Robustness of optimisation algorithms for transonic aerodynamic design
Creators: name:  F. SANCHEZ-MORENO 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: PROPULSION PHYSICS
AIR BREATHING
RAMJET
SCRAMJET

description: It is expected that future aero-engines will operate with higher bypass ratios to decrease the specific thrust and therefore reduce the fuel consumption [1]. This typically results in larger fan diameters and consequently an increase in the nacelle size, mass and total drag [2]. Compact nacelle architectures have been proposed as a way to reduce this drag penalty. However, compact nacelle designs increase the surface curvature gradients and make the designs more sensitive to important off-design operating conditions [3]. Therefore, several operating conditions must be considered during the nacelle optimisation process to achieve aerodynamic robust designs. This justifies the need for a multi-point optimisation method. The choice of the most suitable optimisation algorithms is highly dependant on the nature of the problem [4]. The key considerations to choose an optimisation algorithm are: (a) robust convergence to the optimal design space and (b) high convergence rate to reduce the computational overhead. In this paper, three different Gradient-Free (GF) optimisers have been tested for aerodynamic nacelle optimisation. Important advantages of the GF methods are that they do not require prior knowledge on the design space, they can successfully cope with numerical noise, and they can increase the likelihood to find the global optimal compared to Gradient-Based methods [5]. Within the GF methods, Genetic Algorithms (GA) and Particle Swarm Optimisers (PSO) have been used for aerodynamic optimisation purposes [6-8]. The novelty of this work is the performance comparison of GA and PSO algorithms for multi-point, aerodynamic optimisation of compact nacelles. This is challenging due to the non-linear and non-convex nature of this problem (Figure 1). For this purpose, the selected GA were NSGA-II [9] and IBEA [10] and the OMOPSO [11] algorithm was the PSO approach. A full RANS CFD-in-the-loop optimisations approach was applied [12]. A 2D axisymmetric compact aero-engine nacelle architecture with a length over highlight radius of Lnac/rhi = 3.1 has been used. Three different operating points have been considered to account for the nacelle cruise drag performance, sensitivity to an increase in flight Mach number, and a decrease in the aero-engine mass flow capture ratio. A total of 16 optimisations have been done with each optimiser to statistically assess the robustness and the convergence rate for different initial conditions in the optimisation. Pareto fronts (Figure 2) and the Hypervolume index (Figure 3) have been used to evaluate the performance and to identify the most suitable optimisation algorithm. It has been found that OMOPSO and IBEA are more robust than NSGA-II in terms of convergence to the optimal design space. OMOPSO was finally chosen as it requires on average less generations than IBEA to reach 99.5% of a fully converged hypervolume. [1] M. Daly, Janes aero-engines, 28th ed. Surrey: HIS Global Ltd, 2010, p.64 [2] D.L. Daggett, S.T. Brown and R.T. Kawai, Ultra-efficient engine diameter study, report 2003-212309, NASA Contract Report, 2003 [3] B.D.J. Schreiner, F. Tejero, D.G. MacManus and C. Sheaf, Robust aerodynamic design of nacelles for future civil aero-engines, paper GT2020-14470, Proc. of ASME Turbo Expo, virtual 21-25 September 2020 [4] D.W. Zingg, M. Nemec and T.H. Pulliam, A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization, European Journal of Computational Mechanics 17, 103-126 (2008) [5] G. Venter, Particle swarm optimization, paper AIAA 2002-1235, 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Material Conference, Denver 22-25 April 2002 [6] J. Kim, B. Ovgor, K. Cha, J. Kim, S. Lee and K. Kim, Optimization of the aerodynamic and aeroacoustic performance of an axial-flow fan, AIAA Journal 52, 2032-2044 (2014) [7] E. Benini, Three-dimensional multi-objective design optimization of a transonic compressor rotor, Journal of Propulsion and Power 20, 559-565 (2004) [8] S. Haghighat, J.R.R.A. Martins and H.H.T. Lui, Aeroservoelastic design optimization of a flexible wing, Journal of Aircraft 49, 432-443 (2012) [9] K. Deb, A. Member, A. Pratap, S. Agarwal and T. Meyarivan A Fast and Elitist Multi-Objective Genetic Algorithm: NSGAII, IEEE Transactions on Evolutionary Computation 6, 182-197 (2002) [10] E. Zitzler and S. Kunzli, Indicator-Based Selection in Multiobjective Search, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3242, 832-842 (2004) [11] M. R. Sierra and C. A. Coello Coello Improving PSO-Based Multi-Objective Optimization Using Crowding, Mutation and ?-Dominance. Lecture Notes in Computer Science 3410, 505-519 (2005) [12] F. Tejero, M. Robinson, D.G. MacManus and C. Sheaf, Multi-objective optimisation of short nacelles for high bypass ratio engines, Aerospace Science and Technology 91, 410-421 (2019)