A. Mark Savill

Parallel coordinates in computational engineering design

Modern Engineering Design involves the deployment of many computational tools. Research on challenging real-world design problems is focused on developing improvements for the engineering design process through the integration and application of advanced computational search/optimization and analysis tools. Successful application of these methods generates vast quantities of data on potential optimum designs. To gain maximum value from the optimization process, designers need to visualise and interpret this information leading to better understanding of the complex and multimodal relations between parameters, objectives and decision-making of multiple and strongly conflicting criteria. Initial work by the authors has identified that the Parallel Coordinates interactive visualisation method has considerable potential in this regard. This methodology involves significant levels of user-interaction, making the engineering designer central to the process, rather than the passive recipient of a deluge of pre-formatted information. In the present work we have applied and demonstrated this methodology in two different aerodynamic turbomachinery design cases; a detailed 3D shape design for compressor blades, and a preliminary mean-line design for the whole compressor core. The first case comprises 26 design parameters for the parameterisation of the blade geometry, and we analysed the data produced from a three-objective optimization study, thus describing a design space with 29 dimensions. The latter case comprises 45 design parameters and two objective functions, hence developing a design space with 47 dimensions. In both cases the dimensionality can be managed quite easily in Parallel Coordinates space, and most importantly, we are able to identify interesting and crucial aspects of the relationships between the design parameters and optimum level of the objective functions under consideration. These findings guide the human designer to find answers to questions that could not even be addressed before. In this way, understanding the design leads to more intelligent decision-making and design space exploration.

Enhancing and developing the practical optimisation capabilities and intelligence of automatic design software

In the modern engineering design cycle the use of computational tools becomes a necessity. The complexity of the engineering systems under consideration for design increases dramatically as the demands for advanced and innovative design concepts and engineering products is expanding. At the same time the advancements in the available technology in terms of computational resources and power, as well as the intelligence of the design software, accommodate these demands and make them a viable approach towards the challenge of real-world engineering problems. This class of design optimisation problems is by nature multi-disciplinary. In the present work we establish enhanced optimisation capabilities within the Nimrod/O tool for massively distributed execution of computational tasks through cluster and computational grid resources, and develop the potential to combine and benefit from all the possible available technological advancements, both software and hardware. We develop the interface between a Free Form Deformation geometry management in-house code with the 2D airfoil aerodynamic efficiencyevaluation tool XFoil, and the well established multi-objective heuristic optimisation algorithm NSGA-II. A simple airfoil design problem has been defined to demonstrate the functionality of the design system, but also to accommodate a framework for future developments and testing with other state-of-the-art optimisation algorithms such as the Multi-Objective Genetic Algorithm (MOGA) and the Multi-Objective Tabu Search (MOTS) techniques. Ultimately, heavily computationally expensive industrial design cases can be realised within the presented framework that could not be investigated before.

A Self-adaptive Genetic Algorithm Applied to Multi-Objective Optimization of an Airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

Propeller-Wing Interaction Prediction for Early Design

© 2014, American Institute of Aeronautics and Astronautics Inc. All rights reserved. In the current work a methodology to quickly estimate propeller slipstream effects on a finite wing, for early design stage, is developed. The aerodynamic model consists of a Truckenbrodt 3D lifting surface method, coupled with an airfoil numerical solver for taking into account viscosity and camber effects, and a blade element theory for the formulation of the propeller aerodynamics. Results of the proposed method are validated against RANS (Reynolds Averaged Navier Stokes) data for a conventional four-propeller transport aircraft and a novel Contra-Rotating Open Rotor (CROR) aircraft. A good agreement with the high fidelity solutions is obtained. The low computational cost makes the method attractive and suitable for performing extensive studies such as Multi-Disciplinary Optimisation (MDO) or to explore several alternative designs during the conceptual and preliminary design phases.

A Rapid Approach to the Aerodynamic Design of a Flexible High-Lift Wing

The development and application of a rapid methodology for the optimization of HighLift configurations is here presented. The proposed framework includes a quasi-three- dimensional method to simulate the aerodynamic performance, a rapid method for the estimation of the static aero-elastic effects, and two multi-objective optimization algorithms: the well-know NSGA-II and the innovative multi-objective Tabu Search. After an extensive validation study of both the aerodynamic simulation and the aero-structure coupling method, the framework is used to optimize the landing performance of the KH3 Y configuration. The deployment settings of the full-span slat and of the two single-slotted flap elements, inboard and outboard, are identified as design variables. The maximum lift coefficient and the lift over drag ratio at the approach angle of attack are used as objective functions. Two optimization setups are executed to assess the influence of the wing flexibility on the revealed Pareto Front. In both cases the selected optimization algorithm has been able to improve the performance of the datum. Moreover, the results show the importance of considering aero-structure influences early on in the design process. Finally, parallel coordinate visualization techniques are used to analyze the peculiar Search Pattern revealed.

Multifidelity Optimization for High-Lift Airfoils

High-lift airfoil design is subjected to many constraints in terms of regulation and efficiency. It also involves complex flows that are still challenging to be estimated by Computational Fluid Dynamics. The time required to solve flow around a multielement airfoil makes it difficultly applicable to optimization. However, from legacy, approximated or empirical methods are available to engineers to quickly estimate performances of such designs. But those methods are limited in their range of applicability and accuracy. This paper presents a multifidelity optimizer using two models for the fluid dynamics: a low-fidelity that is used under a trust region scheme and a high-fidelity used to validate candidate points. The lowfidelity function is corrected by a Radial Basis Function surrogate model interpolating the error between the highand low-fidelity models at the sampling points. Points are sampled during the optimization and therefore the method does not require (but can use) any sampling strategy prior of the optimization. The method uses an implementation of the Tabu Search that provides non-deterministic behavior for global exploration. The method is applied to a simple low speed single airfoil case and then to a multielement airfoil case provided by the industry. The results show reduction in terms of number of high-fidelity function calls and in terms of time for the convergence for both test cases. The variability of the results, more important for the multielement airfoil, needs to be addressed and is currently under work.

Fast Multi-objective Optimisation of a Micro-fluidic Device by Using Graphics Accelerators☆

Abstract The development of technology that uses widely available and inexpensive hardware for real- world cases is presented in this work. This is part of a long-term approach to minimise the impact of aviation on the environment and aims to enable the users both from industrial and academic background to design more optimal mixing devices. Here, a Multi-Objective Tabu Search is combined with a flow solver based on the Lattice Boltzmann Method (LBM) so as to optimise and simulate the shape and the flow of a micro-reactor, respectively. Several geometrical arrangements of a micro-reactor are proposed so as to increase the mixing capability of the device while minimising the pressure losses and to investigate related flow features. The computational engineering design process is accelerated by harnessing the high computational power of Graphic Processor Units (GPUs). The ultimate aim is to effectively harvest and harness computing cycles while performing design optimisation studies that can deliver higher quality designs of improved performance within shorter time intervals.

Multi-objective shape optimisation for horizontal-axis wind turbine blades

We are developing a wind turbine blade optimisation package CoBOLDT (COmputa- tional Blade Optimisation and Load De ation Tool) for the optimisation of large horizontal- axis wind turbines. The core consists of the Multi-Objective Tabu Search (MOTS), which controls a spline parameterisation module, a fast geometry generation and a stationary Blade Element Momentum (BEM) code to optimise an initial wind turbine blade design. The objective functions we investigate are the Annual Energy Production (AEP) and the fl apwise blade root bending moment (MY0) for a stationary wind speed of 50 m/s. For this task we use nine parameters which define the blade chord, the blade twist (4 parameters each) and the blade radius. Throughout the optimisation a number of binary constraints are defined to limit the noise emission, to allow for transportation on land and to control the aerodynamic conditions during all phases of turbine operation. The test case shows that MOTS is capable to find enhanced designs very fast and eficiently and will provide a rich and well explored Pareto front for the designer to chose from. The optimised blade de- sign could improve the AEP of the initial blade by 5% with the same flapwise root bending moment or reduce MY0 by 7.5% with the original energy yield. Due to the fast runtime of order 10 seconds per design, a huge number of optimisation iterations is possible without the need for a large computing cluster. This also allows for increased design flexibility through the introduction of more parameters per blade function or parameterisation of the airfoils in future.

Electrical Power Grid Network Optimisation by Evolutionary Computing

Abstract A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods.

Multi-objective Optimisation by Self-adaptive Evolutionary Algorithm

Evolutionary algorithms (EAs) have been used to tackle non-linear multi-objective optimisation (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising EA (MOOEA) that uses self-adaptive mutation and crossover, and which is applied to optimisation of an airfoil, for minimisation of drag and maximisation of lift coefficients. The MOOEA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.