Article
Engineering, Electrical & Electronic
Xiaobing Liao, Min Zhang, Jian Le, Lina Zhang, Zicheng Li
Summary: This paper studies a global sensitivity analysis method of static voltage stability based on an extended affine model. The analysis of simulation results shows that this method can effectively suppress the effect of interval expansion and accurately determine the importance of input interval variables.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Aerospace
Ming Huang, Zuohong Zhou, Kaiyuan Zhang, Zhigang Li, Jun Li
Summary: This investigation introduces an efficient parallel framework for uncertainty quantification, significantly reducing sample requirements while maintaining computational precision. The research findings reveal the significant factors contributing to the variability in leakage flow rate and cooling performance, emphasizing potential issues in operational conditions. Additionally, the study identifies the point at which the probability of blade failure sharply increases, indicating the need for mandatory overhaul.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Aerospace
Hongzhi Cheng, Chuangxin Zhou, Ziliang Li, Xingen Lu, Shengfeng Zhao, Junqiang Zhu
Summary: This paper establishes a framework for uncertainty quantification and global sensitivity analysis of a micro transonic compressor. The coupled uncertainties are propagated and the probability density distribution of performance parameters is predicted using the sparse grid-based polynomial chaos expansion method. Sensitivity analysis and flow field analysis reveal the correlations between uncertain variables and performance, as well as the mechanism of uncertain variables affecting performance.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Civil
Hongzhou Zhang, Oh-Sung Kwon, Constantin Christopoulos
Summary: Modeling uncertainty in structural models is crucial in performance-based earthquake engineering. One major source of uncertainty is the parameters of constitutive models, which simulate the behavior of structural components. Previous research has shown that component-level calibration may result in errors in the system-level structural dynamic response. This study investigates calibration relevance incorporating an uncertainty quantification framework, using polynomial chaos expansions metamodels for sensitivity analysis and calibration evaluation.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2022)
Article
Computer Science, Interdisciplinary Applications
K. C. Ujjwal, Jagannath Aryal, Saurabh Garg, James Hilton
Summary: Environmental models often involve inherent uncertainties, which can be quantified using global sensitivity analysis (GSA) methods such as Morris, Sobol, FAST, and PAWN. The choice of GSA method depends on the model complexity and computational constraints, with a trade-off between convergence and computational costs. Sobol method is recommended for detailed sensitivity information, while Morris or PAWN methods are preferred for balanced trade-off under computational constraints.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Chemistry, Multidisciplinary
Xuejun Liu, Hailong Tang, Xin Zhang, Min Chen
Summary: A fast, accurate, and robust uncertainty quantification method is proposed in this paper to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The method utilizes Gaussian process model and Latin hypercube sampling to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. The study explores two main scenarios where uncertain parameters are considered to be mutually independent and partially correlated, respectively, providing new insights into engine performance uncertainty and important component performance parameters.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Xiaoxu Zhang, Nana Wang, Qing Xie, Hua Zhou, Zhuyin Ren
Summary: This work investigates the soot formation in turbulent spray flames through global sensitivity analysis and uncertainty quantification. The numerical simulation accurately predicts the properties of the flame and provides a good baseline for analysis. The active subspace method is used to analyze the sensitivity of the formation process, and response surfaces are constructed to quantify the uncertainty. The study demonstrates the potential of the active subspace method for analyzing and quantifying the uncertainty of soot formation.
Article
Mathematics, Applied
Barry Lee
Summary: This article presents a new approach of applying uncertainty quantification techniques to probe the operator itself in order to uncover intrinsic structures and uncertainty propagation. By examining eigenvectors, an adaptive parameter procedure based on the relationship between spatial and uncertainty parameters dimensions is constructed. It also explores uncertainty propagation in multicomponent systems and componentized UQ processing based on the eigenvalues/vectors of the state-equation operator.
NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS
(2021)
Article
Engineering, Manufacturing
Xiaoyue Li, Liang Li, Yinfei Yang, Guolong Zhao, Ning He, Eric Schmidt
Summary: This study introduces a novel variance-based sensitivity analysis method to evaluate the effects of residual stress on machining deformation uncertainty. Experimental results show that in sidewall structures, the variance contribution of surface IRS is significantly greater than SRS.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Engineering, Mechanical
Muhammad Bilal Ghori, Yanmei Kang
Summary: This paper investigates the electrophysiology of a hippocampal CA3 pyramidal neuron model under the influence of magnetic flux. It is found that the conductivities of calcium and calcium-activated potassium channels and their interactions have the greatest impact on the quantities of interest, such as average interspike interval and spike frequency. The transition between complex periodic and aperiodic bursting electrical modes is found to significantly affect the electrical activities of the neuron model.
NONLINEAR DYNAMICS
(2023)
Article
Thermodynamics
Ming Huang, Zhigang Li, Jun Li, Liming Song
Summary: This paper proposes an uncertainty quantification method using the non-intrusive polynomial chaos expansion method and Smolyak sparse grids, and applies it to analyze the aerodynamic and heat transfer performance of the GE-E3 rotor blade squealer tip. The results of parameter sensitivity analysis show that the inlet flow angle is the main factor affecting the uncertainty of the blade's performance.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2022)
Article
Engineering, Chemical
Jose M. Gozalvez-Zafrilla, J. Carlos Garcia-Diaz, Asuncion Santafe-Moros
Summary: Uncertainty and sensitivity analyses were used to study the joint effects of model parameter uncertainty and feed variability on methanol-water continuous distillation. The results showed a high impact of model parameter uncertainty on the response, encouraging the use of the methods to obtain robust designs and quantify simulation accuracy.
CHEMICAL ENGINEERING SCIENCE
(2021)
Article
Ecology
Ranju Chapagain, Neil Huth, Tomas A. Remenyi, Caroline L. Mohammed, Jonathan J. Ojeda
Summary: The three major sources of uncertainty in crop models are model inputs, structure and parameters. This study aims to quantify the contribution of structural uncertainty to model outputs produced by the Agricultural Production Systems sIMulator (APSIM). Eight model structures were developed and tested under three contrasting environments. It was found that most structural uncertainty resulted from the choice of model components, rather than interactions between components. The effects of structural uncertainty were strongly impacted by site and climate conditions.
ECOLOGICAL MODELLING
(2023)
Article
Computer Science, Interdisciplinary Applications
Mario Miguel Valero, Lluis Jofre, Ricardo Torres
Summary: Predictions of wildfire behavior are often uncertain, with modeling uncertainties largely unquantified in the literature due to computing constraints. However, new multifidelity techniques show promise in overcoming these limitations, as demonstrated in this study's exploration of their applicability to wildland fire spread prediction. The study achieved notable speedups in performance compared to standard methods, allowing for the quantification of uncertainties and sensitivity analysis in a cost-effective manner.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Ecology
Jody R. Reimer, Frederick R. Adler, Kenneth M. Golden, Akil Narayan
Summary: Uncertainty in parameters in ecological models can be incorporated by treating parameters as random variables with distributions. Recent advances in uncertainty quantification methods provide new approaches for analyzing models with random parameters. Modelling key parameters as random variables changes the characteristics of the model. The computational efficiency of polynomial chaos methods helps in better predicting and synthesizing models with data.
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)