Multi-disciplinary intelligent systems approach to solid rocket motor design Part. II - Multiple goal optimization

Anderson, M.; Afb, Eglin; Jenkins, Rhonald M.

doi:10.2514/6.2001-3600

Cited by 9 publications

(7 citation statements)

References 1 publication

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“…Using this GA and the objective function, in 2003 and 2006, Burkhalter, Jenkins, and Hartfield took the design optimization of missile systems further [3][4][5]. Significant research has been performed in rocket-based vehicle design optimization using various evolutionary techniques [6][7][8][9][10][11][12][13][14][15][16]. Cost has also been considered in some studies [17][18][19] [20].…”

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

View full text Add to dashboard Cite

show abstract

“…[2][3][4] Significant research has been performed in rocket-based vehicle design optimization using various evolutionary techniques. [5][6][7][8][9][10][11][12][13][14][15][16] Cost has also been considered in some studies; [17][18][19] however, this paper represents an effort to minimize launch vehicle cost, for liquid-fueled launch vehicles, at the preliminary design stage using a GA.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of Space Launch Vehicles for Minimum Cost Using a Genetic Algorithm

Bayley¹,

Hartfield²

2007

43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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This paper describes an effort to optimize liquid-fueled space launch vehicles for minimum cost. The entire launch vehicle is modeled to achieve a low-Earth (circular) orbit using multiple stages. The optimization scheme employs a genetic algorithm (GA) with the goals of minimizing both vehicle weight and vehicle cost per launch. Two and three-stage liquid propellant launch vehicles have been analyzed. In addition, results from an air-launched, two-stage liquid propellant launch vehicle are presented. The vehicle performance modeling requires that analysis from four separate disciplines be integrated into the design optimization process. Modeling of propulsion systems, aerodynamics, mass properties and flight dynamics have been integrated to produce a high fidelity system model of the entire vehicle. An existing, two-stage liquid propellant launch vehicle has been used to validate the two-stage liquid propellant system model. The cost model is mass-based and uses extensive historical data to produce a cost estimating relationship for a liquid propellant vehicle. For the design optimization, the primary goal is for the GA to minimize the differences between the desired and actual orbital parameters. This ensures the payload achieves the desired orbit. The third goal is to minimize total launch vehicle mass and system cost per launch.

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“…Significant research has been performed in rocket-based vehicle design optimization using various evolutionary techniques [7][8][9][10][11][12][13][14][15][16][17][18]. The cost has also been considered in some studies [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

Bayley

Hartfield

Burkhalter

et al. 2008

Journal of Spacecraft and Rockets

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This paper describes an effort to optimize the design of an entire space launch vehicle to low Earth (circular) orbit, consisting of multiple stages using a genetic algorithm with the goal of minimizing vehicle weight and ultimately vehicle cost. The entire launch vehicle system is analyzed using various multistage configurations to reach low Earth orbit. Specifically, three-and four-stage solid propellant vehicles have been analyzed. The vehicle performance modeling requires that analysis from four separate disciplines be integrated into the design optimization process. The disciplines of propulsion characteristics, aerodynamics, mass properties, and flight dynamics have been integrated to produce a high-fidelity system model of the entire vehicle. In addition, the system model has been validated using the existing launch vehicle data. The cost model is mass based and uses extensive historical data to produce a cost estimating relationship for a solid propellant vehicle. For the design optimization, the goal is for the genetic algorithm to minimize the differences between the desired and actual orbital parameters. This ensures that the payload achieves the desired orbit. One final goal is to minimize the overall vehicle mass, thus minimizing the system cost per launch. This paper will represent the first effort of its kind to minimize the solid propellant launch vehicle cost at the preliminary design level using a genetic algorithm.

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Multi-disciplinary intelligent systems approach to solid rocket motor design Part. II - Multiple goal optimization

Cited by 9 publications

References 1 publication

Multidisciplinary design optimization of an expendable launch vehicle

Multidisciplinary design optimization of an expendable launch vehicle

Design Optimization of Space Launch Vehicles for Minimum Cost Using a Genetic Algorithm

Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

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