A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship

Weatheritt, Jack; Sandberg, Richard D.

doi:10.1016/j.jcp.2016.08.015

Cited by 252 publications

(154 citation statements)

References 22 publications

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“…This approach is crucial where data is abundant (e.g., geophysical flows, finance, neuroscience) but the accurate physical law/model is not available. When it comes to large-scale datasets, gene expression programming (GEP), for example, has been applied to recover turbulence closures [376]. The authors tailored original GEP algorithm [377] to handle tensor regression and recover complex functional forms.…”

Section: Nonintrusive Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Section: Nonintrusive Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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“…In [25,28,29] this was done using ordinary least-squares regression for problems in the domains of dynamical systems and biological networks. As mentioned above, the ability of the CFD solver, in which the models will be implemented, to produce a converged solution is sensitive to large coefficients, which has been reported in [11,12,22]. We take this additional constraint into account by performing a Ridge regression…”

Section: Model Inference For Cfdmentioning

confidence: 99%

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

Schmelzer¹,

Dwight²,

Cinnella³

2019

Flow Turbulence Combust

182

102

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A novel deterministic symbolic regression method SpaRTA is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine-learning method is based on elastic net regularisation which promotes sparsity of the inferred models. By being data-driven the method relaxes assumptions commonly made in the process of model development. Model-discovery and cross-validation is performed for three cases of separating flows, i.e. periodic hills (Re=10595), converging-diverging channel (Re=12600) and curved backward-facing step (Re=13700). The predictions of the discovered models are significantly improved over the k-ω SST also for a true prediction of the flow over periodic hills at Re=37000. This study shows a systematic assessment of SpaRTA for rapid machine-learning of robust corrections for standard RANS turbulence models.

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“…2.1. GEP for model development GEP, first introduced for turbulence model development by Weatheritt & Sandberg (2016), is applied in the present study to train an EASM-like Reynolds stress relation by adding an extra anisotropic stress a x ij as…”

Section: Methodology and Case Setupmentioning

confidence: 99%

“…In a series of studies, the gene-expression programming (GEP) method has been introduced (Weatheritt & Sandberg 2016) to develop explicit algebraic (Reynolds) stress models (EASM) based on the stress tensor decomposition proposed by Pope (1975). EASM-like turbulence closures are represented in the form…”

Section: Introductionmentioning

confidence: 99%

“…In order to train models adaptive to RANS environments, a novel model development framework named CFD-driven training is introduced in the present work. By integrating RANS calculations in the GEP model training method introduced by Weatheritt & Sandberg (2016), the objective is to develop Reynolds stress closures of the form given in equation (1.2), which are i) straightforward to be implemented in RANS solvers; ii) robust in a sense of providing stable solutions in RANS calculations; and iii) showing improved predictive accuracy in different a posteriori tests.…”

Section: Introductionmentioning

confidence: 99%

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RANS turbulence model development using CFD-driven machine learning

Zhao¹,

Akolekar²,

Weatheritt³

et al. 2020

Journal of Computational Physics

Self Cite

198

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This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. For the CFD-driven training, the gene expression programming (GEP) method (Weatheritt & Sandberg, J. Comput. Phys., 325, 22-37 (2016)) uses RANS calculations in an integrated way to evaluate the fitness of candidate models. The resulting model, which is the one providing the most accurate CFD results at the end of the training process, is thus expected to show good performance in RANS calculations. To demonstrate the potential of this new approach, the CFD-driven machine learning is applied to develop a model for improved prediction of wake mixing in turbomachines. A new model is trained based on a high-pressure turbine training case with particular physical features. The developed model is shown to have a more compact functional form than models trained without CFD assistance. Furthermore, the trained model has been evaluated a posteriori for the training case and three additional test cases with different physical flow features, and the predicted wake mixing profiles are significantly improved in all cases. With the present framework, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is due to the extra diffusion introduced by the CFD-driven model.

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A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship

Cited by 252 publications

References 22 publications

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

RANS turbulence model development using CFD-driven machine learning

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