Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “scientific data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Engaging Learners with NASA's Antarctic Meteorites and Apollo Moon Rocks

A primary goal of our Astromaterials Research and Exploration Science (ARES) Science Engagement team at NASA’s Johnson Space Center (JSC) has been to engage students, educators, the science community, and the public with NASA’s Astromaterials assets including Astromaterials samples, specialized facilities, and Subject Matter Experts (SMEs). Our team has facilitated this through in-person events, hands-on activities, and virtual connections with educator and student (grades 3-12+) audiences. During COVID, our team initiated an effort, focusing on NASA’s Antarctic Meteorite and Apollo Lunar Collections, to reimagine existing resources and content to facilitate access to these assets whether an educator or SME is connecting with audiences virtually or in-person, or ultimately when a learner is exploring on their own. These collections provide rich scientific data for the science community but they can also be used to help inspire the public, including the next generation of scientists.

P. V. Graff↗
NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA): Formation of an International Working Group

There is a natural partitioning of scientific interest amongst three specialties of aerosol research: modeling, in situ measurements, and remote sensing. The community sees enhanced measurement capabilities when these groups interact, and this strengthens the overall scientific impact on climate and air quality. The Models, In situ, and Remote sensing of Aerosols (MIRA) working group connects members of the different aerosol communities through collaborative projects. What is MIRA? MIRA is forum that fosters international collaborations amongst the aerosol specialties. MIRA is also a collection of interdisciplinary projects with clear goals that are pursued by small working groups. Finally, MIRA projects are generally characterized by requests for additional scientific data (both observational and modeled). Why? The purpose of MIRA is to contextualize both observations and model results through the encouragement of holistic projects and collaborations. How does MIRA differ from other projects? MIRA focuses on interdisciplinarity to improve measurements and their utility, so MIRA complements the activities of other groups. For example, ensemble model runs of AeroCom could be used in a MIRA project with greater robustness than a similar effort that uses single-model analyses. We first present a description of MIRA and how your project can become a part of the MIRA community. This is followed by brief descriptions of some of the current MIRA projects, which include the Mapping of Aerosol lidar ratios for CALIPSO (MAC), the Tables of Aerosol Optics (TAO), and the Harmonization of aerosol Assimilation Models and Retrievals (HAMR). Finally, we discuss the immediate science goals and organization of MIRA.

Gregory Schuster↗
NASA NTRS2022

On-Orbit Performance of the Spitzer Space Telescope: Science Meets Engineering

The Spitzer Space Telescope operated for over 16 years in an Earth-trailing solar orbit, returning not only a wealth of scientific data but also, as a by-product, spacecraft and instrument engineering data that will be of interest to future mission planners. These data will be particularly useful because Spitzer operated in an environment essentially identical to that at the L2 LaGrange point, where many future astrophysics missions will operate. In particular, the radiative cooling demonstrated by Spitzer has been adopted by other infrared space missions, from JWST to SPHEREx. We aim to facilitate the utility of the Spitzer engineering data by collecting the more unique and potentially useful portions into a single, readily accessible publication. We avoid discussion of less unique systems, such as the telecom, flight software, and electronics systems, and do not address the innovations in mission and science operations that the Spitzer team initiated. These and other items of potential interest are addressed in references supplied in an appendix to this paper.

Spitzer↗
NASA NTRS2022

Night Sky Testing of the Lunar Flashlight Star Tracker

Lunar Flashlight (LF) is a 6U, 14 kg spacecraft being built by the NASA Jet Propulsion Laboratory (JPL). The mission will use IR laser technology to search for volatiles and surface water ice deposits in the permanently shadowed regions (PSR) of the moon in preparation for future human lunar exploration. The fine pointing provided by the Guidance, Navigation and Control (GNC) system is crucial for the spacecraft to successfully reach the moon, enter a lunar orbit, and acquire scientific data. At the center of the GNC system’s ability to determine the spacecraft’s attitude is a star tracker. The LF star tracker is a commercial-off-the-shelf (COTS) unit built into the integrated GNC system provided by Blue Canyon Technologies. Blue Canyon tested the star tracker in a lab environment for basic functionality and attitude estimation capabilities; upon delivery of the hardware to JPL, further testing with a real night sky was desired to determine expected in-flight operational characteristics. JPL maintains the Table Mountain Facility with its suite of ground telescopes, including a 0.6m telescope with an equatorial mount. This stable platform provided a highly accurate method of providing slews and attitude holds akin to what the spacecraft will perform in flight. Therefore, by analyzing the data from these representative night-sky ground tests performed in March, 2021, it is possible to acquire expected flight performance of the star tracker, particularly in regard to the ability of the star tracker to maintain lock on the starfield during planned maneuver types. This paper presents the various tests that have been performed to ensure that the LF star tracker will function as part of the overall GNC system and integrated spacecraft once launched. Test data is included for several key tests, and a path forward towards future integration and testing at the spacecraft level is described.

Baker, John↗
NASA NTRS2023

Space Networking Implementation for Lunar Operations

The High-Rate Delay Tolerant Networking (HDTN) project at NASA has developed a performance optimized and open-source Delay Tolerant Networking (DTN) implementation. The primary goal is to create a scalable networking solution to increase the scientific data return rate of space missions. To reach this goal, HDTN must span multiple edge cases in space networking by including tools and configurations to accommodate a wide range of space systems. Typically, HDTN evaluations are conducted on a laboratory emulation test bed, made up of hardware accelerated x86 based systems capable of data rates over 10 Gbps. HDTN must have an effective implementation process on a wide range of systems to increase the sustainability of the design. One important implementation option is with low-level embedded systems which could be used on small robotic missions. This paper details the implementation process, benchmark testing, and performance results of HDTN in multiple configurations on Raspberry Pi 4 devices. By implementing HDTN on a Raspberry Pi 4, a process for building HDTN onto ARM processors was developed and utilized to conduct benchmark tests in multiple network configurations, achieving a data rate performance exceeding 600 Mbps. Based on these results, HDTN proved to run on small ARM based systems with slight modifications to the build procedure. These results were then extended to evaluating an implementation of the HDTN software parsed across several Raspberry Pi 4 nodes. To test this capability, HDTN was configured in a simplified cut-through setup and distributed among multiple Raspberry Pi 4 processors. This distributed architecture was benchmark tested in a similar fashion to the testing of a singular HDTN implementation. The results from the benchmark testing are used to examine how these implementation options and capabilities can expand the use cases for DTN, and particularly with small robotic missions.

Space Networking↗
NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA)

There is a natural partitioning of scientific interest amongst three specialties of aerosol research: modeling, in situ measurements, and remote sensing. The community sees enhanced measurement capabilities when these groups interact, and this strengthens the overall scientific impact on climate and air quality. The Models, In situ, and Remote sensing of Aerosols (MIRA) working group connects members of the different aerosol communities through collaborative projects. What is MIRA? MIRA is a forum that fosters international collaborations amongst the aerosol specialties. MIRA is also a collection of interdisciplinary projects with clear goals that are pursued by small working groups. Finally, MIRA projects are generally characterized by requests for additional scientific data (both observational and modeled). Why? The purpose of MIRA is to contextualize both observations and model results through the encouragement of holistic projects and collaborations. How does MIRA differ from other projects? MIRA focuses on interdisciplinarity to improve measurements and their utility, so MIRA complements the activities of other groups. For example, ensemble model runs of AeroCom could be used in a MIRA project with greater robustness than a similar effort that uses single-model analyses. We present a description of MIRA and brief descriptions of some of the current MIRA topics, which include Satellite-Assisted Particulate Matter (SAPM), Mapping of Aerosol lidar ratios for CALIPSO (MAC), Tables of Aerosol Optics (TAO), and the Harmonization of aerosol Assimilation Models and Retrievals (HAMR). Finally, we discuss the immediate science goals and organization of MIRA. See https://science.larc.nasa.gov/mira-wg/ for more information.

Travis Toth↗
NASA NTRS2023

First results from the Solar Orbiter Heavy Ion Sensor

Aims . Solar Orbiter launched in February 2020 with the goal of revealing the connections between the Sun’s interior, atmosphere, and the heliosphere. The Solar Orbiter Heavy Ion Sensor (HIS) is a time-of-flight ion mass spectrometer dedicated to measuring heavy ions in the solar wind. Methods . We present an overview of the first measurements of heavy ion composition from HIS, reviewing the methods used to transform the spectra obtained on board into scientific data products and examining two solar wind case studies as well as the statistical properties of the heavy ion composition observed by HIS. We also carried out a comparison with prior measurements of heavy ions at L1. Results . The HIS data set provides the first mass- and charge-resolved heavy ion measurements in the inner heliosphere. Conclusions . These high temporal resolution data have the potential to transform our understanding of the connections between the solar wind and its origin at the Sun, as well as the interaction between the solar wind and the environment around planets, comets, and in the interstellar medium.

solar wind↗
NASA NTRS2023

Expanding the Deep Space Network to Support the Heliophysics System Observatory

Difficulties associated with receiving telemetry from satellites severely limit the volume of scientific data that can be downlinked to the ground. Current missions employ techniques such as compressing and pruning datasets to reduce the data volume they transmit. While existing mission designs are already restricted by limited telemetry budgets, future Heliophysics System Observatory missions will produce ever larger data volumes with higher resolution and cadence observations from constellations of satellites spread throughout the heliosphere1. In addition, heliophysics missions often produce data for the operational Space Weather community that requires a low latency between observation and downlink. In light of current limitations, the infrastructure to receive NASA satellite telemetry must be expanded and modernized to support the science needs of future data-rich heliophysics missions.

deep space network (DSN)↗
NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote Sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that provides a forum for collaborations amongst these three atmospheric aerosol communities. MIRA consists of a collection of interdisciplinary and independently funded Topic/Project groups with clear goals and generally characterized by requests for additional scientific data. The Satellite-Assisted Particulate Matter (SAPM) Topic group focuses on the study of fine particulate matter (PM2.5), as it is a major contributor to air pollution and negatively impacts human health. Our group aims to provide intercomparisons of various methods and techniques for estimating surface PM2.5 assisted by satellite remote sensors (passive and active), global aerosol models, and in situ aerosol measurements. The overall motivation of our work is to enhance PM2.5 coverage beyond in situ ground stations, which can be limited. In this presentation, we provide an overview of various PM2.5 estimation approaches by current SAPM team members, as each approach has its own strengths and limitations. One approach uses near-surface aerosol extinction retrievals from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and an assumed value of mass extinction efficiency to derive PM2.5 concentrations (Toth et al 2019). A study using this method found a promising agreement (R=0.60; slope=0.89) with U.S. Environmental Protection Agency (EPA) in situ PM2.5 measurements over the contiguous United States (CONUS) for a 12-year (2007-2018) period (Toth et al 2022). In a different approach combining spaceborne lidar and a model, the Cloud Aerosol Transport System (CATS) lidar and Goddard Earth Observing System (GEOS) model are blended in an ensemble variational assimilation scheme to retrieve aerosol extinction and convert speciated mass concentrations to total PM2.5. A recent study (Matus et al in prep.) found agreement within 2 μg/m3 over the CONUS on the 2016 annual mean when compared to EPA ground-based measurements. Another study explored trends in city aerosol optical depth (AOD) from a spaceborne passive remote sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and found that these trends agree well with trends in city surface PM2.5, which provides confidence in the use of city AOD for city PM2.5 trend studies (Vohra et al 2021). In a follow-on study, steep and significant trends (~2.5 to 7.8% a-1) in AOD are found for several South Asian cities (e.g., Bangalore and Hyderabad), suggesting rapid growth in PM2.5 (Vohra et al 2022). Also in this presentation, we report results on our current collective SAPM study of India, a country that exhibits high levels of PM2.5 pollution. We show the temporal (i.e., seasonal) and spatial variability of PM2.5 over India using the CALIOP and CATS+model approaches, comparisons between the estimated PM2.5 from the retrieval schemes, and validation with in situ PM2.5 ground-based observations from the Central Pollution Control Board in India. This region provides us with an excellent case study to test our PM2.5 retrievals in heavily polluted scenes and over a complex and varying topography. The SAPM Topic group actively seeks international participants/collaborators in our group, including those working with in situ aerosol measurements (e.g., ACTRIS). We are interested in aerosol datasets in order to either improve our PM2.5 estimates from various approaches (e.g., using in situ mass scattering/absorption coefficient and aerosol hygroscopic properties for various aerosol species) and/or validate the PM2.5 estimates (e.g., using in situ ground-based PM2.5 concentrations).

Travis D Toth↗
DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗
DOE OSTI2026

Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice

Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies.

Amram, Oz [Fermilab] (ORCID:0000000237653123)↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Reduction and scientific analysis of data from the charge-energy-mass (CHEM) spectrometer on the AMPTE/CCE spacecraft

The Charge-Energy-Mass (CHEM) spectrometer instrument on the AMPTE/Charge Composition Explorer (CCE) spacecraft is designed to measure the mass and charge-state abundance of magnetospheric and magnetosheath ions between 0.3 and 315 keV/e, an energy range that includes the bulk of the ring current and the dynamically important portion of the plasma sheet population. Continuing research is being conducted using the AMPTE mission data set, and in particular, that of the CHEM spectrometer which has operated flawlessly since launch and still provides excellent quality data. The requirted routine data processing and reduction, and software develpment continues to be performed. Scientific analysis of composition data in a number of magnetospheric regions including the ring current region, near-earth plasma sheet and subsolar magnetosheath continues to be undertaken. Correlative studies using data from the sister instrument SULEICA, which determines the mass and charge states of ions in the energy range of approximately 10 to 250 keV/e on the IRM, as well as other data from the CCE and IRM spacecraft, particularly in the upstream region and plasma sheet have also been undertaken.

Gloeckler, G.↗
DOE OSTI2020

FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.

Underwood, Robert↗
NASA NTRS2003

Lessons Learned From Developing A Streaming Data Framework for Scientific Analysis

We describe the development and usage of a streaming data analysis software framework. The framework is used for three different applications: Earth science hyper-spectral imaging analysis, Electromyograph pattern detection, and Electroencephalogram state determination. In each application the framework was used to answer a series of science questions which evolved with each subsequent answer. This evolution is summarized in the form of lessons learned.

Wheeler. Kevin R.↗
NASA NTRS1980

The payload connection - Approaches to onboard data processing for scientific systems on the Space Shuttle

The integration of payload instruments into the Command and Data Management Subsystem (CDMS) of the Space Shuttle Spacelab is discussed. Specific emphasis is on the distributed (self-contained processor) vs centralized (Spacelab Computer) approach to science payload utilization of these control and data handling capabilities. An overview of the Spacelab CDMS is first given. A synopsis of the integration process for Spacelab Missions 1, 2, and 3 is discussed. Three different hardware approaches to payload command and data handling are then categorized from which Spacelab implementations to date are derived. The different types of Spacelab Mission 3 payload CDMS hardware and software being used are discussed, including examples of microprocessors and data acquisition systems. Preliminary costs for Spacelab application software vs other flight software approaches are compared. A standard RAU interface that would permit a range of ECOS services to be provided, yet be implemented as a mass-produceable unit for incorporation into payloads, is proposed, in keeping with the trend toward simplifying the integration effort, standardizing CDMS interfaces, and ultimately lowering cost.

Konkel, C. R.↗