Heuristics applying stochastic information as tools for thermoacoustic standing-wave engine optimization

In this article, two numerical methods for solving engineering problems defined as multicriteria optimization and inverse problem are presented. In particular, this study deals with the optimization of the design of thermoacoustic engine in the frame in which both types of tasks are solved. The first proposed heuristic serves to find many p-optimal solutions simultaneously, which represents a compromise between usually mutually contradictory goals at work. Based on them, the full Pareto front is approximated. The inverse problem solution reproduces parameters for solutions located on a designated front but those that are not found in multicriteria optimization. In this article, the RACO heuristics are proposed for determining p-optimal solutions and the Bayesian approach is introduced as a method for solving ill-conditioned inverse problems. Optimization of the construction of the thermoacoustic engine is aimed at verifying proposed methodology and present the possibility of using both methods in engineering problems. The problem discussed in this article is formulated and the numerical methods used in the solution are presented in details.