The rapid development of bespoke small unmanned aircraft: a proposed design loop
The rapid development of bespoke small unmanned aircraft: a proposed design loop
The ability to quickly fabricate small unmanned aircraft (sUAS) through additive manufacturing (AM) methods opens a range of new possibilities for the design and optimization of these vehicles. In this paper we propose a design loop that makes use of surrogate modelling and AM to reduce the design and optimization time of scientific sUAS. AM reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimizer is used to search this model to find an optimal design for a bespoke aircraft. This paper presents the design loop and a case study which demonstrates its application.
1683-1710
Paulson, Christopher
264d154a-07d6-4272-aefc-e645c78878bd
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Scanlan, James
7ad738f2-d732-423f-a322-31fa4695529d
November 2017
Paulson, Christopher
264d154a-07d6-4272-aefc-e645c78878bd
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Scanlan, James
7ad738f2-d732-423f-a322-31fa4695529d
Paulson, Christopher, Sobester, Andras and Scanlan, James
(2017)
The rapid development of bespoke small unmanned aircraft: a proposed design loop.
Aeronautical Journal, 121 (1245), .
(doi:10.1017/aer.2017.99).
Abstract
The ability to quickly fabricate small unmanned aircraft (sUAS) through additive manufacturing (AM) methods opens a range of new possibilities for the design and optimization of these vehicles. In this paper we propose a design loop that makes use of surrogate modelling and AM to reduce the design and optimization time of scientific sUAS. AM reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimizer is used to search this model to find an optimal design for a bespoke aircraft. This paper presents the design loop and a case study which demonstrates its application.
Text
The Aeronautical Journal_Draft_v2_4_15_2016
- Accepted Manuscript
More information
Accepted/In Press date: 5 June 2017
e-pub ahead of print date: 30 October 2017
Published date: November 2017
Identifiers
Local EPrints ID: 412245
URI: http://eprints.soton.ac.uk/id/eprint/412245
ISSN: 0001-9240
PURE UUID: 1a54cfee-59c5-4097-ae40-03ad42288031
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Date deposited: 14 Jul 2017 16:30
Last modified: 16 Mar 2024 05:32
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Author:
Christopher Paulson
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