Rocket-powered single-stage vehicle configuration selection and design

Stanley, Douglas O.; Engelund, Walter C.; Lepsch, Roger A.; McMillin, Mark L.; Wurster, Kathryn E.; Powell, Richard W.; Guinta, Tony; Unal, Resit

doi:10.2514/3.26514

Cited by 32 publications

(6 citation statements)

References 7 publications

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“…According to Ref. [30], it is considered that the mass of the reentry vehicle is m = 104, 035 kg, and the aerodynamic reference area is S = 391.22 m 2 . In this paper, the fight mission of the vehicle is to plan an optimal trajectory with the minimum flight time under various constraints.…”

Section: Numerical Verificationmentioning

confidence: 99%

A Reentry Trajectory Planning Algorithm via Pseudo-Spectral Convexification and Method of Multipliers

Liang,

Luo,

Che

et al. 2024

Mathematics

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The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem, and it constitutes a critical challenge within the field of aerospace engineering. In this paper, an improved sequential convexification algorithm is proposed to solve it and achieve online trajectory planning. In the proposed algorithm, the Chebyshev pseudo-spectral method with high-accuracy approximation performance is first employed to discretize the continuous dynamic equations. Subsequently, based on the multipliers and linearization methods, the original nonconvex trajectory planning problem is transformed into a series of relaxed convex subproblems in the form of an augmented Lagrange function. Then, the interior point method is utilized to iteratively solve the relaxed convex subproblem until the expected convergence precision is achieved. The convex-optimization-based and multipliers methods guarantee the promotion of fast convergence precision, making it suitable for online trajectory planning applications. Finally, numerical simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the algorithm possesses better convergence performance, and the solution time can reach the level of seconds, which is more than 97% less than nonlinear programming algorithms, such as the sequential quadratic programming algorithm.

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Section: Numerical Verificationmentioning

confidence: 99%

A Reentry Trajectory Planning Algorithm via Pseudo-Spectral Convexification and Method of Multipliers

Liang,

Luo,

Che

et al. 2024

Mathematics

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“…Rocket powered vehicle advocates claim lower dry (empty) weights, high engine thrust-toweight ratios, fewer propulsion/airframe complexities, and reduced technology requirements. Several rocket SSTO vehicles are candidates including winged vehicles powered by either SSME-derivative engines [3] or dual-fuel concepts based on derivatives of the Russian RD-701 engine [4]. Non-winged concepts such as the Delta Clipper [5,6] are also being examined.…”

Section: Acc Advanced Carbon-carbonmentioning

confidence: 99%

“…That is, the predicted maximum S/N will be at a point represented by a combination of either the low or high bound on each design variable. Since there are only three design variables and eight (2 3 ) possible combinations, we should expect that one of the rows of the L 8 experimental array will correspond to the predicted maximum S/N. By using Taguchi's orthogonal (i.e.…”

Section: Robust Designmentioning

confidence: 99%

Results of a Rocket-Based Combined-Cycle SSTO Design Using Parametric MDO Methods

Olds¹

1994

SAE Technical Paper Series

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This paper reports the results of the second phase of a research project to characterize and optimize the design of an advanced launch vehicle for human access to low earth orbit. The vehicle makes use of rocket-based combined-cycle (RBCC) propulsion-a concept combining operating modes of an ejector, ramjet, scramjet, and rocket in a single engine. This research builds on previous work focused on advanced multiple mode propulsion concepts and advanced conical acceleration-class single-stage-toorbit (SSTO) launch vehicles.

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“…The physical difficulty of designing entry vehicles originates from the large degree of coupling between the various disciplines involved in the design [1][2][3]. The disciplines which can be accounted for and integrated during the design are: trajectory optimization [4][5][6], guidance, navigation, and control (GN&C) technology [7,8], aerodynamics and aerothermodynamics [9][10][11], thermal-structural analysis [12][13][14], and thermal protection system (TPS) development [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Multi-Disciplinary Design Optimization under Uncertainty for Thermal Protection System Applications

Sun¹,

Zhang²,

Vlahopoulos

et al. 2006

11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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This paper presents an approach for performing Multidisciplinary Design Optimization under Uncertainty (MDO-U) and an application for minimizing the thickness of the Thermal Protection System (TPS) for an entry vehicle configuration. Uncertainties due to material variability and the operating environment are considered. The value of considering uncertainty during the optimization process is demonstrated by comparing the performance of two optimal configurations, one identified through a deterministic approach and the other by considering uncertainty. Been able to account for uncertainty in engineering simulations and in product design is both important and challenging. In particular, during design optimization the performance of the optimized design typically lays closely to the boundaries of some of the constraints. Since variability alters the actual performance of a system, the optimal design can exhibit performance in the infeasible domain when the impact of the uncertainty has not been accounted properly during the optimization.

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Rocket-powered single-stage vehicle configuration selection and design

Cited by 32 publications

References 7 publications

A Reentry Trajectory Planning Algorithm via Pseudo-Spectral Convexification and Method of Multipliers

A Reentry Trajectory Planning Algorithm via Pseudo-Spectral Convexification and Method of Multipliers

Results of a Rocket-Based Combined-Cycle SSTO Design Using Parametric MDO Methods

Multi-Disciplinary Design Optimization under Uncertainty for Thermal Protection System Applications

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