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At least 91 records · Page 5

Spaceborne GPS Current Status and Future Visions

The Global Positioning System (GPS), developed by the Department of Defense, is quickly revolutionizing the architecture of future spacecraft and spacecraft systems. Significant savings in spacecraft life cycle cost, in power, and in mass can be realized by exploiting Global Positioning System (GPS) technology in spaceborne vehicles. These savings are realized because GPS is a systems sensor-it combines the ability to sense space vehicle trajectory, attitude, time, and relative ranging between vehicles into one package. As a result, a reduced spacecraft sensor complement can be employed on spacecraft and significant reductions in space vehicle operations cost can be realized through enhanced on- board autonomy. This paper provides an overview of the current status of spaceborne GPS, a description of spaceborne GPS receivers available now and in the near future, a description of the 1997-1999 GPS flight experiments and the spaceborne GPS team's vision for the future.

Bauer, Frank H.↗

Spaceborne GPS: Current Status and Future Visions

The Global Positioning System (GPS), developed by the Department of Defense is quickly revolutionizing the architecture of future spacecraft and spacecraft systems. Significant savings in spacecraft life cycle cost, in power, and in mass can be realized by exploiting GPS technology in spaceborne vehicles. These savings are realized because GPS is a systems sensor--it combines the ability to sense space vehicle trajectory, attitude, time, and relative ranging between vehicles into one package. As a result, a reduced spacecraft sensor complement can be employed and significant reductions in space vehicle operations cost can be realized through enhanced on-board autonomy. This paper provides an overview of the current status of spaceborne GPS, a description of spaceborne GPS receivers available now and in the near future, a description of the 1997-2000 GPS flight experiments, and the spaceborne GPS team's vision for the future.

Bauer, Frank H.↗

Analysis of the Tropospheric Doppler Radar Wind Profiler Measurement Accuracy

Space launch vehicle trajectory design uses atmospheric winds to determine vehicle performance and structural margin assessments prior to flight. For launches at the United States Space Force’s Eastern Range (ER) at the Cape Canaveral Space Force Station (CCSFS), tropospheric wind measurements come from either in-situ or remote sensing instrumentation. The use of the National Aeronautics and Space Administration’s (NASA) Kennedy Space Center (KSC) 48-MHz Tropospheric Doppler Radar Wind Profiler (TDRWP) has become the primary wind measurement source for several launch vehicles. Extensive evaluations of TDRWP data have occurred to quantify the system performance. This includes quantifying the accuracy of wind estimates over the entire sampling altitude as well as at discrete altitudes. This paper will describe methodology and analyses used to quantify the measurement accuracy of TDRWP data when operating in an alternative mission support mode over a 2-year period from March 2020 to March 2022.

Meghan E. Carrico↗

Analysis of the Tropospheric Doppler Radar Wind Profiler Measurement Accuracy

Space launch vehicle trajectory design uses atmospheric winds to determine vehicle performance and structural margin assessments prior to flight. For launches at the United States Space Force’s Eastern Range (ER) at the Cape Canaveral Space Force Station (CCSFS), tropospheric wind measurements come from either in-situ or remote sensing instrumentation. The use of the National Aeronautics and Space Administration’s (NASA) Kennedy Space Center (KSC) 48-MHz Tropospheric Doppler Radar Wind Profiler (TDRWP) has become the primary wind measurement source for several launch vehicles. Extensive evaluations of TDRWP data have occurred to quantify the system performance. This includes quantifying the accuracy of wind estimates over the entire sampling altitude as well as at discrete altitudes. This paper will describe methodology and analyses used to quantify the measurement accuracy of TDRWP data when operating in an alternative mission support mode over a 2-year period from March 2020 to March 2022.

Meghan E Carrico↗

NASA Advanced Concepts Office, Earth-To-Orbit Team Design Process and Tools

The Earth-to-Orbit Team (ETO) of the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) is considered the pre-eminent go-to group for pre-phase A and phase A concept definition. Over the past several years the ETO team has evaluated thousands of launch vehicle concept variations for a significant number of studies including agency-wide efforts such as the Exploration Systems Architecture Study (ESAS), Constellation, Heavy Lift Launch Vehicle (HLLV), Augustine Report, Heavy Lift Propulsion Technology (HLPT), Human Exploration Framework Team (HEFT), and Space Launch System (SLS). The ACO ETO Team is called upon to address many needs in NASA s design community; some of these are defining extremely large trade-spaces, evaluating advanced technology concepts which have not been addressed by a large majority of the aerospace community, and the rapid turn-around of highly time critical actions. It is the time critical actions, those often limited by schedule or little advanced warning, that have forced the five member ETO team to develop a design process robust enough to handle their current output level in order to meet their customer s needs. Based on the number of vehicle concepts evaluated over the past year this output level averages to four completed vehicle concepts per day. Each of these completed vehicle concepts includes a full mass breakdown of the vehicle to a tertiary level of subsystem components and a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. A structural analysis of the vehicle to determine flight loads based on the trajectory output, material properties, and geometry of the concept is also performed. Due to working in this fast-paced and sometimes rapidly changing environment, the ETO Team has developed a finely tuned process to maximize their delivery capabilities. The objective of this paper is to describe the interfaces between the three disciplines used in the design process: weights and sizing, trajectory, and structural analysis. The tools used to perform such analysis are INtegrated Rocket Sizing (INTROS), Program to Optimize Simulated Trajectories (POST), and Launch Vehicle Analysis (LVA) respectively. The methods each discipline uses to streamline their particular part of the design process will also be discussed.

Waters, Eric D.↗

NASA Advanced Concepts Office, Earth-To-Orbit Team Design Process and Tools

The Earth-to-Orbit Team (ETO) of the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) is considered the pre-eminent "go-to" group for pre-phase A and phase A concept definition. Over the past several years the ETO team has evaluated thousands of launch vehicle concept variations for a significant number of studies including agency-wide efforts such as the Exploration Systems Architecture Study (ESAS), Constellation, Heavy Lift Launch Vehicle (HLLV), Augustine Report, Heavy Lift Propulsion Technology (HLPT), Human Exploration Framework Team (HEFT), and Space Launch System (SLS). The ACO ETO Team is called upon to address many needs in NASA's design community; some of these are defining extremely large trade-spaces, evaluating advanced technology concepts which have not been addressed by a large majority of the aerospace community, and the rapid turn-around of highly time critical actions. It is the time critical actions, those often limited by schedule or little advanced warning, that have forced the five member ETO team to develop a design process robust enough to handle their current output level in order to meet their customer's needs. Based on the number of vehicle concepts evaluated over the past year this output level averages to four completed vehicle concepts per day. Each of these completed vehicle concepts includes a full mass breakdown of the vehicle to a tertiary level of subsystem components and a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. A structural analysis of the vehicle to determine flight loads based on the trajectory output, material properties, and geometry of the concept is also performed. Due to working in this fast-paced and sometimes rapidly changing environment, the ETO Team has developed a finely tuned process to maximize their delivery capabilities. The objective of this paper is to describe the interfaces between the three disciplines used in the design process: weights and sizing, trajectory, and structural analysis. The tools used to perform such analysis are INtegrated Rocket Sizing (INTROS), Program to Optimize Simulated Trajectories (POST), and Launch Vehicle Analysis (LVA) respectively. The methods each discipline uses to streamline their particular part of the design process will also be discussed.

Waters, Eric D.↗

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.↗

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. 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.3 As noted in [4] work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points.

Zwack, Mathew R.↗

A manned Mars artificial gravity vehicle

Data are presented on an artificial-gravity vehicle that is being designed for a manned Mars mission, using a 'split-mission' concept, in which an unmanned cargo vehicle is sent earlier and stored in a Mars orbit for a rendezvous with a manned vehicle about 1.5 years later. Special attention is given to the vehicle trajectory and configuration, the tether design, and the vehicle weight and launch requirements. It is shown that an artificial-G vehicle for a manned Mars missions is feasible technically and programmatically. Using an artificial-G vehicle instead of a zero-G vehicle for the piloted portion of a split mission provides physiological and human-factor-related benefits, does not eliminate requirements for zero-G countermeasures research (since zero-G is an abort mode), and could possibly reduce some life science activities. Diagrams are included.

Schultz, David N.↗

The Best Estimated Trajectory Analysis for Pad Abort One

I. Best Estimated Trajectory (BET) objective: a) Produce reconstructed trajectory of the PA-1 flight to understand vehicle dynamics and aid other post flight analyses. b) Leverage all measurement sources taken of vehicle during flight to produce the most accurate estimate of vehicle trajectory. c) Generate trajectory reconstructions of the Crew Module (CM), Launch Abort System (LAS), and Forward Bay Cover (FBC). II. BET analysis was started immediately following the PA-1 mission and was completed in September, 2010 a) Quick look version of BET released 5/25/2010: initial repackaging of SIGI data. b) Preliminary version of BET released 7/6/2010: first blended solution using available sources of external measurements. c) Final version of BET released 9/1/2010: final blended solution using all available sources of data.

Kutty, Prasad↗

Estimating Average Wind Velocity Along a Trajectory

Average Wind Velocity (VWAVE) program calculates average wind velocity over time for particular vehicle trajectory. Calculation based on wind profile, which is wind magnitude at various altitudes. Average of wind profile over altitude does not correlate well with actual apparent effect of wind. Wind profiles with low average velocities more severe than some wind profiles with high average velocities. VWAVE written in FORTRAN V for interactive execution.

Bertsch, P.↗

Predicted Weather Display and Decision Support Interface for Flight Deck

A system and method for providing visual depictions of a predictive weather forecast for in-route vehicle trajectory planning. The method includes displaying weather information on a graphical display, displaying vehicle position information on the graphical display, selecting a predictive interval, displaying predictive weather information for the predictive interval on the graphical display, and displaying predictive vehicle position information for the predictive interval on the graphical display, such that the predictive vehicle position information is displayed relative to the predictive weather information, for in-route trajectory planning.

Johnson, Walter W.↗