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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Small Spacecraft Sample Return Mission Concept to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Alan Cassell↗
DOE OSTI2026

arco (Assembled Resource-Constrained Optimization) [SWR-26-030]

Arco (Assembled Resource-Constrained Optimization) is a memory-smart optimization DSL and solver for LP and MIP problems on constrained hardware. The software is an optimization framework built around a KDL-based domain-specific language and a CLI compiler/solver. You write optimization models in .kdl files, and the arco CLI compiles, validates, inspects, and solves them. Language bindings (Python today, more planned) provide programmatic access to the same engine. Built for harder optimization problems on constrained resources, Arco is intentional about every allocation, careful with stack and heap behavior, and relentless about minimizing memory usage so more systems can run real workloads. Arco is built primarily for internal use within our organization. You are welcome to try it, but we make no guarantees about API stability or robustness at this stage

Sanchez Perez, Pedro Andres [National Laboratory o↗
NASA NTRS2014

Acoustics Research of Propulsion Systems

The liftoff phase induces high acoustic loading over a broad frequency range for a launch vehicle. These external acoustic environments are used in the prediction of the internal vibration responses of the vehicle and components. Present liftoff vehicle acoustic environment prediction methods utilize stationary data from previously conducted hold-down tests to generate 1/3 octave band Sound Pressure Level (SPL) spectra. In an effort to update the accuracy and quality of liftoff acoustic loading predictions, non-stationary flight data from the Ares I-X were processed in PC-Signal in two flight phases: simulated hold-down and liftoff. In conjunction, the Prediction of Acoustic Vehicle Environments (PAVE) program was developed in MATLAB to allow for efficient predictions of sound pressure levels (SPLs) as a function of station number along the vehicle using semi-empirical methods. This consisted of generating the Dimensionless Spectrum Function (DSF) and Dimensionless Source Location (DSL) curves from the Ares I-X flight data. These are then used in the MATLAB program to generate the 1/3 octave band SPL spectra. Concluding results show major differences in SPLs between the hold-down test data and the processed Ares I-X flight data making the Ares I-X flight data more practical for future vehicle acoustic environment predictions.

Gao, Ximing↗
NASA NTRS2014

Acoustics Research of Propulsion Systems

The liftoff phase induces some of the highest acoustic loading over a broad frequency for a launch vehicle. These external acoustic environments are used in the prediction of the internal vibration responses of the vehicle and components. Thus, predicting these liftoff acoustic environments is critical to the design requirements of any launch vehicle but there are challenges. Present liftoff vehicle acoustic environment prediction methods utilize stationary data from previously conducted hold-down tests; i.e. static firings conducted in the 1960's, to generate 1/3 octave band Sound Pressure Level (SPL) spectra. These data sets are used to predict the liftoff acoustic environments for launch vehicles. To facilitate the accuracy and quality of acoustic loading, predictions at liftoff for future launch vehicles such as the Space Launch System (SLS), non-stationary flight data from the Ares I-X were processed in PC-Signal in two forms which included a simulated hold-down phase and the entire launch phase. In conjunction, the Prediction of Acoustic Vehicle Environments (PAVE) program was developed in MATLAB to allow for efficient predictions of sound pressure levels (SPLs) as a function of station number along the vehicle using semiempirical methods. This consisted, initially, of generating the Dimensionless Spectrum Function (DSF) and Dimensionless Source Location (DSL) curves from the Ares I-X flight data. These are then used in the MATLAB program to generate the 1/3 octave band SPL spectra. Concluding results show major differences in SPLs between the hold-down test data and the processed Ares IX flight data making the Ares I-X flight data more practical for future vehicle acoustic environment predictions.

Gao, Ximing↗