Wind Field Digital Twins Sandbox System for Transmission Towers
<p>Distribution of forces and moments on the individual components.</p> "> Figure 2
<p>Relationship between two-dimensional and three-dimensional coordinate systems.</p> "> Figure 3
<p>Wind direction decomposition (<b>a</b>) 16 wind directions; (<b>b</b>) direction of wind force application.</p> "> Figure 4
<p>Wind load application procedure diagram.</p> "> Figure 5
<p>Positions of monitoring points.</p> "> Figure 6
<p>Comparative results.</p> "> Figure 7
<p>Sandbox model and digital model (<b>a</b>) sandbox model; (<b>b</b>) digital model.</p> "> Figure 8
<p>Physical and numerical models (<b>a</b>) physical model; (<b>b</b>) numerical model.</p> "> Figure 9
<p>Grouping of transmission tower models and distribution of monitoring points.</p> "> Figure 10
<p>Displacement–time curve under strong wind conditions (<b>a</b>) X-direction displacement–time graph; (<b>b</b>) Y-direction displacement–time graph.</p> "> Figure 11
<p>Total displacement in the x-direction of transmission tower.</p> "> Figure 12
<p>Total displacement in the y-direction of transmission tower.</p> "> Figure 13
<p>Displacement–time curve under complex wind field conditions (<b>a</b>) x-direction displacement graph; (<b>b</b>) y-direction displacement graph.</p> "> Figure 14
<p>Acceleration–time curve under complex wind field conditions (<b>a</b>) Acceleration of monitoring point 1; (<b>b</b>) Acceleration of monitoring point 2; (<b>c</b>) Acceleration of monitoring point 3; (<b>d</b>) Acceleration of monitoring point 4.</p> ">
Abstract
:1. Introduction
- The 3D numerical model is established through the algorithmic processing of two-dimensional images and the creation of corresponding digital three-dimensional scenes. The accuracy of the model is verified by comparing the results from physical and digital models.
- Within the framework of CDEM, we simulated the mechanical characteristics of transmission towers under unidirectional and variable wind conditions. We analyzed the impact of loads on structural displacement and acceleration.
- We converted historical on-site wind speeds into loads, accounting for wind direction effects. We coupled multiple factors through coding, such as load, direction and time, for transmission towers. We confirmed the feasibility and high accuracy of the model, algorithms and simulation methods by comparing the experimental data with the numerical simulation results.
2. Numerical Calculation Method
2.1. Continuum-Discontinuum Element Method (CDEM)
2.2. The Transformation Relationship between Images and 3D Models
2.3. The Relationship between Wind Speed and Load Conversion
2.4. Wind Field Calculation
2.5. Method Validation
3. Image-Based Modeling
3.1. Digital Model
3.2. Transmission Tower Model and Materials
4. Results and Discussion
4.1. Initial and Boundary Conditions
4.2. Simulation of Strong Winds on Transmission Tower
4.3. Simulation of Wind Fields on Transmission Towers
5. Conclusions
- The implementation approach of a digital twin system for transmission towers in a sandbox is proven to be effective and reliable. It involves the utilization of image recognition modeling, camera sensor equipment and the CDEM. The transfer of information data from the physical model to the virtual model is achieved through the camera equipment. The numerical models, created from processed two-dimensional images, exhibit geometric dimensions consistent with the physical model. The dimensions have a 5 mm error, demonstrating the accuracy of the system.
- The functionality developed in Java language successfully achieved the application of multi-variable loads on the structure, including load, time and direction. The load application algorithm was shown to be effective and accurate, with the simulation results demonstrating an error of less than 5% when compared to the experimental data.
- Under unidirectional wind conditions, the displacement of the transmission tower exhibits a distinct “step-like” variation, with a simple linear relationship between the displacement and load magnitude as the load magnitude increases. However, under variable-direction wind loads, the variations in amplitude peaks are caused by differences in projected force areas resulting from different structural forms. The maximum acceleration at each monitoring point is approximately twice that of its lower position. With increasing height, both the displacement and acceleration of the transmission tower also increase accordingly.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Density (kg/m3) | Young’s Modulus (MPa) | Poisson’s Ratio (-) | Tensile Strength (MPa) | Compressive Strength (MPa) |
---|---|---|---|---|
1050 | 2500 | 0.38 | 60 | 80 |
Time/s | X-Direction/N | Y-Direction/N | ||||
---|---|---|---|---|---|---|
1 | −4.506 | −3.884 | −11.590 | 10.725 | 9.535 | 7.031 |
2 | 0.000 | 0.000 | 0.000 | 11.607 | 10.319 | 7.610 |
3 | −2.003 | −1.726 | −5.151 | 4.767 | 4.238 | 3.125 |
4 | −2.003 | −1.726 | −5.151 | −4.767 | −4.238 | −3.125 |
5 | −4.832 | −4.164 | −12.427 | −1.976 | −1.757 | −1.295 |
6 | 0.000 | 0.000 | 0.000 | −5.159 | −4.586 | −3.382 |
7 | −3.697 | −3.186 | −9.509 | 3.647 | 3.243 | 2.391 |
8 | −8.318 | −7.169 | −21.395 | 8.206 | 7.296 | 5.380 |
9 | −8.011 | −6.904 | −20.605 | 19.066 | 16.951 | 12.500 |
10 | −8.011 | −6.904 | −20.605 | 19.066 | 16.951 | 12.500 |
11 | −3.697 | −3.186 | −9.509 | 3.647 | 3.243 | 2.391 |
12 | 0.924 | 0.797 | 2.377 | −0.912 | −0.811 | −0.598 |
13 | 0.924 | 0.797 | 2.377 | 0.912 | 0.811 | 0.598 |
14 | −0.501 | −0.432 | −1.288 | 1.192 | 1.059 | 0.781 |
15 | −4.506 | −3.884 | −11.590 | 10.725 | 9.535 | 7.031 |
16 | −2.003 | −1.726 | −5.151 | 4.767 | 4.238 | 3.125 |
17 | −2.003 | −1.726 | −5.151 | 4.767 | 4.238 | 3.125 |
18 | 5.229 | 4.507 | 13.450 | 0.000 | 0.000 | 0.000 |
19 | 0.924 | 0.797 | 2.377 | 0.912 | 0.811 | 0.598 |
20 | −0.501 | −0.432 | −1.288 | 1.192 | 1.059 | 0.781 |
21 | −0.924 | −0.797 | −2.377 | 0.912 | 0.811 | 0.598 |
22 | 0.000 | 0.000 | 0.000 | 5.159 | 4.586 | 3.382 |
23 | 8.318 | 7.169 | 21.395 | 8.206 | 7.296 | 5.380 |
24 | 5.229 | 4.507 | 13.450 | 0.000 | 0.000 | 0.000 |
25 | 11.765 | 10.140 | 30.261 | 0.000 | 0.000 | 0.000 |
26 | 1.307 | 1.127 | 3.362 | 0.000 | 0.000 | 0.000 |
27 | −0.501 | −0.432 | −1.288 | 1.192 | 1.059 | 0.781 |
28 | −0.924 | −0.797 | −2.377 | 0.912 | 0.811 | 0.598 |
29 | 0.501 | 0.432 | 1.288 | 1.192 | 1.059 | 0.781 |
30 | 0.924 | 0.797 | 2.377 | −0.912 | −0.811 | −0.598 |
31 | 0.000 | 0.000 | 0.000 | −11.607 | −10.319 | −7.610 |
32 | 2.003 | 1.726 | 5.151 | −4.767 | −4.238 | −3.125 |
33 | 0.501 | 0.432 | 1.288 | −1.192 | −1.059 | −0.781 |
34 | 0.000 | 0.000 | 0.000 | −1.290 | −1.147 | −0.846 |
35 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
36 | 4.832 | 4.164 | 12.427 | 1.976 | 1.757 | 1.295 |
37 | 4.506 | 3.884 | 11.590 | −10.725 | −9.535 | −7.031 |
38 | 19.326 | 16.657 | 49.709 | −7.903 | −7.026 | −5.181 |
39 | 0.000 | 0.000 | 0.000 | −46.428 | −41.277 | −30.439 |
40 | 5.229 | 4.507 | 13.450 | 0.000 | 0.000 | 0.000 |
41 | −0.501 | −0.432 | −1.288 | 1.192 | 1.059 | 0.781 |
42 | 1.208 | 1.041 | 3.107 | 0.494 | 0.439 | 0.324 |
43 | 0.000 | 0.000 | 0.000 | 5.159 | 4.586 | 3.382 |
44 | 0.924 | 0.797 | 2.377 | −0.912 | −0.811 | −0.598 |
45 | −4.506 | −3.884 | −11.590 | −10.725 | −9.535 | −7.031 |
46 | 0.000 | 0.000 | 0.000 | −46.428 | −41.277 | −30.439 |
47 | 0.000 | 0.000 | 0.000 | −46.428 | −41.277 | −30.439 |
48 | 0.000 | 0.000 | 0.000 | −5.159 | −4.586 | −3.382 |
49 | 4.832 | 4.164 | 12.427 | 1.976 | 1.757 | 1.295 |
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Zhang, C.; Li, Y.; Feng, C.; Zhang, Y. Wind Field Digital Twins Sandbox System for Transmission Towers. Sensors 2023, 23, 8657. https://doi.org/10.3390/s23218657
Zhang C, Li Y, Feng C, Zhang Y. Wind Field Digital Twins Sandbox System for Transmission Towers. Sensors. 2023; 23(21):8657. https://doi.org/10.3390/s23218657
Chicago/Turabian StyleZhang, Chenshuo, Yunpeng Li, Chun Feng, and Yiming Zhang. 2023. "Wind Field Digital Twins Sandbox System for Transmission Towers" Sensors 23, no. 21: 8657. https://doi.org/10.3390/s23218657