) dislocation density evolution (visualisation with OVITO [<a href="#B29-materials-17-04327" class="html-bibr">29</a>]).</p>
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<p>Development of the initial digital microstructure morphology for the DRX simulation: (<b>a</b>) EBSD map, (<b>b</b>) extracted digital microstructure morphology model, and (<b>c</b>) finite element discretisation of the model.</p>
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<p>The starting FE meshes employed in the numerical simulation of plane strain compression: (<b>a</b>) 25 × 25, (<b>b</b>) 50 × 50, (<b>c</b>) 75 × 75, (<b>d</b>) 100 × 100, (<b>e</b>) 150 × 150, (<b>f</b>) 200 × 200, and (<b>g</b>) 250 × 250 elements with emphasis on a selected grain in red square.</p>
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<p>Boundary conditions of numerical simulation of plane strain compression.</p>
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<p>Homogenised stress–strain responses for the different mesh sizes used during the simulation.</p>
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<p>Equivalent plastic stress field at the end of loading for increasing discretisation levels: (<b>a</b>) 25 × 25, (<b>b</b>) 50 × 50, (<b>c</b>) 75 × 75, (<b>d</b>) 100 × 100, (<b>e</b>) 150 × 150, (<b>f</b>) 200 × 200, and (<b>g</b>) 250 × 250 finite elements.</p>
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<p>Equivalent plastic strain field at the end of loading for increasing discretisation levels: (<b>a</b>) 25 × 25, (<b>b</b>) 50 × 50, (<b>c</b>) 75 × 75, (<b>d</b>) 100 × 100, (<b>e</b>) 150 × 150, (<b>f</b>) 200 × 200, and (<b>g</b>) 250 × 250 finite elements.</p>
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<p>Material morphology for an increasing number of RCA cells in the computational space at the (<b>a</b>) initial step and (<b>b</b>) 0.1 and (<b>c</b>) 0.2 strain levels.</p>
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<p>Comparison of (<b>a</b>) recrystallisation volume fractions for multiple runs of simulations with an increasing number of cells in space and (<b>b</b>) averaged responses.</p>
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<p>Comparison of (<b>a</b>) the average grain size for multiple runs of simulations with an increasing number of cells in space and (<b>b</b>) averaged responses.</p>
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<p>Comparison of final model results after the parameter identification procedure.</p>
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18 pages, 6151 KiB  
Article
Enhancing the Fire Resistance of Ablative Materials: Role of the Polymeric Matrix and Silicon Carbide Reinforcement
by Juana Abenojar, Sara López de Armentia and Miguel Angel Martínez
Polymers 2024, 16(17), 2454; https://doi.org/10.3390/polym16172454 - 29 Aug 2024
Viewed by 369
Abstract
The primary characteristic of ablative materials is their fire resistance. This study explored the development of cost-effective ablative materials formed into application-specific shapes by using a polymer matrix reinforced with ceramic powder. A thermoplastic (polypropylene; PP) and a thermoset (polyester; UPE) matrix were [...] Read more.
The primary characteristic of ablative materials is their fire resistance. This study explored the development of cost-effective ablative materials formed into application-specific shapes by using a polymer matrix reinforced with ceramic powder. A thermoplastic (polypropylene; PP) and a thermoset (polyester; UPE) matrix were used to manufacture ablative materials with 50 wt% silicon carbide (SiC) particles. The reference composites (50 wt% SiC) were compared to those with 1 and 3 wt% short glass fibers (0.5 mm length) and to composites using a 1 and 3 wt% glass fiber mesh. Fire resistance was tested using a butane flame (900 °C) and by measuring the transmitted heat with a thermocouple. Results showed that the type of polymer matrix (PP or UPE) did not influence fire resistance. Composites with short glass fibers had a fire-resistance time of 100 s, while those with glass fiber mesh tripled this resistance time. The novelty of this work lies in the exploration of a specific type of material with unique percentages of SiC not previously studied. The aim is to develop a low-cost coating for industrial warehouses that has improved fire-protective properties, maintains lower temperatures, and enhances the wear and impact resistance. Full article
(This article belongs to the Special Issue Flame-Retardant Polymer Composites II)
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<p>(<b>a</b>) Micrograph of SiC. (<b>b</b>) Macrograph of GFs taken with a mobile phone camera.</p>
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<p>Fire test setup: (<b>a</b>) in front of the sample, (<b>b</b>) behind the sample.</p>
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<p>PP + 50SiC + 3GF sample after the fire test.</p>
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<p>UPE composites after the fire test (<b>a</b>) UPE + 50SiC + 3GF, (<b>b</b>) UPE + 50SiC + mesh at 200 s and (<b>c</b>) UPE + 50SiC + mesh at the end of test.</p>
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<p>Fire test setup for UPE + 50SiC + mesh: (<b>a</b>) side where the fire hit, (<b>b</b>) opposite side to the fire, connected to a thermocouple.</p>
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<p>Examples of friction coefficient curves for UPE + 50SiC and UPE + 50SiC + mesh.</p>
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<p>Friction coefficients for PP and UPE matrices and their composites.</p>
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<p>Wear of PP and UPE matrices and their composites.</p>
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<p>3D panoramic wear tracks by OM: (<b>a</b>) PP, (<b>b</b>) PP + 50SiC, (<b>c</b>) PP + 50SiC + 1GF, and (<b>d</b>) PP + 50SiC + 3GF.</p>
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<p>3D panoramic wear tracks by OM: (<b>a</b>) UPE, (<b>b</b>) UPE + 50SiC, (<b>c</b>) UPE + 50SiC + 3GF, and (<b>d</b>) UPE + 50SiC + mesh.</p>
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<p>Micrograph of wear tracks: (<b>a</b>) PP track; (<b>b</b>) detail of the PP track: debris powder (yellow circle); (<b>c</b>) PP + SiC track, accumulation area (red circle) and abrasion line (blue arrow), (<b>d</b>) PP + SiC track, showing the detail of the SiC particle on the track; (<b>e</b>) PP + SiC + 1GF track, showing the SiC particle and GF in the accumulation track (red circle); and (<b>f</b>) PP + SiC + 3GF track, showing the accumulation area (red circle) and abrasion line (blue arrow).</p>
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<p>Micrographs of wear tracks: (<b>a</b>) UPE + 50SiC, (<b>b</b>) UPE + 50SiC + 1GF, (<b>c</b>) UPE + 50SiC + 3GF, and (<b>d</b>) UPE + 50SiC + mesh. (Red circles mark areas of agglomerates or deposits, and blue circles mark areas of abrasion and fatigue).</p>
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<p>Resilience values obtained from impact tests for both matrices and their composites.</p>
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<p>Micrographs of the impact test fracture surface: (<b>a</b>) PP fractography; (<b>b</b>) PP + SiC fractography with ductile and brittle areas and SiC cleavage; (<b>c</b>) PP + SiC + 1GF fractography, showing the detail of the GF in ductile area; (<b>d</b>) PP + SiC + 3GF track, showing the detail of the GF in ductile area.</p>
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<p>Micrographs of the impact test fracture surface: (<b>a</b>) UPE fractography; (<b>b</b>) UPE + SiC fractography, (<b>c</b>) UPE + SiC + 1GF fractography, showing the SiC cleavage and GFs; and (<b>d</b>) UPE + SiC + 3GF fractography, showing the accumulation of GFs.</p>
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13 pages, 2294 KiB  
Article
Thermodynamic Work of High-Grade Uterine Prolapse Patients Undergoing Transvaginal Mesh Repair with Total Hysterectomy
by Hui-Hsuan Lau, Cheng-Yuan Lai, Ming-Chun Hsieh, Hsien-Yu Peng, Dylan Chou, Tsung-Hsien Su, Jie-Jen Lee and Tzer-Bin Lin
Bioengineering 2024, 11(9), 875; https://doi.org/10.3390/bioengineering11090875 - 28 Aug 2024
Viewed by 502
Abstract
The objective benefit of transvaginal mesh with concomitant total hysterectomy (TVM-HTX) repair to high-grade uterine prolapse (UP) patients has not been fully established. This study aimed to clarify the impact of TVM-HTX on the voiding function of high-grade UP patients by comparing pre- [...] Read more.
The objective benefit of transvaginal mesh with concomitant total hysterectomy (TVM-HTX) repair to high-grade uterine prolapse (UP) patients has not been fully established. This study aimed to clarify the impact of TVM-HTX on the voiding function of high-grade UP patients by comparing pre- and post-operatively measured pressure–flow and pressure–volume investigations. Urodynamic and thermodynamic studies were conducted on 15 high-grade UP patients (stage III/IV, POP-Q system) who underwent TVM-HTX (January 2019–December 2022) in a tertiary-care university hospital. The parameters analyzed included voiding resistance (Rvod), voiding pressure (Pvod), voiding flow (Fvod), voided volume (Vvod), voiding time (Tvod), and the trajectory-enclosed area of the pressure–volume loop (Apv). Post-operative results showed a significant reduction in Rvod (p < 0.001, N = 15), Pvod (p = 0.021, N = 15), and Apv (p = 0.006, N = 15), along with an increase in Fvod (p = 0.003, N = 15) and a decrease in Tvod (p < 0.001, N = 15). The operation-associated changes in Rvod (ΔRvod) correlated with alterations in Pvod and Fvod (ΔPvod and ΔFvod, r = 0.444, p = 0.004, r = 0.717, p = 0.003, respectively; both N = 15); ΔFvod correlated with the change in Tvod (ΔTvod, r = 0.629, p = 0.012, N = 15) but not with that in ΔVvod (r = 0.166, p = 0.555, N = 15). Changes in Apv (ΔApv) correlated with ΔPvod (r = 0.563, p = 0.029, N = 15) but not with ΔVvod (r = 0.353, p = 0.197, N = 15). Collectively, TVM-HTX reduced the voiding resistance, which improved the voiding efficacy and decreased the pressure gradient required for driving urine flow, thereby lessening the bladder’s workload. Full article
(This article belongs to the Special Issue Biomechanics, Health, Disease and Rehabilitation, 2nd Edition)
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<p><b>Pressure–flow/–volume analyses of voiding in response to TVM-HTX.</b> (<b>A</b>) <b>PRE and</b> (<b>B</b>) <b>POST</b> Representative cystometry of a high-grade UP patient measured pre- and post-operatively. Pdet: detrusor pressure, Pves: vesical pressure, Pabd: abdominal pressure, Flow: urethral flow, Vinf: infused volume, Vvod: voided volume, and Vive: intra-vesical volume. (<b>C</b>) <b>PRE</b> and (<b>D</b>) <b>POST</b> pre- (green) and post-operatively (blue) PVA derived from the cystometry. Hatched areas denote the trajectory-enclosed areas.</p>
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<p><b>Voiding resistance, pressure, and flow in response to TVM-HTX.</b> Box plot (left) and mean values (right) of (<b>A</b>) voiding resistance (Rvod), (<b>B</b>) voiding pressure (Pvod), and (<b>C</b>) voiding flow (Fvod) measured pre- and post-operatively. (PRE: green and POST: blue. ** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> = 0.021, ** <span class="html-italic">p</span> = 0.003, respectively, vs. PRE, all N = 15. (<b>D</b>,<b>E</b>) Correlation analyses of the operation-associated change in voiding resistance (ΔRvod) with the changes in voiding pressure (ΔPvod; r = 0.444, <span class="html-italic">p</span> = 0.004, N = 15) and voiding flow (ΔFvod; r = 0.717, <span class="html-italic">p</span> = 0.003, N = 15), respectively.</p>
Full article ">Figure 3
<p><b>Voided volume and voiding time in response to TVM-HTX.</b> Box plot (left) and mean values (right) of (<b>A</b>) voided volume (Vvod) and (<b>B</b>) voiding time (Tvod) measured pre- and post-operatively (PRE: green and POST: blue; NS <span class="html-italic">p</span> = 0.756, ** <span class="html-italic">p</span> &lt; 0.001, respectively, vs. PRE, both N = 15). (<b>C</b>,<b>D</b>) Correlation analyses of the operation-associated change in voiding flow (ΔFvod) with the changes in voided volume (ΔVvod; r = 0.166, <span class="html-italic">p</span> = 0.555, N = 15) and voiding time (ΔTvod; r = 0.629, <span class="html-italic">p</span> = 0.012, N = 15), respectively.</p>
Full article ">Figure 4
<p><b>Trajectory-enclosed area in response to TVM-HTX.</b> (<b>A</b>) Box plot (left) and mean values (right) of the loop-enclosed area (Apv) measured pre- and post-operatively (PRE: green and POST: blue. * <span class="html-italic">p</span> = 0.006, vs. PRE, N = 15). (<b>B</b>,<b>C</b>) Correlation analyses of the operation-associated change in Apv (ΔApv) with the change in voiding pressure (ΔPvod; r = 0.563, <span class="html-italic">p</span> = 0.029, N = 15) and voided volume (ΔVvod; r = 0.353, <span class="html-italic">p</span> = 0.197, N = 15), respectively. (<b>D</b>) Heatmap displaying Pearson’s correlation analysis r values between all pairs of variables. Red and yellow colors represent positive and negative r, respectively.</p>
Full article ">
28 pages, 90455 KiB  
Article
Lessons Learnt from the Simulations of Aero-Engine Ground Vortex
by Wenqiang Zhang, Tao Yang, Jun Shen and Qiangqiang Sun
Aerospace 2024, 11(9), 699; https://doi.org/10.3390/aerospace11090699 - 26 Aug 2024
Viewed by 350
Abstract
With the startup of the aero-engine, the ground vortex is formed between the ground and the engine intake. The ground vortex leads to total pressure and swirl distortion, which reduces the performance of the engine. The inhalation of the dust and debris through [...] Read more.
With the startup of the aero-engine, the ground vortex is formed between the ground and the engine intake. The ground vortex leads to total pressure and swirl distortion, which reduces the performance of the engine. The inhalation of the dust and debris through a ground vortex can erode the fan blade, block the seals and degrade turbine cooling performance. As the diameter of the modern fan blade becomes larger, the clearance between the intake lip and the ground surface is smaller, which enhances the strength of the ground vortex. Though considerable numerical studies have been conducted with the predictions of the ground vortex, it is noted that the accurate simulation of the ground vortex is still a tough task. This paper presents authors’ simulation work of the ground vortex into an intake model with different crosswind speeds. This paper tackles the challenge with a parametric study to provide useful guidelines on how to obtain a good match with the experimental data. The influence of the mesh density, performance of different turbulence models and how the boundary layer thickness affects the prediction results are conducted and analysed. The detailed structure of the flow field with ground vortex is presented, which can shed light on the experimental observations. A number of suggestions are presented that can pave the road to the accurate flow field simulations with strong vorticities. Full article
Show Figures

Figure 1

Figure 1
<p>Computational domain for the ground vortex simulation.</p>
Full article ">Figure 2
<p>Definition of (<b>a</b>) DC60 and (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>Γ</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Vortex visualisation with Q-criterion.</p>
Full article ">Figure 4
<p>Comparisons of the total pressure between the CFD (<b>left</b>) and experimental data (experimental plot courtesy of Cranfield University library) (<b>right</b>) at the AIP [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>], <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4 Cont.
<p>Comparisons of the total pressure between the CFD (<b>left</b>) and experimental data (experimental plot courtesy of Cranfield University library) (<b>right</b>) at the AIP [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>], <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Comparisons of the average velocity vector and vorticity vector field at the PIV measurement surface for different velocity ratios [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>] from CFD (<b>left</b>) and experimental data (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5 Cont.
<p>Comparisons of the average velocity vector and vorticity vector field at the PIV measurement surface for different velocity ratios [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>] from CFD (<b>left</b>) and experimental data (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>The grid topology of (<b>a</b>) Mesh A and (<b>b</b>) Mesh B.</p>
Full article ">Figure 7
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; and (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; and (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Q-criterion of the ground vortex for the case <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model.</p>
Full article ">Figure 9
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math> turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math> turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Time-averaged total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, DDES computation. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Simulated and experimental total pressure at a fixed radial position crossing the vortex core.</p>
Full article ">Figure 13
<p>Simulated and measured circulation at different crosswind speeds.</p>
Full article ">Figure 14
<p>Simulated and measured distortion indexes at different crosswind speeds.</p>
Full article ">Figure 15
<p>Vortex configuration presented by different turbulence models: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST; (<b>b</b>) SA; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math>; and (<b>d</b>) DDES.</p>
Full article ">Figure 16
<p>Slices of the vortex to perform vorticity integration.</p>
Full article ">Figure 17
<p>Velocity profile of the approaching boundary layer.</p>
Full article ">Figure 18
<p>Thickness of the approaching boundary layer, DDES: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.12</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>1.03</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 19
<p>Flow structure of the vortex system with crosswind: (<b>a</b>) helical structure of the flow around the ground vortex and surface streamline of the potential vortex beneath the intake; (<b>b</b>) stream traces of the ground vortex at the PIV measurement plane; and (<b>c</b>) near-ground shear vortices.</p>
Full article ">Figure 20
<p>Normalised vertical velocity measured at the PIV plane: (<b>a</b>) CFD and (<b>b</b>) measurement.</p>
Full article ">Figure 21
<p>Axial slice through the ground vortex: (<b>a</b>) vertical velocity; (<b>b</b>) zoomed view of the vortex near ground surface; (<b>c</b>) Y velocity; and (<b>d</b>) static temperature.</p>
Full article ">Figure 21 Cont.
<p>Axial slice through the ground vortex: (<b>a</b>) vertical velocity; (<b>b</b>) zoomed view of the vortex near ground surface; (<b>c</b>) Y velocity; and (<b>d</b>) static temperature.</p>
Full article ">
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