基于物理的多保真度数据融合在不确定条件下有效表征模态振型变化

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
K. Zhou, J. Tang
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引用次数: 0

摘要

在不确定条件下有效预测模态振型变化对设计和控制具有重要意义。蒙特卡罗模拟(MCS)方法简单,但计算成本高,对于高维复杂结构不可行。为了解决这个问题,在本研究中,我们开发了一种具有增强高斯过程(GP)架构的多保真度数据融合方法来评估模态振型变化。由于从全尺寸物理模型中获取高保真度数据的过程通常是昂贵的,因此我们采用降阶模型来快速生成相对大量的低保真度数据。将这些数据与少量高保真度数据相结合,我们可以建立一个高斯过程元模型,并将其用于有效的模型形状预测。这种增强的元模型允许人们通过结合多响应策略来捕获不同位置模型形状振幅的内在相关性。全面的案例研究进行了方法学验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics Based Multi-Fidelity Data Fusion for Efficient Characterization of Mode Shape Variation Under Uncertainties
Efficient prediction of mode shape variation under uncertainties is important for design and control. While Monte Carlo simulation (MCS) is straightforward, it is computationally expensive and not feasible for complex structures with high dimensionalities. To address this issue, in this study we develop a multi-fidelity data fusion approach with an enhanced Gaussian process (GP) architecture to evaluate mode shape variation. Since the process to acquire high-fidelity data from full-scale physical model usually is costly, we involve an order-reduced model to rapidly generate a relatively large amount of low-fidelity data. Combining these with a small amount of high-fidelity data altogether, we can establish a Gaussian process meta-model and use it for efficient model shape prediction. This enhanced meta-model allows one to capture the intrinsic correlation of model shape amplitudes at different locations by incorporating a multi-response strategy. Comprehensive case studies are performed for methodology validation.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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