Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems

Morita, Yuki; Rezaeiravesh, Saleh; Tabatabaei, Narges; Vinuesa, Ricardo; Fukagata, Koji; Schlatter, Philipp

doi:10.1016/j.jcp.2021.110788

Cited by 60 publications

(19 citation statements)

References 63 publications

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“…The applications of active machine learning span all length scales of chemical engineering from ab initio calculations [12,13,21], material, molecule and catalyst design [22][23][24][25][26][27][28][29][30][31], reaction design [32][33][34][35][36][37][38][39] up to reactor design [40][41][42]. For example, the design of catalysts is an important asset in achieving carbon neutrality as catalysts can enable more sustainable processes, and increase the energy efficiency of chemical processes in general.…”

Section: Active Machine Learning In Chemical Engineeringmentioning

confidence: 99%

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Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Ureel

Dobbelaere

Ouyang

et al. 2022

Preprint

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By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine learning algorithms are maturing, its applications are lacking behind. Three types of challenges faced by active machine learning are identified and ways to overcome them are discussed: the convincing of the experimental researcher, the flexibility of data creation, and the robustness of the active machine learning algorithms. A bright future lies ahead for active machine learning in chemical engineering thanks to increasing automation and more efficient algorithms to drive novel discoveries.

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Section: Active Machine Learning In Chemical Engineeringmentioning

confidence: 99%

“…Next to constraints resulting from how the experimental equipment operates, these are also important for simulations [40,42]. Consider the case when optimizing a reactor in silico with CFD.…”

Section: Constrained Active Machine Learningmentioning

confidence: 99%

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Ureel

Dobbelaere

Ouyang

et al. 2022

Preprint

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“…Mahfoze et al [4] utilized Bayesian optimization on a four-dimensional problem to locate optimal low-amplitude wall-normal blowing strategies to reduce the skin-friction drag of a turbulent boundary-layer with a net power saving of up to 5%, within 20 optimization evaluations. Morita et al [5] considered three CFD problems: the first two problems concerned the shape optimization of a cavity and a channel flow. The third problem optimized the hyperparameters of a spoiler-ice model.…”

Section: Introductionmentioning

confidence: 99%

Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics

Diessner

O’Connor

Wynn

et al. 2022

Front. Appl. Math. Stat.

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Bayesian optimization (BO) provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g., in machine learning to optimize hyperparameters of neural networks, and in engineering, e.g., in fluid dynamics to optimize control strategies that maximize drag reduction. This paper empirically studies and compares the performance and the robustness of common BO algorithms on a range of synthetic test functions to provide general guidance on the design of BO algorithms for specific problems. It investigates the choice of acquisition function, the effect of different numbers of training samples, the exact and Monte Carlo (MC) based calculation of acquisition functions, and both single-point and multi-point optimization. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of BO to specific challenges, and in general. To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics (CFD) are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems. The results of our investigation can similarly be applied to other areas, such as machine learning and physical experiments, where objective functions are expensive to evaluate and their mathematical expressions are unknown.

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“…Fine tuning and optimization tasks suit the application of Bayesian inversion. The authors of [22] used Bayesian inversion for the shape optimization of a wall to obtain a prescribed pressure gradient distribution. This application presents similarities with the present work aiming at optimizing the surface roughness to obtain a given heat flux distribution.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Calibration of Rough Heat Transfer Prediction Using Bayesian Inversion and Genetic Algorithm

et al. 2022

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The prediction of heat transfers in Reynolds-Averaged Navier–Stokes (RANS) simulations requires corrections for rough surfaces. The turbulence models are adapted to cope with surface roughness impacting the near-wall behaviour compared to a smooth surface. These adjustments in the models correctly predict the skin friction but create a tendency to overpredict the heat transfers compared to experiments. These overpredictions require the use of an additional thermal correction model to lower the heat transfers. Finding the correct numerical parameters to best fit the experimental results is non-trivial, since roughness patterns are often irregular. The objective of this paper is to develop a methodology to calibrate the roughness parameters for a thermal correction model for a rough curved channel test case. First, the design of the experiments allows the generation of metamodels for the prediction of the heat transfer coefficients. The polynomial chaos expansion approach is used to create the metamodels. The metamodels are then successively used with a Bayesian inversion and a genetic algorithm method to estimate the best set of roughness parameters to fit the available experimental results. Both calibrations are compared to assess their strengths and weaknesses. Starting with unknown roughness parameters, this methodology allows calibrating them and obtaining between 4.7% and 10% of average discrepancy between the calibrated RANS heat transfer prediction and the experimental results. The methodology is promising, showing the ability to finely select the roughness parameters to input in the numerical model to fit the experimental heat transfer, without an a priori knowledge of the actual roughness pattern.

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Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems

Cited by 60 publications

References 63 publications

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics

Data-Driven Calibration of Rough Heat Transfer Prediction Using Bayesian Inversion and Genetic Algorithm

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