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Relevant bibliographies by topics / Trajectory optimization. Launch vehicles (Astronautics)

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Academic literature on the topic 'Trajectory optimization. Launch vehicles (Astronautics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Trajectory optimization. Launch vehicles (Astronautics)"

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Mukundan, Vijith, Arnab Maity, Shashi Ranjan Kumar, and U. P. Rajeev. "Ascent Trajectory Optimization of Launch Vehicles with Air-Breathing Propulsion." IFAC-PapersOnLine 52, no. 12 (2019): 274–79. http://dx.doi.org/10.1016/j.ifacol.2019.11.255.

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Rao, Prabhakara P., Brian M. Sutter, and Philip E. Hong. "Six-Degree-of-Freedom Trajectory Targeting and Optimization for Titan Launch Vehicles." Journal of Spacecraft and Rockets 34, no. 3 (May 1997): 341–46. http://dx.doi.org/10.2514/2.3214.

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Wang, Zhen, and Zhong Wu. "Six-DOF trajectory optimization for reusable launch vehicles via Gauss pseudospectral method." Journal of Systems Engineering and Electronics 27, no. 2 (April 20, 2016): 434–41. http://dx.doi.org/10.1109/jsee.2016.00044.

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Zhou, Hongyu, Xiaogang Wang, and Naigang Cui. "Ascent trajectory optimization for air‐breathing vehicles in consideration of launch window." Optimal Control Applications and Methods 41, no. 2 (October 28, 2019): 349–68. http://dx.doi.org/10.1002/oca.2546.

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Roshanian, Jafar, Ali A. Bataleblu, and Masoud Ebrahimi. "Robust ascent trajectory design and optimization of a typical launch vehicle." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 24 (January 15, 2018): 4601–14. http://dx.doi.org/10.1177/0954406217753460.

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Robustness and reliability of the designed trajectory are crucial for flight performance of launch vehicles. In this paper, robust trajectory design optimization of a typical LV is proposed. Two formulations of robust trajectory design optimization problem using single-objective and multi-objective optimization concept are presented. Both aleatory and epistemic uncertainties in model parameters and operational environment characteristics are incorporated in the problem, respectively. In order to uncertainty propagation and analysis, the improved Latin hypercube sampling is utilized. A comparison between robustness of the single-objective robust trajectory design optimization solution and deterministic design optimization solution is illustrated using probability density functions. The multi-objective robust trajectory design optimization is executed through NSGA-II and a set of feasible design points with a good spread is obtained in the form of Pareto frontier. The final Pareto frontier presents a trade-off between two conflicting objectives namely maximizing injection robustness and minimizing gross lift-off mass of launch vehicle. The resulted Pareto frontier of the multi-objective robust trajectory design optimization shows that with 1% increase in gross mass, the robustness of the design point to the considered uncertainties can be increased about 80%. Also, numerical simulation results show that the multi-objective formulation is a necessary approach to achieve a good trade-off between optimality and robustness.

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Li, Yuan, Baojun Pang, Changzhu Wei, Naigang Cui, and Yongbei Liu. "Online trajectory optimization for power system fault of launch vehicles via convex programming." Aerospace Science and Technology 98 (March 2020): 105682. http://dx.doi.org/10.1016/j.ast.2020.105682.

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Jamilnia, Reza, and Abolghasem Naghash. "Simultaneous optimization of staging and trajectory of launch vehicles using two different approaches." Aerospace Science and Technology 23, no. 1 (December 2012): 85–92. http://dx.doi.org/10.1016/j.ast.2011.06.013.

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Federici, Lorenzo, Alessandro Zavoli, Guido Colasurdo, Lucandrea Mancini, and Agostino Neri. "Integrated Optimization of First-Stage SRM and Ascent Trajectory of Multistage Launch Vehicles." Journal of Spacecraft and Rockets 58, no. 3 (May 2021): 786–97. http://dx.doi.org/10.2514/1.a34930.

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Song, Yongjun, Young-Joo Song, Seongwhan Lee, Kap-Sung Kim, and Ho Jin. "Potential Launch Opportunities for a SmallSat Mission around the Moon Injected during a Lunar Flyby En Route to Mars." Mathematical Problems in Engineering 2019 (September 24, 2019): 1–14. http://dx.doi.org/10.1155/2019/1245213.

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In this work, the concept of a multipurpose mission that can explore both the Moon and Mars with a single launch is proposed, and potential launch opportunities are analyzed to establish an early-phase trajectory concept. The proposed mission applies the concept of a piggyback ride to a small-sized lunar probe, i.e., the daughtership, of the main Mars orbiter, i.e., the mothership. For the trajectory design, the Earth-Moon-Mars gravity assist (EMMGA) trajectory is adopted for the mothership to reach Mars, and the daughtership is assumed to be released from the mothership during lunar flyby. To investigate the early-phase feasibility of the proposed mission, the launch windows have been analyzed and the associated delta-Vs have been directly compared with the solutions obtained for typical Earth-Mars direct (EMD) transfer options. The identified launch windows (in the years 2031 and 2045) could be the strongest candidates for the proposed conceptual mission. Under the current assumptions, up to approximately 15% (in 2031) and 9% (in 2045), more dry mass is expected to be delivered to Mars by appropriately selecting one of the currently available launch vehicles, regardless of whether the EMMGA transfer option is used. For missions around the Moon using a SmallSat in 2031, the feasibility of a lunar orbiter case and an impactor case is briefly analyzed based on the delta-Vs required to divert the SmallSat from the mothership. Although the current work is performed under numerous assumptions for a simplified problem, the narrowed candidate launch window from the current work represents a good starting point for more detailed trajectory design optimization and analysis to realize the proposed conceptual mission.

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Hwang, Feng-Nan. "Three-dimensional trajectory optimization for multi-stage launch vehicle mission using a full-space quasi-Lagrange–Newton method." ANZIAM Journal 60 (August 30, 2019): C172—C186. http://dx.doi.org/10.21914/anziamj.v60i0.14067.

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Many aerospace industrial applications require robust and efficient numerical solutions of large sparse nonlinear constrained parameter optimization problems arising from optimal trajectory problems. A three-dimensional multistage launcher problem is taken as a numerical example for studying the performance and applicability of the full-space Lagrange–Newton–Krylov method. The typical optimal trajectory, control history and other important physical qualities are presented, and the efficiency of the algorithm is also investigated. References J. T. Betts. Practical methods for optimal control and estimation using nonlinear programming. Advances in Design and Control. SIAM, 2nd edition, 2010. doi:10.1137/1.9780898718577. R. T. Marler and J. S. Arora. Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Opt., 26(6):369395, 2004. doi:10.1007/s00158-003-0368-6. W. Roh and Y. Kim. Trajectory optimization for a multi-stage launch vehicle using time finite element and direct collocation methods. Eng. Opt., 34:1532, 2002. doi:10.1080/03052150210912. G. D. Silveira and V. Carrara. A six degrees-of-freedom flight dynamics simulation tool of launch vehicles. J. Aero. Tech. Manag., 7:231239, 2015. doi:10.5028/jatm.v7i2.433. H.-H. Wang, Y.-S. Lo, F.-T. Hwang, and F.-N. Hwang. A full-space quasi-LagrangeNewtonKrylov algorithm for trajectory optimization problems. Electron. T. Numer. Anal., 49:103125, 2018. doi:10.1553/etna_vol49s103. H. Yang, F.-N. Hwang, and X.-C. Cai. Nonlinear preconditioning techniques for full-space Lagrange-Newton solution of PDE-constrained optimization problems. SIAM J. Sci. Comput., 38:A2756A2778, 2016. doi:10.1137/15M104075X.

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Dissertations / Theses on the topic "Trajectory optimization. Launch vehicles (Astronautics)"

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Shaffer, Patrick J. "Optimal trajectory reconfiguration and retargeting for the X-33 reusable launch vehicle." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FShaffer.pdf.

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Steffens, Michael J. "A combined global and local methodology for launch vehicle trajectory design-space exploration and optimization." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51884.

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Trajectory optimization is an important part of launch vehicle design and operation. With the high costs of launching payload into orbit, every pound that can be saved increases affordability. One way to save weight in launch vehicle design and operation is by optimizing the ascent trajectory. Launch vehicle trajectory optimization is a field that has been studied since the 1950’s. Originally, analytic solutions were sought because computers were slow and inefficient. With the advent of computers, however, different algorithms were developed for the purpose of trajectory optimization. Computer resources were still limited, and as such the algorithms were limited to local optimization methods, which can get stuck in specific regions of the design space. Local methods for trajectory optimization have been well studied and developed. Computer technology continues to advance, and in recent years global optimization has become available for application to a wide variety of problems, including trajectory optimization. The aim of this thesis is to create a methodology that applies global optimization to the trajectory optimization problem. Using information from a global search, the optimization design space can be reduced and a much smaller design space can be analyzed using already existing local methods. This allows for areas of interest in the design space to be identified and further studied and helps overcome the fact that many local methods can get stuck in local optima. The design space included in trajectory optimization is also considered in this thesis. The typical optimization variables are initial conditions and flight control variables. For direct optimization methods, the trajectory phase structure is currently chosen a priori. Including trajectory phase structure variables in the optimization process can yield better solutions. The methodology and phase structure optimization is demonstrated using an earth-to-orbit trajectory of a Delta IV Medium launch vehicle. Different methods of performing the global search and reducing the design space are compared. Local optimization is performed using the industry standard trajectory optimization tool POST. Finally, methods for varying the trajectory phase structure are presented and the results are compared.

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Dyckman, Theodore R. (Theodore Robert) 1978. "Benchmark characterization for reusable launch vehicle onboard trajectory generation using a Legendre psuedospectral [sic] optimization method." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/82221.

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Girerd, André R. (André René) 1977. "Onboard trajectory generation for the unpowered landing of autonomous Reusable Launch Vehicles." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8713.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.
Includes bibliographical references (p. 167-168).
Onboard trajectory generation dispenses with the pre-defined trajectories used. in Reusable Launch Vehicle (RLV) guidance since the early days of the Shuttle era. This shift, enabled by a new breed of algorithms harnessing modern computer power, offers improvements in performance, robustness, operational cost, and safety. This thesis develops a set of algorithms providing onboard trajectory generation for low lift-over-drag gliding RLVs in subsonic flight below 40,000 ft. The NASA/Orbital Sciences X-34 is used as a representative model for which feasible trajectories are designed over a range of initial conditions without human intervention. In addition to being autonomous, the guidance output of the onboard trajectory generator differs from current Shuttle-based approaches, providing a realistic "future history" in a propagated plan, rather than output commands reacting to perceived instantaneous vehicle needs. Hence, this approach serves an enabling role in a larger research effort to develop a next generation guidance system using an integrated control function. To assess feasibility, the onboard trajectory generator is benchmarked against traditional X-34 guidance for a drop test scenario. The results match in basic form, with differences showcasing the autonomous algorithms' preference for maximum robustness. The true strength of the onboard trajectory generator lies in its ability to handle off-nominal conditions. A series of test cases highlight the ability of the algorithms to effectively cope with anomalous initial drop conditions, reach the desired terminal states, and provide maximum late-trajectory robustness. Computation time is sufficiently brief to suggest a real-time application, after straightforward improvements are made.
by André R. Girerd.
S.M.

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Bollino, Kevin P. "High-fidelity real-time trajectory optimization for reusable launch vehicles." Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FBollino%5FPhD.pdf.

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Thesis (Ph.D. in Astronautical Engineering)--Naval Postgraduate School, December 2006.
Dissertation supervisor(s): I. Michael Ross. "December 2006." Includes bibliographical references (p. 395-411). Also available in print.

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Bayley, Douglas James. "Design optimization of space launch vehicles using a genetic algorithm." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2007%20Spring%20Dissertations/BAYLEY_DOUGLAS_5.pdf.

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Ponda, Sameera S. "Trajectory optimization for target localization using small unmanned aerial vehicles." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47794.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.
Includes bibliographical references (p. 189-197).
Small unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance missions. One particular mission of interest involves computing location estimates for targets detected by onboard sensors. Combining UAV state estimates with information gathered by the imaging sensors leads to bearing measurements of the target that can be used to determine the target's location. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods produce biased and uncertain estimates, occasionally leading to filter instabilities. Careful selection of the measurement locations greatly enhances filter performance, motivating the development of UAV trajectories that minimize target location estimation error and improve filter convergence. The objective of this work is to develop guidance algorithms that enable the UAV to fly trajectories that increase the amount of information provided by the measurements and improve overall estimation observability, resulting in proper target tracking and an accurate target location estimate. The performance of the target estimation is dependent upon the positions from which measurements are taken relative to the target and to previous measurements. Past research has provided methods to quantify the information content of a set of measurements using the Fisher Information Matrix (FIM). Forming objective functions based on the FIM and using numerical optimization methods produce UAV trajectories that locally maximize the information content for a given number of measurements. In this project, trajectory optimization leads to the development of UAV flight paths that provide the highest amount of information about the target, while considering sensor restrictions, vehicle dynamics and operation constraints.
(cont.) The UAV trajectory optimization is performed for stationary targets, dynamic targets and multiple targets, for many different scenarios of vehicle motion constraints. The resulting trajectories show spiral paths taken by the UAV, which focus on increasing the angular separation between measurements and reducing the relative range to the target, thus maximizing the information provided by each measurement and improving the performance of the estimation. The main drawback of information based trajectory design is the dependence of the Fisher Information Matrix on the true target location. This issue is addressed in this project by executing simultaneous target location estimation and UAV trajectory optimization. Two estimation algorithms, the Extended Kalman Filter and the Particle Filter are considered, and the trajectory optimization is performed using the mean value of the target estimation in lieu of the true target location. The estimation and optimization algorithms run in sequence and are updated in real-time. The results show spiral UAV trajectories that increase filter convergence and overall estimation accuracy, illustrating the importance of information-based trajectory design for target localization using small UAVs.
by Sameera S. Ponda.
S.M.

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Steele, Steven Cory Wyatt. "Optimal Engine Selection and Trajectory Optimization using Genetic Algorithms for Conceptual Design Optimization of Resuable Launch Vehicles." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51771.

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Proper engine selection for Reusable Launch Vehicles (RLVs) is a key factor in the design of low cost reusable launch systems for routine access to space. RLVs typically use combinations of different types of engines used in sequence over the duration of the flight. Also, in order to properly choose which engines are best for an RLV design concept and mission, the optimal trajectory that maximizes or minimizes the mission objective must be found for that engine configuration. Typically this is done by the designer iteratively choosing engine combinations based on his/her judgment and running each individual combination through a full trajectory optimization to find out how well the engine configuration performed on board the desired RLV design. This thesis presents a new method to reliably predict the optimal engine configuration and optimal trajectory for a fixed design of a conceptual RLV in an automated manner. This method is accomplished using the original code Steele-Flight. This code uses a combination of a Genetic Algorithm (GA) and a Non-Linear Programming (NLP) based trajectory optimizer known as GPOPS II to simultaneously find the optimal engine configuration from a user provided selection pool of engine models and the matching optimal trajectory. This method allows the user to explore a broad range of possible engine configurations that they wouldn't have time to consider and do so in less time than if they attempted to manually select and analyze each possible engine combination. This method was validated in two separate ways. The codes ability to optimize trajectories was compared to the German trajectory optimizer suite known as ASTOS where only minimal differences in the output trajectory were noticed. Afterwards another test was performed to verify the method used by Steele-Flight for engine selection. In this test, Steele-Flight was provided a vehicle model based on the German Saenger TSTO RLV concept and models of turbofans, turbojets, ramjets, scramjets and rockets. Steele-Flight explored the design space through the use of a Genetic Algorithm to find the optimal engine combination to maximize payload. The results output by Steele-Flight were verified by a study in which the designer manually chose the engine combinations one at a time, running each through the trajectory optimization routine to determine the best engine combination. For the most part, these methods yielded the same optimal engine configurations with only minor variation. The code itself provides RLV researchers with a new tool to perform conceptual level engine selection from a gathering of user provided conceptual engine data models and RLV structural designs and trajectory optimization for fixed RLV designs and fixed mission requirement.
Master of Science

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Dukeman, Greg A. "Closed-Loop Nominal and Abort Atmospheric Ascent Guidance for Rocket-Powered Launch Vehicles." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6820.

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An advanced ascent guidance algorithm for rocket-powered launch vehicles is developed. The ascent guidance function is responsible for commanding attitude, throttle and setting during the powered ascent phase of flight so that the vehicle attains target cutoff conditions in a near-optimal manner while satisfying path constraints such as maximum allowed bending moment and maximum allowed axial acceleration. This algorithm cyclically solves the calculus-of-variations two-point boundary-value problem starting at vertical rise completion through orbit insertion. This is different from traditional ascent guidance algorithms which operate in an open-loop mode until the high dynamic pressure portion of the trajectory is over, at which time there is a switch to a closed loop guidance mode that operates under the assumption of negligible aerodynamic forces. The main contribution of this research is an algorithm of the predictor-corrector type wherein the state/costate system is propagated with known (navigated) initial state and guessed initial costate to predict the state/costate at engine cutoff. The initial costate guess is corrected, using a multi-dimensional Newtons method, based on errors in the terminal state constraints and the transversality conditions. Path constraints are enforced within the propagation process. A modified multiple shooting method is shown to be a very effective numerical technique for this application. Results for a single stage to orbit launch vehicle are given. In addition, the formulation for the free final time multi-arc trajectory optimization problem is given. Results for a two-stage launch vehicle burn-coast-burn ascent to orbit in a closed-loop guidance mode are shown. An abort to landing site formulation of the algorithm and numerical results are presented. A technique for numerically treating the transversality conditions is discussed that eliminates part of the analytical and coding burden associated with optimal control theory.

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Bratkovich, Thomas E. (Thomas Edward). "An integrated design, control, and trajectory optimization algorithm for future planetary (Martian) entry/lander vehicles." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/47323.

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Books on the topic "Trajectory optimization. Launch vehicles (Astronautics)"

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Guan yu hang tian qi zui jia fa she gui dao de li lun ji qi ta wen ti de yan jiu. Beijing Shi: Zhongguo yu hang chu ban she, 2004.

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Zhiwen, Zhu, and Zhu Xuejun, eds. Rao di fei xing hang tina qi zui jia fa she gui dao li lun ji qi ta wen ti de yan jiu. Beijing Shi: Zhongguo yu hang chu ban she, 2011.

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Riehl, John. SRM-assisted trajectory for the GTX reference vehicle. Cleveland, Ohio: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Trajectory optimization for an asymmetric launch vehicle. [Cambridge, Mass.]: Massachusetts Institute of Technolgy, 1990.

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SRM-assisted trajectory for the GTX reference vehicle. Cleveland, Ohio: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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SRM-assisted trajectory for the GTX reference vehicle. Cleveland, Ohio: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Charles, Trefny, Kosareo Daniel, and NASA Glenn Research Center, eds. SRM-assisted trajectory for the GTX reference vehicle. Cleveland, Ohio: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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Book chapters on the topic "Trajectory optimization. Launch vehicles (Astronautics)"

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Palaia, Guido, Marco Pallone, Mauro Pontani, and Paolo Teofilatto. "Ascent Trajectory Optimization and Neighboring Optimal Guidance of Multistage Launch Vehicles." In Springer Optimization and Its Applications, 343–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10501-3_13.

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Conference papers on the topic "Trajectory optimization. Launch vehicles (Astronautics)"

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Civek, Ezgi, and M. Kemal Özgören. "Space Launch Vehicle Design with Simultaneous Optimization of Thrust Profile and Trajectory." In AIAA SPACE and Astronautics Forum and Exposition. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-5333.

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Li Yongyuan, Zhang Xuemei, Shi Jianbo, and Li Hongbo. "Trajectory Optimization and Analysis for Reusable Launch Vehicles." In 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2013). IEEE, 2013. http://dx.doi.org/10.1109/icmtma.2013.314.

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Schierman, John, and Jason Hull. "In-Flight Entry Trajectory Optimization for Reusable Launch Vehicles." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-6434.

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McGuire, M., Peter Gage, Eric Galloway, Loc Huynh, Jennie Nguyen, Jeffrey Bowles, and Robert Windhorst. "Trajectory and Thermal Protection System Design for Reusable Launch Vehicles." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4490.

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Briese, Lale Evrim, Klaus Schnepper, and Paul Acquatella B. "Advanced modeling and trajectory optimization framework for reusable launch vehicles." In 2018 IEEE Aerospace Conference. IEEE, 2018. http://dx.doi.org/10.1109/aero.2018.8396704.

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Rao, Prabhakara, Brian Sutter, and Philip Hong. "Six degrees-of-freedom trajectory targeting and optimization for Titan launch vehicles." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-3776.

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"Multi-Level, Multi-Objective and Multidisplinary Optimization Design of a Series of Launch Vehicles." In 55th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.iac-04-w.3.01.

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Qazi, Mateen-ud-Din, He Linshu, and Tarek Elhabian. "Rapid Trajectory Optimization Using Computational Intelligence for Guidance and Conceptual Design of Multistage Space Launch Vehicles." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-6062.

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BELTRACCHI, TODD, and HAL NGUYEN. "Experience with post optimality parameter sensitivity analysis in FONSIZE (A conceptual sizing and trajectory optimization code for launch vehicles)." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4749.

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