检索结果-南通市图书馆

Optimization on the Crosswind Stability of Trains Using Neural Network surrogate model

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2021年第4期34卷 208-224页

作者： Le Zhang Tian Li Jiye Zhang Ronghuan Piao State Key Laboratory of Traction Power Southwest Jiaotong UniversityChengdu 610031China

Under the influence of crosswinds,the running safety of trains will decrease sharply,so it is necessary to optimize the suspension parameters of *** paper studies the dynamic performance of high-speed trains under cross-wind conditions,and optimizes the running safety of train.A computational fluid dynamics simulation was used to determine the aerodynamic loads and moments experienced by a train.A series of dynamic models of a train,with different dynamic parameters were constructed,and analyzed,with safety metrics for these being ***,a surrogate model was built and an optimization algorithm was used upon this surrogate model,to find the minimum possible values for:derailment coefficient,vertical wheel-rail contact force,wheel load reduction ratio,wheel lateral force and overturning *** were 9 design variables,all associated with the dynamic parameters of the *** the train was running with the speed of 350 km/h,under a crosswind speed of 15 m/s,the benchmark dynamic model performed *** derailment coefficient was *** vertical wheel-rail contact force was 133.30 *** wheel load reduction rate was *** wheel lateral force was 85.67 kN,and the overturning coefficient was *** optimization,under the same running conditions,the metrics of the train were 0.268,100.44 kN,0.474,34.36 kN,and 0.421,*** paper show that by combining train aerodynamics,vehicle system dynamics and many-objective optimization theory,a train’s stability can be more comprehensively analyzed,with more safety metrics being considered.

关键词： Safety surrogate model Optimization High-speed train Crosswind

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2020年第6期33卷 1573-1588页

作者： Jun TAO Gang SUN Liqiang GUO Xinyu WANG Department of Aeronautics&Astronautics Fudan UniversityShanghai 200433China Department of Mechanical&Aerospace Engineering University of CaliforniaIrvine 92697USA

An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this *** order to reduce the number of design variables for aerodynamic optimizations,the PCA technique is implemented to the geometric parameters obtained by parameterization *** the purpose of predicting aerodynamic parameters,the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output,and it is trained using the k-step contrastive divergence *** established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils,and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate *** the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization(PSO)framework,and applied to the robust aerodynamic design optimizations of Natural Laminar Flow(NLF)airfoil and transonic *** optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic *** employing the PCA-DBN-based surrogate model,the developed efficient method improves the optimization efficiency obviously.

关键词： Aerodynamic design optimization Deep neural networks Particle swarm optimization Principal component analysis surrogate model

A surrogate model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm

在线全文

学校读者我要写书评

暂无评论

Journal of Thermal Science 2023年第6期32卷 2081-2092页

作者： WANG Xing ZHU Yangli LI Wen ZUO Zhitao CHEN Haisheng National Energy Large Scale Physical Energy Storage Technologies R&D Center(Bijie) Bijie551712China Institute of Engineering Thermophysics Chinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing101408China

It is usually to conduct a full-scale three-dimensional flow analysis for a radial turbine to find a way to increase the efficiency of a Compressed Air Energy Storage(CAES)***,long solving time and huge consumption of computing resources become a major obstacle to the ***,in present study,a surrogate model with test data-based multi-layer perceptron(MLP)Neural Network is proposed to overcome the *** of complex flow field solving process,it provides reliable turbine aerodynamic performance and flow field distribution characteristics in a short solution time by“learning the measurement results”.The validation results illustrated that the predicted maximum relative errors of isentropic efficiency,corrected mass flow rate and corrected power are only 0.03%,0.22%and 0.26%*** predicted flow distribution parameters in chamber,shroud cavity and outlet region of rotor are also basically consistent with the experimental *** the chamber,it can be found that a pressure stagnation point is observed at circumferential angle of 270°when total pressure ratio is *** the shroud cavity,obvious pressure variation is found near outlet of shroud cavity which although labyrinth seals *** outlet of rotor,obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade *** the same time,obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height and this is because the influence of upper passage vortex,lower passage vortex and end wall secondary *** present study can provide further reference for the dynamic performance evaluation of CAES radial inflow turbine.

关键词： CAES surrogate model radial inflow turbine MLP neural network

Uncertain Multidisciplinary Design Optimization on Next Generation Subsea Production System by Using surrogate model and Interval Method

在线全文

学校读者我要写书评

暂无评论

China Ocean Engineering 2021年第4期35卷 609-621页

作者： WU Jia-hao ZHEN Xing-wei LIU Gang HUANG Yi School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024China

The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water *** definition of NextGen SPS involves several disciplines,which makes the design process *** this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)*** deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are *** model technique is applied in the latter two *** optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed *** the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established *** optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.

关键词： next generation subsea production system multidisciplinary design optimization uncertain optimization collaborative optimization surrogate model interval method

A gradient-based method assisted by surrogate model for robust optimization of turbomachinery blades

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2022年第10期35卷 1-7页

作者： Jiaqi LUO Zeshuai CHEN Yao ZHENG School of Aeronautics and Astronautics Zhejiang UniversityHangzhou 310027China

The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced,verified and validated in the *** gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance *** gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast *** reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other *** the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference ***,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.

关键词： Robust aerodynamic design optimization Turbomachinery Adjoint method surrogate model Uncertainty quantification

Integrated optimization analyses of aerodynamic/stealth characteristics of helicopter rotor based on surrogate model

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2015年第3期28卷 737-748页

作者： Jiang Xiangwen Zhao Qijun Zhao Guoqing Li Peng National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics Nanjing University of Aeronautics and Astronautics

Abstract Based on computational fluid dynamics （CFD） method, electromagnetic high-frequency method and surrogate model optimization techniques, an integration design method about aerody- namic/stealth has been established for helicopter rotor. The developed integration design method is composed of three modules： integrated grids generation （the moving-embedded grids for CFD sol- ver and the blade grids for radar cross section （RCS） solver are generated by solving Poisson equa- tions and folding approach）, aerodynamic/stealth solver （the aerodynamic characteristics are simulated by CFD method based upon NavieStokes equations and Spalart-Allmaras （S-A） tur- bulence model）, and the stealth characteristics are calculated by using a panel edge method combining the method of physical optics （PO）, equivalent currents （MEC） and quasi-stationary （MQS）, and integrated optimization analysis （based upon the surrogate model optimization technique with full factorial design （FFD） and radial basis function （RBF）, an integrated optimization analyses on aerodynamic/stealth characteristics of rotor are conducted. Firstly, the scattering characteristics of the rotor with different blade-tip swept and twist angles have been carried out, then timfrequency domain grayscale with strong scattering regions of rotor have been given. Meanwhile, the effects of swept-tip and twist angles on the aerodynamic characteristic of rotor have been performed. Furthermore, by choosing suitable object function and constraint condition, the compromised design about swept and twist combinations of rotor with high aerodynamic performances and low scattering characteristics has been given at last.

关键词： Aerodynamic characteristicsEquivalent currents Physical optics Quasi stationary Rotor Radar cross section Stealth characteristics surrogate model

A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale dome structures

在线全文

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2023年第12期17卷 1813-1829页

作者： Huidong ZHANG Yafei SONG Xinqun ZHU Yaqiang ZHANG Hui WANG Yingjun GAO School of Civil Engineering Tianjin Chengjian UniversityTianjin 300384China Tianjin Key Laboratory of Civil Building Protection and Reinforcement Tianjin 300384China School of Civil and Environmental Engineering University of Technology SydneySydney NSW 2007Australia

Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method(FEM).To reduce the heavy computational burden,a surrogate model of a dome structure was constructed to solve this *** dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance-and distribution-based methods through the surrogate *** model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome *** effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance ***,the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are *** results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.

关键词： large-scale dome structure surrogate model global sensitivity analysis uncertainty quantification structural performance

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

在线全文

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2020年第4期14卷 907-929页

作者： Hamed FATHNEJAT Behrouz AHMADI-NEDUSHAN Department of Civil Engineering Yazd UniversityYazd 89195-741Iran

In this study,the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been *** the first stage,in order to locate the damage accurately,the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion *** the second stage,to estimate the damage extent,the sensitivity of most used modal properties due to damage,such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy(MNMSE)of suspected damaged ***,a modal property change vector is evaluated using the group method of data handling(GMDH)network as a surrogate model during damage extent estimation by optimization algorithm;in this part of methodology,the performance of the three popular optimization algorithms including particle swarm optimization(PSO),bat algorithm(BA),and colliding bodies optimization(CBO)is examined and in this regard,root mean square deviation(RMSD)based on the modal property change vector has been proposed as an objective ***,the effect of noise in the measurement of structural responses by the sensors has also been ***,in order to achieve the most generalized neural network as a surrogate model,GMDH performance is compared with a properly trained cascade feed-forward neural network(CFNN)with log-sigmoid hidden layer transfer *** results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and ***,the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

关键词： two-stage method modal strain energy surrogate model GMDH optimization damage detection

A systematic framework of constructing surrogate model for slider track peeling strength prediction

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Technological Sciences) 2024年第10期67卷 3261-3274页

作者： DONG XingJian CHEN Qian LIU WenBo WANG Dong PENG ZhiKe MENG Guang State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China KEIPER Seating Mechanisms Co. Ltd.Soochow 215500China Department of Industrial Engineering and Management Shanghai Jiao Tong UniversityShanghai 200240China

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety *** methods for determining slider peeling strength are primarily physical testing and numerical ***,these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately ***,the efficient and low-cost surrogate model emerges as a promising ***,currently used surrogate models suffer from inefficiencies and complexity in data sampling,lack of robustness in local model predictions,and isolation between data sampling and model *** overcome these challenges,this paper aims to set up a systematic framework for slider track peeling strength prediction,including sensitivity analysis,dataset sampling,and model ***,the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling *** on the variable sensitivity,a distance metric is constructed to measure the disparity of different variable ***,the sparsity-targeted sampling(STS)is proposed to formulate a representative ***,the sequentially selected local weighted linear regression(SLWLR)is designed to achieve accurate track peeling strength ***,a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a *** results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization ***,the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements,achieving an average absolute error of 3.3 kN in the simulated test dataset.

关键词： slider track peeling strength surrogate model sensitivity analysis data sampling local weighted linear regression