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Uncertainty quantification for the ascent phase of launch vehicles using Bayesian inference 认领 引用
1
作者 CHAO Tao LI Xiaonan +2 位作者 SHANG Xiaobing MA Ping YANG Ming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期485-503,共19页
The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajec... The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch,and uncertainty quantification serves as a crucial method to address this challenge.In traditional uncertainty quantification for launch vehicles,unknown parameters are often assigned specific distributions based on prior knowledge.However,prior knowledge is sometimes subjective,and unknown parameters are often assigned conservative ranges to meet safety margins.In addition,the flight data of the past launch is precious,especially in quantifying the uncertainty of reusable or same-type launch vehicles.This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge,which can more objectively set the distribution of uncertain parameters.Reasonable distribution has a positive impact on uncertainty quantification,which can avoid control strategies that are not robust enough or overly redundant.Therefore,the uncertainty quantification for launch vehicles is discussed under different information sources.In addition,the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions. 展开更多
关键词 launch vehicle uncertainty quantification Bayesian inference launch experience Gaussian process regression polynomial chaos expansions
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Probabilistic framework for uncertainty quantification in the seismic response of buildings 认领 引用
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作者 Moussa Leblouba Samer Barakat Raghad Awad 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期393-410,共18页
This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead ... This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead to either overly conservative or unreliable designs.The proposed method integrates uncertainties in vibration periods and damping ratios as random variables,using elastic response spectra and the ASCE 7-16 design response spectrum for a more accurate seismic risk assessment.The framework effectively identifies discrepancies between measured and predicted vibration periods and damping ratios through numerical examples and case studies,highlighting the risk of non-conservative designs with nominal values.It emphasizes the need to account for biases in vibration period approximations as per ASCE 7 to prevent under-conservative designs.This approach allows engineers and researchers to estimate building responses more realistically,which is crucial for appropriate seismic design and performance evaluation. 展开更多
关键词 uncertainty quantification seismic design response spectra probabilistic approach
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Hybridndiff-UQ:Uncertainty quantification for hybrid neural differentiable modeling 认领 引用
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作者 Deepak Akhare Tengfei Luo Jian-Xun Wang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期1-24,共24页
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer en... The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems.However,a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources.Addressing this gap,we introduce a novel method,uncertainty quantification for hybrid neural differentiable modeling,for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models,leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.Specifically,our approach effectively discerns and quantifies both aleatoric uncertainties,arising from data noise,and epistemic uncertainties,resulting from model-form discrepancies and data sparsity.This is achieved within a Bayesian model averaging framework,where aleatoric uncertainties are modeled through hybrid neural models.The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model.In contrast,epistemic uncertainties are estimated using an ensemble of stochastic gradient descent trajectories.This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters.Notably,our framework is designed for simplicity in implementation and high scalability,making it suitable for parallel computing environments.The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations. 展开更多
关键词 Differentiable programming Scientific machine learning Grey box modeling Uncertainty quantification Scalable bayesian learning
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Continental-scale mapping of forest tree density in North America using remote sensing and deep learning with uncertainty quantification 认领 引用
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作者 Mustak Ahmad Yun Tang +34 位作者 Andrew J.Lister Javier G.P.Gamarra William G.Powell Nathan R.Beane Wook Jin Choi Ankita Mitra Amit Kumar Anibal Cuchietti Alain Paquette Eric Searle Jiaxin Chen Han Y.H.Chen Frans Bongers Jorge A.Meave Mario Guevara Aylin Barreras Jose Armando Alanís de la Rosa Rafael Mayorga Saucedo Rubi Angélica Cuenca Lara César Moreno García Carlos Isaías Godínez Valdivia Carina Edith Delgado Caballero María de los Angeles Soriano Luna Metzli Ileana Aldrete Leal Sandra Liliana Medina Casillas Johny Romero Correa Sergio Armando Villela Gaytán J.Javier Corral Rivas Jose Daniel Vega-Nieva Jaime Briseño-Reyes Pablito Marcelo López-Serrano Tom M.Fayle Jan Altman Daniel J.Johnson Jingjing Liang 《Forest Ecosystems》 SCIE CAS CSCD 2026年第3期757-776,共20页
Accurate,spatially consistent estimates of tree density remain elusive at continental scales,limiting our ability to assess forest structure,carbon stocks,and biodiversity.Existing global assessments have relied on si... Accurate,spatially consistent estimates of tree density remain elusive at continental scales,limiting our ability to assess forest structure,carbon stocks,and biodiversity.Existing global assessments have relied on simplified statistical models and sparse,heterogeneous ground data that are insufficient to capture nonlinear ecological interactions and spatial variability.To address these limitations,we integrated more than 600,000 harmonized ground-based forest inventory plots with satellite-derived vegetation indices,climate surfaces,soil properties,and topographic covariates to develop a deep learning framework for high-resolution mapping of tree density across North America.We evaluated four modeling approaches-generalized linear models(GLMs),ridge regression(RR),random forest(RF),and a feedforward neural network(FFNN).Among all models tested,the FFNN achieved the highest predictive accuracy(RMSE=344.8;R 2=39.53%),and was used to produce a wall-to-wall tree density map at 3 km resolution for the continent.We estimated that the total number of forest trees with diameter at breast height(DBH)≥10 cm across North America ranges from 339 to 514 billion,substantially lower than the widely cited estimate of 603 billion trees reported by Crowther et al.(2015).When smaller stems were included(no DBH threshold),totals more than doubled,reaching 738 billion to 1.12 trillion trees.We quantified uncertainty using Monte Carlo(MC)Dropout,generating pixel-level error estimates and confidence intervals.Spatial patterns reveal high tree densities in boreal and temperate forests,intermediate densities in mixed broadleaf regions,and relatively low densities in deserts,Mediterranean systems,and tundra.Compared to the global GLM-based benchmark by Crowther et al.(2015),our deep learning framework achieves markedly higher predictive accuracy,aligns more closely with national forest inventory statistics,and provides explicit uncertainty quantification,supporting applications in carbon accounting,biodiversity modeling,and ecosystem monitoring at scales through region specific calibration and validation. 展开更多
关键词 Tree density estimation Feedforward neural network(FFNN) Remote sensing Deep learning Uncertainty quantification Monte Carlo(MC)dropout
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Deterministic modeling and uncertainty quantification of wind waves in Ilha Solteira Reservoir,Brazil 认领 引用
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作者 Germano de Oliveira Mattosinho Fabiana de Oliveira Ferreira Geraldo de Freitas Maciel 《Water Science and Engineering》 EI CAS CSCD 2026年第2期291-301,共11页
Wind waves in reservoirs represent a key hydrodynamic process influencing shoreline stability,navigation safety,and the design of hydraulic infrastructure.Despite their practical relevance,wave prediction in inland wa... Wind waves in reservoirs represent a key hydrodynamic process influencing shoreline stability,navigation safety,and the design of hydraulic infrastructure.Despite their practical relevance,wave prediction in inland waters remains subject to significant uncertainties,particularly related to wind forcing and empirical model parameters.This study integrated deterministic and probabilistic approaches for predicting wind waves in reservoirs.Using a deterministic approach,the Simulating Waves Nearshore(SWAN)model was applied to estimate wave height and period.Key variables analyzed included wind velocity,wind direction,the Joint North Sea Wave Project(JONSWAP)bottom friction coefficient,the whitecapping coefficient,and the depth-induced breaking index.Through a probabilistic approach,uncertainties were quantified using polynomial chaos expansion(PCE),and sensitivity analysis was performed via Sobol indices.This framework was applied to a case study of the Tiete—Parana Waterway in the Ilha Solteira Reservoir,Sao Paulo,Brazil.Simulations using the Janssen formulation yielded the most accurate wave height estimates.Sensitivity analysis based on Sobol indices identified wind velocity and the whitecapping coefficient as the most influential factors governing wave behavior.This integrated approach enables the generation of contour maps for wave height and period,offering valuable insights for project planning.Thus,the combination of deterministic and probabilistic analyses enhances the understanding of wind wave dynamics in inland waters. 展开更多
关键词 Uncertainty quantification Wave modeling Wind waves SWAN model Metamodeling Sobol index
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Toward Reliable Battery Life Prediction:A Hybrid Data-Driven Framework with Uncertainty Quantification 认领 引用
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作者 Mingqi Liu Ying Wang +2 位作者 Wujiang Li Juyong Cao Fuyong Yang 《Energy Engineering》 EI 2026年第8期297-312,共16页
Accurately predicting battery life is essential for performance management and system safety.Due to the complexity and diversity of internal mechanisms in lithium-ion batteries,their nonlinear characteristics directly... Accurately predicting battery life is essential for performance management and system safety.Due to the complexity and diversity of internal mechanisms in lithium-ion batteries,their nonlinear characteristics directly give rise to uncertainty in the battery degradation process.However,most existing prediction methods do not fully account for the uncertainty caused by various factors and only provide a point estimate finally.To address this issue,this paper proposes a new framework that combines Random Forest and Conformal Prediction to predict battery life and quantify the uncertainty of the results.This approach leverages the efficiency of Random Forest while enhancing computational robustness and reliability through conformal prediction.The method utilizes early degradation data to select relevant features.Based on this,high-importance feature combinations are selected,and a Random Forest model is used to obtain point estimates.Then,the Conformal Prediction method is introduced to quantify uncertainty and generate prediction intervals with confidence levels and sample-specific bounds.Furthermore,the proposed method is compared against existing uncertainty quantification approaches,with coverage evaluation conducted to enhance the credibility of the prediction results.This method offers a new perspective for the practical application of battery lifetime prediction.Integrating uncertainty quantification into lithium-ion battery research can improve the reliability of the results and support decision-making in practical applications. 展开更多
关键词 Lithium-ion battery uncertainty quantification conformal prediction random forest
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Multiparameter Bayesian full-waveform inversion with uncertainty quantification based on regularized inverse scattering theory for elastic transversely isotropic media 认领 引用
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作者 Wen-Rui Ye Xing-Guo Huang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3180-3212,共33页
Complex subsurface structures exhibit significant anisotropic characteristics,making multi-parameter imaging techniques important for achieving a more comprehensive geological interpretation.Fullwaveform inversion(FWI... Complex subsurface structures exhibit significant anisotropic characteristics,making multi-parameter imaging techniques important for achieving a more comprehensive geological interpretation.Fullwaveform inversion(FWI)as a state-of-the-art method for reconstructing subsurface properties based on seismic wavefield modeling and data misfit minimization has been widely applied to isotropic media in both synthetic and field datasets.However,challenges such as crosstalk correlation and inaccuracy of the initial model indicate that further advancements are required to enhance resolution and computational efficiency.We propose an elastic FWI in the frequency domain for two-dimensional(2D)TI media to characterize their physical properties appropriately,as they are common in sedimentary basin environments.Different from traditional inversion schemes,our approach is formulated based on Bayesian inference,which automatically facilitates uncertainty analysis of the inversion results.Seismic data are acquired via the integral equation(IE)method grounded in scattering theory,where the sensitivity kernel is explicitly constructed using Green's functions,hence facilitating the calculation of gradient and Hessian.A Krylov subspace iterative method provides the approximated solution of the Lippmann-Schwinger(L-S)equation without sacrificing the accuracy.Furthermore,we incorporate the minimum support(MS)stabilizing functional as a model misfit term to regularize the objective function.A randomized singular value decomposition(SVD)approach is used to approximate and decompose the prior preconditioned Hessian.Both the model and covariance are updated through the iterative extended Kalman filter(IEKF)that implemented in the form of the Levenberg-Marquardt(LM)algorithm,thereby enabling practical uncertainty quantification.Numerical tests are conducted on two synthetic TI models with vertical and tilted symmetry axes,respectively,illustrating the precision and robustness of our method. 展开更多
关键词 Inverse scattering theory Full waveform inversion Anisotropy Multi-parameter inversion Uncertainty quantification Regularization term Randomized singular value decomposition
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Sequential search-based Latin hypercube sampling scheme for digital twin uncertainty quantification with application in EHA 认领 引用 被引量：1
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作者 Dong LIU Shaoping WANG +1 位作者 Jian SHI Di LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第4期176-192,共17页
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin... For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes. 展开更多
关键词 Digital Twin(DT) Genetic algorithms(GA) Optimal Latin Hypercube Design(Opt LHD) Sequential test Uncertainty Quantification(UQ) EHA
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Uncertainty Quantification of Dynamic Stall Aerodynamics for Large Mach Number Flow around Pitching Airfoils 认领 引用
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作者 Yizhe Han Guangjing Huang +2 位作者 Fei Xiao Zhiyin Huang Yuting Dai 《Fluid Dynamics & Materials Processing》 EI 2025年第7期1657-1671,共15页
During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a com... During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a complex,unsteady flow phenomenon highly sensitive to inlet conditions such asMach and Reynolds numbers.The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil.In this study,we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils,accounting for uncertainties in inlet parameters.A computational fluid dynamics(CFD)model based on the compressible unsteady Reynolds-averagedNavier–Stokes(URANS)equations,coupledwith sliding mesh techniques,is developed to simulate the unsteady aerodynamic behavior and associated flow fields.To efficiently capture the aerodynamic responses while maintaining high accuracy,a multi-fidelity Co-Kriging surrogate model is constructed.This model integrates the precision of high-fidelity wind tunnel experiments with the computational efficiency of lower-fidelity URANS simulations.Its accuracy is validated through direct comparison with experimental data.Building upon this surrogate model,we employ interval analysis and the Sobol sensitivity method to quantify the uncertainty and parameter sensitivity of the unsteady aerodynamic forces resulting frominlet condition variability.Both the inlet Mach number and Reynolds number are treated as uncertain inputs,modeled using interval representations.Our results demonstrate that variations inMach number contribute far more significantly to aerodynamic uncertainty than those in Reynolds number.Moreover,the presence of dynamic stall vortices markedly amplifies the aerodynamic sensitivity to Mach number fluctuations. 展开更多
关键词 Dynamic stall uncertainty quantification multi-fidelity surrogate modeling sensitivity analysis
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Nonlinear uncertainty quantification of the impact of geometric variability on compressor performance using an adjoint method 认领 引用 被引量：16
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作者 Qian ZHANG Shenren XU +3 位作者 Xianjun YU Jiaxin LIU Dingxi WANG Xiuquan HUANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第2期17-21,共5页
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 ... 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. 展开更多
关键词 Adjoint method Aerodynamics Compressor Manufacturing Monte Carlo method Nonlinearity Uncertainty quantification
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Advances in the study of uncertainty quantification of large-scale hydrological modeling system 认领 引用 被引量：23
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作者 SONG Xiaomeng ZHAN Chesheng +1 位作者 KONG Fanzhe XIA Jun 《Journal of Geographical Sciences》 SCIE CSCD 2011年第5期801-819,共19页
The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions.And the hydrological models play a very important role in simulating the complex s... The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions.And the hydrological models play a very important role in simulating the complex system.However,there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty.The uncertainties in hydrological modeling come from four important aspects：uncertainties in input data and parameters,uncertainties in model structure,uncertainties in analysis method and the initial and boundary conditions.This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources.Also,the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out.And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced,which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models.Finally,some future perspectives on uncertainty quantification are put forward. 展开更多
关键词 uncertainty quantification hydrological model PSUADE land-atmosphere coupling model large scale
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Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming 认领 引用 被引量：6
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作者 Hongbo Zhao Shaojun Li +3 位作者 Xiaoyu Zang Xinyi Liu Lin Zhang Jiaolong Ren 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第3期895-908,共14页
Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conv... Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems. 展开更多
关键词 Geological engineering Geotechnical engineering Inverse analysis Uncertainty quantification Probabilistic programming
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Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system 认领 引用 被引量：3
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作者 Jinsong Tang Linfang Qian +3 位作者 Longmiao Chen Guangsong Chen Mingming Wang Guangzu Zhou 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第3期125-133,共9页
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 pro... 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. 展开更多
关键词 Ammunition delivery system Electromechanical coupling dynamics Uncertainty quantification Generalized probability density evolution
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Computational intelligence approach for uncertainty quantification using evidence theory 认领 引用 被引量：7
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作者 Bin Suo Yongsheng Cheng +1 位作者 Chao Zeng Jun Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第2期250-260,共11页
As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification（UQ） analyses is subject of intense research in recent years.Two state-... As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification（UQ） analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method. 展开更多
关键词 uncertainty quantification（UQ） evidence theory hybrid algorithm interval algorithm genetic algorithm（GA）.
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Uncertainty Quantification of Numerical Simulation of Flows around a Cylinder Using Non-intrusive Polynomial Chaos 认领 引用 被引量：2
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作者 王言金 张树道 《Chinese Physics Letters》 SCIE EI CAS CSCD 2016年第9期17-21,共5页
The uncertainty quantification of flows around a cylinder is studied by the non-intrusive polynomial chaos method. Based on the validation with benchmark results, discussions are mainly focused on the statistic proper... The uncertainty quantification of flows around a cylinder is studied by the non-intrusive polynomial chaos method. Based on the validation with benchmark results, discussions are mainly focused on the statistic properties of the peak lift and drag coefficients and base pressure drop over the cylinder with the uncertainties of viscosity coefficient and inflow boundary velocity. As for the numerical results of flows around a cylinder, influence of the inflow boundary velocity uncertainty is larger than that of viscosity. The results indeed demonstrate that a five-order degree of polynomial chaos expansion is enough to represent the solution of flow in this study. 展开更多
关键词 of in on is it Uncertainty Quantification of Numerical Simulation of Flows around a Cylinder Using Non-intrusive Polynomial Chaos for
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Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification 认领 引用 被引量：3
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作者 Wenting Wang Yaguo Lei +2 位作者 Tao Yan Naipeng Li Asoke KNandi 《Journal of Dynamics, Monitoring and Diagnostics》 2022年第1期2-8,共7页
Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)prediction.Among various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,h... Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)prediction.Among various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,have gained extensive attention for their ability to capture temporal dependence.Although existing RNN-based methods have demonstrated their RUL prediction effectiveness,they still suffer from the following two limitations:1)it is difficult for the RNN to directly extract degradation features from original monitoring data and 2)most RNN-based prognostics methods are unable to quantify RUL uncertainty.To address the aforementioned limitations,this paper proposes a new prognostics method named residual convolution LSTM(RC-LSTM)network.In the RC-LSTM,a new ResNet-based convolution LSTM(Res-ConvLSTM)layer is stacked with a convolution LSTM(ConvLSTM)layer to extract degradation representations from monitoring data.Then,under the assumption that the RUL follows a normal distribution,an appropriate output layer is constructed to quantify the uncertainty of prediction results.Finally,the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests. 展开更多
关键词 Deep learning residual convolution LSTM network remaining useful life prediction uncertainty quantification
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Surrogate model uncertainty quantification for active learning reliability analysis 认领 引用
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作者 Yong PANG Shuai ZHANG +4 位作者 Pengwei LIANG Muchen WANG Zhuangzhuang GONG Xueguan SONG Ziyun KAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第12期55-70,共16页
Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate... Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classification function to quantify the impact of surrogate model uncertainty on failure probability estimation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the proposed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches. 展开更多
关键词 Reliability analysis Kriging model Uncertainty quantification Active learning Monte Carlo simulation
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Compressor geometric uncertainty quantification under conditions from near choke to near stall 认领 引用 被引量：9
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作者 Junying WANG Baotong WANG +3 位作者 Heli YANG Zhenzhong SUN Kai ZHOU Xinqian ZHENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2023年第3期16-29,共14页
Geometric and working condition uncertainties are inevitable in a compressor,deviating the compressor performance from the design value.It’s necessary to explore the influence of geometric uncertainty on performance ... Geometric and working condition uncertainties are inevitable in a compressor,deviating the compressor performance from the design value.It’s necessary to explore the influence of geometric uncertainty on performance deviation under different working conditions.In this paper,the geometric uncertainty influences at near stall,peak efficiency,and near choke conditions under design speed and low speed are investigated.Firstly,manufacturing geometric uncertainties are analyzed.Next,correlation models between geometry and performance under different working conditions are constructed based on a neural network.Then the Shapley additive explanations(SHAP)method is introduced to explain the output of the neural network.Results show that under real manufacturing uncertainty,the efficiency deviation range is small under the near stall and peak efficiency conditions.However,under the near choke conditions,efficiency is highly sensitive to flow capacity changes caused by geometric uncertainty,leading to a significant increase in the efficiency deviation amplitude,up to a magnitude of-3.6%.Moreover,the tip leading-edge radius and tip thickness are two main factors affecting efficiency deviation.Therefore,to reduce efficiency uncertainty,a compressor should be avoided working near the choke condition,and the tolerances of the tip leading-edge radius and tip thickness should be strictly controlled. 展开更多
关键词 Compressor Geometric uncertainty quantification Interpretable machine learning Multiple conditions Neural network
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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 认领 引用 被引量：4
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作者 Mingming Wang Linfang Qian +3 位作者 Guangsong Chen Tong Lin Junfei Shi Shijie Zhou 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第2期209-221,共13页
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 establi... 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. 展开更多
关键词 Truck-mounted howitzer Projectile motion Uncertainty quantification Probability density evolution method
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Smolyak Type Sparse Grid Collocation Method for Uncertainty Quantification of Nonlinear Stochastic Dynamic Equations 认领 引用
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作者 石红芹 何军 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期612-617,共6页
This paper develops a Smolyak-type sparse-grid stochastic collocation method(SGSCM) for uncertainty quantification of nonlinear stochastic dynamic equations.The solution obtained by the method is a linear combination ... This paper develops a Smolyak-type sparse-grid stochastic collocation method(SGSCM) for uncertainty quantification of nonlinear stochastic dynamic equations.The solution obtained by the method is a linear combination of tensor product formulas for multivariate polynomial interpolation.By choosing the collocation point sets to coincide with cubature point sets of quadrature rules,we derive quadrature formulas to estimate the expectations of the solution.The method does not suffer from the curse of dimensionality in the sense that the computational cost does not increase exponentially with the number of input random variables.Numerical analysis of a nonlinear elastic oscillator subjected to a discretized band-limited white noise process demonstrates the computational efficiency and accuracy of the developed method. 展开更多
关键词 sparse grid Smolyak algorithm stochastic dynamic equation uncertainty quantification
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