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Design search and optimization in aerospace engineering

Design search and optimization in aerospace engineering
Design search and optimization in aerospace engineering
In this paper we take a design-led perspective on the use of computational tools in the aerospace sector. We briefly review the current state-of-the-art in Design Search and Optimization (DSO) as applied to problems from aerospace engineering, focusing on those problems that make heavy use of Computational Fluid Dynamics (CFD). This ranges over issues of representation, optimization problem formulation and computational modelling. We then follow this with a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design, an area where this kind of approach is becoming essential for companies to maintain their competitive edge. Our example considers the structure and weight of a transonic civil transport wing, its aerodynamic performance at cruise speed and its manufacturing costs. The goals are low drag and cost while holding weight and structural performance at acceptable levels. The constraints and performance metrics are modelled by a linked series of analysis codes, the most expensive of which is a CFD analysis of the aerodynamics using an Euler code with coupled boundary layer model. Structural strength and weight are assessed using semi-empirical schemes based on typical airframe company practice. Costing is carried out using a newly developed generative approach based on a hierarchical decomposition of the elements involved in the wing bill of parts. To carry out the DSO process in the face of multiple, competing goals a recently developed multi-objective probability of improvement formulation is invoked along with stochastic process response surface models (Krigs). This approach both mitigates the significant run times involved in CFD computation and also provides an elegant way of balancing competing goals while still allowing the deployment of the whole range of single objective optimizers commonly available to design teams. Finally, we make some observations about the current state of the DSO-art and draw a few conclusions about the directions we believe this field should go in if it is to best support the aerospace design sector.
1364-503X
2501-2529
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Scanlan, J.P.
7ad738f2-d732-423f-a322-31fa4695529d
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Scanlan, J.P.
7ad738f2-d732-423f-a322-31fa4695529d

Keane, A.J. and Scanlan, J.P. (2007) Design search and optimization in aerospace engineering. Philosophical Transactions of The Royal Society A, 365 (1859), 2501-2529. (doi:10.1098/rsta.2007.2019).

Record type: Article

Abstract

In this paper we take a design-led perspective on the use of computational tools in the aerospace sector. We briefly review the current state-of-the-art in Design Search and Optimization (DSO) as applied to problems from aerospace engineering, focusing on those problems that make heavy use of Computational Fluid Dynamics (CFD). This ranges over issues of representation, optimization problem formulation and computational modelling. We then follow this with a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design, an area where this kind of approach is becoming essential for companies to maintain their competitive edge. Our example considers the structure and weight of a transonic civil transport wing, its aerodynamic performance at cruise speed and its manufacturing costs. The goals are low drag and cost while holding weight and structural performance at acceptable levels. The constraints and performance metrics are modelled by a linked series of analysis codes, the most expensive of which is a CFD analysis of the aerodynamics using an Euler code with coupled boundary layer model. Structural strength and weight are assessed using semi-empirical schemes based on typical airframe company practice. Costing is carried out using a newly developed generative approach based on a hierarchical decomposition of the elements involved in the wing bill of parts. To carry out the DSO process in the face of multiple, competing goals a recently developed multi-objective probability of improvement formulation is invoked along with stochastic process response surface models (Krigs). This approach both mitigates the significant run times involved in CFD computation and also provides an elegant way of balancing competing goals while still allowing the deployment of the whole range of single objective optimizers commonly available to design teams. Finally, we make some observations about the current state of the DSO-art and draw a few conclusions about the directions we believe this field should go in if it is to best support the aerospace design sector.

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More information

Submitted date: June 2006
Published date: 15 October 2007
Additional Information: Theme issue ‘Computational fluid dynamics in aerospace engineering’ compiled by Paul G. Tucker

Identifiers

Local EPrints ID: 28621
URI: http://eprints.soton.ac.uk/id/eprint/28621
ISSN: 1364-503X
PURE UUID: 03575cb8-ad15-4258-b96f-c58890c109a9

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Date deposited: 09 May 2006
Last modified: 16 Dec 2019 19:22

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