Waverider Aerodynamics and Preliminary Design for Two-Stage-to-Orbit Missions, Part 1

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

2011

This version is available at https://strathprints.strath.ac.uk/43206/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r P e e r R e v i e w 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 y r q h q d p T t u ! E j p s j t s r E r £ x r t ± r t s A r Ex r d d h q j x r Ç E j u r ) x t u U h s x t ) Ũ u 1 h l ! u E s ) x r Ũ x r h q x ì l P t u j 8 r E p Â Ũ £ r x t s rr H y £ h q j x y p s r q h q r r r x r E j £ r Ex r U d h q Á t u h E j r q x r u « p s f gG1 0.664 p ρ 2 e v 3 e µ * C * /ρ * 0.037v 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 ...

Section: Introductionmentioning

confidence: 99%

Robust design of a re-entry unmanned space vehicle by multi-fidelity evolution control

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

2011

“…For this study, the parameterized shape is based on an ideal waverider configuration [15][16][17], but the geometry of the ideal waverider is here modified in order to introduce more realistic rounded edges.…”

Section: Introductionmentioning

confidence: 99%

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

Proceedings of the Institution of Mechanical Engineers, Part G:

Hou

2011

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials

“…This paper presents the robust design optimization of a small scale USV, whose shape is based on an ideal waverider configuration [6][7][8] ). The geometry of the ideal waverider is here modified in order to introduce more realistic rounded edges.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Design of a Micro-USV for Reentry Operations

Hou

AIAA/AAS Astrodynamics Specialist Conference

2010

This paper presents the robust multidisciplinary design of a small scale unmanned space vehicle for re-entry operations. Uncertainties on the aerodynamic forces, thermal flux and characteristics of the thermal protection material are incorporated into the vehicle model. A Monte-Carlo sampling procedure is used to compute the mean and variance of the maximum heat flux and internal temperature. Then a population-based multi-objective optimization algorithm of the EDA type (Estimation of Distribution Algorithms) searches for geometries that minimize the mean value of the maximum heat flux, mean value of the maximum internal temperature, and the weighted sum of their variance. The EDA is hybridized with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. A multi-fidelity approach is adopted during the optimization process to estimate the correct aerodynamic properties of the vehicle. Initially a low-fidelity analytical model, fitted on a wave-rider type of vehicle, is used to train an artificial neural network (ANN) which approximates the aerodynamic forces. Through the evolution a mix of analytical and CFD high-fidelity calculations are used to update the ANN. CFD runs progressively become the main source of updates for the ANN till, close to convergence of the EA, the analytical model is almost completely dropped. Some preliminary results show the achievable performance of such a small spacecraft and the requirements in terms of thermal protection materials.