Application of a Physics-Informed Neural Network Surrogate Model Based on CFD Data for Modeling Flow Around a Cylinder Under Thermal Effects
Issakhov A. Daminov A. Sabyrkulova A. Abylkassymova A.
March 2026John Wiley and Sons Ltd
International Journal for Numerical Methods in Fluids
2026#98Issue 3262 - 273 pp.
This paper examines the application of PINN models to solving a two-dimensional cylinder flow problem with limited data. Using data obtained by direct numerical simulation, a surrogate PINN model was developed and trained. The model utilizes the governing equations of fluid dynamics and heat transfer, enabling it to accurately predict flow parameters such as velocity components, pressure, and temperature. The direct computational flow model was numerically solved using the SIMPLE algorithm, which couples pressures and velocities. The results showed that the PINN model, which does not contain initial and boundary conditions from direct numerical simulation, is capable of reproducing complex dynamic processes such as the formation of a Kármán vortex street behind a cylinder. However, limitations were identified due to the lack of initial and boundary conditions, which led to increased errors at the boundaries of the computational domain. For example, from the data obtained using the PINN model, a very small absolute difference in error for the velocity and temperature components between the reference data and the predicted values can be noted. Thus, for the horizontal velocity component, the maximum relative error was no more than 2.5%. For the temperature component, the relative error was no more than 0.02%. However, the relative error for pressure was 60%–75%. The main reason for this large error is the lack of a reference pressure value or initial pressure conditions in the loss function. The results show that the PINN surrogate model with eight hidden layers of 200 neurons successfully copes with the task of modeling complex unsteady flow. The integration of physical laws made it possible to achieve relatively satisfactory accuracy using only 10,000 data points.
direct numerical modeling , flow around a cylinder , fluid dynamic , physics-informed neural networks , surrogate model
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Kazakh British Technical University, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kazakh American University, Almaty, Kazakhstan
Kazakh British Technical University
Al-Farabi Kazakh National University
Kazakh American University
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026