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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1995

Abort performance for a winged-body single-stage to orbit vehicle

Optimal control theory is employed to determine the performance of abort to orbit (ATO) and return to launch site (RTLS) maneuvers for a single-stage to orbit vehicle. The vehicle configuration examined is a seven engine, winged-body vehicle, that lifts-off vertically and lands horizontally. The abort maneuvers occur as the vehicle ascends to orbit and are initiated when the vehicle suffers an engine failure. The optimal control problems are numerically solved in discretized form via a nonlinear programming (NLP) algorithm. A description highlighting the attributes of this NLP method is provided. ATO maneuver results show that the vehicle is capable of ascending to orbit with a single engine failure at lift-off. Two engine out ATO maneuvers are not possible from the launch pad, but are possible after launch when the thrust to weight ratio becomes sufficiently large. Results show that single engine out RTLS maneuvers can be made for up to 180 seconds after lift-off and that there are scenarios for which RTLS maneuvers should be performed instead of ATP maneuvers.

Lyon, Jeffery A.↗

NASA NTRS1993

DUKSUP - A high thrust trajectory optimization code

Designing missions on expendable launch vehicles (ELV's) includes determining launch vehicle performance capabilities and trajectory characteristics over the range of mission requirements for suitable launch periods. This analysis depends on mathematically modeling both the launch vehicle and the mission requirements. Generally the result is a mathematical model that is described by an objective function to be optimized subject to an assortment of algebraic and dynamic constraints. About 30 years ago, engineers at Lewis Research Center (LeRC) undertook the task of creating a software code to solve 3D versions of such problems and based it on the Calculus of Variations/Optimal Control Theory. One of these codes, DUKSUP, has been in use at LeRC for nearly 25 years, during which time it has played an important role in a large number of studies and actual missions. Currently it is being used by about 12 analysts in the Center's Advanced Space Analysis Office (ASAO) to do mission design, feasibility studies, corroboration of contractor data and planning studies for the Space Exploration Initiative (SEI). Today, it is one of the few ELV mission analysis production codes based on variational methods. With future ELV missions in mind, ASAO is presently creating a new code to upgrade DUKSUP's capabilities.

Balkanyi, Leslie R.↗

NASA NTRS1965

Launch vehicle nose shroud optimization

Nose fairing cone shape effect on Saturn V launch vehicle performance

NOSE CONE↗

NASA NTRS1974

A study of unmanned mission opportunities to comets and asteroids

Several unmanned multiple-target mission opportunities to comets and asteroids were studied. The targets investigated include Grigg-Skjellerup, Giacobini-Zinner, Tuttle-Giacobini-Kresak, Borrelly, Halley, Schaumasse, Geographos, Eros, Icarus, and Toro, and the trajectories consist of purely ballistic flight, except that powered swingbys and deep space burns are employed when necessary. Optimum solar electric rendezvous trajectories to the comets Giacobini-Zinner/85, Borrelly/87, and Temple (2)/83 and /88 employing the 8.67 kw Sert III spacecraft modified for interplanetary flight were also investigated. The problem of optimizing electric propulsion heliocentric trajectories, including the effects of geocentric launch asymptote declination on launch vehicle performance capability, was formulated, and a solution developed using variational calculus techniques. Improvements were made to the HILTOP trajectory optimization computer program. An error analysis of high-thrust maneuvers involving spin-stabilized spacecraft was developed and applied to a synchronous meteorological satellite mission.

Mann, F. I.↗

NASA NTRS2007

Impact to Space Shuttle Vehicle Trajectory on Day of Launch from change in Low Frequency Winds

The National Aeronautics and Space Administration's (NASA) Space Shuttle utilizes atmospheric winds on day of launch to develop throttle and steering commands to best optimize vehicle performance while keeping structural loading on the vehicle within limits. The steering commands and resultant trajectory are influenced by both the high and low frequency component of the wind. However, the low frequency component has a greater effect on the ascent design. Change in the low frequency wind content from the time of trajectory design until launch can induce excessive loading on the vehicle. Wind change limits have been derived to protect from launching in an environment where these temporal changes occur. Process of developing wind change limits are discussed followed by an observational study of temporal wind change in low frequency wind profiles at the NASA's Kennedy Space Center area are presented.

Decker, Ryan K.↗

NASA NTRS2011

Space Shuttle Day-of-Launch Trajectory Design Operations

A top priority of any launch vehicle is to insert as much mass into the desired orbit as possible. This requirement must be traded against vehicle capability in terms of dynamic control, thermal constraints, and structural margins. The vehicle is certified to specific structural limits which will yield certain performance characteristics of mass to orbit. Some limits cannot be certified generically and must be checked with each mission design. The most sensitive limits require an assessment on the day-of-launch. To further minimize vehicle loads while maximizing vehicle performance, a day-of-launch trajectory can be designed. This design is optimized according to that day s wind and atmospheric conditions, which increase the probability of launch. The day-of-launch trajectory design and verification process is critical to the vehicle s safety. The Day-Of-Launch I-Load Update (DOLILU) is the process by which the National Aeronautics and Space Administration's (NASA) Space Shuttle Program tailors the vehicle steering commands to fit that day s environmental conditions and then rigorously verifies the integrated vehicle trajectory s loads, controls, and performance. This process has been successfully used for almost twenty years and shares many of the same elements with other launch vehicles that execute a day-of-launch trajectory design or day-of-launch trajectory verification. Weather balloon data is gathered at the launch site and transmitted to the Johnson Space Center s Mission Control. The vehicle s first stage trajectory is then adjusted to the measured wind and atmosphere data. The resultant trajectory must satisfy loads and controls constraints. Additionally, these assessments statistically protect for non-observed dispersions. One such dispersion is the change in the wind from the last measured balloon to launch time. This process is started in the hours before launch and is repeated several times as the launch count proceeds. Should the trajectory design not meet all constraint criteria, Shuttle would be No-Go for launch. This Shuttle methodology is very similar to other unmanned launch vehicles. By extension, this method would likely be employed for any future NASA launch vehicle. This paper will review the Shuttle s day-of-launch trajectory optimization and verification operations as an example of a more generic application of day-of-launch design and validation. With Shuttle s retirement, it is fitting to document the current state of this critical process and capture lessons learned to benefit current and future launch vehicle endeavors.

Harrington, Brian E.↗

NASA NTRS1994

Multidisciplinary design optimization using genetic algorithms

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Unal, Resit↗

NASA NTRS1995

Optimal technology investment strategies for a reusable launch vehicle

Within the present budgetary environment, developing the technology that leads to an operationally efficient space transportation system with the required performance is a challenge. The present research focuses on a methodology to determine high payoff technology investment strategies. Research has been conducted at Langley Research Center in which design codes for the conceptual analysis of space transportation systems have been integrated in a multidisciplinary design optimization approach. The current study integrates trajectory, propulsion, weights and sizing, and cost disciplines where the effect of technology maturation on the development cost of a single stage to orbit reusable launch vehicle is examined. Results show that the technology investment prior to full-scale development has a significant economic payoff. The design optimization process is used to determine strategic allocations of limited technology funding to maximize the economic payoff.

Moore, A. A.↗

NASA NTRS1962

Optimum Interplanetary Rendezvous Trajectories With Powerlimited Vehicles

The optimum-thrust equations for both variable and constant thrust are presented. These thrust programs are used to generate rendezvous trajectories from the Earth to Mars for various flight times and launch dates during the years 1968-71. The manner in which the propulsion requirements vary with flight time and launch date are considered, and a comparison of vehicle performance using the variable- and constant-thrust programs is presented. The optimization of the pro- pulsion system parameters is discussed, and the existence of optimum launch dates is interpreted in terms of certain transversality conditions derivable from the calculus of variations. A brief comparison of the advanced propulsion vehicle and the ballistic vehicle propulsion requirements is made for Earth-Mars rendezvous trajectories. An appendix considering the analytical basis for this work is included.

INTERPLANETARY TRAJECTORY↗

NASA NTRS1982

A combined Halley flyby/Galileo mission

The Galileo mission to Jupiter utilizes change of velocity earth gravity assist trajectories which leave earth in 1985 on 2-year orbits, have maneuvers of approximately 500 m/sec near aphelion, and then use earth flybys to send the spacecraft to Jupiter. It was noted that in Nov. 1985, one family of these trajectories passes fairly close to Halleys comet near its preperihelion node. It is shown that an arbitrarily close flyby of Halley can be added to these trajectories for about a 500 m/sec increase in the aphelion maneuver. Optimal trajectory design and mission performance as a function of launch vehicle capability are discussed in detail.

Byrnes, D. V.↗

NASA NTRS2018

Navigation Requirements Development and Performance Assessment of a Martian Ascent Vehicle

To support development of Martian Ascent Vehicles, analysis tools are needed to support the development of Guidance, Navigation, and Control requirements. This paper presents a focused approach to Navigation analysis to capture development of requirements on initial state knowledge and inertial sensor capabilities. A simulation and analysis framework was used to assess the capability of a range of sensors to operate inertially along a range of launch trajectories. The baseline Martian Ascent Vehicle was used as the input for optimizing a set of trajectories from each launch site. These trajectories were used to perform Monte Carlo analysis dispersing error sensor terms and their effects on integrated vehicle performance. Additionally, this paper provides insight into the use of optical navigation techniques to assess state determination and the potential to use observations of local extraplanetary bodies to estimate state. This paper provides an initial level of performance assessment of navigation components to support continued requirements development of a Martian Ascent Vehicle with applications to both crew and sample return missions.

Anzalone, Evan J.↗

NASA NTRS2016

Efficiency Management in Spaceflight Systems

Efficiency in spaceflight is often approached as “faster, better, cheaper – pick two”. The high levels of performance and reliability required for each mission suggest that planners can only control for two of the three. True efficiency comes by optimizing a system across all three parameters. The functional processes of spaceflight become technical requirements on three operational groups during mission planning: payload, vehicle, and launch operations. Given the interrelationships among the functions performed by the operational groups, optimizing function resources from one operational group to the others affects the efficiency of those groups and therefore the mission overall. This paper helps outline this framework and creates a context in which to understand the effects of resource trades on the overall system, improving the efficiency of the operational groups and the mission as a whole. This allows insight into and optimization of the controlling factors earlier in the mission planning stage.

Murphy, Karen↗

NASA NTRS2016

Efficiency Management in Spaceflight Systems

Efficiency in spaceflight is often approached as "faster, better, cheaper - pick two". The high levels of performance and reliability required for each mission suggest that planners can only control for two of the three. True efficiency comes by optimizing a system across all three parameters. The functional processes of spaceflight become technical requirements on three operational groups during mission planning: payload, vehicle, and launch operations. Given the interrelationships among the functions performed by the operational groups, optimizing function resources from one operational group to the others affects the efficiency of those groups and therefore the mission overall. This paper helps outline this framework and creates a context in which to understand the effects of resource trades on the overall system, improving the efficiency of the operational groups and the mission as a whole. This allows insight into and optimization of the controlling factors earlier in the mission planning stage.

Murphy, Karen↗

NASA NTRS2014

Coupled Solid Rocket Motor Ballistics and Trajectory Modeling for Higher Fidelity Launch Vehicle Design

Multi-stage launch vehicles with solid rocket motors (SRMs) face design optimization challenges, especially when the mission scope changes frequently. Significant performance benefits can be realized if the solid rocket motors are optimized to the changing requirements. While SRMs represent a fixed performance at launch, rapid design iterations enable flexibility at design time, yielding significant performance gains. The streamlining and integration of SRM design and analysis can be achieved with improved analysis tools. While powerful and versatile, the Solid Performance Program (SPP) is not conducive to rapid design iteration. Performing a design iteration with SPP and a trajectory solver is a labor intensive process. To enable a better workflow, SPP, the Program to Optimize Simulated Trajectories (POST), and the interfaces between them have been improved and automated, and a graphical user interface (GUI) has been developed. The GUI enables real-time visual feedback of grain and nozzle design inputs, enforces parameter dependencies, removes redundancies, and simplifies manipulation of SPP and POST's numerous options. Automating the analysis also simplifies batch analyses and trade studies. Finally, the GUI provides post-processing, visualization, and comparison of results. Wrapping legacy high-fidelity analysis codes with modern software provides the improved interface necessary to enable rapid coupled SRM ballistics and vehicle trajectory analysis. Low cost trade studies demonstrate the sensitivities of flight performance metrics to propulsion characteristics. Incorporating high fidelity analysis from SPP into vehicle design reduces performance margins and improves reliability. By flying an SRM designed with the same assumptions as the rest of the vehicle, accurate comparisons can be made between competing architectures. In summary, this flexible workflow is a critical component to designing a versatile launch vehicle model that can accommodate a volatile mission scope.

Ables, Brett↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

Zwack, Mathew R.↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

During early conceptual design of complex systems, concept down selection can have a large impact upon program life-cycle cost. Therefore, any concepts selected during early design will inherently commit program costs and affect the overall probability of program success. For this reason it is important to consider as large a design space as possible in order to better inform the down selection process. For conceptual design of launch vehicles, trajectory analysis and optimization often presents the largest obstacle to evaluating large trade spaces. This is due to the sensitivity of the trajectory discipline to changes in all other aspects of the vehicle design. Small deltas in the performance of other subsystems can result in relatively large fluctuations in the ascent trajectory because the solution space is non-linear and multi-modal [1]. In order to help capture large design spaces for new launch vehicles, the authors have performed previous work seeking to automate the execution of the industry standard tool, Program to Optimize Simulated Trajectories (POST). This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases [2]. Additional work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points [3]. The conclusion of the previous work illustrated the utility of the graph theory approach for completing a DOE through POST. However, this approach was still dependent upon the use of random repetitions to generate seed points for the graph. As noted in [3], only 8% of these random repetitions resulted in converged trajectories. This ultimately affects the ability of the random reps method to confidently approach the global optima for a given vehicle case in a reasonable amount of time. With only an 8% pass rate, tens or hundreds of thousands of reps may be needed to be confident that the best repetition is at least close to the global optima. However, typical design study time constraints require that fewer repetitions be attempted, sometimes resulting in seed points that have only a handful of successful completions. If a small number of successful repetitions are used to generate a seed point, the graph method may inherit some inaccuracies as it chains DOE cases from the non-global-optimal seed points. This creates inherent noise in the graph data, which can limit the accuracy of the resulting surrogate models. For this reason, the goal of this work is to improve the seed point generation method and ultimately the accuracy of the resulting POST surrogate model. The work focuses on increasing the case pass rate for seed point generation.

Zwack, Matthew R.↗

NASA NTRS2014

Space Launch System Implementation of Adaptive Augmenting Control

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to robustly demonstrate stable and high performance flight. On its development path to preliminary design review (PDR), the stability of the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant dynamics. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an adaptive augmenting control (AAC) algorithm previously presented by Orr and VanZwieten, has been shown to extend the envelope of failures and flight anomalies for which the SLS control system can accommodate while maintaining a direct link to flight control stability criteria (e.g. gain & phase margin). In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the SLS digital 3-axis autopilot, including existing load-relief elements, and necessary steps for integration with the production flight software prototype have been implemented. Several updates to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

VanZwieten, Tannen S.↗

NASA NTRS2014

Space Launch System Implementation of Adaptive Augmenting Control

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to provide stable and high-performance flight. On its development path to Preliminary Design Review (PDR), the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an Adaptive Augmenting Control (AAC) algorithm has been shown to extend the envelope of failures and flight anomalies the SLS control system can accommodate while maintaining a direct link to flight control stability criteria such as classical gain and phase margin. In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the full SLS digital 3-axis autopilot, including existing load-relief elements, and the necessary steps for integration with the production flight software prototype have been implemented. Several updates which have been made to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are also shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Wall, John H.↗