Interactive optimisation for high-lift design.

Interactivity always involves two entities; one of them by default is a human user. The specialised subject of human factors is introduced in the context of computational aerodynamics and optimisation, specifically a high-lift aerofoil. The trial and error nature of a design process hinges on designer’s knowledge, skill and intuition. A basic, important assumption of a man-machine system is that in solving a problem, there are some steps in which the computer has an advantageous edge while in other steps a human has dominance. Computational technologies are now an indispensable part of aerospace technology; algorithms involving significant user interaction, either during the process of generating solutions or as a component of post-optimisation evaluation where human decision making is involved are increasingly becoming popular, multi-objective particle swarm is one such optimiser. Several design optimisation problems in engineering are by nature multi-objective; the interest of a designer lies in simultaneous optimisation against two or more objectives which are usually in conflict. Interactive optimisation allows the designer to understand trade-offs between various objectives, and is generally used as a tool for decision making. The solution to a multi-objective problem, one where betterment in one objective occurs over the deterioration of at least one other objective is called a Pareto set. There are multiple solutions to a problem and multiple betterment ideas to an already existing design. The final responsibility of identifying an optimal solution or idea rests on the design engineers and decision making is done based on quantitative metrics, displayed as numbers or graphs. However, visualisation, ergonomics and human factors influence and impact this decision making process. A visual, graphical depiction of the Pareto front is oftentimes used as a design aid tool for purposes of decision making with chances of errors and fallacies fundamentally existing in engineering design. An effective visualisation tool benefits complex engineering analyses by providing the decision-maker with a good imagery of the most important information. Two high-lift aerofoil data-sets have been used as test-case examples; a multi-element solver, an optimiser based on swarm intelligence technique, and visual techniques which include parallel co-ordinates, heat map, scatter plot, self-organising map and radial coordinate visualisation comprise the module. Factors that affect optima and various evaluation criteria have been studied in light of the human user. This research enquires into interactive optimisation by adapting three interactive approaches: information trade-off, reference point and classification, and investigates selected visualisation techniques which act as chief aids in the context of high-lift design trade studies. Human-in-the-loop engineering, man-machine interaction & interface along with influencing factors, reliability, validation and verification in the presence of design uncertainty are considered. The research structure, choice of optimiser and visual aids adapted in this work are influenced by and streamlined to fit with the parallel on-going development work on Airbus’ Python based tool. Results, analysis, together with literature survey are presented in this report. The words human, user, engineer, aerodynamicist, designer, analyst and decision-maker/ DM are synonymous, and are used interchangeably in this research. In a virtual engineering setting, for an efficient interactive optimisation task, a suitable visualisation tool is a crucial prerequisite. Various optimisation design tools & methods are most useful when combined with a human engineer's insight is the underlying premise of this work; questions such as why, what, how might help aid aeronautical technical innovation.PhD in Aerospac

Modelling and aerodynamic design of optimisation of the twin-boom aegis UAV.

The aircraft industry gives considerable attention to computational optimisation tools in order to enhance the design process and product quality in terms of efficiency and performance, respectively. In reality, most real-world applications contain many complicating factors and constraints that affect system behaviour. Consequently, finding optimal solutions, or even only those viable for a given design problem, in an economical computational time is a difficult task, even with the availability of superfast computers. Thus, it is important to optimise the use of available computational resources. This research project presents a method for using stochastic multi-objective optimisation approaches combined with Artificial Intelligence and Interactive Design techniques to support the decision-making process. The improved ability of the developed methods to accelerate the search while retaining all the useful information in the design space was the main area of work. Both the efficiency and reliability of the proposed methodology have been demonstrated through the aerodynamic design of the Aegis-UAV. Initially, the optimisation platform Nimrod/O was deployed to enable the designer to manipulate and better understand different design scenarios. This happened before any commitment to a specific design architecture to allow for a wider exploration of the design space before a decision was made for a more detailed study of the problem. This had the potential to improve the quality of the product and reduce the design cycle time. The optimisation was performed using the Multi-Objective Tabu Search (MOTS) algorithm, chosen for its suitability for this type of complex aerodynamic design problem. Prior to the optimisation process, a parametric study was performed using the Sweep Method (SM) to explore the design space and identify design limitations. Analysis and investigation of the SM results were used to help determine the formulation of the design problem. SM was chosen because it has been proven to be reliable, effective, and able to provide a large amount of structured information about the design problem to the decision maker (DM) at this stage. Next, since most decisions of a DM in practical applications concern regions of the Pareto front, an interactive optimisation framework was proposed where the DM was involved with the optimisation process in real time. The framework used the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm for its suitability to this type of design problem. The results obtained confirmed the ability of the DM to use its preferences effectively, to steer the search to the Region of Interest (ROI) without degrading the aerodynamic performance of the optimised configurations. Even using only half the evaluations, the DM was able to obtain results similar to, or better than those obtained by the non-interactive use of MOTS and MOPSO. Furthermore, it was possible for the DM to stop the search at any iteration, which is not possible in non-interactive approaches even though the solutions do not converge or may be infeasible. Finally an Artificial Neural Network (ANN) was introduced to guide the MOPSO algorithm in deciding whether the trial solution was worthy of full evaluation, or not. The results obtained showed the success of the ANN in recognising non-valid particles. Consequently, the solver avoided wasting computational efforts on non-worthwhile particles. The optimisation process provides particles that are more valid for almost the same computational time. Demonstrating the algorithm’s effectiveness was done by comparing results of the ANN-MOPSO solutions with those obtained by the other approaches for the same design problems. In conclusion, future avenues of research have been identified and presented in the final chapter of the thesis.PhD in Aerospac

Enhancing MOPSO through the guidance of ANNs

In existing work, Artificial Neural Networks (ANNs) are often used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO) or MOPSO is used to aid in ANN-training. We instead use an ANN to guide the optimisation algorithm by deciding if a trial solution is worthy of full evaluation. This should be particularly helpful for computationally expensive calculations. We also introduce a level of scepticism to the result produced by the ANN, to account both for inaccuracy in the ANN and the loss of performance in a MOPSO if the reinitialisation of particles is too extreme. As a case study we used a multi-objective optimisation problem that seeks to optimise the shape of an airfoil to minimise drag and maximise lift. We evaluated several different methods for training an ANN: pre-training vs live training, continuous vs single training, and varied initial training set size. For applying the ANN's output to MOPSO we looked at various levels of scepticism and verified ANN quality before applying it. Attainment surfaces were then used to compare the performance of guided and unguided MOPSOs. Our analysis showed the performance of guided MOPSO was significantly better than unguided MOPSO. We further analysed the results to derive guidance for selecting appropriate variations for specific problems