A new and better optimization of supersonic ejector design

Aim. The introduction of the full paper reviews a number of papers in the open literature and then, in its fifth paragraph, outlines what we believe to be a new and better optimization, which is explained in sections 1, 2 and 3. The core of section 2 consists of: (1) we study the parameters of an ejector configuration and determine its design variables; (2) we build the surrogate model of an ejector to approximate the entrained flow rate and pressure lift; for the design of experiment, we build its optimal Latin hypercube (OLH) to ensure that every design point have its corresponding value and be evenly distributed to design object; (3) we use the multi-objective permutation genetic algorithm (GA) to optimize the fitness and uniformity of the building points and validation points; (4) we use the moving least squares (MLS) to establish the metamodel of ejector and carry out its global optimization with GA, whose response is calculated with the surrogate model; (5) we use the sequential quadratic programming to adjust the design variables optimized by GA and to ensure that they be optimal. Section 3 presents the optimization results, given in Figs.6, 9, 10, 11 and 12; the results and their analysis show preliminarily that our optimization method can obtain the Pareto optimal front and conduct rapid optimization of ejectors working in a wide range of entrained flow rate.