Engineering topics

Jason R Neuhaus

Publications and source records attributed to Jason R Neuhaus.

5 records

NASA NTRS2024

Expansion of Check-Cases for 6DOF Simulation

This effort expands upon a previous NASA activity that developed flight simulation benchmark check-cases to include new check-cases for the Cislunar domain, comparing multiple NASA simulation tools. The results of this effort describe the benefits of standardizing inputs, simulation comparisons and describe an interactive website that enables comparison of externally provided simulation data. Participating simulations improved their software and identified implementation errors. This activity elevated simulation credibility and provided a measure of validation for the simulations actively in use for NASA’s Human Landing Systems (HLS).

Modeling↗

NASA NTRS2024

Expansion of Check-Cases for 6DOF Simulation: Appendix A

This is the Appendix containing figures of simulation output data plots for comparison from the assessment, “Expansion of Check-Cases for 6DOF Simulation”. This effort expands upon a previous NASA activity that developed flight simulation benchmark check-cases to include new check-cases for the Cislunar domain, comparing multiple NASA simulation tools. The results of this effort describe the benefits of standardizing inputs, simulation comparisons and describe an interactive website that enables comparison of externally provided simulation data. Participating simulations improved their software and identified implementation errors. This activity elevated simulation credibility and provided a measure of validation for the simulations actively in use for NASA’s Human Landing Systems (HLS).

Modeling↗

NASA NTRS2024

Expansion of Check-Cases for 6DOF Simulation

This is the Appendix containing a description of the solution for Case 1 in the assessment, “Expansion of Check-Cases for 6DOF Simulation”. For cases of spherical gravity, it is possible to provide a two-body solution without recourse to numerical integration and thus it is accurate to machine precision. Python code for a Keplerian Propagator (propagate.py) which produced a reference trajectory for Case 1 is provided in this appendix. There is also code for generating test cases which was used as an independent verification of the propagator. This is a high-level description of the algorithm employed. The documentation of each function includes implementation details, including equations for each task.

Modeling↗

NASA NTRS2018

Persistent Assets in Zero-G and on Planetary Surfaces: Enabled by Modular Technology and Robotic Operations

Space operations are on the cusp of a revolutionary new operational paradigm that leverages modular systems and recurring robotic visits to “Persistent Assets” enabling asset maintenance, repair, and enhancement. A “Persistent Asset” is defined here as any zero-g or planetary surface system that benefits from in-space assembly (ISA) or multiple visits for servicing, repairs, and upgrades. This term is an extension of the term “Persistent Platform” used by Ms. Pam Melroy at the Defense Advanced Research Projects Agency to describe the vision of the Agency for a geosynchronous Earth orbiting platform. In this paper, the term “Persistent Asset” is introduced to encompass not only zero-g systems; such as telecommunication platforms, Earth observing science platform, Department of Defense platforms, and scientific telescope systems, but also planetary surface systems that support missions such as human outposts, science stations, and in-situ resource utilization systems. In contrast to the current state of the art, where space systems are typically launched as a single unit and operated without any further physical intervention after launch; future systems will be maintained, enhanced and reconfigured in-situ as new technology becomes available or mission needs change. Visits to the persistent asset can be regularly scheduled or dictated by funding constraints enabling a pay-as-you-go approach, which is largely independent of time-constraints and able to exploit launches of opportunity. In this paper, historical in-space assembly activities which relied heavily on astronaut extra-vehicular activity will be reviewed as well as early robotic assembly activities. These approaches will be contrasted with emerging modular approaches supporting realization of a new Persistent Asset operational paradigm. The paper will define attributes of the Persistent Asset paradigm and illustrate advantages by applying the paradigm to two relevant applications: 1) a large space telescope backing structure and 2) the backbone structure for a solar electric transport vehicle. Finally, recently developed unique Persistent Asset elements (modules, and interface approaches) will be described.

William R Doggett↗

NASA NTRSDate not supplied

Supervised Autonomous Assembly to Create and Evolve Persistent Assets

Supervised autonomous assembly (SAA) will create a paradigm shift in the planning and design of future persistent assets (PAs), both in near zero-g environments and on planetary surfaces. SAA refers to an autonomy approach that has the benefits of autonomous assembly as well as the benefits provided by a supervisor (operator) who is available to resolve unexpected situations. SAA provides both increased design freedom as well as reduced programmatic risk. SAA enables evolution of future PAs over decades as in-space operations transition from single purpose missions to creation of PAs, such as laboratories and experimental stations which more closely resembling terrestrial laboratories that can easily adapt and evolve to new missions leveraging repeated visits to the PA. The ability to evolve enables PAs to rapidly respond to changing objectives resulting from new questions as our understanding improves. A recently initiated National Aeronautics and Space Administration (NASA) project in the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) Program called the Precision Assembled Space Structure (PASS), leverages the advantages of SAA to develop technologies that enable efficient creation and evolution of hexagonal topologies; both planar (example: fuel depots) and curved (examples: telescopes and shelters). PASS will be used to provide context for the philosophy and concepts discussed as well as the decision and selections made. PASS objectives are: a) Develop confidence in SAA and on-orbit servicing, assembly and manufacturing (OSAM) technologies by executing a test campaign that uses a path-to-flight autonomous precision assembly process directly applicable to future space telescopes. b) Test autonomous technologies including automated path planning and error recovery, to emphasize a robust approach that relies on generic robots and special purpose tools. c) Validate critical component models using a digital twin that includes the assembled primary mirror support structure and assembly process. A digital twin is a high-fidelity simulation of the asset capable of predicting the on-orbit performance. The paper concludes after identifying the critical need for a modest assembly flight experiment to validate and develop confidence in the SAA paradigm, thus accelerating adoption of the benefits described. SAA is a game changing paradigm that enhances the ability of an organization to infuse new technology through rapid evolution of PAs while leveraging OSAM technologies.

Structural Modeling↗