Dong-Seop Lee

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimizer

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bezier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Evolutionary Optimisation Methods with Uncertainty for Modern Multidisciplinary Design in Aeronautical Engineering

One of the new challenges in aeronautics is combining and accounting for multiple disciplines while considering uncertainties or variability in the design parameters or operating conditions. This paper describes a methodology for robust multidisciplinary design optimisation when there is uncertainty in the operating conditions. The methodology, which is based on canonical evolution algorithms, is enhanced by its coupling with an uncertainty analysis technique. The paper illustrates the use of this methodology on two practical test cases related to Unmanned Aerial Systems (UAS). These are the ideal candidates due to the multi-physics involved and the variability of missions to be performed. Results obtained from the optimisation show that the method is effective to find useful Pareto non-dominated solutions and demonstrate the use of robust design techniques.

Multi-objective design optimization of morphing UAV aerofoil/wing using hybridised MOGA

The paper investigates two advanced Computational Intelligence Systems (CIS) for a morphing Unmanned Aerial Vehicle (UAV) aerofoil/wing shape design optimisation. The first CIS uses Genetic Algorithm (GA) and the second CIS uses Hybridized GA (HGA) with the concept of Nash-Equilibrium to speed up the optimisation process. During the optimisation, Nash-Game will act as a pre-conditioner. Both CISs; GA and HGA, are based on Pareto optimality and they are coupled to Euler based Computational Fluid Dynamic (CFD) analyser and one type of Computer Aided Design (CAD) system during the optimisation. For the practical test case, one type of morphing techniques; Leading and Trailing Edge Deformation (LTED) is considered to control flow over the aerofoil/wing. LTED to a Natural Laminar Flow (NLF) aerofoil is applied to maximise the lift coefficients (Cl) at both the take-off and landing conditions. Two applications on LTED with low/middle and high design complexities are optimised using GA and HGA. The optimisation efficiency for GA and HGA are compared in terms of computational cost and design quality. Numerical results clearly show that Nash-Game helps a GA based CIS to accelerate the optimisation process and also to produce higher performance solutions in solving both the low/middle and high complex design optimisation problems. In addition numerical CFD study demonstrates that the implementation of morphing technique on the aerofoil/wing significantly improves the lift coefficients at both the take-off and landing conditions when compared to the baseline design.

Active flow control of airfoil using mesh/meshless methods coupled to hierarchical genetic algorithms for drag reduction design

Purpose – The purpose of this paper is to investigate an active flow control technique called Shock Control Bump (SCB) for drag reduction using evolutionary algorithms.Design/methodology/approach – A hierarchical genetic algorithm (HGA) consisting of multi‐fidelity models in three hierarchical topological layers is explored to speed up the design optimization process. The top layer consists of a single sub‐population operating on a precise model. On the middle layer, two sub‐populations operate on a model of intermediate accuracy. The bottom layer, consisting of four sub‐populations (two for each middle layer populations), operates on a coarse model. It is well‐known that genetic algorithms (GAs) are different from deterministic optimization tools in mimicking biological evolution based on Darwinian principle. In HGAs process, each population is handled by GA and the best genetic information obtained in the second or third layer migrates to the first or second layer for refinement.Findings – The method wa...

Advanced Computational Intelligence System for Inverse Aeronautical Design Optimisation

Computational Intelligence Systems (CIS) is one of advanced softwares. CIS has been important position for solving single-objective / reverse / inverse and multi-objective design problems in engineering. The paper hybridise a CIS for optimisation with the concept of Nash-Equilibrium as an optimisation pre-conditioner to accelerate the optimisation process. The hybridised CIS (Hybrid Intelligence System) coupled to the Finite Element Analysis (FEA) tool and one type of Computer Aided Design (CAD) system, GiD is applied to solve an inverse engineering design problem, reconstruction of High Lift Systems (HLS). Numerical results obtained by the hybridised CIS are compared to the results obtained by the original CIS. The benefits of using the concept of Nash-Equilibrium are clearly demonstrated in terms of solution accuracy and optimisation efficiency.

Double Shock Control Bump design optimization using hybridised evolutionary algorithms

The paper investigates two advanced optimisation methods for solving active flow control device shape design problem and also compares their optimisation efficiency in terms of computational cost and design quality. The first optimisation method uses Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithm (HAPMOEA) and the second uses Hybridized EA with Nash-Game strategies. Both optimisation method are based on a canonical evolution strategy and incorporates the concepts of parallel computing and asynchronous evaluation. For the practical test case, one of active flow control devices named Shock Control Bump (SCB) is considered and it is applied to Natural Laminar Flow (NLF) aerofoil. The concept of SCB is to decelerate supersonic flow on upper/lower surface of transonic aerofoil that leads delay of shock occurrence. Such active flow technique reduces a total drag at transonic speeds. Numerical results clearly show that Hybrid-Game helps EA to accelerate optimisation process, and also applying SCB on the suction and pressure sides significantly reduces transonic wave drag and improves lift on drag (L/D) value when compared to the baseline design.

Improving soft frequency reuse for realistic OFDMA-based cellular deployments

Effective interference management is a technical challenge of utmost importance for emerging OFDMA-based technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). Static Intercell Interference Coordination techniques including Soft Frequency Reuse (SFR) have enjoyed acceptance among mobile operators as a solution to deal with this problem mainly due to their low complexity and easy implementability. However, recent results indicate that the performance of default SFR settings directly applied to realistic cellular deployments is quite suboptimal and hence optimization is mandatory. This paper addresses this issue by presenting a novel multiobjective framework able to achieve accurate fine tuning of SFR and hence, enhance system capacity and cell edge performance while reducing energy consumption.

Hybrid Game Strategies for MO Problems in Aerospace Design

Game strategies have been developed in past decades and used in the field of economics, engineering, computer science and biology due to their efficiency in solving design optimisation problems. In addition, research on Multi-Objective (MO) and Multidisciplinary Design Optimisation (MDO) has focused on developing robust and efficient optimisation method to produce quality solutions with less computational time. In this paper, a new optimisation method Hybrid Game Strategy for MO problems is introduced and compared to CMA-ES based optimisation approach. Numerical results obtained from both optimisation methods are compared in terms of computational expense and model quality. The benefits of using Game-strategies are demonstrated.