D.S. Lee

Active Transonic Aerofoil Design Optimization Using Robust Multiobjective Evolutionary Algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment

This paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables - aerofoil sections - supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.

Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm

Unmanned Aerial Vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as Evolutionary Algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions amongst many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described. Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts

Aerodynamic Shape Optimisation of Unmanned Aerial Vehicles using Hierarchical Asynchronous Parallel Evolutionary Algorithms

One of the challenges in Unmanned (Combat) Aerial Vehicles (UCAV) is the improvement of aerodynamic performance to complete diverse missions, increase endurance and lower fuel consumption. Recent advances in design tools, materials, electronics and actuators have opened the door for implementation of transonic flow control technologies to improve aerodynamic efficiency. This paper explores the application of a robust Multi-Objective Evolutionary Algorithm (MOEA) for the design and optimisation of aerofoil sections and wing planform of UAVs and UCAVs. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. For the design and optimisation of UCAV wing planform shape, an aero-diamond planform shape with a jagged trailing edge is considered like saw tooth. Results obtained from the combination between the approach and the aerodynamic analysis tools show the improvement of the aerodynamic efficiency, a set of shock-free aerofoils and the supercritical aero-diamond wing. Results also indicate that the method is capable to produce non-dominated solutions.

Adaptive Wing/Aerofoil Design Optimisation Using MOEA Coupled to Uncertainty Design Method

The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction.