CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries

Hakan Bayrakcı; Arif Çutay; Özdeş Çermik

doi:10.30855/gmbd.070526A07

CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries

Abstract

Transient hemodynamic analysis using Computational Fluid Dynamics (CFD) provides detailed insight into blood flow behavior in aneurysmal aortic geometries; however, repeated simulations for multiple configurations are computationally expensive. In this study, a hybrid CFD–Machine Learning (ML) surrogate framework was developed to predict time-dependent outlet velocity profiles for unseen vascular geometries characterized by different aneurysm diameters (45 mm and 50 mm). High-fidelity transient CFD simulations were conducted under pulsatile flow conditions, and the resulting outlet velocity data were used to train a data-driven interpolation-based surrogate model. The trained model was evaluated on geometries not included in the training dataset. Results show that the ML framework accurately reproduces peak systolic velocity, waveform morphology, and phase alignment across all outlets, with RMSE values below 0.026 m/s and mean relative errors generally under 7%. Under a fixed inlet flow rate of 8.75 L/min, global mass conservation was maintained, while minor local flow redistribution differences were observed. The proposed approach significantly reduces computational cost and offers a rapid, reliable alternative for parametric hemodynamic analysis.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Biomechanical Engineering, Numerical Methods in Mechanical Engineering

Journal Section

Research Article

Authors

Hakan Bayrakcı ^*
0009-0001-0885-1972
Türkiye

Arif Çutay
0000-0002-0057-9417
Türkiye

Özdeş Çermik
0000-0001-9308-4589
Türkiye

Publication Date

August 31, 2026

Submission Date

March 5, 2026

Acceptance Date

August 22, 2026

Published in Issue

Year 2026 Volume: 12 Number: 2

DOI

https://doi.org/10.30855/gmbd.070526A07

IZ

https://izlik.org/JA89JZ78ZN

Cite

RIS / Bibtex

APA

Bayrakcı, H., Çutay, A., & Çermik, Ö. (2026). CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries. Gazi Journal of Engineering Sciences, 12(2), 255-275. https://doi.org/10.30855/gmbd.070526A07

AMA

1.Bayrakcı H, Çutay A, Çermik Ö. CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries. GJES. 2026;12(2):255-275. doi:10.30855/gmbd.070526A07

Chicago

Bayrakcı, Hakan, Arif Çutay, and Özdeş Çermik. 2026. “CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries”. Gazi Journal of Engineering Sciences 12 (2): 255-75. https://doi.org/10.30855/gmbd.070526A07.

EndNote

Bayrakcı H, Çutay A, Çermik Ö (August 1, 2026) CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries. Gazi Journal of Engineering Sciences 12 2 255–275.

IEEE

[1]H. Bayrakcı, A. Çutay, and Ö. Çermik, “CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries”, GJES, vol. 12, no. 2, pp. 255–275, Aug. 2026, doi: 10.30855/gmbd.070526A07.

ISNAD

Bayrakcı, Hakan - Çutay, Arif - Çermik, Özdeş. “CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries”. Gazi Journal of Engineering Sciences 12/2 (August 1, 2026): 255-275. https://doi.org/10.30855/gmbd.070526A07.

JAMA

1.Bayrakcı H, Çutay A, Çermik Ö. CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries. GJES. 2026;12:255–275.

MLA

Bayrakcı, Hakan, et al. “CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries”. Gazi Journal of Engineering Sciences, vol. 12, no. 2, Aug. 2026, pp. 255-7, doi:10.30855/gmbd.070526A07.

Vancouver

1.Hakan Bayrakcı, Arif Çutay, Özdeş Çermik. CFD-Driven Machine Learning Surrogate for Predicting Transient Outlet Velocity Profiles in Aneurysmal Aortic Geometries. GJES. 2026 Aug. 1;12(2):255-7. doi:10.30855/gmbd.070526A07