Data-driven reduced order modeling for mechanical oscillators using Koopman approaches
Frontiers in Applied Mathematics and Statistics, 2023 Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced,
Charlotte Geier +4 more
doaj +1 more source
Reduced order modeling of fluid flows using convolutional neural networks
Journal of Fluid Science and Technology, 2023 Application of machine learning is currently one of the hottest topics in the fluid mechanics field. While machine learning seems to have a great possibility, its limitations should also be clarified.
Koji FUKAGATA
doaj +1 more source
Vortex-induced vibration of catenary riser: reduced-order modeling and lock-in analysis using wake oscillator [PDF]
, 2009 A new reduced-order model capable of analyzing the vortex-induced vibration of catenary riser in the ocean current has been developed. This semi analytical-numerical approach is versatile and allows for a significant reduction in computational effort for
O'Brien, P. +4 more
core +3 more sources
Reduced order models for Lagrangian hydrodynamics
Computer Methods in Applied Mechanics and Engineering, 2022 42 pages, 7 figure, 8 ...
Dylan Matthew Copeland +3 more
openaire +2 more sources
Computational fluid dynamics modeling of a wafer etch temperature control system
Digital Chemical Engineering, 2023 Next-generation etching processes for semiconductor manufacturing exploit the potential of a variety of operating conditions, including cryogenic conditions at which high etch rates of silicon and very low etch rates of the photoresist are achieved. Thus,
Henrique Oyama +5 more
doaj +1 more source
A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models
Journal of Advances in Modeling Earth Systems, 2021 We present a comprehensive inter‐comparison of linear regression (LR), stochastic, and deep‐learning approaches for reduced‐order statistical emulation of ocean circulation.
Niraj Agarwal +4 more
doaj +1 more source
Reduced-Order Modeling of Gust Responses [PDF]
Journal of Aircraft, 2017 This paper describes two approaches to the construction of reduced-order models from computational fluid dynamics to predict the gust response of airfoils and wings.
Wales, Christopher +2 more
openaire +2 more sources
Verification of Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE)
Nuclear Engineering and Technology, 2019 This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to
Bassam Khuwaileh +3 more
doaj +1 more source
Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil
Fluids, 2021 In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack.
Hamayun Farooq +3 more
doaj +1 more source
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics [PDF]
Wind Energy Science, 2022 This article presents a reduced-order model of the highly turbulent wind turbine wake dynamics. The model is derived using a large eddy simulation (LES) database, which cover a range of different wind speeds. The model consists of several sub-models: (1)
S. J. Andersen, J. P. Murcia Leon
doaj +1 more source