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

Abstract Weight estimation methods are categorised in different classes based on their level of fidelity. The lower class methods are based on statistical data, while higher class methods use physics based calculations. Statistical weight estimation methods are usually utilised in early design stages when the knowledge of designers about the new aircraft is limited. Higher class methods are applied in later design steps when the design is mature enough. Lower class methods are sometimes preferred in later design stages, even though the designers have enough knowledge about the design to use higher class methods. In high level multidisciplinary design optimisation (MDO) fidelity is often sacrificed to obtain models with shorter computation times. There is always a compromise required to select the proper weight estimation method for an MDO project. An investigation has been performed to study the effect of using different weight estimation methods, with low and medium levels of fidelity, on the results of a wing design using multidisciplinary design optimisation techniques. An MDO problem was formulated to design the wing planform of a typical turboprop and a turbofan passenger aircraft. The aircraft maximum take-off weight was selected as the objective function. A quasi-three-dimensional aerodynamic solver was developed to calculate the wing aerodynamic characteristics. Five various statistical methods and a quasi-analytical method are used to estimate the wing structural weight. These methods are compared to each other by analysing their accuracy and sensitivity to different design variables. The results of the optimisations showed that the optimum wing shape is affected by the method used to estimate the wing weight. Using different weight estimation methods also strongly affects the optimisation convergence history and computational time.

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High fidelity multidisciplinary design optimisation of an electromagnetic device

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-015-1375-0 ◽

2015 ◽

Vol 53 (5) ◽

pp. 1113-1127 ◽

Cited By ~ 1

Author(s):

J. N. Stander ◽

G. Venter ◽

M. J. Kamper

Keyword(s):

Multidisciplinary Design ◽

High Fidelity ◽

Design Optimisation ◽

Electromagnetic Device ◽

Multidisciplinary Design Optimisation

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Multidisciplinary Design Optimisation of a Ship Hull Using Metamodels

Ship Technology Research ◽

10.1179/str.2011.58.3.004 ◽

2011 ◽

Vol 58 (3) ◽

pp. 156-166

Author(s):

Jim He ◽

Shari Hannapel ◽

David Singer ◽

Nickolas Vlahopoulos

Keyword(s):

Ship Hull ◽

Multidisciplinary Design ◽

Design Optimisation ◽

Multidisciplinary Design Optimisation

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Global Optimisation of Car Front-End Geometry to Minimise Pedestrian Head Injury Levels

Proceedings of the Design Society: International Conference on Engineering Design ◽

10.1017/dsi.2019.294 ◽

2019 ◽

Vol 1 (1) ◽

pp. 2873-2882

Author(s):

Mohammed Reza Kianifar ◽

Felician Campean

Keyword(s):

Head Injury ◽

Global Optimum ◽

Global Optimisation ◽

Multidisciplinary Design ◽

Parametric Modelling ◽

Optimization Strategy ◽

Design Optimisation ◽

Front End ◽

The Individual ◽

Multidisciplinary Design Optimisation

AbstractThe paper presents a multidisciplinary design optimisation strategy for car front-end profile to minimise head injury criteria across pedestrian groups. A hybrid modelling strategy was used to simulate the car- pedestrian impact events, combining parametric modelling of front-car geometry with pedestrian models for the kinematics of crash impact. A space filling response surface modelling strategy was deployed to study the head injury response, with Optimal Latin Hypercube (OLH) Design of Experiments sampling and Kriging technique to fit response models. The study argues that the optimisation of the front-end car geometry for each of the individual pedestrian models, using evolutionary optimisation algorithms is not an effective global optimization strategy as the solutions are not acceptable for other pedestrian groups. Collaborative Optimisation (CO) multidisciplinary design optimisation architecture is introduced instead as a global optimisation strategy, and proven that it can enable simultaneous minimisation of head injury levels for all the pedestrian groups, delivering a global optimum solution which meets the safety requirements across the pedestrian groups.

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