Noise optimization of multi-stage orifice plates based on RBF neural network response surface and adaptive NSGA-II

Abstract

In the multi-stage orifice plates (MSOP) design of the pipeline system of the nuclear power plant, since the multi-parameter coupling and matching technology has not been broken through, the high noise cannot be effectively suppressed under large flow rate and high pressure drop conditions. This paper proposes an optimization method for the low noise design of the MSOP based on the RBF neural network response surface and the adaptive NSGA-II algorithm. Cavitation and flow velocity are proposed as the characteristic parameter for evaluating the noise. The relationship between coupling parameters such as diameter ratio, plates thickness, and plates spacing is clarified by correlation analysis. The method increased the minimum pressure by 28.57% and reduced the maximum velocity by 11.49% under the constraints of the high pressure drop, structure size and flow rate. The results prove that this method can effectively realize the design of the MSOP with low noise.