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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 NTRS2014

STK Integrated Message Production List Editor (SIMPLE) for CEO Operations

Late in fiscal year 2011, the Crew Earth Observations (CEO) team was tasked to upgrade and replace its mission planning and mission operations software systems, which were developed in the Space Shuttle era of the 1980s and 1990s. The impetuses for this change were the planned transition of all workstations to the Windows 7 64-bit operating system and the desire for more efficient and effective use of Satellite Tool Kit (STK) software required for reliable International Space Station (ISS) Earth location tracking. An additional requirement of this new system was the use of the same SQL database of CEO science sites from the SMMS, which was also being developed. STK Integrated Message Production List Editor (SIMPLE) is the essential, all-in-one tool now used by CEO staff to perform daily ISS mission planning to meet its requirement to acquire astronaut photography of specific sites on Earth. The sites are part of a managed, long-term database that has been defined and developed for scientific, educational, and public interest. SIMPLE's end product is a set of basic time and location data computed for an operator-selected set of targets that the ISS crew will be asked to photograph (photography is typically planned 12 to 36 hours out). The CEO operator uses SIMPLE to (a) specify a payload operations planning period; (b) acquire and validate the best available ephemeris data (vectors) for the ISS during the planning period; (c) ingest and display mission-specific site information from the CEO database; (d) identify and display potential current dynamic event targets as map features; (e) compute and display time and location information for each target; (f) screen and select targets based on known crew availability constraints, obliquity constraints, and real-time evaluated constraints to target visibility due to illumination (sun elevation) and atmospheric conditions (weather); and finally (g) incorporate basic, computed time and location information for each selected target into the daily CEO Target List product (message) for submission to ISS payload planning and integration teams for their review and approval prior to uplink. SIMPLE requires and uses the following resources: an ISS mission planning period Greenwich Mean Time start date/time and end date/time), the best available ISS mission ephemeris data (vectors) for that planning period, the STK software package configured for the ISS, and an ISS mission-specific subset of the CEO sites database. The primary advantages realized by the development and implementation of SIMPLE into the CEO payload operations support activity are a smooth transition to the Windows 7 operating system upon scheduled workstation refresh; streamlining of the input and verification of the current ISS ephemeris (vector data); seamless incorporation of selected contents of the SQL database of science sites; the ability to tag and display potential dynamic event opportunities on orbit track maps; simplification of the display and selection of encountered sites based on crew availability, illumination, obliquity, and weather constraints; the incorporation of high-quality mapping of the Earth with various satellite-based datasets for use in describing targets; and the ability to encapsulate and export the essential selected target elements in XML format for use by onboard Earth-location systems, such as Worldmap. SIMPLE is a carefully designed and crafted in-house software package that includes detailed help files for the user and meticulous internal documentation for future modifications. It was delivered in February 2012 for test and evaluation. Following acceptance, it was implemented for CEO mission operations support in May 2012.

Trenchard, Mike↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2017

GES DISC Datalist Improves Earth Science Data Discoverability

At American Geophysical Union(AGU) 2016 Fall Meeting, Goddard Earth Sciences Data Information Services Center (GES DISC) unveiled a novel way to access data: Datalist. Currently, datalist is a collection of predefined data variables from one or more archived datasets, curated by our subject matter expert (SME). Our science support team has curated a predefined Hurricane Datalist and received very positive feedback from the user community. Datalist uses the same architecture our new website uses and have the same look and feel as other datasets on our web site. and also provides a one-stop shopping for data, metadata, citation, documentation, visualization and other available services. Since the last AGU Meeting, we have further developed a few new datalists corresponding to the Big Earth Data Initiative (BEDI) Societal Benefit Areas and A-Train data. We now have four datalists: Hurricane, Wind Energy, Greenhouse Gas and A-Train. We have also started working with our User Working Group members to create their favorite datalists and working with other DAAC to explore the possibility to include their products in our datalists that may also lead to a future of potential federated (cross-DAAC) datalists. Since our datalist prototype effort was a success, we are planning to make datalist operational. It's extremely important to have a common metadata model to support datalist, this will also be the foundation of federated datalist. We mapped our datalist metadata model to the unpublished UMM(Universal Metadata Model)-Var (Variable) (June version) and found that the UMM-var together with UMM-C (Collection) and possible UMM-S (Service) will meet our basic requirements. For example: Dataset shortname, and version are already specified in UMM-C, variable name, long name, units, dimensions are all specified in UMM-Var. UMM-Var also facilitates Science Keywords to allow tagging at variable level and Characteristics for optional variable characteristics. Measurements is useful for grouping of the variables and Set is promising to define datalist. And finally, the UMM-Service model to specify the available services for the variable will be very beneficial. In summary, UMM-Var, UMM-C and UMM-S are the basis of federated datalist and the development and deployment of datalist will contribute to the evolution of the UMM.

datalist↗
NASA NTRS2010

High Resolution, Non-Dispersive X-Ray Calorimeter Spectrometers on EBITs and Orbiting Observatories

X-ray spectroscopy is the primary tool for performing atomic physics with Electron beam ion trap (EBITs). X-ray instruments have generally fallen into two general categories, 1) dispersive instruments with very high spectral resolving powers but limited spectral range, limited count rates, and require an entrance slit, generally, for EBITs, defined by the electron beam itself, and 2) non-dispersive solid-state detectors with much lower spectral resolving powers but that have a broad dynamic range, high count rate ability and do not require a slit. Both of these approaches have compromises that limit the type and efficiency of measurements that can be performed. In 1984 NASA initiated a program to produce a non-dispersive instrument with high spectral resolving power for x-ray astrophysics based on the cryogenic x-ray calorimeter. This program produced the XRS non-dispersive spectrometers on the Astro-E, Astro-E2 (Suzaku) orbiting observatories, the SXS instrument on the Astro-H observatory, and the planned XMS instrument on the International X-ray Observatory. Complimenting these spaceflight programs, a permanent high-resolution x-ray calorimeter spectrometer, the XRS/EBIT, was installed on the LLNL EBIT in 2000. This unique instrument was upgraded to a spectral resolving power of 1000 at 6 keV in 2003 and replaced by a nearly autonomous production-class spectrometer, the EBIT Calorimeter Spectrometer (ECS), in 2007. The ECS spectrometer has a simultaneous bandpass from 0.07 to over 100 keV with a spectral resolving power of 1300 at 6 keV with unit quantum efficiency, and 1900 at 60 keV with a quantum efficiency of 30%. X-ray calorimeters are event based, single photon spectrometers with event time tagging to better than 10 us. We are currently developing a follow-on instrument based on a newer generation of x-ray calorimeters with a spectral resolving power of 3000 at 6 keV, and improved timing and measurement cadence. The unique capabilities of the x-ray calorimeter spectrometer, coupled with higher spectral resolution dispersive spectrometers to resolve line blends, has enabled many science investigations, to date mostly in our x-ray laboratory astrophysics program. These include measurements of absolute cross sections for Land K shell emission from Fe and Ni, charge exchange measurements in many astrophysically abundant elements, lifetime measurements, line ratios, and wavelength measurements. In addition, we have performed many additional measurements in nuclear physics, and in support of diagnostics for laser fusion, for example. In this presentation we will give a detailed overview of x-ray calorimeter instruments in general and in our EBIT laboratory astrophysics program in particular. We will also discuss the science yield of our measurements at EBIT over the last decade) prospects for future science enabled by the current generation of spectrometers and that will be expanded in the near future by the next generation of spectrometers starting in 2611.

Porter, Frederick S.↗
NASA NTRS2022

NIAC Phase-2 Final Report for Astrophysics and Technical Lab Studies of a Solar Neutrino Spacecraft Detector

The Sun provides all the energy that our planet needs for life and has been doing so for five billion years. Understanding our Sun and its interior is one of the major goals of the NASA Science program. Still this is a very difficult task because very little makes it directly out of the Sun’s interior. The energy we see today, that warms the Earth, was made 50,000 to 80,000 years ago and is only now coming to the surface to make light. However, neutrinos penetrate matter almost without interaction and make it to Earth in only eight minutes from creation. Since neutrinos interact only weakly they are hard to detect; never-the-less within the last ten years neutrino detectors on Earth have started to reliably detect neutrinos from the fusion reactions in the interior of the Sun and scientists have started to use this information to investigate the Sun’s nuclear furnace. Changes in solar neutrino flux make it advantageous to take a neutrino detector into space since the solar neutrino intensity changes dramatically as the inverse square of the distance from the Sun, by five orders of magnitude when going from the Earth to the Sun. Launch of a neutrino detector into space toward the Sun will: a) aim to significantly increase the neutrino flux 10,000x allowing for a smaller detector which improves detector energy resolution and performance, b) attempt to completely eliminate background terrestrial neutrino sources for improved measurement accuracy, and c) conduct unique science experiments near the Sun not achievable with much larger detectors on the Earth. NASA's interest in deep space exploration has been a key factor in its unmanned spacecraft development and launch of exploration science satellites and spacecraft. NASA has done exceptional experiments in space where science benefits from the unique platform of spacecraft that provides unprecedented views. For example, the Hubble Space Telescope is really a small and very common instrument, but when it is put into an orbit high above the Earth, it becomes one of the most powerful optical observatories man has ever made. Moving neutrino observations to space is the next obvious step. The concept of putting a neutrino detector in close orbit of the sun is completely unexplored and innovative. Its scientific return is to vastly enhance the understanding of the solar interior which is a NASA major goal as stated in the decadal survey. Preliminary calculations show that such a spacecraft if properly shielded, can operate in this environment both taking data of neutrino interactions which can be distinguished from random background rates of solar Electromagnetic emissions, Galactic charged cosmic-ray, and gamma-rays by using a double pulsed signature. The NIAC Phase-1 simulations have shown this idea to be very successful in eliminating background and identifying the neutrino interaction signal, hence this spacecraft detector concept once demonstrated to be Technical Readiness Level 7 and flight mission-ready would enable a whole new type of mission to explore and study our Sun, in details that could neither be done with the largest neutrino detectors on Earth nor other types of space-craft measurements that are not using neutrino detection. Our goal in this NIAC Phase-2 was to take the detector simulation ideas and construct a prototype for testing in the lab with tagged sources to evaluate and demonstrate that the performance in the simulations are borne out by a prototype.

Neutrino↗