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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 NTRS2005

Recent Progress on the Stretched Lens Array (SLA)

At the last Space Photovoltaic Research and Technology Conference, SPRAT XVII, held during the fateful week of 9/11/01, our team presented a paper on the early developments related to the new Stretched Lens Array (SLA), including its evolution from the successful SCARLET array on the NASA/JPL Deep Space 1 spacecraft. Within the past two years, the SLA team has made significant progress in the SLA technology, including the successful fabrication and testing of a complete four-panel prototype solar array wing (Fig. 1). The prototype wing verified the mechanical and structural design of the rigid-panel SLA approach, including multiple successful demonstrations of automatic wing deployment. One panel in the prototype wing included four fully functional photovoltaic receivers, employing triple-junction solar cells.

O'Neill, Markl↗
NASA NTRS2016

Pick and Eat Crop Testing: Dwarf Tomato and Pepper as Candidate Space Crops

Several dwarf tomato and pepper varieties were evaluated under International Space Station (ISS)-simulated growth conditions (22 degrees Centigrade, 50 percent relative humidity, 1500 parts per million CO2, and 300 micromoles per square meter per second of light for 16 hours per day) with the goal of selecting those with the best growth, nutrition, and organoleptic potential for use in a pick and eat salad crop system on ISS and future exploration flights. Testing included six cultivars of tomato (Red Robin, Scarlet Sweet 'N' Neat, Tiny Tim, Mohamed, Patio Princess, and Tumbler) and six cultivars of pepper (Red Skin, Fruit Basket, Cajun Belle, Chablis, Sweet Pickle, and Pompeii). Plants were grown to an age sufficient to produce fruit (up to 106 days for tomato and 109 days for pepper) using Turface (arcillite) potting media with 18-6-8 control-release fertilizer and supplemental nutrient solution beginning around 60-days-age. Tomato fruits were harvested when they showed full red color, beginning around 70-days age and then at weekly intervals thereafter, while peppers were grown until fruits showed color and were harvested twice (first test) and just once at the end of the second test, with the final harvests including colored and green fruit. Plant sizes, yields, and nutritional attributes were measured and used to down-select to three cultivars for each species. In particular, we were interested in cultivars that were short (dwarf) but still produced high yields. Nutritional data included elemental (Ca, Mg, Fe, and K) content, vitamin K, phenolics, lycopene (for tomato), anthocyanin, lutein, and zeaxanthin. The three down-selected cultivars for each species were grown again and the harvested fruit sent to NASA's Johnson Space Center for sensory evaluation, which included overall acceptability, appearance, color intensity, aroma, flavor and texture. The combined data were compared and given weighting factors to rank the cultivars as candidates for testing in space. Weightings gave maximum importance to plant size (smaller being good) and fruit yield (greater yields being good). For tomato, the ranking was 1) cultivar Mohamed and cultivar Red Robin (tied), and 3) cultivar Sweet N' Neat. For pepper, the ranking was 1) cultivar Pompeii, 2) cultivar Red Skin, and 3) cultivar Fruit Basket. These rankings are somewhat subjective but provide a starting point for conducting higher fidelity testing with these crops (e.g., testing with light emitting diode lighting similar to the Veggie plant unit on ISS), and ultimately conducting a flight experiment.

Food↗
NASA NTRS2019

Dwarf Tomato and Pepper Cultivars for Space Crops

Several dwarf tomato and pepper varieties were evaluated under ISS-simulated growth conditions (22ºC, 50% RH, 1500 ppm CO2, and 300 μmol m(exp -2) s(exp -1) of light for 16 h per day) with the goal of selecting those with the best growth, nutrition, and organoleptic potential for use in a pick and eat salad crop system on ISS and future exploration flights. Testing included six cultivars of tomato (Red Robin, Scarlet Sweet ’N’ Neat, Tiny Tim, Mohamed, Patio Princess, and Tumbler) and six cultivars of pepper (Red Skin, Fruit Basket, Cajun Belle, Chablis, Sweet Pickle, and Pompeii). Plants were grown to an age sufficient to produce fruit (70 to 106 days for tomato and 109 days for pepper). Tomato fruits were harvested when they showed full red color, beginning ca. 70-days age and then at weekly intervals thereafter, while peppers were grown until numerous fruits showed color and all fruits (green and colored) were harvested once at the end of the test. Plant sizes, yields, and nutritional attributes were measured and used to down-select to three cultivars for each species. In particular, we were interested in cultivars that were short (dwarf) but still produced high yields. Nutritional data included elemental (Ca, Mg, Fe, and K) composition, vitamin K, phenolics, lycopene, anthocyanin, lutein, and zeaxanthin. The three down-selected cultivars for each species were evaluated for sensory attributes, including overall acceptability, appearance, color intensity aroma, flavor and texture. The combined data were compared and given weighting factors to rank the cultivars as potential candidates for testing in space. For tomato, the ranking was 1) cv. Mohamed, 2) cv. Red Robin, and 3) cv. Sweet N’ Neat. For pepper, the ranking was 1) cv. Pompeii, 2) cv. Red Skin, and 3) cv. Fruit Basket. These rankings are somewhat subjective but provide a good starting point for conducting higher fidelity testing with these crops (e.g., testing with LED lighting similar to the Veggie plant unit), and ultimately conducting flight experiments.

Spencer, LaShelle E.↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment outside the International Space Station

If crop seeds are to be stored on space vehicles for long-duration missions, the effects of the space environment on the seeds must be understood for developing mitigation strategies to prevent seed degradation. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also tested the Materials International Space Station Experiment (MISSE) platform for exposing biological samples to the space environment and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seeds, or other biological samples, to be preserved upon exposure to the space environment outside the ISS. The experiment was launched to the ISS on NG-15and returned to Earth aboard SpX-24. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). The exposure lasted eight months outside the ISS in the MISSE hardware at the Zenith position. Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record the environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. In this presentation, the hardware and experimental design, the environmental profiles, and the results of post-flight germination tests will be presented.

Jeffrey T Richards↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment Outside the International Space Station

If crop seeds are to be stored on space vehicles for long-duration missions, the effects of the space environment on the seeds must be understood for developing mitigation strategies to prevent seed degradation. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also tested the Materials International Space Station Experiment (MISSE) platform for exposing biological samples to the space environment and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seeds, or other biological samples, to be preserved upon exposure to the space environment outside the ISS. The experiment was launched to the ISS on NG-15and returned to Earth aboard SpX-24. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). The exposure lasted eight months outside the ISS in the MISSE hardware at the Zenith position. Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record the environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. In this presentation, the hardware and experimental design, the environmental profiles, and the results of post-flight germination tests will be presented.

Jeffrey T Richards↗
NASA NTRSDate not supplied

Exposure of Plant Seeds to the Space Environment Outside the International Space Station

Crop seeds may prove to be a vital component of future long-term spaceflight missions, and the impacts of extended exposure on seed degradation is not well understood. The MISSE-Seed project was designed to investigate the effects of space exposure on seed quality and storage. The project also served as a test of biological materials aboard the Materials International Space Station Experiment (MISSE) platform, and evaluated the capability of a newly designed passive sample containment vessel for the provision of acceptable storage conditions for seed and other biological samples to be preserved upon exposure to the space environment outside the ISS. MISSE-Seed was launched to the ISS on NG-15 in July of 2021 and returned to Earth aboard SpX-24 after approximately 8 months of exposure to the space environment. The specimens consisted of eleven seed varieties (Lettuce, Scarlet Frills, Amara, Garnet Giant, Pac Choi, Radish, Mizuna, Tomato, Cauliflower, Pepper, and Arabidopsis). Data-loggers and thermoluminescent dosimeters (TLDs) were included in each container to record environmental data. Temperature profiles and radiation/UV exposure data were also provided by Aegis Aerospace, the MISSE Implementation Partner. Hardware and experimental design, environmental profiles, and post-flight germination and growth results will be presented.

Alexander D Meyers↗
DOE OSTI2022

Denoising Convolutional Networks to Accelerate Detector Simulation

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNNs) to "denoise" faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

Banerjee, Sunanda↗
DOE OSTI2022

Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Experiment Logistics for an International Blind Intercomparison Exercise for Nuclear Accident Dosimetry at the Armed Forces Radiobiology Research Institute's TRIGA Reactor

This document is the Experimental Set-up and Design (CED-3a) Report for IER-602, “Dosimetry Exercise with Armed Forces Radiobiology Research Institute (AFRRI) - Exercise” The report discusses the structure of the exercise consisting of three reactor exposures, identifying the participating laboratories and their points of contact. The report also includes details of all dosimetry each laboratory will submit to be placed in proximity to AFRRI on aluminum plates or BOMAB phantoms. Each laboratory lists the counting and spectroscopy equipment to be utilized at AFFRI. The exercise is tentatively scheduled for one week in June, 2024 (FY24 Q4).

42 ENGINEERING↗
DOE OSTI2024

Initial Results of the Nuclear Accident Dosimetry Intercomparison at the Armed Forces Radiobiology Research Institute's TRIGA Reactor

This report presents the initial results for IER-602, the international nuclear accident dosimetry intercomparison performed at the Armed Forces Radiobiology Research Institute (AFRRI). Ten groups participated (six DOE laboratories, three international groups, and one DOD group) in a week-long exercise in late June 2024. Two additional groups observed the exercise (one DOE laboratory, one university). Three days of irradiations were performed, and participants reported results within 24 hours of the irradiations. Results were compared to the reference values and performance criteria established by IER-484 AFRRI ER1 Dosimetry Characterization and ANSI/HPS N13.3-2013, respectively. Passing performance varied from 17% to 100%, with an average of 67%.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Updated Results of the Nuclear Accident Dosimetry Intercomparison at the Armed Forces Radiobiology Research Institute’s TRIGA Reactor (IER-602 CED-4a Report)

The IER-602 3b document (Angus, et al. 2024) reported the initial, 24-hour results of the international nuclear accident intercomparison, which took place at the Armed Forces Radiobiological Research Institute during June 24-28, 2024. This report provides updated results and further analysis of the intercomparison.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
NASA NTRS2022

Northeast US Ecological Forecasting: Modeling Invasive Plant Habitat Suitability to Support Management Efforts in the American Northeast

Invasive plant species threaten environmental and economic interests when they spread into new areas, outcompete native species, and disrupt ecosystem services. If the spread is not controlled early, species can become well-established and increasingly difficult to manage. The National Park Service (NPS) Invasive Plant Management Teams (IPMTs) strive for an “early detection, rapid response” approach to reducing invasive species spread. Management teams can better prioritize their work with the help of species distribution models (SDMs), which map habitat suitability by combining species occurrences with environmental predictor variables. Scarce invaded range data for newly arrived invasive species presents a particular challenge for producing accurate models. To improve future modeling efforts, this project compared SDM methods using different spatial scales to model two plant species invasive to the Northeast US: the well-established Japanese stiltgrass (Microstegium vimineum) and newer invasive species wavyleaf basketgrass (Oplismenus undulatifolius). The team used NASA Earth observations and climate datasets to model occurrence data and predictor layers at a US-specific extent (90m2 spatial resolution) and global extent (1 km2 spatial resolution). Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) provided data for US Normalized Difference Moisture Indices (NDMI), while global NDMI and topographic predictor layers were derived from Shuttle Radar Topography Mission (SRTM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting models indicated important predictor variables for each species and explored the benefits and tradeoffs of using global data to model habitat suitability for new-arrival invasive species.

Rebecca Ohman↗
NASA NTRS2022

Grand Canyon Ecological Forecasting: Using NASA Earth Observations to Monitor and Model Juniper Woodland Mortality in Grand Canyon National Park

Significant die-offs of the drought tolerant species Utah juniper (Juniperus osteosperma) and one-seeded juniper (Juniperus monosperma) have been observed throughout central and northern Arizona, including Grand Canyon National Park (GCNP). As climate models project rising temperatures and continuous drought, land managers are concerned for the future of juniper in and around GCNP. This project incorporated data from Landsat 8 Operational Land Imager (OLI), the Shuttle Radar Topography Mission (SRTM), and ocular samples of the National Agriculture Imagery Program (NAIP) in a random forest model to identify patterns between characteristics of the landscape and locations of juniper woodland mortality and to model areas subject to vulnerability. This study found no significant correlation between ocularly sampled juniper tree mortality and remotely sensed environmental variables used thus, accurately modeling mortality vulnerability in the future was not feasible. The ocular sampling, however, allows the partners at the GCNP’s Science & Resource Management Division to better understand areas of juniper tree woodland mortality and the relative amount of mortality in the park. Additionally, the areas of juniper tree mortality found in this project provide the partners with guidance for future field sampling.

Scarlet Jackson↗