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

Initial investigation of aerodynamic shape design optimisation for the Aegis UAV

This paper presents an aerodynamic design optimisation methodology used in further developing an already existing Unmanned Aerial Vehicle (UAV) platform called Aegis. This paper aims to deliver a medium altitude long endurance UAV for civilian purposes. The methodology used is also applicable to conceptual and preliminary design phases of any aerial vehicle platform. It combines a low fidelity aerodynamic analysis tool, Athena Vortex Lattice Code, with a design optimisation tool (Nimrod/O). The meta-heuristic algorithm, Multi-Objective Tabu Search-2 (MOTS2), is used to perform the optimisation process. This new methodological study optimises the UAV wing planform for level flight. It was used successfully to obtain a set of optimal wing shapes for the Aegis UAV flying at different speeds. Prior to the formulation of the design problem, a parametric study was performed to explore the design space and provide an insight into how the objective functions behave with respect to the design variables. The methodology presented here is not finalized, it is a first step to constructing a general framework that can be used to optimise the design of a twin-boom UAV aerodynamic shape. The interfacing of the already successful packages Nimrod/O, MOTS2, and AVL software produces an initial result that shows the capability of the new methodology to provide correct support decisions making for a design optimisation process that will benefit the entire community of UAV researchers and designers when it is complete

The interactive design approach for aerodynamic shape design optimisation of the Aegis UAV

In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of interactive optimisation—reduction of computational time with high optimality levels. Progress towards the most preferred solutions is made by having the Decision Maker (DM) periodically provide preference information once the MOPSO iterations are underway. By involving the DM within the optimisation process, the search is directed to the region of interest, which accelerates the process. The flexibility and eciency of undertaking optimisation interactively have been demonstrated by comparing the interactive results with the non-interactive results of an optimum design case obtained using Multi-Objective Tabu Search (MOTS) for the Aegis UAV. The obtained results show the superiority of using an interactive approach for the aerodynamic shape design, compared to posteriori approaches. By carrying out the optimisation using interactive MOPSO it was shown to be possible to obtain similar results to non-interactive MOTS with only half the evaluations. Moreover, much of the usual complexity of post-data-analysis with posteriori approaches is avoided, since the DM is involved in the search process

Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV

This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem

The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV

In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of interactive optimisation—reduction of computational time with high optimality levels. Progress towards the most preferred solutions is made by having the Decision Maker (DM) periodically provide preference information once the MOPSO iterations are underway. By involving the DM within the optimisation process, the search is directed to the region of interest, which accelerates the process. The flexibility and efficiency of undertaking optimisation interactively have been demonstrated by comparing the interactive results with the non-interactive results of an optimum design case obtained using Multi-Objective Tabu Search (MOTS) for the Aegis UAV. The obtained results show the superiority of using an interactive approach for the aerodynamic shape design, compared to posteriori approaches. By carrying out the optimisation using interactive MOPSO it was shown to be possible to obtain similar results to non-interactive MOTS with only half the evaluations. Moreover, much of the usual complexity of post-data-analysis with posteriori approaches is avoided, since the DM is involved in the search process