Results 1 to 10 of about 3,842 (98)
Estimating flow fields with reduced order models
Heliyon, 2023 The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands.
Kamil David Sommer +4 more
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Augmented reduced order models for turbulence
Frontiers in Physics, 2022 The authors introduce an augmented-basis method (ABM) to stabilize reduced-order models (ROMs) of turbulent incompressible flows. The method begins with standard basis functions derived from proper orthogonal decomposition (POD) of snapshot sets taken ...
Kento Kaneko +3 more
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Mechanics & Industry, 2022 We consider the dictionary-based ROM-net (Reduced Order Model) framework [Daniel et al., Adv. Model. Simul. Eng. Sci. 7 (2020) https://doi.org/10.1186/s40323-020-00153-6] and summarize the underlying methodologies and their recent improvements.
Daniel Thomas +4 more
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A Class of Reduced-Order Regenerator Models
Energies, 2021 We present a novel class of reduced-order regenerator models that is based on Endoreversible Thermodynamics. The models rest upon the idea of an internally reversible (perfect) regenerator, even though they are not limited to the reversible description ...
Raphael Paul, Karl Heinz Hoffmann
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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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Deep learning-based reduced order models in cardiac electrophysiology.
PLoS ONE, 2020 Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems.
Stefania Fresca +3 more
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Learning stable reduced-order models for hybrid twins
Data-Centric Engineering, 2021 The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time ...
Abel Sancarlos +6 more
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Topology Optimization Based Material Design for 3D Domains Using MATLAB
Applied Sciences, 2022 In this work, a simple, easy to use MATLAB code is presented for the optimal design of materials for 3D domains. For the optimal design of materials, the theoretical framework of topology optimization and that of homogenization were utilized to develop a
George Kazakis, Nikos D. Lagaros
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Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey
Fluids, 2020 Reduced order models (ROMs) are computational models whose dimension is significantly lower than those obtained through classical numerical discretizations (e.g., finite element, finite difference, finite volume, or spectral methods).
Changhong Mou +4 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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