Bibliographic Information: Apte, D., Razaaly, N., Fang, Y., Ge, M., Sandberg, R., & Coutier-Delgosha, O. (2024). A novel data-driven method for augmenting turbulence modelling for unsteady cavitating flows. Elsevier.
Research Objective: This study aims to develop a more accurate and computationally efficient method for modeling turbulence in unsteady cavitating flows, addressing the limitations of traditional RANS and hybrid RANS-LES models.
Methodology: The researchers propose a data-driven approach that integrates Gene-Expression Programming (GEP) with a traditional RANS method. GEP is employed to generate an additional corrective term for the Boussinesq approximation, enhancing its accuracy in predicting Reynolds shear stress and turbulent kinetic energy. The model is trained using high-fidelity experimental data from a converging-diverging nozzle (venturi) case study. A Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is used to optimize the GEP-derived expressions, minimizing the error between the model predictions and experimental data. The performance of the proposed GEP-CFD approach is compared against both baseline RANS simulations and a linear regression model.
Key Findings: The GEP-CFD approach demonstrates superior accuracy compared to standard RANS simulations in predicting both Reynolds shear stress and turbulent kinetic energy fields. Incorporating the standard deviation of velocity profiles significantly improves the model's ability to capture the turbulent kinetic energy dynamics. Sensitivity analysis reveals the significant influence of void fraction, time-averaged velocities, and their standard deviations on the model's predictive performance.
Main Conclusions: The study concludes that integrating GEP with traditional RANS methods offers a promising approach for augmenting turbulence modeling in unsteady cavitating flows. The proposed method demonstrates improved accuracy and computational efficiency compared to traditional approaches, paving the way for more reliable simulations of complex multi-phase flow phenomena.
Significance: This research contributes to the growing field of data-driven turbulence modeling, offering a novel approach to address the limitations of traditional methods in simulating complex flows. The findings have significant implications for various engineering applications involving cavitation, including hydraulic machinery design and optimization.
Limitations and Future Research: The study acknowledges the limitations of using a single case study for model training and validation. Future research should focus on testing the GEP-CFD approach on a wider range of cavitating flow scenarios and exploring the potential of incorporating additional flow features and physics-informed constraints into the GEP algorithm to further enhance its accuracy and generalizability.
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by Dhruv Apte, ... at arxiv.org 10-10-2024
https://arxiv.org/pdf/2410.06282.pdfDeeper Inquiries