Karkenahalli Srinivas

Efficient Hybrid-Game Strategies Coupled to Evolutionary Algorithms for Robust Multidisciplinary Design Optimization in Aerospace Engineering

A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated.

Interaction of Two Parallel Plane Jets of Different Velocities

The interaction of two parallel plane jets of different velocities is studied by flow visualization and PIV measurement to examine the influence of velocity ratio on the development ofjets in the initial region. It is found that the parallel plane jets develop toward the high velocity side and the jet width is reduced with a decrease in the jet velocity ratio. Corresponding to the variation of mean velocity field to the velocity ratio, the magnitudes of turbulence intensities, Reynolds stress and static pressure are weakened in the merging region of the jets and their peak locations of the properties are shifted to the high velocity side. These results indicate that the interaction of two parallel jets is weakened with a decrease in the velocity ratio of the jets.

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.

Incompressible Viscous Flow

In this chapter no assumption is made about the relative magnitude of the velocity components, consequently, reduced forms of the Navier-Stokes equations (Chap. 16) are not available. Instead the full Navier-Stokes equations must be considered; however, it will be assumed that the flow is incompressible.

Multi-Objective / Multidisciplinary Design Optimisation of Blended Wing Body UAV via Advanced Evolutionary Algorithms

Improvement of wing aerodynamic efficiency is one of the common challenges in Unmanned (Combat) Aerial Vehicles (UCAV) to provide a short distance take-off, long endurance that leads to lower fuel consumption. In addition, the stealth function is one of the essential requirements to complete diverse missions and the survivability of UAVs. This paper explores the application of a robust Evolutionary Algorithm (EA) for aerofoil sections and wing planform design and optimisation for the improvement of aerodynamic performance and the reduction of Radar Cross Section (RCS). The method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Results obtained from the optimisation show that utilising the designing transonic wing aerofoil sections and planform in combination with Evolutionary techniques improve the aerodynamic efficiency and this produced a set of shock-free aerofoils and achieved the supercritical aero-diamond wings. Results also indicate that the method is efficient and produces optimal and non-dominated solutions.

A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems Using Evolutionary Computing

This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of Unmanned Aerial Vehicles (UAVs). The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser, several design modules, mesh generators and post-processing capabilities in an integrated platform. Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. A new class of optimisation techniques named Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) have 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, can be executed asynchronously in parallel and adapted easily to arbitrary solver codes without major modifications. The application of the methodology is illustrated on multi-criteria and multidisciplinary design problems. Results indicate the practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplinary analyses and producing a set of non dominated individuals.

Aerodynamic Optimisation using a Robust Evolutionary Algorithm and Grid-free Flowsolver

It is well known that Evolutionary Algorithms (EAs) can provide solutions to problems that are difficult to solve with conventional deterministic optimisers. In this paper, we present continuing research on the application of a modern Evolutionary Algorithm (EA) for aerodynamic shape optimisation coupled with a grid-free or meshless flowsolver based on Kinetic schemes. The evolutionary method is based upon traditional evolution strategy with the incorporation of an asynchronous function evaluation for the solution and uses a hierarchical topology where the search for the best individual takes place successively in separate hierarchical layers comprising different fidelity models/resolutions or number of points. The grid-free formulation requires the domain discretisation to have very little topological information. A simple random distribution of points along with local connectivity information is sufficient. The connectivity which contains a set of neighbouring points is used to evaluate the special derivatives appearing in the conservation law. The derivatives are evaluated using Least Square (LS) approximation. The application of the methodology is then illustrated on two-dimensional inverse aerofoil optimisation problems. Results indicate that the method is robust and efficient on its application to real world problems.

Uncertainty based MDO of UAS using HAPMOEA

CFD has been successfully used in the optimisation of aerodynamic surfaces using a given set of parameters such as Mach numbers and angle of attack. While carrying out a multidisciplinary design optimisation one deals with situations where the parameters have some uncertaint attached. Any optimisation carried out for fixed values of input parameters gives a design which may be totally unacceptable under off-design conditions. The challenge is to develop a robust design procedure which takes into account the fluctuations in the input parameters. In this work, we attempt this using a modified Taguchi approach. This is incorporated into an evolutionary algorithm with many features developed in house. The method is tested for an UCAV design which simultaneously handles aerodynamics, electromagnetics and maneuverability. Results demonstrate that the method has considerable potential.

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.

Multi-Objective Robust Design Optimisation Using Hierarchical Asynchronous Parallel Asynchronous Evolutionary Algorithms

In this paper, a new Robust Design method is investigated with a hierarchical asynchronous parallel Multi-objective evolutionary optimisation framework to overcome single and multi-point design optimisation problems in Aerodynamics. The single design techniques produce solutions that perform well for the selected design point but have poor off-design performance. Here, we show how the approach can provide robust solutions using game theory in the sense that the solutions are as less insensitive to little changes of the input parameters. Starting from a statistical definition of stability, the method finds, simultaneously Pareto non-dominated solutions for performance and stability which offer alternative choices to the designer.