Article
Computer Science, Interdisciplinary Applications
Chao Dang, Pengfei Wei, Matthias G. R. Faes, Michael Beer
Summary: This paper investigates the estimation, variable importance, and bounds of response expectation function (REF) under hybrid uncertainties using different probability models. A new method called PBQO is proposed, which allows for parallel computing and simultaneous calculation of the REF, its variable importance, and bounds.
COMPUTERS & STRUCTURES
(2022)
Article
Robotics
Aaronkumar Ehambram, Luc Jaulin, Bernardo Wagner
Summary: In this paper, we propose a novel online capable hybrid interval-probabilistic localization method using publicly available 2D building maps. The method first narrows down the uncertainty in orientation and position using 3D LiDAR sensor data and building geometry information. It then determines the best solution within the feasible set using probabilistic Maximum Likelihood Estimation (MLE) and bounded optimization. Experimental results demonstrate the accuracy and reliability of the proposed method.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hui Lu, Zebin Zheng, Xiaoting Huang, Hui Yin, Wen-Bin Shangguan
Summary: A comprehensive methodology for the design optimization of automotive powertrain mounting systems (PMSs) involving hybrid interval-random uncertainties is proposed in this study. The methodology includes the development of a hybrid interval-random perturbation central difference method (HIRP-CDM) for calculating uncertain responses, construction of reliability assessment models, and formulation of a design optimization model considering system inherent characteristics and reliability constraints. The proposed methodology is demonstrated to be effective through a numerical example.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Aerospace
Hao Zhu, Mingyang Xiao, Junhao Zhang, Guobiao Cai
Summary: This study proposed an efficient design optimization method that considers random and interval uncertainties for designing hybrid rocket motors, showing high efficiency and accuracy in uncertainty analysis. The method is capable of searching optimal results and saving computational costs, highlighting its importance in aerospace engineering applications.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Thermodynamics
Yingchao Dong, Hongli Zhang, Ping Ma, Cong Wang, Xiaojun Zhou
Summary: A novel hybrid robust-interval optimization (HRIO) framework is proposed in this study to address the uncertainties in demand response and renewable energy generation in integrated energy system (IES) planning. The framework integrates robust optimization and interval analysis to account for the uncertainties associated with renewable energy generation output and demand response. A constrained multi-objective transition algorithm is developed to solve the deterministic bi-objective optimization problem, with investment operation cost and robustness as the optimization objectives. Simulation results demonstrate the efficacy of the proposed HRIO method in coordinating the system's economy, robustness, and operation reliability, providing theoretical guidance and potential engineering applications for IES operators.
Article
Computer Science, Theory & Methods
Chong Wang, Hermann G. Matthies
Summary: This paper introduces a more general hybrid uncertainty analysis framework to handle coupled uncertain problems with fuzzy-interval variables, and improves the computational efficiency of extreme-value prediction. The effectiveness of the proposed method is validated through two examples.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Jin Cheng, Wei Lu, Zhenyu Liu, Di Wu, Wei Gao, Jianrong Tan
Summary: A novel robust optimization approach is proposed in this study for engineering structures with hybrid uncertainties, incorporating both stochastic and interval uncertain system parameters. The method utilizes generalized beta distribution to model stochastic system uncertainties and introduces the concept of interval angular vector to evaluate the robust feasibility of constraints. A genetic algorithm is presented to systematically solve the robust optimization problem and demonstrate its effectiveness through numerical and realistic engineering examples.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Mechanical
Paul A. Meehan
Summary: Research shows that chaotic instability in brake squeal is caused by mode coupling instability via friction. Conservative analytical conditions were developed and numerically verified for suppressing brake squeal chaos. The results provide predictive insight into conditions under which brake squeal chaos occurs and its suppression.
NONLINEAR DYNAMICS
(2022)
Review
Acoustics
Gongyu Pan, Xiaoman Zhang, Peng Liu, Lin Chen
Summary: The study effectively reduced brake squeal noise by optimizing the brake caliper structure to achieve symmetrical contact pressure distribution. The method improved brake system stability and decreased noise incidence, providing an effective solution for reducing brake squeal noise.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Engineering, Mechanical
Chen Yang, Yuanqing Xia
Summary: Conventional optimal sensor placement methods do not consider actual load cases or structural responses, leading to errors and failures. Therefore, this paper proposes a novel uncertain load-dependent sensor placement method based on non-probabilistic theory, which uses a two-step strategy to select optimal displacement sensor configurations and constitutes a novel interval time series model based on the ratio of reduced and full intervals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Fangqi Hong, Pengfei Wei, Jingwen Song, Marcos A. Valdebenito, Matthias G. R. Faes, Michael Beer
Summary: Uncertainty quantification is crucial for reliability-oriented analysis and design of engineering structures. Three groups of mathematical models have been developed for different forms of uncertainties: probability models, imprecise probability models, and non-probabilistic models. Propagating these models through expensive simulators to quantify output uncertainties is a challenging task. Collaborative and Adaptive Bayesian Optimization (CABO) has been improved to handle all three categories of uncertainty models and to bound various probabilistic measures of the output.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Jungro Yoon, Joosang Park, Seungjae Min
Summary: This paper proposes an improved disc brake system optimization method for reducing squeal instability using slip-dependent eigenvalue results. The proposed optimal design method produces minimal squeal instability during the full vehicle braking time range.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Gao Hong, Deng Zhongmin
Summary: Nonlinear structural dynamics systems with hybrid uncertainties require mathematical models to accurately predict their behavior. To address the hybrid uncertainty often encountered in engineering applications, the NSDAHU method is proposed, which uses random interval moment and random interval perturbation methods to calculate the statistical characteristics of nonlinear structural dynamic responses. The NSDAHU method is verified using Monte Carlo simulation method and can solve hybrid random interval problems, as well as single interval, single random, and linear structural dynamics problems.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Mechanical
Paul A. Meehan, Andrew C. Leslie
Summary: This study investigates the occurrence, growth, and mitigation of brake squeal noise using a predictive analytical coupled modal model. The closed form analytical criteria for brake squeal reveal that amplitude primarily depends on sliding velocity with minimal differences due to different mechanisms. The results provide important insight into mitigating brake squeal noise.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Mechanics
Xiang Peng, Yuliang Guo, Jiquan Li, Huaping Wu, Shaofei Jiang
Summary: This paper presents a multi-objective uncertainty optimization design methodology for hybrid composite structures considering multiple-scale uncertainties. The methodology involves uncertainty propagation analysis, quantification of macro material uncertainties using a neural network model, and the development of a multiple objective robust optimization design function. An adaptive nondominated sorting genetic algorithm II (NSGA-II) method is proposed to optimize the stacking sequence and material patches simultaneously. Engineering examples demonstrate that the proposed methodology can improve vibration characteristics while maintaining low material cost.
COMPOSITE STRUCTURES
(2022)
Article
Computer Science, Interdisciplinary Applications
Hui Lu, Kun Yang, Wen-bin Shangguan, Hui Yin, D. J. Yu
ENGINEERING COMPUTATIONS
(2020)
Article
Computer Science, Theory & Methods
Hui Yin, Ye-Hwa Chen, Dejie Yu
FUZZY SETS AND SYSTEMS
(2020)
Article
Engineering, Multidisciplinary
Yiyuan Gao, Dejie Yu, Haojiang Wang
Article
Engineering, Multidisciplinary
Hui Yin, Ye-Hwa Chen, Dejie Yu, Hui Lu, Wenbin Shangguan
APPLIED MATHEMATICAL MODELLING
(2020)
Article
Computer Science, Interdisciplinary Applications
Wu Qin, Hui Yin, D. J. Yu, Wen-Bin Shangguan
ENGINEERING COMPUTATIONS
(2020)
Article
Engineering, Multidisciplinary
Dingcheng Zhang, Edward Stewart, Mani Entezami, Clive Roberts, Dejie Yu
Article
Engineering, Mechanical
Yiyuan Gao, Dejie Yu
MECHANISM AND MACHINE THEORY
(2020)
Article
Physics, Applied
Tinggui Chen, Junrui Jiao, Dejie Yu
Summary: The GCM proposed in this study combines gradient and coiled structures to achieve enhanced broadband acoustic sensing, with the ability to amplify acoustic signals up to approximately 80 times over a wide frequency range. By coupling coiled structures, trapped and enhanced frequencies in the GCM can be reduced by nearly 43%. Experimental results demonstrate that GCM can enhance frequency-selective unknown signals and effectively recognize and recover harmonic signals from strong background noise.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2021)
Article
Engineering, Multidisciplinary
Tinggui Chen, Junrui Jiao, Dejie Yu
Summary: The study proposes a method based on the gradient acoustic-grating metamaterial (GAGM) for detecting harmonic and periodic impulse signals more easily. Numerical and experimental investigations demonstrate that GAGM achieves acoustic rainbow trapping to spatially separate different frequency components. This work opens up new vistas for weak signals detection in various areas.
Article
Physics, Applied
Hongqing Dai, Baizhan Xia, Dejie Yu
Summary: Acoustic topological insulators enable non-contact particle manipulations, such as microparticle trapping and separation. Based on the SSH model, we can separate particles of the same size and density.
APPLIED PHYSICS LETTERS
(2021)
Article
Engineering, Electrical & Electronic
Tinggui Chen, Dejie Yu, Bo Wu, Baizhan Xia
Summary: A sensor based on acoustic metamaterials is proposed for detecting weak signals, utilizing a trapezoidal structure to enhance the acoustic pressure field and amplify pressure amplitudes by over 20 times around maximum gain frequencies, achieving broadband acoustic enhancement. Harmonic and periodic impulse signals are detected more easily, and experimental results show effective recovery of signals from background noise due to improved signal to noise ratios.
IEEE SENSORS JOURNAL
(2021)
Article
Automation & Control Systems
Tinggui Chen, Dejie Yu
Summary: This article proposes a novel method to diagnose bearing faults using acoustic metamaterials, which enhances the resonance frequency band to extract fault features. Compared to conventional denoising techniques, this method shows superior performance in low signal to noise ratios.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Acoustics
Tinggui Chen, Junrui Jiao, Dejie Yu
Summary: The detection and localization of acoustic signals are important in many areas, but achieving both high sensitivity and high directivity in an acoustic system remains a challenge. This study proposes a structure that combines phononic crystal point defects with four-sided Helmholtz resonators to enhance acoustics and enable directional sensing. The proposed structure surpasses the detection limit of conventional acoustic sensing systems and provides a new method for developing coupled acoustic sensing devices.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Physics, Applied
Guiju Duan, Shengjie Zheng, Jie Zhang, Zihan Jiang, Xianfeng Man, Dejie Yu, Baizhan Xia
Summary: This study reports the realization of a synthetic gauge field in acoustic Moire superlattices consisting of two superimposed periodic phononic crystals with mismatched lattice constants. The symmetric and antisymmetric Landau levels and interface states are observed in the acoustic Moire superlattices with the help of the synthetic gauge field. Sound pressure field distributions of Landau levels are experimentally measured and consistent with full-wave simulations. This study provides a simple way to generate synthetic gauge fields in phononics and expands the avenues for manipulating sound waves that were previously inaccessible in traditional periodic acoustic systems.
APPLIED PHYSICS LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Yiyuan Gao, Dejie Yu
Summary: The study introduces an intelligent method using directed graphs for fault diagnosis, which improves diagnostic performance by constructing a directed and weighted k-nearest neighbor graph and measuring the similarity between samples using cosine distance. Experimental results show that the method is better than traditional convolutional neural networks and support vector machines in rolling bearing fault diagnosis.
ADVANCED ENGINEERING INFORMATICS
(2021)