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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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40 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Using Neutron Spectroscopy to Constrain the Composition and Provenance of Phobos and Deimos

The origin of the Martian moons Phobos and Deimos is obscure and enigmatic. Hypotheses include the capture of asteroids originally from the outer main belt or beyond, residual material left over from Mars' formation, and accreted ejecta from a large impact on Mars, among others. Measurements of reflectance spectra indicate a similarity to dark, red D-type asteroids, but could indicate a highly space-weathered veneer. Here we suggest a way of constraining the near-surface composition of the two moons, for comparison to known meteoritic compositions. Neutron spectroscopy, particularly the thermal and epithermal neutron flux, distinguishes clearly between various classes of meteorites and varying hydrogen (water) abundances. Perhaps most surprising of all, a rendezvous with Phobos or Deimos is not necessary to achieve this. A low-cost mission based on the LADEE spacecraft design in an eccentric orbit around Mars can encounter Phobos every 2 weeks. As few as five flyby encounters at speeds of 2.3 kilometers per second and closest-approach distance of 3 kilometers provide sufficient data to distinguish between ordinary chondrite, water-bearing carbonaceous chondrite, ureilite, Mars surface, and aubrite compositions. A one-Earth year mission design includes many more flybys at lower speeds and closer approach distances, as well as similar multiple flybys at Deimos in the second mission phase, as described in the Phobos And Deimos Mars Environment (PADME) mission concept. This presentation will describe the expected thermal and epithermal neutron fluxes based on MCNP6 (Monte Carlo N (i.e. Neutron)-Particle transport code (version 6) simulations of different meteorite compositions and their uncertainties.

thtermal and epithermal neutron fluxes↗
NASA NTRSDate not supplied

Modeling Radiation Sources for Experimental Validation

To build on previously completed experimental work, I have been working to implement a model of our lab’s Americium-Beryllium neutron source and Ludlum detection instruments computationally to provide secondary verification of our experimental results. This has been done using the radiation transport code MCNP6 (Monte Carlo N-Particle). I have developed files to calculate the mass ratio of various composite materials that we have exposed to the radiation source, as well as the components of the detection instruments and converted all of these into geometric coordinates that define the experimental setup. Identifying the proper energy distribution and detector efficiencies as well as the correct interpretation of the detector physics have proven to be the most difficult and time-consuming challenges of the work. It is currently too early for results based on the most updated and accurate models. With this experience, the next step is to develop another source model of a proton beam to simulate exposures on other composite targets. This will be developed based on existing experimental datasets, but not used to validate them. Each of these projects will further define the radiation shielding needs for humans in space radiation environments that astronauts will someday be exposed to beyond Low Earth Orbit.

Juliana Simon↗
DOE OSTI2025

Developing a Nuclear Quality Assurance Compliant Design Methodology for Neutronic Analysis of Xe-100 Design

The primary objective of this work is to develop a design methodology compliant with nuclear quality assurance standards for the Xe-100 neutronic design verification studies. To achieve this, a Monte Carlo model of the Xe-100 reactor was constructed using the exclusion principle, transformation technique, and universe-based level specification following Idaho National Laboratory (INL) NQA level-1 compliant standards and an NQA-1 compliant version of MCNP6. The model encompasses the entire reactor core structures, including the upper plenum, core region, and lower plenum sections, along with all sub-components. The active core section was represented using the spectral regions, each comprising a particular fuel composition and temperature averaged over the considered zone, calculated by X-energy using Very Superior Old Programs (VSOP). Additionally, a component-wise temperature map was implemented into the model, not only for the core region but also for the structural components. Temperature-dependent cross-section libraries, along with thermal scattering law libraries, generated using INL NQA-1 compliant version of NJOY21, were utilized for each isotope in the burnt fuel and the structural materials. Furthermore, the volume of each modeled component was estimated using a stochastic approach with the ray tracing method in MCNP and criticality calculations were performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Docker Containers for MCNP ® Development

Containers are a revolutionary technology in software development and deployment that provides a lightweight, portable environment for ensuring consistency across multiple computing environments. In anticipation of the MCNP 6.3.1 release, two Docker container images have been released on DockerHub for general use. The MCNP source code is not included in the images, and users are still required to obtain it through RSICC. The images produced by Docker are compliant with the OCI (Open Container Initiative) standards, ensuring compatibility with other container engines such as Podman or Kubernetes’ CRI-O. Initially, the images are stored under the author’s personal space on DockerHub (docker.io/azukaitis), but they will be relocated to a dedicated MCNP group space once approved. In the future, they will also be available through the registry feature of the https://github.com/lanl/mcnp-containers project. The use of Docker provides a pre-configured environment for building and running MCNP, ensuring reproducibility of results across various host architectures. This significantly improves consistency when running MCNP on different systems. Notably, executables and installers from the Docker images have successfully passed the MCNP development branch testing suite on x86-64 architectures, including Windows, macOS, and Linux operating systems. Furthermore, testing has demonstrated compatibility with macOS Docker in emulation mode on the latest Apple Mac M2 Ultra hardware, ensuring robust support even on the latest platforms. In this document, we will provide a step-by-step guide to using the Docker images across multiple platforms. Additionally, we will present performance numbers for building and running the MCNP test suite.

97 MATHEMATICS AND COMPUTING↗