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
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
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
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
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
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
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
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
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
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 respo...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.展开更多
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
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
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 wi...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.展开更多
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
Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and me...Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and method selection uncertainty, on the basis of information from limited experimental data of uncertain parameters. Two uncertain variables of the actuator coupling system with unknown probability distributions, that is bending and torsional stiffness, which are both described with multiple intervals and the basic belief assignment（BBA） extricated from the modal test of actuator coupling systems, are taken into account. Considering the difference in dealing with experimental data by different persons and the reliability of various information sources, a new combination rule of evidence––the generalized lower triangular matrices method is formed to acquire the combined BBA. Finally the parametric uncertainty and the epistemic uncertainty of flutter analysis method selection are considered in the same system to realize quantification. A typical rudder of missile is selected to examine the present method, and the dangerous range of velocity as well as relevant belief and plausibility functions is obtained. The results suggest that the present method is effective in obtaining the lower and upper bounds of flutter probability and assessing flutter risk of structures with limited experimental data of uncertain parameters and the belief of different methods.展开更多
Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse ...Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric structures.To tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is proposed.This graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and edges.The method effectively captures complex building characteristics,enhancing the generalization abilities.An approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain factors.Additionally,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design stage.The effectiveness of this method is evaluated through comparison with four widely-used data-driven techniques.Results indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,respectively.The proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization abilities.With regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.展开更多
High performance concrete(HPC)properties depend on both its constituent materials and their interaction.This study presents a machine learning framework to quantify the effects of constituents on HPC compressive stren...High performance concrete(HPC)properties depend on both its constituent materials and their interaction.This study presents a machine learning framework to quantify the effects of constituents on HPC compressive strength.We first develop a stochastic constitutive model using experimental data and subsequently employ an uncertainty quantification method to identify key parameters in relation to the compressive strength of HPC.The resultant sensitivity indices indicate that fly ash content has the strongest influence on compressive strength,followed by concrete age at test and blast surface slag content.展开更多
Most problems in uncertainty quantification,despite their ubiquitousness in scientific computing,applied mathematics and data science,remain formidable on a classical computer.For uncertainties that arise in partial d...Most problems in uncertainty quantification,despite their ubiquitousness in scientific computing,applied mathematics and data science,remain formidable on a classical computer.For uncertainties that arise in partial differential equations(PDEs),large numbers M>>1 of samples are required to obtain accurate ensemble averages.This usually involves solving the PDE M times.In addition,to characterise the stochasticity in a PDE,the dimension L of the random input variables is high in most cases,and classical algorithms suffer from the curse-of-dimensionality.We propose new quantum algorithms for PDEs with uncertain coefficients that are more efficient in M and L in various important regimes,compared to their classical counterparts.We introduce transformations that convert the original d-dimensional equation(with uncertain coefficients)into d+L(for dissipative equations)or d+2L(for wave type equations)dimensional equations(with certain coefficients)in which the uncertainties appear only in the initial data.These transformations also allow one to superimpose the M different initial data,so the computational cost for the quantum algorithm to obtain the ensemble average from M different samples is independent of M,while also showing potential advantage in d,L and precisionεin computing ensemble averaged solutions or physical observables.展开更多
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
In this study,we measured the^(58)Ni(n,p)^(58)Co reaction cross section with neutron energies of 1.06,1.86,and 2.85 MeV.The cross section was measured using neutron activation techniques andγ-ray spectroscopy,and it ...In this study,we measured the^(58)Ni(n,p)^(58)Co reaction cross section with neutron energies of 1.06,1.86,and 2.85 MeV.The cross section was measured using neutron activation techniques andγ-ray spectroscopy,and it was compared with cross section data available in the EXFOR.Furthermore,we calculated the covariance matrix of the measured cross section for the aforementioned nuclear reaction.The uncertainties of the theoretical calculation for^(58)Ni(n,p)^(58)Co reaction cross section were calculated via Monte Carlo method.In this study,we used uncertainties in the optical model and level density parameters to calculate uncertainties in the theoretical cross sections.The theoretical calculations were performed by using TALYS-1.96.In this study,we aim to analyze the effect of uncertainties of the nuclear model input as well as different experimental variables used to obtain the values of reaction cross section.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘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.
文摘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.
基金the National Natural Science Foundation of China(Grant No.11472137).
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.11472137 and U2141246)。
文摘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.
基金supported by the National Key Research and Development Program of China(No.2023YFB3406900)the National Natural Science Foundation of China(No.52075068).
文摘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.
基金National Key Basic Research Program of China,No.2010CB428403National Grand Science and Technology Special Project of Water Pollution Control and Improvement,No.2009ZX07210-006
文摘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.
基金funded by the National Natural Science Foundation of China(No.52006177)National Science and Technology Major Project,China(No.2017-II-0009-0023)。
文摘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.
基金supported by the National Science and Technology Major Project,China(No.2017-II-0004-0016)。
文摘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.
基金supported by the Advanced Research of National Defense Foundation of China(426010501)
文摘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.
基金National Science Foundation of China under grant No.51378107Fundamental Research Funds for the Central Universities and Doctoral Research Fund by Southeast University under Grant No.YBJJ-1442
文摘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.
基金Supported by the National Natural Science Foundation of China under Grant No 11371069the Young Foundation of Institute of Applied Physics and Computational Mathematics under Grant No ZYSZ1518-13the Science Foundation of China Academy of Engineering Physics under Grant No 2013A0101004
文摘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.
基金This research was supported by National Natural Science Foundation of China(52005387,52025056)Project funded by China Postdoctoral Science Foundation(2020M673380)Fundamental Research Funds for the Central Universities.
文摘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.
基金supported by the Shanghai Rising-Star Program (20QA1406800)the National Natural Science Foundation of China (22072091,91745102,92045301)。
文摘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.
基金the Scientific Research Foundation of State Education Ministry for the Returned Overseas Scholars(No.14Z102050011)
文摘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.
基金co-supported by the National Natural Science Foundation of China(Nos.:91116005 and 11372023)
文摘Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and method selection uncertainty, on the basis of information from limited experimental data of uncertain parameters. Two uncertain variables of the actuator coupling system with unknown probability distributions, that is bending and torsional stiffness, which are both described with multiple intervals and the basic belief assignment（BBA） extricated from the modal test of actuator coupling systems, are taken into account. Considering the difference in dealing with experimental data by different persons and the reliability of various information sources, a new combination rule of evidence––the generalized lower triangular matrices method is formed to acquire the combined BBA. Finally the parametric uncertainty and the epistemic uncertainty of flutter analysis method selection are considered in the same system to realize quantification. A typical rudder of missile is selected to examine the present method, and the dangerous range of velocity as well as relevant belief and plausibility functions is obtained. The results suggest that the present method is effective in obtaining the lower and upper bounds of flutter probability and assessing flutter risk of structures with limited experimental data of uncertain parameters and the belief of different methods.
基金supported by the National Natural Science Foundation of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028)+1 种基金the Basic Research Funds for the Central Government‘Innovative Team of Zhejiang University’(No.2022FZZX01-09)China Scholarship Fund.
文摘Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric structures.To tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is proposed.This graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and edges.The method effectively captures complex building characteristics,enhancing the generalization abilities.An approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain factors.Additionally,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design stage.The effectiveness of this method is evaluated through comparison with four widely-used data-driven techniques.Results indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,respectively.The proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization abilities.With regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.
文摘High performance concrete(HPC)properties depend on both its constituent materials and their interaction.This study presents a machine learning framework to quantify the effects of constituents on HPC compressive strength.We first develop a stochastic constitutive model using experimental data and subsequently employ an uncertainty quantification method to identify key parameters in relation to the compressive strength of HPC.The resultant sensitivity indices indicate that fly ash content has the strongest influence on compressive strength,followed by concrete age at test and blast surface slag content.
基金supported by the National Natural Science Foundation of China(Grant Nos.12031013,12341104,and 12050410230)the National Natural Science Foundation of China International Young Scientists Project(Grant No.12050410230)+6 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)the Innovation Program of Shanghai Municipal Education Commission(Grant No.2021-01-07-00-02-E00087)the Science and Technology Program of ShanghaiChina(Grant No.21JC1402900)the Shanghai Pujiang Talent Grant(Grant No.20PJ1408400)the Shanghai Jiao Tong University 2030 Initiativethe Fundamental Research Funds for the Central Universities。
文摘Most problems in uncertainty quantification,despite their ubiquitousness in scientific computing,applied mathematics and data science,remain formidable on a classical computer.For uncertainties that arise in partial differential equations(PDEs),large numbers M>>1 of samples are required to obtain accurate ensemble averages.This usually involves solving the PDE M times.In addition,to characterise the stochasticity in a PDE,the dimension L of the random input variables is high in most cases,and classical algorithms suffer from the curse-of-dimensionality.We propose new quantum algorithms for PDEs with uncertain coefficients that are more efficient in M and L in various important regimes,compared to their classical counterparts.We introduce transformations that convert the original d-dimensional equation(with uncertain coefficients)into d+L(for dissipative equations)or d+2L(for wave type equations)dimensional equations(with certain coefficients)in which the uncertainties appear only in the initial data.These transformations also allow one to superimpose the M different initial data,so the computational cost for the quantum algorithm to obtain the ensemble average from M different samples is independent of M,while also showing potential advantage in d,L and precisionεin computing ensemble averaged solutions or physical observables.
基金The authors gratefully acknowledge the support from the National Natural Science Foundation of China(Grant No.42377174)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2022ME198)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.Z020006).
文摘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.
基金Project supported by the Scientific and Industrial Research(CSIR)Government of India(File No 09/013(882)/2019-EMR-1)for providing senior research fellowships+1 种基金the IUAC-UGC,Government of India(Sanction No.IUAC/XIII.7/UFR-71353)Institutions of Eminence(Io E)BHU(Grant No.6031)。
文摘In this study,we measured the^(58)Ni(n,p)^(58)Co reaction cross section with neutron energies of 1.06,1.86,and 2.85 MeV.The cross section was measured using neutron activation techniques andγ-ray spectroscopy,and it was compared with cross section data available in the EXFOR.Furthermore,we calculated the covariance matrix of the measured cross section for the aforementioned nuclear reaction.The uncertainties of the theoretical calculation for^(58)Ni(n,p)^(58)Co reaction cross section were calculated via Monte Carlo method.In this study,we used uncertainties in the optical model and level density parameters to calculate uncertainties in the theoretical cross sections.The theoretical calculations were performed by using TALYS-1.96.In this study,we aim to analyze the effect of uncertainties of the nuclear model input as well as different experimental variables used to obtain the values of reaction cross section.
