Model reduction by the Modal Identification Method in forced convection: Application to a heated flow over a backward-facing step

Rouizi, Yassine; Girault, Manuel; Favennec, Yann; Petit, Daniel

doi:10.1016/j.ijthermalsci.2010.02.011

Cited by 17 publications

(9 citation statements)

References 48 publications

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“…A Particle Swarm Optimization (PSO) algorithm [33] has been used in the present work. Details about the home-made PSO code can be found in [26]. A parallelized version has been used for the present work.…”

Section: Solving the Optimization Problemmentioning

confidence: 99%

Reduced order model of a two-phase loop thermosyphon by modal identification method

Bodjona

Girault

Videcoq

et al. 2018

International Journal of Heat and Mass Transfer

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This study focuses on the model reduction of a two-phase loop thermosyphon. The aim is to propose a nonlinear reduced order model able to mimic the thermo-hydraulic behavior of the loop in order to use it for real-time state feedback control, in future applications. First, the one-dimensional two-phase flow model describing the liquid-gas mixture in both mechanical and thermal equilibrium is recalled. The numerical resolution of this detailed model is carried out using a finite volume approach and a Harten-Lax-van Leer Contact Riemann solver. Then, from this detailed model, a new structure of reduced model is determined via the Galerkin projection method. These reduced models, built by the Modal Identification Method, show a very good agreement between the outputs of the detailed model and those computed by the reduced model,

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Section: Solving the Optimization Problemmentioning

confidence: 99%

Reduced order model of a two-phase loop thermosyphon by modal identification method

Bodjona

Girault

Videcoq

et al. 2018

International Journal of Heat and Mass Transfer

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show abstract

“…Density (kg.m -3 ) is the only property supposed to vary with temperature. In the Boussinesq approximation, density is considered constant (equal to ∞ ∞, ) in all the terms but the buoyancy term, for which density variation depends linearly on temperature variation, thus leading to the term in (2). (m.s -2 ) is the gravity vector and (K -1 ) the thermal expansion coefficient.…”

Section: The Studied Problem: 2d Mixed Convection Past a Heated Cylindermentioning

confidence: 99%

“…A variety of techniques are available for model order reduction. Here, we are focusing on two approaches: Modal Identification Method (MIM), mainly used in thermal systems [1,2], and a POD-Galerkin method (POD-G), based on Proper Orthogonal Decomposition [3] and traditionally employed in fluid mechanics [4]. Briefly, the MIM consists in firstly defining the structure of the reduced order model, secondly generating some inputoutput data characteristic of the system dynamics, thirdly adjusting the model parameters using optimization techniques so that the model outputs fit the output data [1].…”

Section: Introductionmentioning

confidence: 99%

Reduced Order Modeling by Modal Identification Method and POD-Galerkin approach of the heated circular cylinder wake in mixed convection

Cordier

Girault

Petit

2012

J. Phys.: Conf. Ser.

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Abstract. When dealing with control of thermal systems, the first step in the design of a model-based controller is the development of a model which accurately captures heat transfer dynamics. Classical high-fidelity models directly derived from the application of conservation laws on the discretized space domain, lead to sets of Ordinary Differential Equations in time whose size is usually too huge for practical implementation of real-time control. A variety of techniques have been developed for building low order models, involving a small number of degrees of freedom compared to high-fidelity models. Here, we focus on two approaches: Modal Identification Method, mainly used in heat transfer, and a POD-Galerkin method, based on Proper Orthogonal Decomposition and traditionally employed in fluid mechanics. The objective of this work is to compare on a simple 2D mixed convection problem, the accuracy of the reduced order models derived by both methods for describing the flow dynamics. Results are presented for the heated circular cylinder wake at a Reynolds number equal to 200 and a Richardson number equal to 2. Velocity and temperature fields at different time instants, computed with a reference Finite Element model, are used as data for both approaches.

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“…Many of these algorithms are inspired by the nature. Genetic Algorithms [5,6,13,18,30] and Evolutionary Programming [12] [11,20,31,35] and Ant Colony Optimization [10,21] simulate the behaviour of individuals of a biological system, that spread in the environment looking for a food.…”

Section: Global Optimizationmentioning

confidence: 99%

Genetic Algorithm-Based Calibration of Reduced Order Galerkin Models

Stankiewicz

Roszak

Morzyński

2011

Mathematical Modelling and Analysis

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Low-dimensional models, allowing quick prediction of fluid behaviour, are key enablers of closed-loop flow control. Reduction of the model's dimension and inconsistency of high-fidelity data set and the reduced-order formulation lead to the decrease of accuracy. The quality of Reduced-Order Models might be improved by a calibration procedure. It leads to global optimization problem which consist in minimizing objective function like the prediction error of the model.In this paper, Reduced-Order Models of an incompressible flow around a bluff body are constructed, basing on Galerkin Projection of governing equations onto a space spanned by the most dominant eigenmodes of the Proper Orthogonal Decomposition (POD). Calibration of such models is done by adding to Galerkin System some linear and quadratic terms, which coefficients are estimated using Genetic Algorithm.

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Model reduction by the Modal Identification Method in forced convection: Application to a heated flow over a backward-facing step

Cited by 17 publications

References 48 publications

Reduced order model of a two-phase loop thermosyphon by modal identification method

Reduced order model of a two-phase loop thermosyphon by modal identification method

Reduced Order Modeling by Modal Identification Method and POD-Galerkin approach of the heated circular cylinder wake in mixed convection

Genetic Algorithm-Based Calibration of Reduced Order Galerkin Models

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