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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 NTRS2007

Simulation Environment for Orion Launch Abort System Control Design Studies

The development and use of an interactive environment to perform control system design and analysis of the proposed Crew Exploration Vehicle Launch Abort System is described. The environment, built using a commercial dynamic systems design package, includes use of an open-source configuration control software tool and a collaborative wiki to coordinate between the simulation developers, control law developers and users. A method for switching between multiple candidate control laws and vehicle configurations is described. Aerodynamic models, especially in a development program, change rapidly, so a means for automating the implementation of new aerodynamic models is described.

McMinn, J. Dana↗

NASA NTRS2010

Designing and Implementing an OVERFLOW Reader for ParaView and Comparing Performance Between Central Processing Units and Graphical Processing Units

In the Applied Aerosciences and CFD branch at Johnson Space Center, computational simulations are run that face many challenges. Two of which are the ability to customize software for specialized needs and the need to run simulations as fast as possible. There are many different tools that are used for running these simulations and each one has its own pros and cons. Once these simulations are run, there needs to be software capable of visualizing the results in an appealing manner. Some of this software is called open source, meaning that anyone can edit the source code to make modifications and distribute it to all other users in a future release. This is very useful, especially in this branch where many different tools are being used. File readers can be written to load any file format into a program, to ease the bridging from one tool to another. Programming such a reader requires knowledge of the file format that is being read as well as the equations necessary to obtain the derived values after loading. When running these CFD simulations, extremely large files are being loaded and having values being calculated. These simulations usually take a few hours to complete, even on the fastest machines. Graphics processing units (GPUs) are usually used to load the graphics for computers; however, in recent years, GPUs are being used for more generic applications because of the speed of these processors. Applications run on GPUs have been known to run up to forty times faster than they would on normal central processing units (CPUs). If these CFD programs are extended to run on GPUs, the amount of time they would require to complete would be much less. This would allow more simulations to be run in the same amount of time and possibly perform more complex computations.

Chawner, David M.↗

NASA NTRS2012

Demystifying Kepler Data: A Primer for Systematic Artifact Mitigation

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like stars, Kepler has provided high quality data for many areas of investigation. Unconditioned simple aperture time-series photometry are however affected by systematic structure. Examples of these systematics are differential velocity aberration, thermal gradients across the spacecraft, and pointing variations. While exhibiting some impact on Kepler's primary science, these systematics can critically handicap potentially ground-breaking scientific gains in other astrophysical areas, especially over long timescales greater than 10 days. As the data archive grows to provide light curves for 10(exp 5) stars of many years in length, Kepler will only fulfill its broad potential for stellar astrophysics if these systematics are understood and mitigated. Post-launch developments in the Kepler archive, data reduction pipeline and open source data analysis software have occurred to remove or reduce systematic artifacts. This paper provides a conceptual primer for users of the Kepler data archive to understand and recognize systematic artifacts within light curves and some methods for their removal. Specific examples of artifact mitigation are provided using data available within the archive. Through the methods defined here, the Kepler community will find a road map to maximizing the quality and employment of the Kepler legacy archive.

Kinemuchi, K.↗

NASA NTRS2017

SPHERES and Astrobee: Space Station Robotic Free Flyers

Free-flying space robots can be used when humans are present to off-load routine work, to increase astronaut productivity, and to handle contingencies. The International Space Station (ISS), for example, is a continuously manned orbital laboratory the size of a large house, which contains many thousands of inventory items and hundreds of diverse payloads and experiments - all of which have to be managed by 6 person crew. To help with this, NASA is developing and testing robotic free-flyers on the ISS. SPHERES (Synchronized Position Hold, Engage, Reorient, Experimental Satellites) is an ISS facility with three nano-satellites designed to research estimation, control, and autonomy algorithms. SPHERES are volleyball-sized, have their own power, propulsion and navigation systems, and work on the ISS under astronaut supervision. For more than 10 years, NASA has made SPHERES available to other U.S. government agencies, schools, commercial companies and students as a platform for science, technology development, and education. SPHERES will soon be succeeded by the new Astrobee free-flying robot. Astrobee builds on the success of SPHERES, but in addition to research, the robot will also be used for housekeeping and monitoring duties without astronaut supervision. Astrobee makes extensive use of open-source (the complete software stack is available on GitHub) and is scheduled to be installed on the ISS in late Spring 2018.

Benavides, Jose V.↗

NASA NTRS2019

Data Preservation: Final Step in the Life Cycle of a Mission

Many NASA Earth Observing System (EOS) have either already reached the end of their active life or are nearing it. Preservmissionsation of data products is a fairly well defined task for the NASA EOS Data Centers or DAACs.The Goddard Earth Sciences Data and Information Services Center (GES-DISC) has implemented a repository system, which is capable of long-term archive of documentation artifacts and other associated digital content. The existing GES-DISC Repository System is based on Fedora Commons, an open-source repository management software, for cost savings and flexibility.The first mission to utilize the GES-DISC Repository System was the High Resolution Dynamics Limb Sounder (HIRDLS) on the Aura spacecraft. Since then, the GES DISC has gathered documentation from the UARS and TOMS into the Repository. The Microwave Limb Sounder (MLS) team has begun delivering some early pre-launch documents to the GES-DISC Repository System as well. Other missions in planning or progress include AIRS, OMI, SORCE, SNPP Sounder, and TRMM.

Johnson, J.↗

NASA NTRSDate not supplied

Bridging the Gap Between Microscale Modeling and Additive Manufacturing for TPS

An overview of the now open-source NASA ARC software PuMA will be provided. On top of the well-documented ability to import and compute material properties from micro-CT images, PuMA has the ability to design microstructures and compute/predict material properties such as: porosity, permeability, tortuosity, thermal and electrical conductivity, tensile strength, etc. Therefore, a whole new range of capabilities is available, and provides users with the ability to build TPS materials (both fibrous and woven types) and optimize their properties based on missions and requirements. This talk will aim to provide a seed to start bridging the gap between microscale modeling of TPS materials and additive manufacturing.

Conductivity↗

NASA NTRS2022

Solar Temperature Variations Computed from SORCE SIM Irradiances Observed During 2003-2020

NASA’s Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) instrument produced about 17 years of daily average Spectral Solar Irradiance ( SSI ) data for wavelengths 240 nm – 2416 nm. We choose a day of minimal solar activity, 2008-08-24, during the 2008 − 2009 minimum between cycles 23 and 24, and compute the brightness temperature (𝑇 o ) from that day’s solar spectral irradiance (𝑆𝑆𝐼 o ). We consider small variations of T and SSI about these reference values, and derive linear and quadratic analytic approximations by Taylor expansion about the reference day values. To determine approximation accuracy, we compare to exact brightness temperatures T computed from the Planck spectrum, by solving analytically for T , or equivalent root-finding in Wolfram Mathematica. We find that the linear analytic approximation overestimates, while the quadratic underestimates the exact result. This motivates search for statistical “fit” models “in between” the two analytic models, with minimum root-mean-square-error RMSE. We make this search using open-source statistical R software, determine coefficients for linear and quadratic fit models, and compare statistical with analytic RMSE’s. When only linear analytic and fit models are compared, the fit model is superior at ultraviolet, visible, and near infrared wavelengths. This again holds true when comparing only quadratic models. Quadratic is superior to linear for both analytic and statistical models, and statistical fits give smallest RMSE’s. Lastly, we use linear analytic and fit models to find an interpolating function in wavelength, useful in case the SIM results need adjustment to another choices of wavelengths, to compare or extend to any other instrument.

SORCE↗

NASA NTRS2021

ExEP: Initial super-Nyquist wavefront control experiments in the Decadal Survey Testbed

In order to characterize exoplanets around nearby stars, upcoming and proposed space telescopes are being designed with high contrast coronagraph instruments. Coronagraphs are generally designed to suppress only a single, on-axis star, but there are numerous binary star systems within our observational range. Multi-star wavefront correction (MSWC) is a technique that uses deformable mirror(s) to simultaneously suppress light from more than one star to create a high contrast dark zone in the image. MSWC has already been well demonstrated in simulation and in air-based testbeds. In this paper, we describe our prepatory vacuum experiments in the Decadal Survey Testbed (DST) at the Jet Propulsion Laboratory. First we implemented super-Nyquist wavefront control (SNWC, a critical component of MSWC), and then we proceeded to simultaneous sub- and super-Nyquist control. With our existing calibration methods, SNWC reached a contrast of $8.6{\times}10^{-9}$ in a 10\% bandpass around a first-order diffraction spot; calibration improvements are needed to improve model agreement for higher-order spots. We have also implemented changes to our software (the open-source package FALCO) required for MSWC, and identified the necessary changes in hardware. Here we report our best SNWC lab results and our plans for implementing MSWC during a future set of experiments.

Bendek, Eduardo↗

DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2016

Finding Stuff Underwater: Open-Source Tools for Underwater Field Science

Since 2009, we have been developing and testing Exploration Ground Data Systems (xGDS). xGDS is a software suite, built on open-source web technologies, for supporting NASA terrestrial field science analog missions. We will describe our recent work supporting underwater mapping and dive operations at the Pavilion Lake Research Project (PLRP - www.pavilionlake.com). At PLRP we used open-source mapping technologies to meet an operational need to quickly guide divers and underwater vehicles to locations of interest, and the scientific goal of building a map showing the locations and characteristics of the microbialites at the bottom of the lake. We will discuss some of the benefits and lessons-learned from our recent transition from Google Earth to OpenLayers as the primary mapping engine to support our work. Well also explain the challenges we faced and solutions weve developed to support mapping and data collection in an underwater environment.Beyond their immediate value for advancing scientific research on Earth, NASA analogs explore new ideas for supporting scientists working in extreme environments (e.g. underwater) to develop best practices for future human missions to the Moon or Mars. Science at PLRP involved imaging, sampling and characterizing microbialites growing beneath the surface of an alpine lake in British Columbia. To support PLRP operations and science, xGDS helped plan data collection tasks and provided accurate tracking and mapping of the locations of the underwater assets (human-piloted submersibles, remotely operated vehicles (ROVs) and human divers) collecting imagery and samples from the lake. The data collected during our field work was registered with precursor bathymetry data collected by an autonomous underwater vehicle (AUV) and was globally located on a satellite base layer map of the area surrounding the lake.

Earth analogs↗

NASA NTRSDate not supplied

The State of NOS3

The NASA Operational Simulator for Small Satellites (NOS3) showcases some of the Jon McBride Software Testing and Research (JSTAR) laboratories technologies on an open-source platform. NOS3 is a software digital twin providing a virtualized platform inside which you have your traditional flight software, ground software, environmental simulators, and middleware to keep all pieces in sync. NOS3 leverages the core Flight System (cFS), OpenC3 COSMOS, and NASA GSFC’s 42 software as the baseline to which additional research technologies can be developed. Current technologies to be demonstrated include NOS3 Igniter, constellation support, NASA JPL’s SYNOPSIS integration, and NASA GSFC’s OnAir. NOS3 Igniter is a GUI in which you can configure, build, and run your simulation. This along with improvements to the documentation and training available open source aims to reduce the ramp up time with new users and improve accessibility. As constellations introduce another level of complexity, it is important to ensure the baseline design reference mission covers all the basics required and allows users to experiment, understand, and test at all levels of the system. The Science Yield improvement via Onboard Prioritization and Summary of Information Systems (SYNOPSIS) is an open-source tool developed by NASA JPL to enable data prioritization and planning. GSFC’s Onboard Artificial Intelligence Research (OnAIR) enables custom algorithm development written in python to interface with the flight software allowing scientists to develop what they need for the next generation of missions and easily interface back to the traditional flight software. During the presentation, a review and demonstration of the above technologies is planned along with a roadmap.

NOS3↗

NASA NTRSDate not supplied

Open Source Principles Utilized by the CERES Edition 5 Level-3 Framework

The Clouds and the Earth’s Radiant Energy System (CERES) Science Team integrates and fuses observations from six CERES instruments aboard the Terra, Aqua, S-NPP, and NOAA-20 missions with data from twenty-five geostationary imagers, in creating a nearly 25-year, ongoing record of the Earth’s radiation budget. In preparation for the next version of CERES data products, the team has been exploring software development methods leveraging open-source principles and software to streamline data product configuration and algorithm implementation. This effort will provide robust software for production while maintaining greater flexibility for the algorithm developers to explore new science.

T. Nelson Hillyer↗

NASA NTRSDate not supplied

Open Source Principles Utilized by the CERES Edition 5 Level-3 Framework

Thomas N Hillyer↗

DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2021

Leveraging Open-Source Development to Enhance the F Prime Flight Software Framework

No abstract provided

Starch, Michael↗

NASA NTRS2013

Towards Efficient Scientific Data Management Using Cloud Storage

A software prototype allows users to backup and restore data to/from both public and private cloud storage such as Amazon's S3 and NASA's Nebula. Unlike other off-the-shelf tools, this software ensures user data security in the cloud (through encryption), and minimizes users operating costs by using space- and bandwidth-efficient compression and incremental backup. Parallel data processing utilities have also been developed by using massively scalable cloud computing in conjunction with cloud storage. One of the innovations in this software is using modified open source components to work with a private cloud like NASA Nebula. Another innovation is porting the complex backup to- cloud software to embedded Linux, running on the home networking devices, in order to benefit more users.

He, Qiming↗

DOE OSTI2024

Asynchronous GPU-based DEM solver embedded in commercial CFD software with polyhedral mesh support

A novel graphical processing unit-based discrete element method solver is introduced to improve stability, performance, and provide seamless integration into commercial or open-source computational fluid dynamics software. A key innovation is eliminating a need for network communication between solvers, which was previously required for cross-platform coupling. This is accomplished by a direct coupling method that employs dynamic-linked libraries. Furthermore, the solver optimizes memory usage by streamlining the particle-cell search algorithm by eliminating the cells' searching grid. This ensures the solver is compatible with a wide range of mesh types, providing high geometric flexibility. The approach simplifies the simulation process by directly incorporating computational fluid dynamics mesh information into the discrete element method solver. The performance analysis indicates about sixteen times boost in computational speed compared to benchmark central processing unit-based solvers. Finally, the solver's compatibility with polyhedral meshes, a vital advantage for complex geometries, is tested against a referenced study regarding the simulation of an immersed-tube fluidized bed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Software Quality Assurance for the MOOSE-Based Open-Source Multiphysics Code Cardinal - An Expanded CI Testing Suite

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing and, maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program has been initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as a safety analysis tool. These gaps have been captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents a snapshot of the verification test cases currently available for Cardinal and NekRS in their assimilation into a Continuous Integration (CI) platform. Following the CI practice permits the integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. It should be noted that the SQA program itself, including the program plans, procedures, configuration management, and testing strategies, need to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗