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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

Overview of Ablative TPS Modeling at NASA Ames

Over the past decade, NASA has invested in efforts to build predictive thermal protection system (TPS) material models from the micro-scale to the macro-scale. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. Traditional material response and ablation modeling tools, such as the heritage code FIAT, and its multidimensional siblings, TITAN and 3dFIAT, are being complemented with newly developed software such as Icarus and PATO. Both of these programs are three-dimensional, finite-volume solvers that use unstructured meshes and 21st century programming paradigms to allow for efficient parallel simulations. FIAT and Icarus are also used for TPS sizing purposes. Today, these traditional tools are being supplemented with computational materials models at the atomistic level. The scales of interest range from computational chemistry (Density Functional Theory [DFT]), to atomistic simulations (Molecular Dynamics [MD]), to the microscale with the Porous Microstructure Analysis (PuMA) software that was recently awarded the 2022 NASA Software of the Year award. Finally, thermo-structural modeling is also of interest to the TPS Materials branch and done using commercial tools such as MSC MARC, MENTAT, NASTRAN and PATRAN. The present talk will also link the use of these computational tools to current NASA missions and projects associated with challenging and complex vehicles entries/reentries.

materials modeling↗
NASA NTRS2020

Exploring Carbon Mineral Systems: Recent Advances in C Mineral Evolution, Mineral Ecology, and Network Analysis

Large and growing data resources on the spatial and temporal diversity and distribution of the more than 400 carbon-bearing mineral species reveal patterns of mineral evolution and ecology. Recent advances in analytical and visualization techniques leverage these data and are propelling mineralogy from a largely descriptive field into one of prediction within complex, integrated, multidimensional systems. These discoveries include: (1) systematic changes in the character of carbon minerals and their networks of coexisting species through deep time; (2) improved statistical predictions of the number and types of carbon minerals that occur on Earth but are yet to be discovered and described; and (3) a range of proposed and ongoing studies related to the quantification of network structures and trends, relation of mineral “natural kinds” to their genetic environments, prediction of the location of mineral species across the globe, examination of the tectonic drivers of mineralization through deep time, quantification of preservational and sampling bias in the mineralogical record, and characterization of feedback relationships between minerals and geochemical environments with microbial populations. These aspects of Earth’s carbon mineralogy underscore the complex coevolution of the geosphere and biosphere and highlight the possibility for scientific discovery in Earth and planetary systems.

Carbon↗
NASA NTRSDate not supplied

A New Method for the Accurate Calculation of Derivatives at Structured Multiblock Grid Singularities

The new Structured Mapping At Reduced dimension Topology elements (SMART) method for the accurate calculation of derivatives at multiblock structured grid block interfaces is presented. In the SMART method, a ’local’ curvilinear mapping is defined at each interface point, giving a consistent multidimensional definition of the derivatives. The resulting system of equations is overdetermined, and a least squares method is used to determine the value of the derivatives in the local coordinate system. In the special case of a nonsingular interface point, the SMART method automatically recovers the spatial differencing stencils used at the interior points in the neighboring grid blocks.

Computational Aeroacoustics↗
NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles Using Python

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗
NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗
NASA NTRSDate not supplied

NASA’s Earth System Observatory Formulation Progress

The 2017 Earth Science Decadal Survey by the National Academies of Science, Engineering, and Medicine identified five foundational observations, or Designated Observables (DO), to be implemented as missions by NASA. To address these five DO areas – Surface Biology and Geology; Mass Change; Aerosols; Cloud, Convection, and Precipitation; and Surface Deformation and Change – NASA is developing the Earth System Observatory (ESO) as an array of Earth-focused, interconnected satellite missions. The ESO will build on the strong history of international partnerships in NASA Earth science, with initial participation and collaboration on these missions across space agency partners including the Japanese Aerospace Exploration Agency (JAXA), Centre National D'Etudes Spatiales (CNES), Canadian Space Agency (CSA), Deutsches Zentrum für Luft- und Raumfahrt (DLR), and Agenzia Spaziale Italiana (ASI). Targeting launch dates in the late 2020s and early 2030s, the ESO will work as a single observatory, with each satellite in the ESO delivering its own valuable information, but taken together, the data and imagery will provide the global community with a multidimensional, holistic view of the Earth. In addition to furthering the world’s scientific knowledge, data gathered by ESO missions will provide actionable science to inform decisions related to climate change, disaster mitigation, and wildfires, improve real-time agricultural processes, and many other applications. This paper will provide an overview of the ESO development activities, as well as an update on the individual ESO missions currently in formulation – Atmosphere Observing System, Gravity Recovery and Climate Experiment-Continuity, and Surface Biology and Geology – including current mission architectures and concepts, international partner collaboration, science community engagement, and applications efforts, as well as how ESO data will be freely open and accessible to users all over the world.

NASA Earth Science↗
NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗
NASA NTRSDate not supplied

High-Enthalpy Testing to Validate Simulation of an Aerosol Capture Probe

AERACEPT (Aerosol Rapid Analysis Combined Entry Probe/sonde Technology) is a developing technology enabling in-situ aerosol particle sampling and analysis in a small spacecraft mission envelope. It integrates a passive aerosol sample collection system into a probe’s thermal protection system (TPS) to remove the need for heat shield separation and active descent control (parachutes, gliders, etc.). AERACEPT is being validated against the requirements of the Nephele mission concept, which targets the middle and lower Venus cloud layers. The proposed technology employs 3D Carbon-Carbon (3D-CC) at the probe’s nose to withstand the extreme environments encountered in a Venus entry without producing pyrolysis gases that would contaminate the mission sample collection. Any shape change of this 3D-CC sample inlet will affect airflow through the sampling system, thereby impacting aerosol sample collection bias and efficiency. Accurate prediction of the nose’s material response during entry is there-fore of fundamental importance to the development and practical use of this technology. As a result, the AERACEPT project is planning a test campaign in the newly built PlasmatronX facility with a novel “Open Iso-Q” test article to validate state-of-the-art material response tools for a simulated Venus entry. The PlasmatronX is a 350 kW inductively-coupled plasma facility developed and run by the Center for Hypersonics and Entry Systems Studies at the University of Illinois at Urbana-Champaign. The facility can support ground testing for a variety of planetary destinations by simulating entry conditions in Nitrogen, Air, and Carbon Dioxide. To best simulate the Venus atmosphere and match flight-like recession, the planned AERACEPT test campaign will use Carbon Dioxide as its test gas. AERACEPT has designed two test articles for this campaign: 1) a standard test article whose curved surface approximates a constant applied heat flux (“Closed Iso-Q” model) and 2) a novel inlet test article with a through-hole at the stagnation point (“Open Iso-Q” model). Both article types consist of a 3D-CC sample bonded to a graphite fixture. All articles will be laser scanned before and after testing to estimate shape change and recession, and the “Closed Iso-Q” articles will be instrumented with thermocouples to provide temperature histories. The test article temperatures and inlet shape change will be compared against simulations run with the Porous material Analysis Toolbox based on OpenFOAM (PATO) to validate the AERACEPT 3D-CC material response model. Due to the high thermal conductivity of 3D-CC, multidimensional effects have a large influence on the test article temperature and the standard 1-D material response tools are not sufficient for test planning. The test campaign features (e.g. article design, run duration, heat fluxes) are therefore informed by a series of 3-D simulations using state-of-the-art material response tools such as PATO. The test campaign is scheduled for spring of 2024. Expected results include 1) Key drivers of selected test conditions (e.g. matching mission flight atmosphere & recession), 2) Simulation results that informed test article design, 3) Acquired test data such as recession measurements, temperature histories, and photos, and 4) Comparisons between test results and simulation predictions.

AERACEPT↗
NASA NTRSDate not supplied

High-Enthalpy Testing to Validate Simulation of an Aerosol Capture Probe

AERACEPT (Aerosol Rapid Analysis Combined Entry Probe/sonde Technology) is a developing technology enabling in-situ aerosol particle sampling and analysis in a small spacecraft mission envelope. It integrates a passive aerosol sample collection system into a probe’s thermal protection system (TPS) to remove the need for heat shield separation and active descent control (parachutes, gliders, etc.). AERACEPT is being validated against the requirements of the Nephele mission concept, which targets the middle and lower Venus cloud layers. The proposed technology employs 3D Carbon-Carbon (3D-CC) at the probe’s nose to withstand the extreme environments encountered in a Venus entry without producing pyrolysis gases that would contaminate the mission sample collection. Any shape change of this 3D-CC sample inlet will affect airflow through the sampling system, thereby impacting aerosol sample collection bias and efficiency. Accurate prediction of the nose’s material response during entry is there-fore of fundamental importance to the development and practical use of this technology. As a result, the AERACEPT project is planning a test campaign in the newly built PlasmatronX facility with a novel “Open Iso-Q” test article to validate state-of-the-art material response tools for a simulated Venus entry. The PlasmatronX is a 350 kW inductively-coupled plasma facility developed and run by the Center for Hypersonics and Entry Systems Studies at the University of Illinois at Urbana-Champaign. The facility can support ground testing for a variety of planetary destinations by simulating entry conditions in Nitrogen, Air, and Carbon Dioxide. To best simulate the Venus atmosphere and match flight-like recession, the planned AERACEPT test campaign will use Carbon Dioxide as its test gas. AERACEPT has designed two test articles for this campaign: 1) a standard test article whose curved surface approximates a constant applied heat flux (“Closed Iso-Q” model) and 2) a novel inlet test article with a through-hole at the stagnation point (“Open Iso-Q” model). Both article types consist of a 3D-CC sample bonded to a graphite fixture. All articles will be laser scanned before and after testing to estimate shape change and recession, and the “Closed Iso-Q” articles will be instrumented with thermocouples to provide temperature histories. The test article temperatures and inlet shape change will be compared against simulations run with the Porous material Analysis Toolbox based on OpenFOAM (PATO) to validate the AERACEPT 3D-CC material response model. Due to the high thermal conductivity of 3D-CC, multidimensional effects have a large influence on the test article temperature and the standard 1-D material response tools are not sufficient for test planning. The test campaign features (e.g. article design, run duration, heat fluxes) are therefore informed by a series of 3-D simulations using state-of-the-art material response tools such as PATO. The test campaign is scheduled for spring of 2024. Expected results include 1) Key drivers of selected test conditions (e.g. matching mission flight atmosphere & recession), 2) Simulation results that informed test article design, 3) Acquired test data such as recession measurements, temperature histories, and photos, and 4) Comparisons between test results and simulation predictions.

AERACEPT↗
NASA NTRSDate not supplied

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗
NASA NTRSDate not supplied

Anticipating Hazard Impacts through Capacity Building and Co-development

Improving forecast accuracy, extending lead time, understanding hazard susceptibility, and integrating exposure and vulnerability data to generate impact-based forecasts are critical in mitigating disaster impacts. These efforts enable anticipatory action through enhancing the efficacy of early warning systems to assess potential multi-dimensional hazard impacts. In response to this need, SERVIR has co-developed a series of hazard services providing vital information from national to regional scales. This poster showcases examples of geospatial services co-developed with partners through a capacity building approach, supporting the establishment of hazard early warning / early action systems. These services integrate multidimensional vulnerability and exposure data to comprehensively assess impacts. Furthermore, these services demonstrate how co-development and capacity building can advance the development of impact-based forecasts and multi-hazards services. By prioritizing collaboration, human insights, local knowledge, capacity building, and employing applied science approaches in geospatial service development, this work has helped create inclusive and customized solutions. These solutions are tailored to meet the needs of local communities and are readily adaptable into decision-making processes.

weather↗
NASA NTRSDate not supplied

Swift Mission Gyro Patch and Re-Calibration Without A Calibration Campaign

The Swift spacecraft has three Two-Axis-Rate-Assemblies (TARAs) (designated as TARA 1, TARA 2, and TARA 3, and generically referred to as gyros) which until March of 2024 were all used in rate estimation as an operational workaround (dubbed “corrector-gyro”) of a software defect discovered in 2007. TARA 1 performance degraded throughout 2023 and 2024, making it an unreliable rate source. It was determined that shifting TARA 1 to be the corrector-gyro and using mainly TARAs 2 and 3, with the existing flight software (FSW) defect, would not meet performance requirements for science operations. Rather the FSW was patched to remove the defect found in 2007. This patch was delivered by the vendor in 2009, but was not fully tested or installed at that time. A decision was made in 2024 to install the patch despite limited simulation and testing capabilities. The problem facing the team was to come up with a set of alignment parameters that would work with the new patch, using data from the spacecraft operating normally in the old configuration. Calibration activities would have been possible in either the old or new FSW configuration, but were ultimately deemed unnecessary, saving staff time and pre-serving time on the spacecraft for science data collection. This paper describes the old and new FSW configurations related to gyro processing and alignment, and how the raw gyro and star tracker data from normal operation were used with a multidimensional unconstrained nonlinear minimization in MATLAB (Nelder-Mead1) to produce a fully calibrated set of gyro alignment parameters compatible with the patched FSW. Flight data is presented showing that not only did the process work, it improved performance significantly, rivaling the best slew performance of the mission to date.

MATLAB↗
NASA NTRS2026

Mathematical Model of a Regenerative Fuel Cell for System Optimization

This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.

Electrochemistry↗
DOE OSTI2016

GazeAppraise v. 0.1

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths. An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.

Wilson, Andrew↗
DOE OSTI2016

Pole Figure Explorer v. 1.8

This software is employed for 3D visualization of X-ray diffraction (XRD) data with functionality for slicing, reorienting, isolating and plotting of 2D color contour maps and 3D renderings of large datasets. The program makes use of the multidimensionality of textured XRD data where diffracted intensity is not constant over a given set of angular positions (as dictated by the three defined dimensional angles of phi, chi, and two-theta). Datasets are rendered in 3D with intensity as a scaler which is represented as a rainbow color scale. A GUI interface and scrolling tools along with interactive function via the mouse allow for fast manipulation of these large datasets so as to perform detailed analysis of diffraction results with full dimensionality of the diffraction space.

Van Benthem, Mark H.↗
DOE OSTI2016

Leveraging Interaction History for Intelligent Configuration of Multiple Coordinated Views in Visualization Tools

Visualization tools can take advantage of multiple coordinated views to support analysis of large, multidimensional data sets. Effective design of such views and layouts can be challenging, but understanding users analysis strategies can inform design improvements. We outline an approach for intelligent design configuration of visualization tools with multiple coordinated views, and we discuss a proposed software framework to support the approach. The proposed software framework could capture and learn from user interaction data to automate new compositions of views and widgets. Such a framework could reduce the time needed for meta analysis of the visualization use and lead to more effective visualization design.

infovis↗
DOE OSTI2016

Modelling of LOCA Tests with the BISON Fuel Performance Code

BISON is a modern finite-element based, multidimensional nuclear fuel performance code that is under development at Idaho National Laboratory (USA). Recent advances of BISON include the extension of the code to the analysis of LWR fuel rod behaviour during loss-of-coolant accidents (LOCAs). In this work, BISON models for the phenomena relevant to LWR cladding behaviour during LOCAs are described, followed by presentation of code results for the simulation of LOCA tests. Analysed experiments include separate effects tests of cladding ballooning and burst, as well as the Halden IFA-650.2 fuel rod test. Two-dimensional modelling of the experiments is performed, and calculations are compared to available experimental data. Comparisons include cladding burst pressure and temperature in separate effects tests, as well as the evolution of fuel rod inner pressure during ballooning and time to cladding burst. Furthermore, BISON three-dimensional simulations of separate effects tests are performed, which demonstrate the capability to reproduce the effect of azimuthal temperature variations in the cladding. The work has been carried out in the frame of the collaboration between Idaho National Laboratory and Halden Reactor Project, and the IAEA Coordinated Research Project FUMAC.

BISON code↗
DOE OSTI2017

Bellerophon Environment for Analysis of Materials (BEAM)

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now--with the rise of multimodal acquisition systems and the associated processing capability--the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation and manage uploaded data files via an intuitive, cross-platform client user interface. This framework delivers authenticated, "push-button" execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing compute-and-data cloud infrastructures and HPC environments like Titan at the Oak Ridge Leadershp Computing Facility (OLCF).

Lingerfelt, Eric J↗