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

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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 problem.The 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.The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure.The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states.Finally,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 discussed.The 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

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Fast Uncertainty quantification of Electromechanical Oscillation Frequency on Varying Generator Damping

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 2043-2048页

作者： Yongli Zhu Chanan Singh Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX77840USA

This letter develops a fast analytical method for Uncertainty quantification of electromechanical oscillation frequency due to varying generator dampings. By employing the techniques of matrix determinant reduction, two types of uncertainty analysis are investigated to quantify the impact of the generator damping on electromechanical oscillation frequency, i.e., interval analysis and probabilistic analysis. The proposed analytical frequency estimation formula is verified against conventional methods on two transmission system models. Then, Monte Carlo experiments and interval analysis are respectively conducted to verify the established lower/upper bound formulae and probability distribution formulae. Results demonstrate the accuracy and speed of the proposed method.

关键词： Electromechanical oscillation interval analysis Monte Carlo simulation quadratic eigenvalue problem Uncertainty quantification

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Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

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Journal of Energy Chemistry 2023年第6期81卷 118-124,I0005页

作者： Yonghui Zhou Bo Yang School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of Uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

关键词： Uncertainty quantification High-entropy alloys Bayesian neural networks Energy prediction Structure screening

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Nonlinear Uncertainty quantification of the impact of geometric variability on compressor performance using an adjoint method

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Chinese Journal of Aeronautics 2022年第2期35卷 17-21页

作者： Qian ZHANG Shenren XU Xianjun YU Jiaxin LIU Dingxi WANG Xiuquan HUANG School of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Aeroengine Internal Flows Northwestern Polytechnical UniversityXi’an 710129China School of Energy and Power Engineering Beihang UniversityBeijing 100083China Research Institute of Aeroengine Beihang UniversityBeijing 100083China

Manufactured blades are inevitably different from their design intent,which leads to a deviation of the performance from the intended value.To quantify the associated performance uncertainty,many approaches have been developed.The traditional Monte Carlo method based on a Computational Fluid Dynamics solver(MC-CFD)for a three-dimensional compressor is prohibitively expensive.Existing alternatives to the MC-CFD,such as surrogate models and secondorder derivatives based on the adjoint method,can greatly reduce the computational cost.Nevertheless,they will encounter’the curse of dimensionality’except for the linear model based on the adjoint gradient(called MC-adj-linear).However,the MC-adj-linear model neglects the nonlinearity of the performance function.In this work,an improved method is proposed to circumvent the lowaccuracy problem of the MC-adj-linear without incurring the high cost of other alternative models.The method is applied to the study of the aerodynamic performance of an annular transonic compressor cascade,subject to prescribed geometric variability with industrial relevance.It is found that the proposed method achieves a significant accuracy improvement over the MC-adj-linear with low computational cost,showing the great potential for fast Uncertainty quantification.

关键词： Adjoint method Aerodynamics Compressor Manufacturing Monte Carlo method Nonlinearity Uncertainty quantification

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Uncertainty quantification of Store-Separation Simulation Due to Ejector Modeling using a Monte Carlo Approach with Kriging Model

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Advances in Applied Mathematics and Mechanics 2022年第3期14卷 622-651页

作者： Shuling Tian Rongjie Li Jiawei Fu Zihan Jiao Jiangtao Chen Key Laboratory of Unsteady Aerodynamics and Flow Control Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjingJiangsu 210016China Shenyang Aircraft Design&Research Institute ShenyangLiaoning 110000China Science and Technology on Space Physics Laboratory China Academy of Launch Vehicle TechnologyBeijing 100076China China Aerodynamics Research and Development Center MianyangSichuan 621000China

Precise calculation of the trajectory of store separation is critical in assess-ing whether the store can be released safely.Store ejection is the initial stage of the releasing process and any uncertainty introduced at this stage will propagate through the whole trajectory.In this work,the impact of the uncertainties in ejector modeling on the simulation of a generic store separation is investigated by using a Monte-Carlo-based approach.To reduce the extremely large computation cost resulted from the direct use CFD in Monte Carlo simulation,the CFD solutions are represented by a time-dependent Kriging model,which is constructed at each time step by using the samples from the URANS simulations.The stochastic outputs,including the distri-bution of probability density function,expected value and 95%conﬁdence interval of store separation trajectory,are obtained by the Monte Carlo simulations.The sensitiv-ity analysis is also carried out by using the Monte-Carlo-based method to determine the most signiﬁcant variables in ejector modeling,which affect the output uncertainty.Our results show that ejector modeling is one of the main uncertainty sources of store separation simulation and the approximation in ejector modeling can cause a signiﬁ-cant deviation,especially in the angular displacement.

关键词： Uncertainty quantification Monte Carlo simulation Kriging surrogate model store separation

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Uncertainty quantification and reduction in the characterization of subsurface stratigraphy using limited geotechnical investigation data

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Underground Space 2020年第2期5卷 125-143页

作者： Xiangrong Wang Department of Civil and Environmental Engineering and Engineering Mechanics The University of DaytonDaytonOH 45469-0243USA

Subsurface stratigraphy is critical to the design,construction,and subsequent performance of geotechnical structures.However,in practice it is impossible to identify the stratigraphy of a subsurface geological domain with absolute certainty,due to the limitations imposed by geotechnical investigation techniques and project budgets.This paper presents a subsurface stratigraphic modeling and Uncertainty quantification approach,which is established based on an improved and extended geological modeling technique previously established by the author and others,for simulating the stratigraphy of both two-dimensional(2D)and three-dimensional(3D)cases with more complex geological features.Furthermore,this approach provides quantitative evaluation of the amount of stratigraphic uncertainty in the current interpretation and enables the systematic reduction of stratigraphic uncertainty through the investigation of additional targeted borehole drilling locations.Illustrative examples,including artificial cases as well as two real cases from existing geotechnical projects,are presented in this study to demonstrate the use of the proposed analysis approach.

关键词： Site investigation Stratigraphy Stochastic modeling Uncertainty quantification Borehole drilling

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Analysis of actuator delay and its effect on Uncertainty quantification for real-time hybrid simulation

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Earthquake Engineering and Engineering Vibration 2017年第4期16卷 713-725页

作者： Cheng Chen Weijie Xu Tong Guo Kai Chen School of Engineering San Francisco State University Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education Southeast University

Uncertainties in structure properties can result in different responses in hybrid simulations. quantification of the effect of these tmcertainties would enable researchers to estimate the variances of structural responses observed from experiments. This poses challenges for real-time hybrid simulation （RTHS） due to the existence of actuator delay. Polynomial chaos expansion （PCE） projects the model outputs on a basis of orthogonal stochastic polynomials to account for influences of model uncertainties. In this paper, PCE is utilized to evaluate effect of actuator delay on the maximum displacement from real-time hybrid simulation of a single degree of freedom （SDOF） structure when accounting for uncertainties in structural properties. The PCE is first applied for RTHS without delay to determine the order of PCE, the number of sample points as well as the method for coefficients calculation. The PCE is then applied to RTHS with actuator delay. The mean, variance and Sobol indices are compared and discussed to evaluate the effects of actuator delay on Uncertainty quantification for RTHS. Results show that the mean and the variance of the maximum displacement increase linearly and exponentially with respect to actuator delay, respectively. Sensitivity analysis through Sobol indices also indicates the influence of the single random variable decreases while the coupling effect increases with the increase of actuator delay.

关键词： real-time hybrid simulation actuator delay polynomial chaos expansion delay differential equation Uncertainty quantification

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Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system

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Defence Technology（防务技术） 2024年第3期33卷 125-133页

作者： Jinsong Tang Linfang Qian Longmiao Chen Guangsong Chen Mingming Wang Guangzu Zhou Nanjing University of Science and Technology Nanjing 210094China Northwest Institute of Mechanical and Electrical Engineering Xianyang 712099China

In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the Uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and Uncertainty quantification research of mechatronics system.

关键词： Ammunition delivery system Electromechanical coupling dynamics Uncertainty quantification Generalized probability density evolution

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Ensemble-based Uncertainty quantification for coordination and control of thermostatically controlled loads

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Journal of Control and Decision 2018年第2期5卷 148-168页

作者： Weixuan Li Jianming Lian Dave Engel Hong Wang Pacific Northwest National Laboratory RichlandWAUSA

This work investigates an Uncertainty quantification(UQ)framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance.The framework involves solving four problems:identifying uncertain parameters,propagating uncertainty to the quantity of interest,data assimilation and making decisions under quantified uncertainties.A specific group of UQ approaches,known as the ensemble-based methods,are adopted to solve these problems.This UQ framework is applied to coordinating a group of thermostatically controlled loads,which relies on simulating a second-order equivalent thermal parameter model with some uncertain parameters.How this uncertainty affects the prediction and the control of total power is examined.The study shows that uncertainty can be effectively reduced using the measurement of air temperatures.Also,the control objective is achieved fairly accurately with a quantification of the uncertainty.

关键词： Uncertainty quantification ensemble-based method thermostatically controlled loads transactive control smart grid

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High-dimensional Uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method

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Defence Technology（防务技术） 2024年第2期32卷 209-221页

作者： Mingming Wang Linfang Qian Guangsong Chen Tong Lin Junfei Shi Shijie Zhou Nanjing University of Science and Technology Nanjing 210094JiangsuChina Northwest Institute of Mechanical and Electrical Engineering Xianyang 712099ShaanxiChina Nanjing Vocational University of Industry Technology Nanjing 210023JiangsuChina

This paper proposed an efficient research method for high-dimensional Uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional Uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

关键词： Truck-mounted howitzer Projectile motion Uncertainty quantification Probability density evolution method

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