查看更多>>摘要：This study introduces a new approach utilizing an interval finite element method com-bined with a bilevel Kriging model to determine the bounds of structural responses in the presence of spatial uncertainties.A notable benefit of this approach is its ability to determine the response bounds across all degrees of freedom with a small sample size,which means that it has high effi-ciency.Firstly,the spatially varying uncertain parameters are quantified using an interval field model,which is described by a series of standard interval variables within a truncated interval Karhunen-Loeve(K-L)series expansion.Secondly,considering that the bound of structural response is a function of spatial position with the property of continuity,a surrogate model for the response bound is constructed,namely the first-level Kriging model.The training samples required for this surrogate model are obtained by establishing the second-level Kriging model.The second-level Kriging model is established to describe the structural responses at particular loca-tions relative to the interval variables so as to facilitate the upper and lower bounds of the node response required by the first-level Kriging model.Finally,the accuracy and effectiveness of the method are verified through examples.
查看更多>>摘要：The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and epistemic uncertainties,inevitably exist in the performance assessment of EHA systems.Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design.To overcome this drawback,a metamodeling method for hybrid uncertainty propagation of EHA systems is devel-oped via an active learning Gaussian Process(GP)model.The proposed method is bifurcated into three pillars:(A)Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance(OMMD)algorithm,which aims to maximize the minimum distance between the added samples and original samples,(B)maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy,while updating the original GP model,and(C)judging the convergence by three uncertainty metrics,i.e.,the area met-ric,maximum variance metric,and the mean value metric.A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method.Meanwhile,the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional prob-lems.The effects of the uncertainties in the Proportional-Integral-Differential(PID)of the EHA system are also examined.
查看更多>>摘要：Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epistemic uncertainties may be one of the challenging issues in impor-tance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncer-tainty,should be taken into consideration.To solve these problems,this paper proposes the com-ponent Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the compo-nents is described by a Markov model,and the system reliability model is developed using the Mar-kov hierarchal evidential network.The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality,from the perspective of system performance.A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure.The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.
查看更多>>摘要：Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inher-ent 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 classi-fication function to quantify the impact of surrogate model uncertainty on failure probability esti-mation.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 pro-posed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.
查看更多>>摘要：Interval Uncertainty Propagation(IUP)holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters.In the aviation field,the precise determination of probability models for input parameters of aeronautical struc-tures entails substantial costs in both time and finances.As an alternative,the use of interval vari-ables to describe input parameter uncertainty becomes a pragmatic approach.The complex task of solving the IUP for aeronautical structures,particularly in scenarios marked by pronounced non-linearity and multiple outputs,necessitates innovative methodologies.This study introduces an effi-cient deep learning-driven approach to address the challenges associated with IUP.The proposed approach combines the Deep Neural Network(DNN)with intelligent optimization algorithms for dealing with the IUP in aeronautical structures.An inventive extremal value-oriented weighting technique is presented,assigning varying weights to different training samples within the loss func-tion,thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs.Moreover,an adaptive framework is established to strategically balance the glo-bal exploration and local exploitation capabilities of the DNN,resulting in a predictive model that is both robust and accurate.To illustrate the effectiveness of the developed approach,various appli-cations are explored,including a high-dimensional numerical example and two aeronautical struc-tures.The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach,showcasing its potential for addressing complex IUP challenges in aeronautical engineering.
查看更多>>摘要：Deep learning has been widely applied in surrogate modeling for airfoil flow field predic-tion.The success of deep learning relies heavily on large-scale,high-quality labeled samples.How-ever,acquiring labeled samples with complete annotations is prohibitively expensive,and the available annotations in practical engineering are often sparse due to limited observation.To lever-age samples with sparse annotations,this paper proposes an uncertainty-based active transfer learn-ing method.The most valuable positions in the flow field are selected based on uncertainty for annotation,effectively improving prediction accuracy and reducing annotation costs.Our method involves a novel active annotation based on synchronous quantile regression,which can mitigate the computational cost of query annotation.Besides,a novel quantile levels-based consistency regular-ization is proposed to constrain the remaining unlabeled regions and further improve the model per-formance.Experiments show that our method can significantly reduce prediction errors with only 1％extra annotations,and is a promising tool for achieving rapid and accurate flow field prediction.
查看更多>>摘要：Reliability is a crucial metric in aerospace engineering.The results of reliability assess-ments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems.Current methods for analyzing the reliabil-ity of aerospace electromagnetic relays have limitations,such as neglecting the combined effects of multiple uncertain factors,degradation of key component properties,and the influence of fluctua-tions in aerospace environments.Additionally,these methods often assume a single-type uncer-tainty in the manufacturing process,leading to significant deviations between the analysis results and actual measurement results.To address these issues,this study proposes an efficient time-dependent reliability analysis method based on the HL-RF algorithm,considering a hybrid of prob-abilistic and interval uncertainty that accounts for degradation and environmental conditions.The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods,demonstrating significant advantages.The pro-posed method has been applied to the time-dependent reliability analysis of actual aerospace elec-tromagnetic relay products under different environmental conditions.The analysis results exhibit an error margin within 5.12％compared to actual measurement results.Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantifica-tion,this method reduces the analysis error by 52％and 67％respectively.When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification,the error reduction is 23％.These demonstrate the superiority of the proposed method and validates its effectiveness.The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.
查看更多>>摘要：The distribution-free P-box process serves as an effective quantification model for time-varying uncertainties in dynamical systems when only imprecise probabilistic information is avail-able.However,its application to nonlinear systems remains limited due to excessive computation.This work develops an efficient method for propagating distribution-free P-box processes in nonlin-ear dynamics.First,using the Covariance Analysis Describing Equation Technique(CADET),the dynamic problems with P-box processes are transformed into interval Ordinary Differential Equa-tions(ODEs).These equations provide the Mean-and-Covariance(MAC)bounds of the system responses in relation to the MAC bounds of P-box-process excitations.They also separate the pre-viously coupled P-box analysis and nonlinear-dynamic simulations into two sequential steps,including the MAC bound analysis of excitations and the MAC bounds calculation of responses by solving the interval ODEs.Afterward,a Gaussian assumption of the CADET is extended to the P-box form,i.e.,the responses are approximate parametric Gaussian P-box processes.As a result,the probability bounds of the responses are approximated by using the solutions of the inter-val ODEs.Moreover,the Chebyshev method is introduced and modified to efficiently solve the interval ODEs.The proposed method is validated based on test cases,including a duffing oscillator,a vehicle ride,and an engineering black-box problem of launch vehicle trajectory.Compared to the reference solutions based on the Monte Carlo method,with relative errors of less than 3％,the pro-posed method requires less than 0.2％calculation time.The proposed method also possesses the ability to handle complex black-box problems.
查看更多>>摘要：For the design and optimization of advanced aero-engines,the prohibitively computa-tional resources required for numerical simulations pose a significant challenge,due to the extensive exploration of design parameters across a vast design space.Surrogate modeling techniques offer a viable alternative for efficiently emulating numerical results within a notably compressed timeframe.This study introduces parametric Reduced-Order Models(ROMs)based on Convolutional Auto-Encoders(CAE),Fully Connected AutoEncoders(FCAE),and Proper Orthogonal Decomposition(POD)to fast emulate spatial distributions of physical variables for a supercritical jet into a super-sonic crossflow under different operating conditions.To further accelerate the decision-making pro-cess,an optimization model is developed to enhance fuel-oxidizer mixing efficiency while minimizing total pressure loss.Results indicate that CAE-based ROMs exhibit superior prediction accuracy while FCAE-based ROMs show inferior predictive accuracy but minimal uncertainty.The latter may be ascribed to the markedly greater number of hyperparameters.POD-based ROMs underperform in regions of strong nonlinear flow dynamics,coupled with higher overall prediction uncertainties.Both AE-and POD-based ROMs achieve online predictions approximately 9 orders of magnitude faster than conventional simulations.The established optimization model enables the attainment of Pareto-optimal frontiers for spatial mixing deficiencies and total pressure recovery coefficient.
查看更多>>摘要：To improve the computational efficiency and accuracy of multi-objective reliability esti-mation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix the-ory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multi-output response function approximation problem and two engineering application cases(i.e.,land-ing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable ref-erence for the improvement of multi-objective reliability assessment theory.
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