Results 31 to 40 of about 7,720,158 (325)
On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 One of the principal barriers in developing accurate and tractable predictive models in turbulent flows with a large number of species is to track every species by solving a separate transport equation, which can be computationally impracticable. In this
Donya Ramezanian, A. Nouri, H. Babaee
semanticscholar +1 more source
A Method to Minimize the Effort for Damper–Blade Matching Demonstrated on Two Blade Sizes
Applied Sciences, 2021 A method called PCR (Platform Centered Reduction) is designed to more effectively perform complex iterative and nonlinear calculations required for the dynamic response of turbine blades damped by dry friction contacts between rigid dampers and airfoil ...
Chiara Gastaldi, Muzio M. Gola
doaj +1 more source
Reduced-order modelling numerical homogenization [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2014 A general framework to combine numerical homogenization and reduced-order modelling techniques for partial differential equations (PDEs) with multiple scales is described. Numerical homogenization methods are usually efficient to approximate the effective solution of PDEs with multiple scales.
Abdulle Assyr, Bai Yun
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Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders [PDF]
The Physics of Fluids, 2020 A common strategy for the dimensionality reduction of nonlinear partial differential equations relies on the use of the proper orthogonal decomposition (POD) to identify a reduced subspace and the Galerkin projection for evolving dynamics in this reduced
R. Maulik +2 more
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A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders
Fluids, 2022 This paper presents a new nonlinear projection based model reduction using convolutional Variational AutoEncoders (VAEs). This framework is applied on transient incompressible flows.
Nissrine Akkari +3 more
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Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes [PDF]
Theoretical and Computational Fluid Dynamics, 2020 We propose a method to construct a reduced order model with machine learning for unsteady flows. The present machine-learned reduced order model (ML-ROM) is constructed by combining a convolutional neural network autoencoder (CNN-AE) and a long short ...
K. Hasegawa +3 more
semanticscholar +1 more source
Reduced finite element square techniques (RFE2): towards industrial multiscale fe software [PDF]
, 2019 Reduced order modeling techniques proposed by the authors are assessed for an industrial case study of a 3D reinforced composite laminate. Essentially, the main dominant strain micro-structural modes are obtained through standard reduced order modeling ...
Huespe, Alfredo Edmundo +3 more
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Wall‐based reduced‐order modelling [PDF]
International Journal for Numerical Methods in Fluids, 2015 SummaryIn this work, we propose a novel approach to model order reduction for incompressible fluid flows, which focuses on the spatio‐temporal description of the stresses on the surface of a body, that is, of the wall shear stress and of the wall pressure.
Lasagna, Davide, Tutty, Owen
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Linear Reduced-Order Model Predictive Control
IEEE Transactions on Automatic Control, 2022 Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems with low-dimensional models. Nevertheless, high-dimensional models arise in many settings, for example discretization methods for generating finite-dimensional ...
Joseph Lorenzetti +3 more
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Approximate deconvolution reduced order modeling [PDF]
Computer Methods in Applied Mechanics and Engineering, 2017 This paper proposes a large eddy simulation reduced order model(LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition(POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures.
Xie, X. +3 more
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