Results 31 to 40 of about 1,899,215 (187)

CD-ROM: Complemented Deep - Reduced order model

open access: yesComputer Methods in Applied Mechanics and Engineering, 2023
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems. However, the applicability of the method to non linear high-dimensional dynamical systems such as the Navier-Stokes equations has been shown to be limited, producing inaccurate and sometimes unstable models.
Menier, Emmanuel   +4 more
openaire   +6 more sources

Pressure Stabilization Strategies for a LES Filtering Reduced Order Model

open access: yesFluids, 2021
We present a stabilized POD–Galerkin reduced order method (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method.
Michele Girfoglio   +2 more
doaj   +1 more source

Verification of Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE)

open access: yesNuclear 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

Modern methods of mathematical modeling of blood flow using reduced order methods [PDF]

open access: yesКомпьютерные исследования и моделирование, 2018
The study of the physiological and pathophysiological processes in the cardiovascular system is one of the important contemporary issues, which is addressed in many works.
Sergey Sergeevich Simakov
doaj   +1 more source

Low Pressure Experimental Validation of Low-Dimensional Analytical Model for Air–Water Two-Phase Transient Flow in Horizontal Pipelines

open access: yesFluids, 2021
This paper presents a low-pressure experimental validation of a two-phase transient pipeline flow model. Measured pressure and flow rate data are collected for slug and froth flow patterns at the low pressure of 6 bar at the National University of ...
Hamdi Mnasri   +8 more
doaj   +1 more source

An Artificial Compression Reduced Order Model [PDF]

open access: yesSIAM Journal on Numerical Analysis, 2020
We propose a novel artificial compression, reduced order model (AC-ROM) for the numerical simulation of viscous incompressible fluid flows. The new AC-ROM provides approximations not only for velocity, but also for pressure, which is needed to calculate forces on bodies in the flow and to connect the simulation parameters with pressure data. The new AC-
Victor DeCaria   +4 more
openaire   +2 more sources

Reduced order modeling of non-linear monopile dynamics via an AE-LSTM scheme

open access: yesFrontiers in Energy Research, 2023
Non-linear analysis is of increasing importance in wind energy engineering as a result of their exposure in extreme conditions and the ever-increasing size and slenderness of wind turbines.
Thomas Simpson   +4 more
doaj   +1 more source

Predictive and stochastic reduced-order modeling of wind turbine wake dynamics [PDF]

open access: yesWind 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

Reduced order method for finite difference modeling of cardiac propagation

open access: yesCurrent Directions in Biomedical Engineering, 2020
Efficient numerical simulation of cardiac electrophysiology is crucial for studying the electrical properties of the heart tissue. The cardiac bidomain model is the most widely accepted representation of the electrical behaviour of the heart muscle.
Khan Riasat   +2 more
doaj   +1 more source

Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression

open access: yes, 2020
Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output.
Graff, John   +3 more
core   +1 more source

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