Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

Bayley, Douglas; Hartfield, Roy J.; Burkhalter, John E.; Jenkins, Rhonald M.

doi:10.2514/6.2007-1863

Cited by 23 publications

(17 citation statements)

References 21 publications

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“…In 2007 Douglas J. Bayley and et al optimized the design of an entire space launch vehicle to low-Earth orbit, consisting of multiple stages using a genetic algorithm (GA) with the goal of minimizing vehicle weight [1]. In 1998, Anderson assembled these performance codes and created an objective function that could analyze the performance of an entire, single-stage solid propellant rocket vehicle [2].…”

Section: Recent Launch Vehicle Optimization Workmentioning

confidence: 99%

Multidisciplinary design optimization of an expendable launch vehicle

Hosseini

Toloie

Nosratollahi

et al. 2011

Proceedings of 5th International Conference on Recent Advances in Space Technologies - RAST2011

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This paper describes an effort to optimize the design of an expendable launch vehicle (ELV) to low-Earth orbit, consisting of multiple stages with the goal of minimizing total vehicle weight and ultimately vehicle cost. The disciplines of weight & sizing, propulsion characteristics, aerodynamics and flight dynamics have been integrated to produce a system model of the entire vehicle. One multilevel multidisciplinary optimization techniques -Collaborative -is applied to the design of a launch vehicle. The results inclusive designed launch vehicle data has been validated by existing launch vehicle data. Keywords-Launch vehicle (LV), System design, Optimization, MDO, Design framework. I. NOMENCLATURE R = Earth radius E ω = Earth rotational velocity φ = Launch point latitude Hp = Orbit perigee altitude Ha = Orbit apogee altitude i = Orbit inclination ei μ = proportion of empty mass to total mass for (i)th stage ei M = empty mass for (i)th stage i M 0 = total mass for (i)th stage i n = load factor for (i)th stage MDO = multidisciplinary design optimization a, b, c = coefficient of mass & energy PL μ = proportion of payload mass to total mass CD = coefficient of drag D L = proportion of length to diameter N = number of stages ELV = Expendable Launch Vehicle LV = Launch Vehicle FPI = Fixed Point Iteration BLISS = Bi-level integrated system synthesis CO = Collaborative Optimization CAs = contributing analyses

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Section: Recent Launch Vehicle Optimization Workmentioning

confidence: 99%

Multidisciplinary design optimization of an expendable launch vehicle

Hosseini

Toloie

Nosratollahi

et al. 2011

Proceedings of 5th International Conference on Recent Advances in Space Technologies - RAST2011

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“…However, the application was limited to a single-vehicle configuration, the disciplinary models were tailored for the specific case, and the accuracy of the engineering analyses was not extensively evaluated. Many subsequent launch vehicle applications of MDO are documented in literature, as for example [9][10][11][12], introducing more complex engineering-level models but still lacking a rigorous assessment of the global accuracy of the MDA and hence not conveying a precise feeling of the reliability of the MDO design solutions.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Multidisciplinary Design Models for Expendable Launch Vehicles

Castellini¹,

Lavagna²,

Riccardi³

et al. 2014

Journal of Spacecraft and Rockets

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The research effort described in this paper is centered on the development and quantitative assessment of a multidisciplinary design optimization environment for the early design phases of expendable launch vehicles. The focus of the research is on the engineering modeling aspects, with the goal of evaluating in detail the accuracy of engineering-level methods for launch vehicle design, both in terms of disciplinary errors (e.g., engine specific impulse evaluation) and of system-level sensitivities, to assess their applicability to industrial early design. Although aerospace applications of multidisciplinary design optimization can be found in literature, the systematic assessment of the models' accuracies to the extent described in the present research is a rather new endeavor, which is critical for the advancement of this field. In fact, the widespread industrial application of multidisciplinary design optimization has often been obstructed by the difficulty of finding a suitable compromise between the analysis fidelity and computational cost. Considerable effort was therefore spent on a careful, incremental modeling process, with the purpose of overcoming such an obstacle. As a result, although it is clear that the development and tuning of a reliable multidisciplinary environment is a particularly complex and challenging task, detailed investigations showed that a good compromise can indeed be achieved for the expendable launch vehicles application. In particular, the 1Ïƒ accuracy on the payload performance was assessed to be in the order of 12%for a computational time <2 sper design cycle, allowing one to obtain physically sound design changes through the multidisciplinary design optimization, even exploiting only fast engineering-level methods

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“…Bayley et al in [9] for instance performed a full vehicle optimization of a classical solid propellant rocket. While more recently Gong et al in [10] completed a full vehicle and trajectory optimization of a suborbital vehicle powered by an RBCC engine.…”

Section: Introductionmentioning

confidence: 99%

Configurational optimizer of Combined Cycle Propulsion using Genetic Programming

Mogavero

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-In most engineering applications the optimization is employed after the conceptual design is already frozen with the only purpose of perfecting it. Although optimization algorithms capable of optimizing the configuration exist, their application is limited to electronics or control design, while for complex systems the conceptual design is often performed with manual trade off analyses among few options.In hypersonic propulsion for instance, the choice of which flow cycle to use is made mainly by means of experience or in the best case by a trade off between radically different architectures. For Combined Cycle Propulsion (CCP) however many flow cycle possibilities are available and it is very possible that the final design is influenced by the preconceptions of the engineers rather than a pure objective judgement. Therefore a configurational optimization process, not bound to any known configuration, can potentially deliver totally new engine concepts, aiding the creativity of the designer.In this paper the first steps toward a configurational optimization of CCP are shown. This optimization is intended to find the optimal engine design without knowing the engine conceptual design a-priori. The optimization algorithm is based on the Genetic Programming (GP) and it was presented for the first time at the 20 th AIAA International Space Planes and Hypersonic Systems and Technologies Conference. In the present paper the fitness function has been improved with the addition of a constraint like penalty function designed to solve a convergence problem outlined in the previous version.The results presented in this paper demonstrate that the optimizer is able to converge to a reasonable configuration, coherent with the engineering practice. Moreover the improvements proposed in this paper proved to be effective in the mitigation of the previously detected problem, thus demonstrating that the optimization algorithm is robust and that the previous problems were only due to a poorly defined fitness function.

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Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

Cited by 23 publications

References 21 publications

Multidisciplinary design optimization of an expendable launch vehicle

Multidisciplinary design optimization of an expendable launch vehicle

Quantitative Assessment of Multidisciplinary Design Models for Expendable Launch Vehicles

Configurational optimizer of Combined Cycle Propulsion using Genetic Programming

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