Structural Parameter Optimization of a Coaxial Swashplate Engine Based on NSGA-II

Abstract

Abstract. Swashplate engines offer compelling advantages over traditional piston engines for low-altitude applications, particularly in power density, compact design, and fuel versatility. However, traditional design processes for swashplate engines still rely heavily on empirical paradigms, resulting in relatively outdated optimization schemes. To address this issue, this study focuses on a coaxial swashplate engine and proposes an optimization strategy based on the Nondominated Sorting Genetic Algorithm II (NSGA-II). First, a coupled engine power model and a connecting rod inertia moment model are developed, incorporating key structural parameters. Then, a dynamic penalty function-based elimination mechanism is introduced to ensure that the optimization outcomes surpass expected performance thresholds. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to rank and select the Pareto-optimal solutions. The results demonstrate that the dynamic penalty function mechanism effectively overcomes the premature convergence drawback associated with fixed penalty methods, significantly enhancing the algorithm's stability. Through TOPSIS-based evaluation, a synergistic optimization outcome is achieved, with a relative power increase of 19%.