Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

Wu, Haibo; Zhang, Hua; Xu, Shuping; Ren, Chao; Chen, Qian; Jadhav, Tushar

doi:10.4271/2022-01-0899

SAE Technical Paper Series

2022

DOI: 10.4271/2022-01-0899

|View full text |Cite

Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

Haibo Wu¹,

Hua Zhang²,

Shuping Xu³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Influence of Hyperparameters of a Neural Network on the Augmented RANS Model Using Field Inversion and Machine Learning

Tao,

Xia,

Cai

et al. 2024

SAE Technical Paper Series

View full text Add to dashboard Cite

<div class="section abstract"><div class="htmlview paragraph">In the field of vehicle aerodynamic simulation, Reynold Averaged Navier-Stokes (RANS) model is widely used due to its high efficiency. However, it has some limitations in capturing complex flow features and simulating large separated flows. In order to improve the computational accuracy within a suitable cost, the Field Inversion and Machine Learning (FIML) method, based on a data-driven approach, has received increasing attention in recent years. In this paper, the optimal coefficients of the Generalized <i>k-ω</i> (GEKO) model are firstly obtained by the discrete adjoint method of FIML, utilizing the results of wind tunnel experiments. Then, the mapping relationship between the flow field characteristics and the optimal coefficients is established by a neural network to augment the turbulence model. On the basis of that, the study further investigates the effects of hyperparameters such as epoch, batch size, activation function, and learning rate on the accuracy of the augmented GEKO model. The result shows that with the drag coefficient (<i>C<sub>D</sub></i>) as the target, batch size and activation function significantly influence the accuracy of the trained model. When a batch size of 512 and either Softsign or Leaky-ReLU activation function are employed, the trained model predicts <i>C<sub>D</sub></i> value closest to the experimental values in the condition of 2000 epochs and a learning rate of 0.001. Increasing the batch size to 1024 or the learning rate to 0.002 provides some improvement in model accuracy, but the effect is not obvious. This work is an important reference for the debugging and improvement of FIML method.</div></div>

show abstract

The Influence of Hyperparameters of a Neural Network on the Augmented RANS Model Using Field Inversion and Machine Learning

Tao,

Xia,

Cai

et al. 2024

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

Cited by 1 publication

References 42 publications

The Influence of Hyperparameters of a Neural Network on the Augmented RANS Model Using Field Inversion and Machine Learning

The Influence of Hyperparameters of a Neural Network on the Augmented RANS Model Using Field Inversion and Machine Learning

Contact Info

Product

Resources

About