Search Results (2,221)

Thermal management technology based on loop heat pipes (LHPs) has broad application prospects in heat transfer control for aerospace and new energy vehicles. LHPs offer excellent heat transfer performance, reliability, and flexibility, making them suitable for high-heat flux density, high-power heat dissipation, and complex thermal management scenarios. However, due to limitations in heat source temperature and heat transfer power range, LHP-based thermal management systems still face challenges, especially in thermohydraulic modeling, component design, and optimization. Steady-state models improve computational efficiency and accuracy, while transient models capture dynamic behavior under various conditions, aiding performance evaluation during start-up and non-steady-state scenarios. Designs for single/multi-evaporators, compensation chambers, and wick materials are also reviewed. Single-evaporator designs offer compact and efficient start-up, while multi-evaporator designs handle complex thermal environments with multiple heat sources. Innovations in wick materials, such as porous metals, composites, and 3D printing, enhance capillary driving force and heat transfer performance. A comprehensive summary of working fluid selection criteria is conducted, and the effects of selecting organic, inorganic, and nanofluid working fluids on the performance of LHPs are evaluated. The selection process should consider thermodynamic properties, safety, and environmental friendliness to ensure optimal performance. Additionally, the mechanism and optimization methods of the start-up behavior, temperature oscillation, and non-condensable gas on the operating characteristics of LHPs were summarized. Optimizing vapor/liquid distribution, heat load, and sink temperature enhances start-up efficiency and minimizes temperature overshoot. Improved capillary structures and working fluids reduce temperature oscillations. Addressing non-condensable gases with materials like titanium and thermoelectric coolers ensures long-term stability and reliability. This review comprehensively discusses the development trends and prospects of LHP technology, aiming to guide the design and optimization of LHP. Full article

Reconstructive and regenerative medicine are critical disciplines dedicated to restoring tissues and organs affected by injury, disease, or congenital anomalies. These fields rely on biomaterials like synthetic polymers, metals, ceramics, and biological tissues to create substitutes that integrate seamlessly with the body. Personalized implants and prosthetics, designed using advanced imaging and computer-assisted techniques, ensure optimal functionality and fit. Regenerative medicine focuses on stimulating natural healing mechanisms through cellular therapies and biomaterial scaffolds, enhancing tissue regeneration. In bone repair, addressing defects requires advanced solutions such as bone grafts, essential in medical and dental practices worldwide. Bovine bone scaffolds offer advantages over autogenous grafts, reducing surgical risks and costs. Incorporating antimicrobial properties into bone substitutes, particularly with metals like zinc, copper, and silver, shows promise in preventing infections associated with graft procedures. Silver nanoparticles exhibit robust antimicrobial efficacy, while zinc nanoparticles aid in infection prevention and support bone healing; 3D printing technology facilitates the production of customized implants and scaffolds, revolutionizing treatment approaches across medical disciplines. In this review, we discuss the primary biomaterials and their association with antimicrobial agents. Full article

The need for sustainability-oriented K–12 engineering education that expands beyond the classroom and the increased accessibility of educational technologies create an opportunity for examining the affordances of educational technologies in low-stakes informal engineering education settings. In this paper, we share our experiences of using novel technologies to develop sustainability-oriented mental models in K–12 informal engineering education. Through the use of technologies including Augmented Reality (AR), Virtual Reality (VR), Minecraft video games, Tinkercad (browser-based application for computer-aided design (CAD)), and physical computing, we have designed and tested approaches to introduce students to engineering design and engineering habits of mind with an overarching theme of developing sustainability-oriented mental models among K–12 youth in informal engineering education spaces. In this paper, we share our approaches, and lessons learned, and outline directions for future research. Full article

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design. Full article

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing custom-developed photogrammetry software, high-resolution images are processed to generate detailed 3D models, which are subsequently converted to STL files with precision. Adaptive meshing in COMSOL 5.3 Multiphysics, controlled through a MATLAB 2023 API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL’s advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining the desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency. Full article

This study proposes and simulates a smart system that can be used in production car seats to decrease whiplash risk in rear-end crashes. A sliding seat incorporating a semiactively controlled magnetorheological (MR) damper model positioned under the seat-pan is simulated with a validated biofidelic human body model. Since this is the first study that demonstrates a computer controlled anti-whiplash car seat system to the best of the authors’ knowledge, a benchmark analysis is carried out to compare the proposed semiactive seat with a state-of-the-art passive anti-whiplash car seat using 23 different crash pulses, including the moderate and high severity crash pulses within the European New Car Assessment Program (EuroNCAP) whiplash risk assessment framework. The proposed semiactive design outperforms the passive seat design by further reducing the values of the critical EuroNCAP whiplash criteria, such as NIC and $N_{km}$, together with the loads acting on the upper neck. The semiactive seat lowers the upper-neck shear force by an amount of 4 kg and 7 kg while lessening the NIC by 10% and 21% and $N_{km}$ by 9% and 56% for the EuroNCAP crash pulses, having a delta-V of 16 km/h and 24 km/h, respectively. The findings presented in this paper can aid in the design of car seats to further mitigate whiplash risk in rear-end crashes. Full article

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality, but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach. Full article

Unmanned Aerial Vehicles (UAVs) represent a rapidly increasing technology with profound implications for various domains, including surveillance, security, and commercial applications. Among the number of detection and classification methodologies, radar technology stands as a cornerstone due to its versatility and reliability. This paper presents a comprehensive primer written specifically for researchers starting on investigations into UAV detection and classification, with a distinct emphasis on the integration of full-wave electromagnetic computer-aided design (EM CAD) tools. Commencing with an elucidation of radar’s pivotal role within the UAV detection paradigm, this primer systematically navigates through fundamental Frequency-Modulated Continuous-Wave (FMCW) radar principles, elucidating their intricate interplay with UAV characteristics and signatures. Methodologies pertaining to signal processing, detection, and tracking are examined, with particular emphasis placed on the pivotal role of full-wave EM CAD tools in system design and optimization. Through an exposition of relevant case studies and applications, this paper underscores successful implementations of radar-based UAV detection and classification systems while elucidating encountered challenges and insights obtained. Anticipating future trajectories, the paper contemplates emerging trends and potential research directions, accentuating the indispensable nature of full-wave EM CAD tools in propelling radar techniques forward. In essence, this primer serves as an indispensable roadmap, empowering researchers to navigate the complex terrain of radar-based UAV detection and classification, thereby fostering advancements in aerial surveillance and security systems. Full article

Computer-aided design and computer-aided manufacturing (CAD/CAM) dentistry have significantly changed workflows in recent years. Restorations and devices can now be digitally designed and 3D-printed for dental care purposes. This clinical case report provides straightforward protocols for the digital design and 3D manufacture of gingivectomy and tooth preparation guides. These types of guides improved the gingival architecture of the anterior teeth and provided controllable tooth preparations prior to labial ceramic veneers. Thoughtful clinical evaluation started with listening to the patient’s chief complaint and extra- and intra-oral evaluations. Then a digital wax-up was performed, followed by an intra-oral mock-up, to evaluate the shape of the proposed restorations. After patient acceptance, the clinical procedure started with the gingivectomy and tooth preparation. Hand-crafted porcelain veneers were bonded under rubber dam isolation to avoid any contamination and maximize the bonding protocol. The esthetic and functional demands were fully satisfied. Predictable outcomes can be obtained whenever a meticulous evaluation and execution of all the steps are performed. Three dimensional printing technology allows the fabrication of devices such as gingivectomy and tooth reduction guides that help accomplish the desired results. Full article

The development of Ballistic Protection Vests (BPVs) has gained significant attention, particularly focusing on the design of Ballistic Protection Soft Panels (BPSPs), which are crucial to the overall size and configuration of these vests. Despite their critical role, there is a noticeable lack of a standardized design method for surface area patterns of BPSPs in the existing literature. The findings indicate that the National Institute of Justice (NIJ) standard 0101.06 Ballistic Test Templates (BTTs) are only partially applicable to the design of BPSP patterns. While the NIJ standard 0101.06 provides a useful framework, it requires adaptation to meet the specific needs of regional body types and the practicalities of BPV manufacturing. This research aims to address this gap by assessing the suitability of NIJ BTTs for the design of BPSPs and BPVs and to develop a standardized pattern design methodology along with a method for calculating the surface area of the soft amour prior to its creation. Results have to be achieved ready for the production of BPSP patterns tailored to the body types of regional soldiers while adhering to relevant standards and soldier’s physical comfort, thereby saving time and resources for manufacturers and researchers. In this study, we evaluated the applicability of the NIJ standard 0101.06 BTT for configuring these templates into the cutting patterns of BPSPs. To achieve this, patterns for BPSPs were designed and the feasibility of using NIJ BTTs for their configuration was analyzed. The research process involved a comprehensive literature review, an analysis of the dimensions of existing BPV soft panels, and a comparison with NIJ standard 0101.06 BTT. The design and scaling of the panel patterns were executed using computer-aided design (CAD) systems and evaluated through both physical fitting on mannequins and virtual fitting using the Clo3D program. The developed pattern-making methodology includes size specifications tailored to regional covers, incorporating a coefficient K identified to calculate the BPSP surface area prior to design. This approach not only ensures better fitting for the physical comfort and protection of soldiers but also saves time and resources in the manufacturing process of BPSPs. The proposed design methodology offers a significant step forward in standardizing BPSP patterns, promising enhanced protection and efficiency in BPV manufacturing. Full article

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date. Full article

Living organisms are constructed from proteins that assemble into biomolecular complexes, each with a unique shape and function. Our knowledge about the structure–activity relationship of these complexes is still limited, mainly because of their small size, complex structure, fast processes, and changing environment. Furthermore, the constraints of current microscopic tools and the difficulty in applying molecular dynamic simulations to capture the dynamic response of biomolecular complexes and long-term phenomena call for new supplementary tools and approaches that can help bridge this gap. In this paper, we present an approach to comparing biomolecular and origami hierarchical structures and apply it to comparing bacterial microcompartments (BMCs) with spiral-based origami models. Our first analysis compares proteins that assemble the BMC with an origami model called “flasher”, which is the unit cell of an assembled origami model. Then, the BMC structure is compared with the assembled origami model and based on the similarity, a physical scaled-up origami model, which is analogous to the BMC, is constructed. The origami model is translated into a computer-aided design model and manufactured via 3D-printing technology. Finite element analysis and physical experiments of the origami model and 3D-printed parts reveal trends in the mechanical response of the icosahedron, which is constructed from tiled-chiral elements. The chiral elements rotate as the icosahedron expands and we deduce that it allows the BMC to open gates for transmembrane passage of materials. Full article

Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model’s performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model’s adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring. Full article

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure. Full article

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. Full article