Article
Engineering, Manufacturing
Paromita Nath, Matthew Sato, Pranav Karve, Sankaran Mahadevan
Summary: This paper proposes a multi-fidelity modeling approach that combines physics-based models of different fidelity and experimental data to construct a prediction model for the AM process, maximizing accuracy within available computational resources. The method utilizes a Bayesian calibration approach to estimate correction factors and model parameters to address process variability and model uncertainty.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
(2022)
Article
Engineering, Multidisciplinary
Carlos Mora, Jonathan Tammer Eweis-Labolle, Tyler Johnson, Likith Gadde, Ramin Bostanabad
Summary: In this paper, a neural network-based approach for data fusion is proposed, which converts multi-fidelity data into a nonlinear manifold learning problem. Low-fidelity data is encoded into a probabilistic manifold close to high-fidelity data, and uncertainties are quantified using parametric distribution. The network's loss function is reformulated based on proper scoring rules to enhance robustness and accuracy on unseen high-fidelity data.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Nuclear Science & Technology
Riccardo Cocci, Guillaume Damblin, Alberto Ghione, Lucia Sargentini, Didier Lucor
Summary: This paper presents a methodology called Bayesian calibration for the development, validation, and uncertainty quantification of closure laws in thermal-hydraulic system codes. It introduces a robust and reliable assessment, selection, and uncertainty quantification of physical models by tuning parameters and selecting the best-suited model based on statistical indicators. The paper also discusses the application of this methodology to condensation heat transfer correlations.
ANNALS OF NUCLEAR ENERGY
(2022)
Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Hao An, Haofeng Wang, Xueqing Zhang, Weinan Xie, Changhong Wang
Summary: This paper investigates the longitudinal control problem of hypersonic vehicles, considering multiple modelling uncertainties and proposes an adaptive ACS-RCS compound control method to deal with uncertain RCS parameters. The results show that preset references can be achieved with prescribed performance.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Remi Martin, Luc Duong
Summary: Bayesian convolutional neural networks are a cutting-edge computer vision framework that can model two types of uncertainties: epistemic uncertainty and aleatoric uncertainty. Few studies have integrated uncertainty into end-to-end prediction pipelines, but our research proposes a novel way to assess confidence in predictions. We train a model using cross-entropy loss and then use pixel weighting to reduce uncertainty. From the resulting epistemic uncertainty measures, we calculate a histogram and use a final neural network model to determine a confidence interval for predictions. Our approach yields a lower calibration error and higher accuracy compared to existing methods.
MACHINE VISION AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Yangyang Zhao, Fuyuan Xiao, Masayoshi Aritsugi, Weiping Ding
Summary: In this paper, a novel quantum fidelity measure called quantum Tanimoto coefficient (QTC) fidelity is proposed. QTC fidelity not only considers the overlap between quantum states, but also takes into account the separation between quantum states for the first time, leading to better measurement performance. The effectiveness and advantages of QTC fidelity are demonstrated by comparing it with existing fidelities through specific examples. Furthermore, discrimination coefficients d(1)(QTC), d(2)(QTC), and d(3)(QTC) are defined based on QTC fidelity to measure the difference between quantum states, and the practicability of using QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states is shown.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Engineering, Mechanical
A. Gray, A. Wimbush, M. de Angelis, P. O. Hristov, D. Calleja, E. Miralles-Dolz, R. Rocchetta
Summary: This paper presents a framework for addressing engineering design challenges with limited empirical data and partial information, including characterisation of uncertainties, data integration, reliability analysis, and risk/reliability based design optimization. The framework's efficacy has been demonstrated through its application to the NASA 2020 uncertainty quantification challenge.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Kenny Chowdhary, Chi Hoang, Kookjin Lee, Jaideep Ray, V. G. Weirs, Brian Carnes
Summary: This paper explores the effectiveness of combining machine-learning methods with projection-based model reduction techniques to create data-driven surrogate models of computationally expensive, high-fidelity physics models. The method is demonstrated on modeling heat flux and pressure in a turbulent flow and used for Bayesian estimation of parameters in a turbulence model for a high-fidelity flow simulator.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Automation & Control Systems
Tuo Han, Qinglei Hu, Hyo-Sang Shin, Antonios Tsourdos, Ming Xin
Summary: This paper proposes a passive fault tolerant control scheme for the full reentry trajectory tracking of a hypersonic vehicle. The scheme utilizes attitude error dynamics and a multivariable twisting controller to achieve precise tracking, and further optimizes the system by reducing model dependency and system uncertainties through an incremental twisting fault tolerant controller.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Thermodynamics
Michael Ullman, Venkat Raman
Summary: This study develops a calibration procedure for a one-dimensional model of the wall pressure in a scramjet flowpath. Using wall pressure measurements from three-dimensional simulations, the six model parameters are tuned using a Bayesian methodology. The results show that the calibrated model can capture the mean wall pressure in both the isolator and combustor. The procedure can converge for both uniform and Gaussian priors, and physically-consistent correlations between parameters are obtained.
COMBUSTION SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Aerospace
Cunyu Bao, Peng Wang, Guojian Tang
Summary: In this article, a model-free adaptive dynamic planning (MFADP) optimal control method is proposed for attitude control of hypersonic morphing vehicles (HMVs) with variable sweep wings. The method is based on data-driven and finite-time fuzzy disturbance observer. The control scheme is organized by a steady-feedback-compensation framework, and the dynamic model is reconstructed using neural networks for steady-state control. The proposed method utilizes an Off-On serial policy learning strategy based on the MFADP algorithm to obtain a real-time approximate optimal feedback control. A fuzzy disturbance observer with finite-time convergence ability is also proposed to estimate and compensate the multiple uncertainties. The stability of the closed-loop system is theoretically proved, and simulation results demonstrate the improved performance of the proposed method.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Engineering, Industrial
Yuhao Wang, Yi Gao, Yongming Liu, Sayan Ghosh, Waad Subber, Piyush Pandita, Liping Wang
Summary: The novel Bayesian-Entropy Gaussian Process (BEGP) is introduced for surrogate modeling, incorporating extra information as constraints to enhance the extrapolation behavior of the GP model. The method effectively connects local GPs and incorporates physics constraints to improve prediction accuracy and extrapolation capabilities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Environmental Sciences
Ran Wang, Xiaoquan Yi, Liang Yu, Chenyu Zhang, Tongdong Wang, Xiaopeng Zhang
Summary: This paper proposes a method based on sparse Bayesian learning and Bayesian information fusion for precise localization of infrasound sources. The method incorporates the uncertainty of the infrasound propagation environment and infrasound measurement equipment, reducing the localization error.
Article
Engineering, Mechanical
Bo Leng, Da Jin, Lu Xiong, Xing Yang, Zhuoping Yu
Summary: The paper proposes a disturbance observer of tire force and tire-road peak adhesion coefficient based on the modified Burckhardt tire model, and designs a tire-road peak adhesion coefficient estimation method based on vehicle mounted camera. The fusion strategy of dynamic estimator and visual estimator is shown to improve estimation accuracy and convergence speed.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)