Sensitivity-based analysis of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si61.gif" display="inline" overflow="scroll"><mml:mi>k</mml:mi></mml:math>–<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si62.gif" display="inline" overflow="scroll"><mml:mi>ε</mml:mi></mml:math> model for the turbulent flow between two plates

Bardow, André; Bischof, Christian; Bücker, H. Martin; Dietze, Georg F.; Kneer, Reinhold; Leefken, Ansgar; Marquardt, Wolfgang; Renz, Ulrich; Sluşanschi, Emil

doi:10.1016/j.ces.2007.12.029

Cited by 13 publications

(1 citation statement)

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“…This leads to the question of model discrimination. See, e.g., [6,7,13,16,20,25,31,30,11,10,39,14]. While the approaches in [6,7,13,16,20,25,31] sequentially compute designs and conduct experiments to differentiate between N models, our approach requires a one-time computation of N(N − 1)/2 designs to best discriminate all pairs of models and, in a second stage N(N − 1) or, depending on the approach, N(N − 1)/2 experiments to identify the model.…”

Section: Introductionmentioning

confidence: 99%

A method for model identification and parameter estimation

2013

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We propose and analyze a new method for the identification of a parameterdependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive models are available to describe a given material, and one has to determine the best-suited constitutive model for a given material and application from experiments. We assume that the true model is one of N possible parameter-dependent models. To identify the correct model and the corresponding parameters we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e., system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application.

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Section: Introductionmentioning

confidence: 99%

A method for model identification and parameter estimation

2013

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Velocity measurement in the hydrocyclone by oil droplet, doppler ultrasound velocimetry, and CFD modelling

Siangsanun¹,

Guigui²,

Morchain³

et al. 2010

Can J Chem Eng

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To develop the water treatment process, the hydrocyclone is now used as a unit to operate. Understanding hydrodynamics is a key step to improve the separation process efficiency. Recently, a new simple method called the oil droplet method was proposed by Bamrungsri et al. [Chem. Eng. Res. Design 86, 1263-1270] and applied to velocity measurements in a hydrocyclone.The Doppler Velocimetry measurements and computational fluid dynamics (CFD) have been proposed by many researchers as effective for studying the flow field of a hydrocyclone. This work presents a comparison of the experimental results from these two methods along with those obtained from numerical simulations. The numerical calculations of the 3D flow field were performed with FLUENT using the k-ε model and the Reynolds stress model (RSM). Measurements and CFD simulations were performed for two hydrocyclone configurations (5 and 10 cm diameter). Les données de la vélocimétrie ultrasonographique Doppler et les résultats CFD-MTR sont en très bon accord. La méthode de la globule d'huile est moins précise pour les profils de vitesse de phase continus, mais est prometteuse pour la validation des simulations de suivi lagrangiennes.

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Large Eddy simulation for lean premixed combustion

2012

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In comparison to previous numerical studies interested in the ORACLES benchmark (One Rig for Accurate Comparisons with Large Eddy Simulations), the present study demonstrates the advantages of LES-WALE model in both inert and reacting flows using the Fluent-CFD. So, the confirmation is based on the experimental research effort that was involved in the European Union-funded research program MOLECULES (Modelling of Low Emissions Combustors Using Large Eddy Simulations), for three parameters: longitudinal velocity, longitudinal velocity fluctuation, and length of recirculation zone. In line with what was observed by the experimental reference study, the dynamic model (LES-WALE) predicts, respectively, as well as the asymmetry and the symmetry, for both inert and reacting flows. In addition, the simulation succeeds to predict the zones of recirculation and shows the differences between the two cases, inert and reacting flows. Moreover, results have been compared with those of the k-ε model performed by Kurenkov and Obserlack [Kurenkov and Obserlack, Flow Turbulence Combustion 74, 387-407 (2005)] study.

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Sensitivity-based analysis of the k–ε model for the turbulent flow between two plates

Cited by 13 publications

References 30 publications

A method for model identification and parameter estimation

A method for model identification and parameter estimation

Velocity measurement in the hydrocyclone by oil droplet, doppler ultrasound velocimetry, and CFD modelling

Large Eddy simulation for lean premixed combustion

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