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

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.

Influence of weight modelling on the outcome of wing design using multidisciplinary design optimisation techniques

2013 ◽  
Vol 117 (1195) ◽  
pp. 871-895 ◽  
Author(s):  
J. Mariens ◽  
A. Elham ◽  
M. J. L. van Tooren
Keyword(s):  
Estimation Method ◽  
Lower Class ◽  
Multidisciplinary Design ◽  
Estimation Methods ◽  
Computational Time ◽  
Weight Estimation ◽  
Wing Design ◽  
Design Optimisation ◽  
Multidisciplinary Design Optimisation ◽  
Optimisation Techniques

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.

Robust Multidisciplinary Design Optimisation Using CFD and Advanced Evolutionary Algorithms

2010 ◽  
pp. 469-491 ◽  
Author(s):  
DongSeop Lee ◽  
Karkenahalli Srinivas ◽  
Luis Felipe Gonzalez ◽  
Jacques Periaux ◽  
Shigeru Obayashi
Keyword(s):  
Evolutionary Algorithms ◽  
Multidisciplinary Design ◽  
Design Optimisation ◽  
Multidisciplinary Design Optimisation

Multidisciplinary Design Optimisation of a Ship Hull Using Metamodels

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

Multidisciplinary design optimisation of a fully electric regional aircraft wing with active flow control technology

2021 ◽  
pp. 1-25
Author(s):  
V. Mosca ◽  
S. Karpuk ◽  
A. Sudhi ◽  
C. Badrya ◽  
A. Elham
Keyword(s):  
Boundary Layer ◽  
Flow Control ◽  
Electric Propulsion ◽  
Active Flow Control ◽  
Multidisciplinary Design ◽  
Aircraft Wing ◽  
Design Optimisation ◽  
Boundary Layer Suction ◽  
Active Flow ◽  
Multidisciplinary Design Optimisation

Abstract The German research Cluster of Excellence SE2A (Sustainable and Energy Efficient Aviation) is investigating different technologies to be implemented in the following decades, to achieve more efficient air transportation. This paper studies the Hybrid Laminar Flow Control (HLFC) using boundary layer suction for drag reduction, combined with other technologies for load and structural weight reduction and a novel full-electric propulsion system. A multidisciplinary design optimisation framework is presented, enabling physics-based analysis and optimisation of a fully electric aircraft wing equipped with HLFC technologies and load alleviation, and new structures and materials. The main focus is on simulation and optimisation of the boundary layer suction and its influence on wing design and optimisation. A quasi three-dimensional aerodynamic analysis is used for drag estimation of the wing. The tool executes the aerofoil analysis using XFOILSUC, which provides accurate drag estimation through boundary layer suction. The optimisation is based on a genetic algorithm for maximum take-off weight (MTOW) minimisation. The optimisation results show that the active flow control applied on the optimised geometry results in more than 45% reduction in aircraft drag coefficient, compared to the same geometry without HLFC technology. The power absorbed for the HLFC suction system implies a battery mass variation lower than 2%, considering the designed range as top-level requirement (TLR).

Multidisciplinary design optimisation (MDO) methods: their synergy with computer technology in the design process

1999 ◽  
Vol 103 (1026) ◽  
pp. 373-382 ◽  
Author(s):  
Jaroslaw Sobieszczanski-Sobieski
Keyword(s):  
Parallel Processing ◽  
Computer Technology ◽  
Multidisciplinary Design ◽  
Massively Parallel ◽  
Design Optimisation ◽  
Complex Behaviour ◽  
Human Interface ◽  
Massively Parallel Processing ◽  
Computer Environment ◽  
Multidisciplinary Design Optimisation

Abstract The paper identifies speed, agility, human interface, generation of sensitivity information, task decomposition, and data transmission (including storage) as important attributes for a computer environment to have in order to support engineering design effectively. It is argued that when examined in terms of these attributes the presently available environment can be shown to be inadequate. A radical improvement is needed, and it may be achieved by combining new methods that have recently emerged from multidisciplinary design optimisation (MDO) with massively parallel processing computer technology. The caveat is that, for successful use of that technology in engineering computing, new paradigms for computing will have to be developed - specifically, innovative algorithms that are intrinsically parallel so that their performance scales up linearly with the number of processors. It may be speculated that the idea of simulating a complex behaviour by interaction of a large number of very simple models may be an inspiration for the above algorithms; the cellular automata are an example. Because of the long lead time needed to develop and mature new paradigms, development should begin now, even though the widespread availability of massively parallel processing is still a few years away.

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