Article
Engineering, Mechanical
Zhibao Zheng, Hongzhe Dai, Yuyin Wang, Wei Wang
Summary: This paper introduces a new numerical scheme for simulating stochastic processes based on their specified marginal distribution functions and covariance functions. By generating stochastic samples to meet target marginal distribution functions and using an iterative algorithm to match the simulated covariance function to the target, the proposed method can accurately represent stochastic samples in series forms. The approach is applicable to non-stationary non-Gaussian stochastic processes and is demonstrated through three examples to be accurate and efficient.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Industrial
Ruijing Zhang, Hongzhe Dai
Summary: This paper presents a method based on polynomial chaos and fractional moments for constructing non-Gaussian random models from limited observations. The method is able to quantify the randomness and uncertainty of the observed non-Gaussian field simultaneously, and facilitates the implementation of polynomial chaos-based stochastic analysis in practical engineering applications.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Mechanical
Ying Zhang, Wei Qu, He Zhang, Tao Qian
Summary: This paper proposes the adaptive Fourier decomposition (AFD) type methods to solve the two-match problem, which is to simultaneously meet a given marginal distribution condition and be compatible with a given covariance function. The AFD-Type methods offer flexibility and efficiency, and can be applied to various stochastic processes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
Summary: This study introduces an Iterative Polynomial Chaos method to solve structural mechanics problems, reducing the curse of dimensionality by iteratively solving problems with smaller sizes of PC expansions. By combining Polynomial Dimensional Decomposition, the method achieves higher computational efficiency and converged solutions.
PROBABILISTIC ENGINEERING MECHANICS
(2021)
Article
Thermodynamics
Sufia Khatoon, Jyoti Phirani, Supreet Singh Bahga
Summary: We propose a fast Bayesian inference framework for solving inverse heat conduction problems. The framework combines polynomial chaos expansions and dimensionality reduction based on Karhunen-Loeve expansion to generate efficient surrogate models. We demonstrate the potential of this approach using three model problems for heat flux estimation.
APPLIED THERMAL ENGINEERING
(2023)
Article
Engineering, Mechanical
Ming-Na Tong, Yan-Gang Zhao, Zhao Zhao
Summary: A novel method combining Karhunen-Lo & egrave;ve expansion with L-moments-based Hermite polynomial model is proposed for simulating strongly non-Gaussian and non stationary processes. The method effectively transforms non-Gaussian processes into Gaussian processes and addresses incompatibilities that may occur in strongly non-Gaussian processes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Aerospace
Loic Brevault, Mathieu Balesdent
Summary: The early design phase of launch vehicles often involves low fidelity models with high levels of modeling uncertainties. These uncertainties need to be propagated throughout the design process to ensure robustness, which can be computationally costly due to trajectory optimization and uncertainty quantification.
Article
Computer Science, Interdisciplinary Applications
Mohamad Sadeq Karimi, Mehrdad Raisee, Mohamed Farhat, Patrick Hendrick, Ahmad Nourbakhsh
Summary: The study investigates the effects of operational and geometrical uncertainties on Tip Leakage Vortex (TLV) characteristics, showing profound impacts on cavitating tip leakage vortex flow, lift, and drag coefficients, and describing the discrepancies between numerical and experimental results. Operational uncertainties mainly influence TLV characteristics like circulation and velocity field, while geometrical randomness affects the vortex core position and viscous core radius, specifically gap distance.
COMPUTERS & FLUIDS
(2021)
Article
Computer Science, Interdisciplinary Applications
Dilaksan Thillaithevan, Paul Bruce, Matthew Santer
Summary: We demonstrate a methodology for robust optimization using multivariable parameterized lattice microstructures. By introducing material uncertainties at the microscale, we are able to simulate manufacturing variations and design structures tolerant to those variations. We impose different types of material uncertainties to generate more robust structures.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Electrical & Electronic
Yiwei Qiu, Jin Lin, Xiaoshuang Chen, Feng Liu, Yonghua Song
Summary: This paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. The method accurately and efficiently quantifies the probability distribution and high-order moments of system dynamic response and performance index using Ito process and adaptive sparse probabilistic collocation method. Based on commercial simulation software, this method is easy to use for power utility companies.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Yi Gao, Yang Jiao, Yongming Liu
Summary: This paper introduces a novel methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity, addressing challenges of handling uncertainties and dimensionality for probabilistic solvers. By utilizing analytical and hierarchical uncertainty quantification methods and forming a probabilistic solver with adjoint first-order reliability method, the proposed approach demonstrates high efficiency in solving high-dimensional material reliability problems.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Engineering, Industrial
Mishal Thapa, Samy Missoum
Summary: This paper presents a framework for uncertainty quantification (UQ) and global sensitivity analysis (GSA) of composite wind turbine blades using polynomial chaos expansion (PCE) with l(1)-minimization. The framework is capable of handling a large number of random parameters and can assess the relative importance of these parameters using Sobol Indices. It also allows for arbitrary distributions of random inputs and spatial variations of material and geometric properties. The presented framework is applied to three composite wind turbine blade problems, and results are compared to Monte Carlo simulations.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Acoustics
Karl-Alexander Hoppe, Kevin Josef Li, Bettina Chocholaty, Johannes D. Schmid, Simon Schmid, Kian Sepahvand, Steffen Marburg
Summary: This study expands the methods for non-destructively identifying material properties of a structure using modal data. It improves the results significantly by using eigenvectors instead of eigenvalues and accelerates the inversion process with a generalized polynomial chaos surrogate. A methodology for reusing surrogate models across inversion tasks is also developed.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Engineering, Electrical & Electronic
Phil Howlett, Anatoli Torokhti, Peter Pudney, Pablo Soto-Quiros
Summary: This paper introduces a multilinear filter for distributed signal processing and its associated techniques, including multilinear KLT-1 and multilinear KLT-2. These techniques help reduce the dimensionality of observed signals, improve accuracy, and decrease computational cost by breaking down the original problem into smaller matrix problems.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Aerospace
Shaoqing Wu, Yi Zheng, Yincen Geng
Summary: A novel time domain method is proposed to estimate the time-varying statistics of nonstationary random pressure load from structural strain response samples. Numerical simulation is conducted to validate the proposed method and the influence of load correlation length on identification accuracy is discussed. The method is also applied to reconstruct nonstationary gust-induced random pressure load from structural strain data, and practical aspects affecting the performance of the method are discussed.
JOURNAL OF AEROSPACE ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
JOURNAL OF COMPUTATIONAL PHYSICS
(2020)
Article
Computer Science, Interdisciplinary Applications
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
Summary: With the advancement of computational facilities, there is a growing interest in probabilistic analysis for realistic assessment of physical systems. The discretization of random fields is typically done using methods like truncated Karhunen-Loeve (KL) expansion, where accuracy requires fine mesh and a large number of terms, leading to increased computational costs. Simply increasing the number of terms in expansion or elements alone does not accurately represent the random field, hence an adaptive discretization strategy is proposed to appropriately discretize the domain while keeping errors within prescribed limits.
ENGINEERING WITH COMPUTERS
(2022)
Article
Engineering, Mechanical
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
Summary: This study introduces an Iterative Polynomial Chaos method to solve structural mechanics problems, reducing the curse of dimensionality by iteratively solving problems with smaller sizes of PC expansions. By combining Polynomial Dimensional Decomposition, the method achieves higher computational efficiency and converged solutions.
PROBABILISTIC ENGINEERING MECHANICS
(2021)
Article
Multidisciplinary Sciences
Kamaljyoti Nath, Xuhui Meng, Daniel J. Smith, George Em Karniadakis
Summary: This paper presents a physics-informed neural network (PINN) approach for monitoring the health and performance of diesel engines. The PINN model accurately predicts both unknown parameters and dynamics, and the self-adaptive weight in the loss function helps achieve faster convergence. The study also combines deep neural networks (DNNs) to improve the generalizability of the mean value model, offering a comprehensive and versatile method for engine monitoring.
SCIENTIFIC REPORTS
(2023)
