A comparative study of dynamic mode decomposition methods for mode identification in a cryogenic swirl injector

Luong, Hung Truyen; Wang, Yuangang; Sung, Hong-Gye; Sohn, Chae Hoon

doi:10.1016/j.jsv.2021.116108

Cited by 7 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the subsystem side of space applications, DMD was used to find resonant frequencies, damping coefficient and mode shapes in a CFD simulation of a rocket engine's cryogenic swirl injector [27], the critical flow rate at which vibration occurs, or "garden hose instability" (commonly encountered in rocket engines), was investigated using Arnoldi iteration to attain Koopman modes [65]. Remark 4 (Identified Gap in the Literature): Although the objective of the aforementioned aerospace-related studies has been limited to system identification, the ultimate goal of almost 37% of the studies within this vehicle category (which is almost 46% above all vehicle categories combined together) was to obtain equations of motion in a linear form for the purposes of control using model predictive control (MPC) or other state-space methods.…”

Section: Space Systemsmentioning

confidence: 99%

Vehicular Applications of Koopman Operator Theory—A Survey

2023

View full text Add to dashboard Cite

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

show abstract

Section: Space Systemsmentioning

confidence: 99%

Vehicular Applications of Koopman Operator Theory—A Survey

2023

View full text Add to dashboard Cite

show abstract

“…From a practical point of view, it is essential to extract these modes from the experimental data, so as to identify the fundamental dynamics, analyze the underlying physics, and build the low-dimensional model of the system [13,24]. Over the past few decades, various data-based modal decomposition techniques have been proposed and applied to analyze complex dynamical systems, including fluid flow [50], combustion system [39], neural activity recording [10], and spread of infectious disease [45], among many others.…”

mentioning

confidence: 99%

An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise

Lee¹,

Park²

2021

Preprint

View full text Add to dashboard Cite

Dynamic mode decomposition (DMD) is an efficient tool for decomposing spatio-temporal data into a set of low-dimensional modes, yielding the oscillation frequencies and the growth rates of physically significant modes. In this paper, we propose a novel DMD model that can be used for dynamical systems affected by multiplicative noise. We first derive a maximum a posteriori (MAP) estimator for the data-based model decomposition of a linear dynamical system corrupted by certain multiplicative noise. Applying a penalty relaxation to the MAP estimator, we obtain the proposed DMD model whose epigraphical limits are the MAP estimator and the Askham-Kutz optimized DMD model. We also propose an efficient alternating gradient descent method for solving the proposed DMD model, and analyze its convergence behavior. The proposed model is demonstrated on both the synthetic data and the numerically generated one-dimensional combustor data, and is shown to have superior reconstruction properties compared to state-of-the-art DMD models. Considering that multiplicative noise is ubiquitous in numerous dynamical systems, the proposed DMD model opens up new possibilities for accurate data-based modal decomposition.

show abstract

Effects of recess length on combustion instability in a model chamber with a gas-centered swirl coaxial injector

Wang

Cho

et al. 2022

Aerospace Science and Technology

View full text Add to dashboard Cite

A comparative study of dynamic mode decomposition methods for mode identification in a cryogenic swirl injector

Cited by 7 publications

References 21 publications

Vehicular Applications of Koopman Operator Theory—A Survey

Vehicular Applications of Koopman Operator Theory—A Survey

An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise

Effects of recess length on combustion instability in a model chamber with a gas-centered swirl coaxial injector

Contact Info

Product

Resources

About