Searched for: subject%3A%22turbulence%255C+models%22
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Steiner, J. (author)
The Dutch energy strategy expects renewable energy sources like wind and solar to provide around 70% of the yearly electricity by 2030. In order to achieve these targets, models that efficiently and accurately capture the flow around wind turbines would be immensely helpful for both planning and operation of wind farms.<br/><br/>For wind turbine...
doctoral thesis 2023
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Naik, Atharv (author)
In recent years, a considerable amount of research has been directed towards making energy generation more efficient to combat global warming. To aid this goal, the use of supercritical fluids (SCFs) is gaining a lot of traction. SCFs have only one phase and experience a sharp variation of thermophysical properties when heated sufficiently....
master thesis 2023
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van Ede, Matthijs (author)
Classical RANS (Reynolds-Averaged Navier-Stokes) turbulence models have limited accuracy in the prediction of the flow over the wing-body geometry. Therefore, this work focuses on improving the prediction accuracy of the classical k-ω SST turbulence model for the junction flow by means of the data-driven method SpaRTA. In the SpaRTA methodology...
master thesis 2023
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Hoefnagel, Kaj (author)
Computational fluid dynamics (CFD) is an important tool in design involving fluid flow. Scale-resolving CFD methods exist, but they are too computationally expensive for practical design. Instead, the relatively cheap Reynolds-averaged Navier-Stokes (RANS) approach is the industry standard, specifically models based on the Boussinesq hypothesis,...
master thesis 2023
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Diez Sanhueza, R.G. (author), Smit, S.H.H.J. (author), Peeters, J.W.R. (author), Pecnik, Rene (author)
This paper presents a machine learning methodology to improve the predictions of traditional RANS turbulence models in channel flows subject to strong variations in their thermophysical properties. The developed formulation contains several improvements over the existing Field Inversion Machine Learning (FIML) frameworks described in the...
journal article 2023
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Hosseini, A. (author), Hage, Johannes (author), Meijer, Koen (author), Offerman, S.E. (author), Yang, Y. (author)
In this paper a CFD analysis of HIsarna off-gas system for post combustion of CO-H2-carbon particle mixture is presented to evaluate the effect of different sub-models and parameters on the accuracy of predictions and simulation time. The effects of different mesh type, mesh grid size, radiation models, turbulent models, kinetic mechanism,...
journal article 2023
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Grabe, Cornelia (author), Jäckel, Florian (author), Khurana, Parv (author), Dwight, R.P. (author)
Purpose: This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model. Design/methodology/approach: The field inversion and machine learning (FIML) approach is applied to...
journal article 2023
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Elsinga, G.E. (author), Ishihara, Takashi (author), Hunt, J.C.R. (author)
Direct numerical simulations up to Reλ = 1445 show that the scaling exponents for the enstrophy and the dissipation rate extrema are different and depend on the Reynolds number. A similar Reynolds number dependence of the scaling exponents is observed for the moments of the dissipation rate, but not for the moments of the enstrophy. Significant...
journal article 2023
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Miori, Nicolò (author)
Recent years have seen an increase in studies focusing on data-driven techniques to enhance modelling approaches like the two-equation turbulence models of Reynolds-averaged Navier-Stokes (RANS). Different techniques have been implemented to improve the results from these simulations. In particular, the main focus has been on overcoming the...
master thesis 2022
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van Leeuwen, Elske (author)
Computational Fluid Dynamics (CFD) is the main tool to use in industry and engineering problems including turbulent flows. Turbulence modeling relies on solving the Navier-Stokes equations. Solving these equations directly takes a lot of time and computational power. More affordable methods solve the Reynolds Averaged Navier-Stokes (RANS)...
master thesis 2022
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van de Krol, Bart (author)
The most used RANS-model in relation to wind farms, k−epsilon, has significant shortcomings. It over-predicts the eddy viscosity in the near-wake and fails to model the anisotropy of the turbulence quantities. The Sparse Regression of the Turbulence Stress Anisotropy (SpaRTA) method could remedy these shortcomings. This method uses temporally...
master thesis 2022
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Hemmes, Jasper (author)
When simulating fluids the industry standard is Reynolds averaged Navier-Stokes (RANS). However, the results for certain flows are inaccurate. The main source of error in popular RANS turbulence models is the Boussinesq approximation, assuming a linear relationship between the Reynolds stress anisotropy and the mean rate of strain. Experiments...
master thesis 2022
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Volker, Thijs-Gerrit (author)
Implementation of an Explicit Algebraic Reynolds Stress Model in Isogeometric Analysis for seperating flows using a two-equation k-omega model. The model is verified for simple cases such as homogeneous isotropic decaying turbulence, and the turbulent backward facing step
master thesis 2022
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Pathanadka, Chinmaya (author)
Aerodynamics has played a significant role in the industry of motorsports in improving the performance and handling of the race car. Rob Smedley, the former head of vehicle performance at Williams Racing stated that - "Where teams have problems is when their development or simulation environment – so CFD [Computational Fluid Dynamics] or wind...
master thesis 2022
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Ben Hassan Saïdi, Ismaïl (author), Schmelzer, M. (author), Cinnella, Paola (author), Grasso, Francesco (author)
A CFD-driven deterministic symbolic identification algorithm for learning explicit algebraic Reynolds-stress models (EARSM) from high-fidelity data is developed building on the frozen-training SpaRTA algorithm of [1]. Corrections for the Reynolds stress tensor and the production of transported turbulent quantities of a baseline linear eddy...
journal article 2022
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Modesti, D. (author), Pirozzoli, Sergio (author)
We carry out direct numerical simulations (DNS) of flow in a turbulent square duct by focusing on heat transfer effects, considering the case of unit Prandtl number. Reynolds numbers up to are considered that are much higher than in previous studies, and that yield clear scale separation between inner- and outer-layer dynamics. Close...
journal article 2022
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Fehrs, Michael (author), Ritter, Markus (author), Helm, Sebastian (author), Mertens, C. (author)
The Pazy Wing test case is a benchmark for the investigation of aeroelastic effects at very large deflections. Tip deformations in the order of 50% span were measured in wind tunnel tests, which renders this model highly attractive for the validation of numerical aeroelastic methods and tools for geometrically nonlinear, large deflection...
conference paper 2022
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Steiner, J. (author), Dwight, R.P. (author), Viré, A.C. (author)
The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are Reynolds-Averaged Navier–Stokes (RANS) which are two orders of magnitude cheaper, but resolve only mean quantities...
journal article 2022
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Xu, R. (author), Zhou, Xu Hui (author), Han, Jiequn (author), Dwight, R.P. (author), Xiao, Heng (author)
In fluid dynamics, constitutive models are often used to describe the unresolved turbulence and to close the Reynolds averaged Navier–Stokes (RANS) equations. Traditional PDE-based constitutive models are usually too rigid to calibrate with a large set of high-fidelity data. Moreover, commonly used turbulence models are based on the weak...
journal article 2022
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Khurana, Parv (author)
In recent years, many data-driven approaches which leverage high-fidelity reference data have been developed to augment the performance of Reynolds Averaged Navier–Stokes (RANS) turbulence models by providing an improved closure to the governing fluid flow equations. The goal of this M.Sc. thesis is to apply and extend one such data-driven...
master thesis 2021
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