Data-driven quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms

Eidi, Ali; Zehtabiyan-Rezaie, Navid; Ghiassi, Reza; Yang, Xiang; Abkar, Mahdi

doi:10.1063/5.0100076

Cited by 25 publications

(2 citation statements)

References 75 publications

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“…Computational fluid dynamics (CFD) has not been spared. ML has been applied for turbulence modeling [5][6][7] , flow control 8,9 , reduced-order modeling 10 , optimization [11][12][13] , among others. An overview of ML applications in CFD can be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Vadrot¹,

Yang²,

Bae³

et al. 2023

Preprint

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This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially in domains such as games. Despite its potential, RL is still not widely used for turbulence modeling and is primarily used for flow control and optimization purposes. A new RL wall model (WM) called VYBA23 is developed in this work, which uses agents dispersed in the flow near the wall. The model is trained on a single Reynolds number (Re τ = 10 4 ) and does not rely on high-fidelity data, as the back-propagation process is based on a reward rather than output error. The states of the RLWM, which are the representation of the environment by the agents, are normalized to remove dependence on the Reynolds number. The model is tested and compared to another RLWM (BK22) and to an equilibrium wall model, in a half-channel flow at eleven different Reynolds numbers (Re τ ∈ [180; 10 10 ]). The effects of varying agents' parameters such as actions range, time-step, and spacing are also studied. The results are promising, showing little effect on the average flow field but some effect on wall-shear stress fluctuations and velocity fluctuations. This work offers positive prospects for developing RLWMs that can recover physical laws, and for extending this type of ML models to more complex flows in the future.

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Section: Introductionmentioning

confidence: 99%

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Vadrot¹,

Yang²,

Bae³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These outliers show a slight overestimation of f 1 (x) and a reasonably high underestimation of f 2 (x). RANS models the turbulence instead of resolving it and levels of known uncertainties in RANS turbulence models (including k − ω SST) have been documented against high-fidelity methods [30,31,[119][120][121]. Therefore, marginal inaccuracies when modelling complex turbulence are expected as seen in the results.…”

Section: Iv35 Pareto Front Validationmentioning

confidence: 99%

Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

Rincón

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The accurate measurement of fluid flow is essential in a wide range of industrial applications. In this regard, ultrasonic flow meters present an accurate and cost-effective solution; however, these systems introduce challenges due to their complex geometry and interaction with fluid flow. Hence, understanding their flow dynamics is paramount in the improvement of their design and operation. Simultaneously, data-driven turbulence modelling techniques have emerged as promising tools to improve the precision of fluid flow simulations. This doctoral thesis focuses on bridging these two domains by predicting complex flows in ultrasonic flow meters, developing a robust methodology for geometrical optimisation, and enhancing existing Reynolds-averaged Navier-Stokes (RANS) turbulence models with progressive and generalisable data-driven methods. A methodology based on computational fluid dynamics (CFD) is developed to predict the flow dynamics throughout ultrasonic flow meters by the use of RANS k −ω SST. The method is experimentally validated with laser Doppler velocimetry and pressure drop experiments for the whole dynamic range of operation of these systems. Subsequently, a series of flow simulations are driven by surrogate and Bayesian multi-objective optimisation methods to provide improved geometrical designs. These improved geometries are further optimised by an adjoint-based shape optimisation method that enables the 3-dimensional morphing of the geometry, allowing higher improvement gains which cannot be achieved by traditional engineering ingenuity. In the realm of data-driven turbulence modelling, two critical aspects take centre stage: generalisability and the consistency of a posteriori results. This thesis addresses these challenges through a systematic approach that combines the CFD-driven optimisation techniques developed for flow meter improvement with a progressive augmentation strategy in the k −ω SST turbulence model. The goal is to improve the prediction of turbulence anisotropybased secondary flows, and flow separation—two common stumbling blocks in fluid flow simulations — without altering the successful law of the wall prediction by the original model. Two correction terms are carefully defined and introduced in the momentum and specific dissipation rate (ω) transport equations. After extensive numerical verification, the enhanced models exhibit significant improvements in the prediction of secondary flows, boundary-layer detachment and reattachment, and friction coefficients in both training and testing cases, reaffirming their a posteriori reliability and generalisability. In conclusion, this doctoral thesis presents a robust and cost-efficient methodology to systematically improve the performance of complex engineering systems based on CFD simulations. Additionally, two generalisable methods are developed to enhance turbulence modelling based on data-driven progressive approaches. By addressing challenges related to flow separation and secondary flow predictions in complex geometries together with design and shape optimisation techniques, this research contributes to more accurate fluid flow predictions and simulation-based optimisation; benefiting industries dependent on precise flow measurements and simulations. The synergy between data-driven modelling and complex system optimisation demonstrates the potential for innovative solutions in both turbulence modelling and flow measurement technologies, with broad implications across academic and industrial engineering sectors.

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A Novel Geometric-Encoded and Feature-Fused Model for Pressure Distribution Prediction on Airfoils

Fang,

Xiang,

Zhang

et al. 2024

Lecture Notes in Computer Science

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Data-driven quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms

Cited by 25 publications

References 75 publications

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

A Novel Geometric-Encoded and Feature-Fused Model for Pressure Distribution Prediction on Airfoils

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