Optimization of wind turbine TMD under real wind distribution countering wake effects using GPU acceleration and machine learning technologies

Liu, Zhengqing; Wang, Yi–Ze; Hua, Xugang; Zhu, Hongping; Zhu, Zhiwen

doi:10.1016/j.jweia.2020.104436

Cited by 28 publications

(8 citation statements)

References 43 publications

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“…Each graphic element is a feature object (Feature), and many methods of the layer object (Layer) return the feature set of the layer. You can search and locate geographic features such as lines, symbols, or regional characteristics [ 21 – 24 ]. In order to be able to use this method, there must be an index field in the MapInfo table of the search layer:

…”

Section: Gis Technology Based On Rbfnn-ga Algorithm In the Delineation Model Construction Of Geological Hazard Prone Areasmentioning

confidence: 99%

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Lei¹,

Yao²,

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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With the rapid development of the economy and society, geological disasters such as landslides, collapses, and mudslides have shown an intensifying trend, seriously endangering the safety of people’s lives and property, and affecting the sustainable development of the economy and society. Aiming at the problems of merging different data layers and determining the weighting of data stacking in the statistical analysis model based on GIS technology in the evaluation of the risk of geological disasters, this study proposes a logistic regression model combined with the RBFNN-GA algorithm, that is, the determination of the occurrence of geological disasters. The fusion coefficient (CF value) with the RBFNN-GA algorithm model, and with the help of SPSS statistical analysis software, solves the problem of factor selection, heterogeneous data merging, and weighting of each data layer in the risk assessment. In the experimental stage, this study adopts the method of geological hazard certainty coefficients to carry out the sensitivity analysis of the geological hazards in the study area. Using homogeneous grid division, the spatial quantitative evaluation of the risk of geological disasters is realized, and at the same time, the results of the spatial quantitative evaluation of the risk of geological disasters are tested according to the latest landslide points in the region. The existing classification mainly depends on the acquisition of land use/cover information or the processing method of the acquired information, but the existing information acquisition will be limited by time, space, and spectral resolution. The results show that the number of landslide points per unit area in the extremely unstable zone and the unstable zone is 0.0395 points/km2 and 0.0251 points/km2, respectively, which is much higher than 0.0038 points/km2 in the stable zone, indicating the evaluation results and actual landslide conditions.

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…”

Section: Gis Technology Based On Rbfnn-ga Algorithm In the Delineation Model Construction Of Geological Hazard Prone Areasmentioning

confidence: 99%

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Lei¹,

Yao²,

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…Papadopoulos [17], Gravvanis [18] proved that GPU parallel calculation based on the Compute Unified Device Architecture (CUDA) has higher calculation efficiency than multi-CPU processors in solving large sparse matrix. In the field of engineering, researchers were also using GPU to accelerate calculation and simulation [19,20]. With combination of GPU parallel and discrete element method (DEM), Mohammad [21] studied displacement slip faulting through granular soils by a method of GPU-based DEM modelling.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Real Time Hybrid Simulation Based on GPU Computing

Tang

Dong

et al. 2021

Preprint

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With combination of physical experiment and numerical simulation, real-time hybrid simulation (RTHS) can enlarge the dimensions of testing specimens and improve the testing accuracy. However, due to the limitation of computing capacity, the maximum degrees of freedom for numerical substructure are less than 2000 from the reported RTHS testing. It cannot meet the testing requirements for evaluating the dynamic performance of large and complex engineering structures. Taking advantages of parallel computing toolbox (PCT) in Matlab and high-performance computing of graphics processing unit (GPU). A RTHS framework based on MATLAB and GPU was established in this work. Using this framework, a soil-structure interaction system (SSI) was tested by a shaking table based RTHS. Meanwhile, the dynamic response of this SSI system was simulated by finite element analysis. The comparison of simulation and testing results demonstrated that the proposed testing framework can implement RTHS testing successfully. Using this method, the maximum degrees of freedom for numerical substructure can reach to 27,000, which significantly enhance the testing capacity of RTHS testing for large and complex engineering structures.

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“…Papadopoulos[18], Gravvanis [19] proved that GPU parallel calculation based on the Compute Uni ed Device Architecture (CUDA) has higher calculation e ciency than multi-CPU processors in solving large sparse matrix. In the eld of engineering, researchers were also using GPU to accelerate calculation and simulation [20,21]. With combination of GPU parallel and discrete element method (DEM),…”

Section: Introductionmentioning

confidence: 99%

Implementation of Real Time Hybrid Simulation Based On GPU Computing

Tang

Dong

et al. 2021

Preprint

View full text Add to dashboard Cite

With combination of physical experiment and numerical simulation, real-time hybrid simulation (RTHS) can enlarge the dimensions of testing specimens and improve the testing accuracy. However, due to the limitation of computing capacity, the maximum degrees of freedom for numerical substructure are less than 7000 from the reported RTHS testing. It cannot meet the testing requirements for evaluating the dynamic performance of large and complex engineering structures. Taking advantages of parallel computing toolbox (PCT) in Matlab and high-performance computing of graphics processing unit (GPU). A RTHS framework based on MATLAB and GPU was established in this work. Using this framework, a soil-structure interaction system (SSI) was tested by a shaking table based RTHS. Meanwhile, the dynamic response of this SSI system was simulated by finite element analysis. The comparison of simulation and testing results demonstrated that the proposed testing framework can implement RTHS testing successfully. Using this method, the maximum degrees of freedom for numerical substructure can reach to 27,000, which significantly enhance the testing capacity of RTHS testing for large and complex engineering structures.

show abstract

Optimization of wind turbine TMD under real wind distribution countering wake effects using GPU acceleration and machine learning technologies

Cited by 28 publications

References 43 publications

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Implementation of Real Time Hybrid Simulation Based on GPU Computing

Implementation of Real Time Hybrid Simulation Based On GPU Computing

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