Annual Review of Fluid Mechanics · 2021 · 93 citations · 112 references
EngineeringAerospace EngineeringMechanical EngineeringAerodynamicsComputational Fluid DynamicsFluid DynamicsComputational AerodynamicsGas Turbine PropulsionTurbulenceUnsteady AerodynamicsAerodynamic ModelingBlade AerodynamicsGas TurbinesTurbine AerodynamicsTurbine Blade AerodynamicsTurbine DesignTurbinesTurbomachinery
Axial turbomachines have achieved high efficiency and reliability through decades of experience and detailed flow‑physics understanding, and advances in CFD and computing power now provide deeper insights into their complex fluid dynamics and loss mechanisms. The article aims to present complex flow phenomena in gas turbine blade components and show how simulations aid understanding while highlighting modeling challenges. It discusses how key aerodynamic features interact with deterministic unsteadiness from multiple blade rows and stochastic turbulence. High‑fidelity simulations, enhanced by machine learning, promise to further improve turbomachinery performance and reliability, thereby increasing the industrial impact of fluid‑mechanics research.
The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.
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GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS
Joseph Smagorinsky · Monthly Weather Review · 1963 · 13.8K citations
Machine Learning for Fluid Mechanics
Annual Review of Fluid Mechanics · 2019 · 2.6K citations · Full text