Optimization of Endwall Film-Cooling Hole Arrangement Based on Reinforcement Learning
The arrangement of holes is an important geometric factor affecting the effectiveness of end-wall film cooling.This paper proposes a film hole arrangement optimization method based on reinforcement learning.By treating the design of hole arrangements as a series of decisions to open or close holes,it converts the problem into a Markov decision process and solves it using reinforcement learning.The paper abstracts the turbine vane cascade into a bent and contracted passage to simulate the transverse and directional pressure gradient environments of the end-wall,and carries out the optimization of film cooling hole arrangement under this pressure gradient environment.The research results provide a new paradigm for the layout optimization of various cooling forms such as film cooling,impingement cooling,and pin-fin cooling.
air film coolingpressure gradientreinforcement learninghole arrangementopti-mization
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