Multi-objective optimization of helical coil steam generator in high temperature gas reactors with genetic algorithm and response surface method

Abstract

High temperature gas reactors (HTGRs) have broad prospects in industry. The helical coil steam generator, which plays an important role in energy conversion in HTGRs, is widely adopted due to its high thermal efficiency. However, the design and performance analysis for helical coil steam generators is a definitely tough job induced by its complex structure and operation conditions. In this paper, an innovative multi-objective optimization process was proposed to manage the key parameter design of steam generators in HTGRs. Firstly, the system response of steam generators is investigated. The sensitivity of geometric parameters on the steam generator thermal performance is obtained through the response surface methodology (RSM). Finally, the geometric parameters of steam generators are optimized using the genetic algorithm with the goal of a higher heat transfer coefficient and a lower tube side pressure drop. Compared with the original design pressure drop (1.19 MPa) and heat transfer coefficient (1.007 kW∙m−2∙K−1, the optimal solution obtained by multi-objective genetic algorithm (MOGA) decreases the pressure drop by 50.76% and improves the overall heat transfer coefficient by 15.00%. It shows that MOGA performs well in heat transfer optimization of steam generators in HTGRs.