Design-Point Performance Adaptation of Small Gas Turbine Using Particle Swarm Optimization

Abstract

Modeling of design-point performances is an important step for designing the gas turbine engine. It is also a necessary step in an off-design performance analysis where during the modeling of design-point performance, some engine parameters are typically not known. These unknown parameters should be estimated and adapted to obtain design-point target performances. This paper employs meta-heuristic optimizations, namely genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), and whale optimizer algorithm (WOA) to solve the design-point adaptation of a small gas turbine designed for marine applications. Target parameters are shaft power, fuel flow, turbine exit temperature, turbine exit pressure, and thermal efficiency with seven adapted parameters as the optimization parameters. Due to multiple solutions and constraints of the searching area, the meta-heuristic optimization can encounter the computational difficulty. The PSO shows an outstanding performance among other meta-heuristic optimizations where it has the smallest fitness value, i.e., 0.02233. The GWO has the minimum fitness value as compared with the GA and WOA, but the value of turbine inlet temperature (TIT) is approaching its upper bound, in which this condition is not expected. The GA has a problem of escaping from the initial value for the TIT parameter. The WOA has the largest fitness value, i.e., 0.0971, although it does not have a problem of escaping the initial value and approaching the upper/lower bound.