چکیده مقاله
Turbulence, a nonlinear, chaotic, and multiscale phenomenon, governs fluid motion in both natural and engineered systems This paper provides a comprehensive analysis of classical and advanced turbulence modeling techniques, emphasizing their evolution, applications, and integration with emerging computational technologies Traditional methods such as Direct Numerical Simulation DNS , Reynolds Averaged Navier–Stokes RANS , and Large Eddy Simulation LES have laid the foundation for modern hybrid approaches like Detached Eddy Simulation DES and Scale Adaptive Simulation SAS , which balance accuracy and computational cost Recent developments highlight the growing influence of machine learning ML , physics informed neural networks PINNs , and quantum algorithms in overcoming the limitations of classical models These tools enhance predictive capability and efficiency by uncovering hidden patterns within turbulent flow data and accelerating solution convergence Applications span aerospace, energy, environmental, and biomedical fields—from optimizing aircraft aerodynamics and wind turbine efficiency to predicting cardiovascular flow instabilities and pollutant dispersion The paper also explores the role of digital twins, which integrate real time sensor data with adaptive LES models, enabling dynamic updates for industrial systems Despite significant advances, challenges persist in model interpretability, validation, and scalability, particularly in data driven and hybrid frameworks The future of turbulence modeling lies in the synergistic convergence of artificial intelligence, high performance computing, and quantum simulation, promising breakthroughs toward a universal turbulence solver capable of unifying physics based and data driven paradigms for complex, real world fluid dynamic problems
کلیدواژهها
نویسندگان
Aliasghar Azma
School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian ۱۱۶۰۲۴, China
Yakun Liu
School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian ۱۱۶۰۲۴, China
شیوه ارجاع
Azma, Aliasghar and Liu, Yakun,1404,Next-Generation Turbulence Modeling: Integrating Classical Methods, Machine Learning, and Quantum Computing,5th international conference on engineering and information technology
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی و فناوری اطلاعات30 آبان 1404