Multi-source Uncertainties Research Articles

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau

Abstract Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model.

Robust design optimization of a multi-body system with aleatory and epistemic uncertainty

There are several issues in robust design optimization of the multi-body system with aleatory and epistemic uncertainty, such as the difficulty in the multi-source uncertainty characterization and low efficiency in dynamic modeling, analysis and the corresponding solving strategy for its robust design optimization. To address these problems, this paper firstly constructs a unified analysis framework for aleatory and epistemic uncertainty for the multi-body system based on probability theory and evidence theory. Taking a multi-body system of a shipborne artillery model as an example, the transfer matrix method is next employed to rapidly calculate the dynamic response of the muzzle. The results are compared with those by the finite element method to verify the accuracy and efficiency of the transfer matrix method. A decision model of the robust design optimization with both aleatory and epistemic uncertainty is proposed. Based on the polynomial chaos expansion-Kriging model, subset simulation optimization method and a weighted single-objective solving strategy, the multi-objective robust design optimization problem involving aleatory and epistemic uncertainty is then transformed into a single-objective optimization one. Finally, results of both the multi-objective and weighted single-objective robust design optimization methods for the shipborne artillery model are compared to demonstrate the performance of the proposed method.

Remaining useful life prediction of proton exchange membrane fuel cell based on Wiener process and Bayesian GRU network considering multi-source uncertainties

ABSTRACT Proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising green energy devices. Although fruitful results can be available for the remaining useful life (RUL) prediction of PEMFC, stochastic uncertainties have never been considered. To tackle this problem, a hybrid method is proposed in this paper. Wiener process with temporal uncertainty and individual uncertainty is adopted to model the degradation of the state of health (SOH), which is then estimated from monitoring voltage with measurement noise using the unscented Kalman filter (UKF), where unknown filtering and model parameters are jointly identified by expectation-maximization (EM) algorithm and Rauch-Tung-Striebel (RTS) smoother. Finally, gated recurrent unit (GRU) network is employed to realize the RUL prediction with the prediction uncertainty quantified by the Bayesian variational inference technology. The proposed method is verified on the experimental data. Results indicate that smaller mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) values can be obtained compared with other methods. When 60% data are used for prediction, the proposed method can achieve a RUL prediction accuracy with 1.63% and 2.17% relative errors under static and dynamic conditions, respectively, which illustrates the feasibility and superiority of the proposed method.

Disturbance observer-based fixed-time control for hypersonic morphing vehicles with uncertainties

Abstract The attitude-tracking problem of hypersonic morphing vehicles (HMVs) is investigated in this research. After introducing variable-span wings, the optimal aerodynamic shape is available throughout the entire flight mission. However, the morphing wings cause significant changes in aerodynamic coefficients and mass distribution, challenging the attitude control. Therefore, a complete design procedure for the flight control system is proposed to address the issue. Firstly, the original model and the control-oriented model of HMVs are built. Secondly, in order to eliminate the influence caused by the multisource uncertainties, an adaptive fixed-time disturbance observer combined with fuzzy control theory is established. Thirdly, the fixed-time control method is developed to stabilise hypersonic morphing vehicles based on a multivariable sliding mode manifold. The control input can be obtained directly. Finally, the effectiveness of the proposed method is proved with the help of the Lyapunov theory and simulation results.

Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties

Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties

Recent advances in mechanism/data-driven fault diagnosis of complex engineering systems with uncertainties

<p>The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusing on innovative strategies for intelligent fault diagnosis. To this end, conventional fault diagnosis methods are first reviewed, including advances in mechanism-driven, data-driven, and hybrid-driven diagnostic models and their strengths, limitations, and applicability across various scenarios. Subsequently, we provide a thorough exploration of multi-source uncertainty in fault diagnosis, addressing its generation, quantification, and implications for diagnostic processes. Then, intelligent strategies for all stages of fault diagnosis starting from signal acquisition are highlighted, especially in the context of complex engineering systems. Finally, we conclude with insights and perspectives on future directions in the field, emphasizing the need for the continued evolution of intelligent diagnostic systems to meet the challenges posed by modern engineering complexities.</p>

Hybrid reliability evaluation of arch dam during long-term service with multi-dimensional parallelepiped convex model

The loads and material parameters of arch dams during long-term service have multi-source uncertainty, but they are usually disregarded by traditional convex models, which is unfavorable for reliability evaluation. This work is a contribution to study the multi-source uncertainty in the reliability analysis of arch dams combining the multi-dimensional parallelogram convex model and the fuzzy theory. A novel multi-dimensional parallelogram convex model is firstly constructed, and its correlation angle is utilized to indicate the multi-source uncertainty of the arch dam parameters. Subsequently, the safety degree of the arch dam is evaluated to determine the correlation angle between the correlation parameters combining with the comprehensive method. Afterwards, considering with the fuzzy failure criterion of the arch dam, hybrid reliability indexes are proposed, probably failure paths are explored, and the dynamic reliability are further calculated. The effectiveness of the proposed methods is validated employing numerical simulation of the Jinping Ι arch dam. The results indicate that the methodology can evaluate the reliability and explore the possible failure modes of the arch dam during long-term service.

Gaussian process metamodel and Markov chain Monte Carlo-based Bayesian inference framework for stochastic nonlinear model updating with uncertainties

The estimation of the posterior probability density function (PDF) of unknown parameters remains a challenge in stochastic nonlinear model updating with uncertainties; thus, a novel Bayesian inference framework based on the Gaussian process metamodel (GPM) and the advanced Markov chain Monte Carlo (MCMC) method is proposed in this paper. The instantaneous characteristics (ICs) of the decomposed measurement response, calculated using the Hilbert transform and the discrete analytical mode decomposition methodology, are extracted as nonlinear indices and further used to construct the likelihood function. Then, the posterior PDFs of structural nonlinear model parameters are derived based on the Bayesian theorem. To precisely calculate the posterior PDF, an advanced MCMC approach, i.e., delayed rejection adaptive Metropolis-Hastings (DRAM) algorithm, is adopted with the advantages of a high acceptance ratio and strong robustness. However, as a common shortage in most MCMC methods, the resampling technology is still applied, and numerous iterations of nonlinear simulations are conducted to ensure accuracy, thus directly reducing the computational efficiency of the DRAM. To address the abovementioned issue, a mathematical regression metamodel of the GPM with a polynomial kernel function is adopted in this paper instead of the traditional finite element model (FEM) to simulate a nonlinear response for the reduction of computational cost, and the hyperparameters are further estimated using the conjugate gradient optimization methodology. Finally, numerical simulations concerning a Giuffré–Menegotto–Pinto modeled steel-frame structure and a seven-storey base-isolated structure are conducted. Furthermore, a shake-table experimental test of a nonlinear steel framework is investigated to validate the accuracy of the Bayesian inference method. Both simulations and experiment demonstrate that the proposed GPM and DRAM-based Bayesian method effectively estimates the posterior PDF of unknown parameters and is appropriate for stochastic nonlinear model updating even with multisource uncertainties.

Interval strategy-based regularization approach for force reconstruction with multi-source uncertainties

A force reconstruction method for uncertain structures instead of direct measurement is proposed in this paper. Considering the multi-source uncertainties as unknown-but-bounded (UBB) parameters including unknown structural dynamics and measurement errors, this study proposed a novel force reconstruction method using an interval strategy-based regularization approach with truncated singular value decomposition (TSVD). To overcome the drawbacks of incompleted models and limited information, the overall modelling and inversion of the interval force are reconstructed based on the non-probabilistic uncertainty theory. First, The interval uncertainty-based kernel function for force inversion is deduced using the second-order interval perturbation method, in which the uncertainties of the reconstructed force originate from the uncertain singular values and measurement errors. Based on the regularization of the TSVD, the interval bounds of the singular values can be accurately obtained. Combining the two typical regularization approaches, a novel interval strategy-based regularization approach is proposed to optimize the best regularization parameter based on the uncertain interval distribution of singular values. This novel strategy can not only balance the two typical regularization approaches but also reduce the burden of force inversion. Two numerical examples including a truss and space capsule are applied to access the proposed method, and the estimated accurate force can prove the effectiveness.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty

Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty

Event-triggered attitude control for hypersonic morphing vehicles using fixed-time control technique

This research discusses the attitude tracking issue for hypersonic morphing vehicles (HMVs) subject to multisource uncertainty and limited system resources. Hypersonic vehicles with variable-span wings can change the aerodynamic configuration according to various requirement segments. However, A morphing process will cause significant changes in aerodynamic coefficients, mass distribution, and inertial characteristics. Moreover, the additional forces and moments exist in the HMV model, impacting the attitude control. Therefore, a robust and fast flight control system is necessary for HMVs to stabilize the attitude angles in the presence of multisource uncertainty, which always consists of modeling inaccuracies, external disturbances, and coefficient perturbances. Considering such factors, the fixed-time control theory is employed to develop the flight control system with strong robustness and adaptation. Compared to the existing fixed-time control strategies with complex expressions to indicate the convergence time, the setting time of the proposed controller is more explicit and can be exactly preassigned in the design procedure. Fast attitude tracking can be achieved and accurately evaluated with the help of the proposed technique. Furthermore, to avoid excessive occupation of limited aircraft resources, a novel switched dynamic event-triggered technique is constructed, leading to a desired level of control performance with low resource consumption. The proposed event-triggering mechanism will strike a better balance between tracking accuracy and resource occupation than the commonly used triggered control methods, such as the fixed-threshold and relative-threshold event-triggering strategy. Finally, the event-triggered fixed-time controller provides an outstanding tracking performance, with the Zeno behavior avoided. The stability of the closed-loop system is proved by introducing the Lyapunov theory. Simulation results and comparisons validate the effectiveness and advantages of the developed control scheme.

A level set reliability-based topology optimization (LS-RBTO) method considering sensitivity mapping and multi-source interval uncertainties

With the diversification of engineering structure performance requirements and the continuous development of structural design refinement, structural design methods are facing more and more factors to be considered. It is necessary to develop advanced design technology. In this paper, a sensitivity mapping technique is proposed to improve the effect of topology optimization based on a gradient optimization algorithm. The applicability of this technology is analyzed. In addition, considering the influence of multi-source uncertainties in the whole life cycle of the structure, a reliability-based topology optimization strategy based on the level set method is proposed. The sensitivity of displacement constraint and reliability constraint with pseudo time is derived. Numerical examples and Monte Carlo verification results fully illustrate the applicability, effectiveness, and efficiency of the proposed method. This method has been successfully applied to the topology optimization design of the rocket skid structure.

Three-stage hyperelliptic Kalman filter for health and performance monitoring of aeroengine under multi-source uncertainty

Aeroengine operation is inevitably subject to multi-source uncertainty, which consists of epistemic uncertainty related to the aeroengine and stochastic uncertainty associated with the control system. This paper presents a solution for health and performance monitoring under multi-source uncertainty to ensure the safety and reliability of aeroengine whole-life operation in complex environments. Based on the hyperelliptic Kalman filter (HeKF), optimal health monitoring is achieved by treating health parameters as the augmented state. Meanwhile, the conservativeness-reduced output prediction is realized with the extra estimation of the unknown state function bias caused by probabilistic system parameters. Considering the computational effort can be significantly reduced by designing a multi-stage filter, the three-stage hyperelliptic Kalman filter (ThSHeKF) is finally developed, achieving high accuracy health parameter estimation and adaptive performance prediction under multi-source uncertainty. Open-loop and closed-loop numerical simulations demonstrate the effectiveness of the proposed ThSHeKF-based health and performance monitoring with high estimation accuracy. Furthermore, compared to the most commonly used extended Kalman filter (EKF), Monte Carlo (MC) experiments shows that the proposed ThSHeKF is less conservative, has better robustness, and is superior in adaptive monitoring under multi-source uncertainty.

Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

PurposeThe multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.Design/methodology/approachIn this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.FindingsThe accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.Originality/valueThe results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

Predicting and extracting thermal behavior rules of hydronic thermal barrier with interpretable ensemble learning in the heating season

Hydronic thermal barrier (HTB) creates dynamic thermal resistance in buildings, enabling buildings to adapt to outdoor weather fluctuations and regulate indoor environments. However, the lack of a comprehensive model to predict thermal behavior and explain the underlying rules is a significant limitation to its real-life situations. Additionally, the high computational cost of traditional numerical modelling also poses a challenge. To address these issues, this study proposed a framework for predicting HTB thermal behavior and extracting rules. A benchmark model was first introduced that incorporates coupled physical fields and accounts for multi-source uncertainties, reducing rule failures resulting from system changes. A data-driven prediction model was then developed, using machine learning and the finite element method, to clarify thermal behavior during the heating season in cold and severely cold regions of China. To enhance model generalization and applicability, three typical feature selection methods were employed. A comprehensive trial involving five ensemble learning models was conducted. Recursive Feature Elimination (RFE) selected the most representative features for prediction models. For heat storage efficiency, RFE combined with Light Gradient Boosting Machine achieved the best prediction performance with an R2 of 0.877, while the optimal prediction model for exergy efficiency was developed by combining RFE with Categorical Boosting with an R2 of 0.782. Finally, the profound HTB thermal behavior rules were extracted by the Shapley Additive exPlanation framework from both global and individual perspectives. Taken together, these findings can serve as a reference for the practical design and operation management of HTB.

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

An extended hesitant fuzzy set for modeling multi-source uncertainty and its applications in multiple-attribute decision-making

Handling uncertainty has always been a pervasive and formidable challenge in the realm of multiple-attribute decision-making (MADM). Hesitant fuzzy set (HFS) can retain several different values, making it a useful tool for expressing the uncertainty experienced by decision-makers (DMs). Recently, the introduction of the normal wiggly hesitant fuzzy set (NWHFS) has aimed to uncover potential uncertain information within HFS, and it has been empirically demonstrated to enhance the rationality of MADM. However, it is essential to acknowledge the role of probabilistic information, which assesses the likelihood of evaluation values occurring and is a factor that cannot be ignored in MADM. In this current study, we propose an innovative approach known as the mixed-normal-based hesitant fuzzy set (MNHFS), considering both probabilistic information and the potential uncertainty. To distinguish MNHFSs in MADM, we defined a score function and distance measures. Subsequently, we apply the proposed MNHFS to TODIM (an acronym in Portuguese for interactive and multiple attribute decision-making) and investigate its validity and superiority in a case study involving the selection of a promising venture capital investment. Finally, we provide a visualization method to illustrate the internal uncertainty of the decision results and offer insights into investment risk.

Fuzzy disturbance observer-based dynamic sliding mode control for hypersonic morphing vehicles

This research discusses the attitude-tracking problem of hypersonic morphing vehicles (HMVs) in the presence of multi-source uncertainties. The hypersonic flight vehicle with variable-span wings is introduced. Considering the dramatic aerodynamic characteristic changes caused by morphing-span wings, strong robustness and great adaption are demanded. The sliding mode control technique is adopted to build the flight control system since the robustness is what the aircraft orders. In order to obtain fast attitude-tracking performance, the terminal function and adaptive laws are developed to achieve finite-time control with high adaptation to varying aerodynamic characteristics. For a hypersonic flight with guts, robustness enhancement is required. Therefore, a novel fixed-time disturbance observer is proposed to enhance the robustness against multi-source uncertainties. Unlike the existing finite-time observers, the settling time is independent of the initial conditions. Based on the disturbance observer and the adaptive technique, finite-time flight control is developed to provide strong robustness and adaptation, achieving uncertainty compensation. The effectiveness of the designed controller is revealed with the help of Lyapunov synthesis. Several flight scenarios are introduced to analyze the performance.