3-D hypersonic inlet optimization using a Genetic Algorithm

Chernyavsky, Boris; Stepanov, V.; Rasheed, Khaled; Blaize, Michael; Knight, Doyle

doi:10.2514/6.1998-3582

Cited by 9 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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

show abstract

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

“…Optimization designs on the two-dimensional and three-dimensional inlets have generally been extensively studied by using the response surface method and the genetic algorithm to obtain a higher total pressure recovery and more uniform outflow at the inlet exit [19][20][21]. The issues affecting the performance of the inlet are considered comprehensively in the above optimization methods, but they are too complicated by consuming a lot of computational resources, making it difficult to reveal the general rules of shock wave configuration.…”

Section: Introductionmentioning

confidence: 99%

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

Yue

et al. 2022

Aerospace

View full text Add to dashboard Cite

The present paper focuses on the Lagrange optimization of shock waves for a two-dimensional hypersonic inlet by limiting the cowl internal angle and inlet length. The results indicate the significant influences of geometric constraints on the configuration of shock waves and performances of an inlet. Specifically, the cowl internal angle mainly affects the internal compression section; the inlet length affects both the internal and external compression sections where the intensity of internal and external compression shock waves shows a deviation of equal. In addition, the performances of optimized inlets at off-design points are further numerically simulated. A prominent discovery is that a longer inlet favors a higher total pressure recovery at the positive AOA; conversely, a shorter inlet can increase the total pressure recovery at the negative AOA.

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

View full text Add to dashboard Cite

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.

show abstract

3-D hypersonic inlet optimization using a Genetic Algorithm

Cited by 9 publications

References 3 publications

Multidisciplinary design optimization of an expendable launch vehicle

Multidisciplinary design optimization of an expendable launch vehicle

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

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

Contact Info

Product

Resources

About