Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future

Abstract

Utilizing meta-heuristic global optimization algorithms in gas turbine aero-engines modelling and control problems is proposed over the past two decades as a methodological approach. The purpose of the review is to establish evident shortcomings of these approaches and to identify the remaining research challenges. These challenges need to be addressed to enable the novel, cost-effective techniques to be adopted by aero-engine designers. First, the benefits of global optimization algorithms are stated in terms of philosophy and the nature of different types of these methods. Then, a historical coverage is given for the applications of different optimization techniques applied in different aspects of gas turbine modelling, controller design, and tuning fields. The main challenges for the application of meta-heuristic global optimization algorithms in new advanced engine designs are presented. To deal with these challenges, two efficient optimization algorithms, Competent Genetic Algorithm in single objective feature and aggregative gradient-based algorithm in multi-objective feature are proposed and applied in a turbojet engine controller gaintuning problem as a case study. A comparison with the publicly available results show that optimization time and convergence indices will be enhanced noticeably. Based on this comparison and analysis, the potential solutions for the remaining research challenges for application to aerospace engineering problems in the future include the implementation of enhanced and modified optimization algorithms and hybrid optimization algorithms in order to achieve optimal results for the advanced engine modelling and controller design procedure with affordable computational effort.

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

“…According to Equation (18), the constraint inequality function from Equation (25) can be written as Equation (26).…”

“…• Higher flexibility to deal with control requirements and constraints at different flight and weather conditions. So, a lower coupling between the control channels has become a developing trend to improve the performance of classic scheduling technique (e.g., the min-max algorithm) [26].…”

Multidisciplinary assessment of blade number and manufacturing parameters for the performance of centrifugal fans

“…where nn is the total number of the nodes on pressure or suction surface. It yields equation (11), the left of which has the center coordinate and radius.…”

An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations

“…Finally, the algorithms inspired by the living habits of animals and swarms in nature have Particle swarm optimization (PSO) algorithm [19], Bat algorithm (BA) [20], Gray wolf algorithm (GWO) [21], Whale optimization algorithm (WOA) [22], Cuckoo search algorithm (CS) [23], Slime mold algorithm (SMA) [24], Harris hawks optimization (HHO) [25] and Moth flame algorithm (MFO) [26]. Even though the metaheuristic algorithm has been put forward for many years, it is also a research hotspot now [27] [28]. Table 1 lists the symbols, and shortened words are used in the document.…”