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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A center for commercial development of space: Real-time satellite mapping. Remote sensing-based agricultural information expert system

The research project results in a powerful yet user friendly CROPCAST expert system for use by a client to determine the crop yield production of a certain crop field. The study is based on the facts that heuristic assessment and decision making in agriculture are significant and dominate much of agribusiness. Transfer of the expert knowledge concerning remote sensing based crop yield production into a specific expert system is the key program in this study. A knowledge base consisting of a root frame, CROP-YIELD-FORECAST, and four subframes, namely, SATELLITE, PLANT-PHYSIOLOGY, GROUND, and MODEL were developed to accommodate the production rules obtained from the domain expert. The expert system shell Personal Consultant Plus version 4.0. was used for this purpose. An external geographic program was integrated to the system. This project is the first part of a completely built expert system. The study reveals that much effort was given to the development of the rules. Such effort is inevitable if workable, efficient, and accurate rules are desired. Furthermore, abundant help statements and graphics were included. Internal and external display routines add to the visual capability of the system. The work results in a useful tool for the client for making decisions on crop yield production.

Hadipriono, Fabian C.↗
NASA NTRSDate not supplied

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Isolation↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2021

Biological Carbon Sequestration for Climate and Soil Health

The United States recently launched the Net Zero World Initiative on decarbonization. As temperatures and sea levels rise, strong action is needed to mitigate high atmospheric CO 2 levels. In response to this need, a joint workshop between the US Department of Energy’s Oak Ridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) was convened to assess the challenges and opportunities within biological carbon (C) capture. The virtual workshop on Biological Carbon Sequestration for Climate and Soil Health focused on three aspects of biological C capture research: natural systems, engineered systems, and modeling. For this workshop, the scope of natural systems included unmanaged (e.g., grasslands, wetlands, natural forests) and managed (e.g., agricultural lands, commercial forests, forest biofuels) ecosystems. The engineered systems group covered topics in genetically modified plants and microbes, synthetic microbiomes, and altered plant–microbe interactions for enhancing C capture, storage, and utilization. The modeling group focused on computational tools and data collection to predict and assess the effectiveness of new biocarbon sequestration advances. Participant expertise included plant and microbial systems biology, synthetic biology, soil biogeochemistry, and techno-economic analysis. Together, participants outlined key scientific advances in support of biological C capture and storage. Among the major findings of the meeting were the need to better measure C flux within natural, agricultural, and fabricated ecosystems for experimentation; better understand the role of soil composition, plant roots, and the rhizosphere (i.e., the soil surrounding the plant root) in the underground storage of organic C; and design plant–microbe systems for maximal C storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Establishing Isotopic Measurement Capabilities using Laser-Induced Breakdown Spectroscopy for the Molten Salt Reactor Campaign

Proof-of-principle H isotope ratios of aqueous aerosol systems and liquid droplets on filter paper have been measured using laser-induced breakdown spectroscopy (LIBS) with root mean square error of prediction values down to 1.9%. Molten salt reactors (MSRs) will consist of a complex chemical and radiological system consistently producing new fission products as the reactor operates. Some of these fission products and/or their daughter species will leave the salt in the reactor off-gas. Monitoring the composition of the off-gas, as well as the salt itself, is important for monitoring reactor performance, including burnup, corrosion, and the concentration of impurities. Tritium is of concern for MSRs because it will be produced from the irradiation of key salt constituents, including Li and Be. LIBS offers an avenue for in situ salt and off-gas monitoring by firing a laser onto or into the sample stream to generate a plasma. The plasma light can be monitored to measure an elemental fingerprint of the sample. Extending this analysis to include isotopic ratios offers a critical expansion of the in situ monitoring capabilities being developed. This study demonstrates the expansion of Oak Ridge National Laboratory’s LIBS capabilities to monitor isotopes and shows how simple calibrations may provide rapid semiquantitative models.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2016

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

SMOS↗
NASA NTRS2019

Omni-gravity Hydroponics for Space Exploration

As part of the NASA Plant Water Management technology demonstration experiments, a capillary fluidics hydroponic system that can function in a variety of gravity environments has been developed and tested for crop production in space. A passive liquid delivery method is employed that drastically reduces the number of contaminable moving parts providing a high reliability solution requiring minimal resources for operation. The terrestrial, lunar, and Martian environments are managed in a ‘gravity-dominated mode,’ while the low-gravity transit and orbit environments are managed in a ‘capillary fluidics mode,’ where the role of gravity is replaced by the equally passive effects of surface tension, conduit shape, and wettability. The unique considerations for priming, germination, aeration, nutrient supply, root accommodation, layout, crew interaction, etc. are highlighted. Design guides for system function are provided along with high Technology Readiness Level demonstrations of the system during terrestrial and drop tower tests. Long duration tests are planned on short schedule aboard the International Space Station in 2019.

gravity↗
NASA NTRSDate not supplied

The System Modeling and Analysis of Resiliency in STEReO (SMARt-STEReO)

Wildfire emergency response has remained rooted in relatively low-tech solutions for coordination between ground and aerial assets. These low-tech solutions are robust for the remote environments in which wildfires are usually fought, but limit strategic cross-organizational support and the ability to deploy and effectively utilize aerial assets. As aircraft become more advanced and new technology, including drones, become available to firefighters, a new, more modern method of asset coordination is needed. NASA is working on a project called ‘Scalable Traffic Management for Emergency Response Operations’ (STEReO) to integrate unmanned aerial systems (UAS)and UAS traffic management (UTM)into wildfire response. STEReO’s goals include simplifying the coordination of aerial assets, improving the existing UAS framework, and increasing the role of additional autonomous systems to reduce human risk and to increase system resilience. This paper describes the development of the ‘System Modeling and Analysis of Resiliency in STEReO’ (SMARt-STEReO) project, which aims to model wildfire response and to quantify the additional system resilience that STEReO technology provides firefighters. This paper verifies SMARt-STEReO and defines its scope; it includes experimental and statistical analysis of the impact that the addition of UAS has on both performance metrics and also on performance resiliency response to a given fault. SMARt-STEReO is a grid-based model of fire propagation that incorporates varying crew responses. Through the use of a Python package called ‘fmdtools’, the model easily allows for the addition of faults to the system. These faults allow analysts to investigate various response parameters. Factors including terrain, fuel type and wind speed can be modified to affect the fire propagation; additionally, the number of ground crews, engines, fixed wing aircraft, helicopters, and UAS can be changed to affect the crew response. The communication lines between actors mimic those used in real life situations. This paper explains the development of SMARt-STEReO including background research, verification and validation, and preliminary experimental analysis of system resilience to both a minor and major fault in systems with and without UAS.

Resiliency↗
DOE OSTI2024

Vadose zone flushing of fertilizer tracked by isotopes of water and nitrate

Abstract A substantial fraction of nitrogen (N) fertilizer applied in agricultural systems is not incorporated into crops and moves below the rooting zone as nitrate (NO 3 − ). Understanding mechanisms for soil N retention below the rooting zone and leaching to groundwater is essential for our ability to track the fate of added N. We used dual stable isotopes of nitrate (δ 15 N–NO 3 − and δ 18 O–NO 3 − ) and water (δ 18 O–H 2 O and δ 2 H–H 2 O) to understand the mechanisms driving nitrate leaching at three depths (0.8, 1.5, and 3.0 m) of an irrigated corn field sampled every 2 weeks from 2016 to 2020 in the southern Willamette Valley, Oregon, USA. Distinct periods of high nitrate concentrations with lower δ 15 N–NO 3 − values indicated that a portion of that nitrate was from recent fertilizer applications. We used a mixing model to quantify nitrate fluxes associated with recently added fertilizer N versus older, legacy soil N during these “fertilizer signal periods.” Nitrate leached below 3.0 m in these periods made up a larger proportion of the total N leached at that depth (∼52%) versus the two shallower depths (∼13%–16%), indicating preferential movement of recently applied fertilizer N through the deep soil into groundwater. Further, N associated with recent fertilizer additions leached more easily when compared to remobilized legacy N. A high volume of fall and winter precipitation may push residual fertilizer N to depth, potentially posing a larger threat to groundwater than legacy N. Optimizing fertilizer N additions could minimize fertilizer losses and reduce nitrate leaching to groundwater.

Agriculture↗
DOE OSTI2025

Ab-initio nucleon-nucleon correlations and their impact on high energy 16 O+ 16 O collisions

Investigating nucleon-nucleon correlations inherent to the strong nuclear force is one of the core goals in nuclear physics research. We showcase the unique opportunities offered by collisions of 16 O nuclei at high-energy facilities to reveal detailed many-body properties of the nuclear ground state. We interface existing knowledge about the geometry of 16 O coming from ab-initio calculations of nuclear structure with transport simulations of high-energy 16 O+ 16 O collisions. Bulk observables in these processes, such as the elliptic flow or the fluctuations of the mean transverse momentum, are found to depend significantly on the input nuclear model and to be sensitive to realistic clustering and short-range repulsive correlations, effectively opening a new avenue to probe these features experimentally. This finding demonstrates collisions of oxygen nuclei as a tool to elucidate initial conditions of small collision systems while fostering connections with effective field theories of nuclei rooted in quantum chromodynamics (QCD).

Zhang, Chunjian [Fudan University, Shanghai (China↗
DOE OSTI2021

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

42 ENGINEERING↗
DOE OSTI2024

Reproducible growth of Brachypodium in EcoFAB 2.0 reveals that nitrogen form and starvation modulate root exudation

Understanding plant-microbe interactions requires examination of root exudation under nutrient stress using standardized and reproducible experimental systems. We grew Brachypodium distachyon hydroponically in fabricated ecosystem devices (EcoFAB 2.0) under three inorganic nitrogen forms (nitrate, ammonium, and ammonium nitrate), followed by nitrogen starvation. Analyses of exudates with liquid chromatography–tandem mass spectrometry, biomass, medium pH, and nitrogen uptake showed EcoFAB 2.0’s low intratreatment data variability. Furthermore, the three inorganic nitrogen forms caused differential exudation, generalized by abundant amino acids–peptides and alkaloids. Comparatively, nitrogen deficiency decreased nitrogen-containing compounds but increased shikimates-phenylpropanoids. Subsequent bioassays with two shikimates-phenylpropanoids (shikimic and p-coumaric acids) on soil bacteria or Brachypodium seedlings revealed their distinct capacity to regulate both bacterial and plant growth. Our results suggest that (i) Brachypodium alters exudation in response to nitrogen status, which can affect rhizobacterial growth, and (ii) EcoFAB 2.0 is a valuable standardized plant research tool.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

3P Program: Phenotyping X Prediction = Productivity (Final Scientific/Technical Report)

The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Validation of Weather Forecasting Products

Meteorological modeling plays a pivotal role in operational safety and emergency response at the Savannah River Site (SRS). This study focuses on verifying the Regional Atmospheric Modelling System (RAMS) Version 4.3 through Mean Bias Error (MBE) and Root Mean Square Error (RMSE) analyses of temperature, dew point, and wind speed over a decade. Using observed data from SRS, we assessed RAMS' accuracy, revealing seasonal biases and error trends. Results indicate RAMS' strengths in mild weather conditions but challenges during seasonal extremes and wind speed predictions due to measurement disparities. Future research aims to expand verification to other models and parameters, advocating for enhanced forecasting accuracy crucial for safeguarding personnel and community well-being.

42 ENGINEERING↗
NASA NTRS2000

Comparison of Aerosol Optical Properties and Water Vapor Among Ground and Airborne Lidars and Sun Photometers During TARFOX

We compare aerosol optical thickness (AOT) and precipitable water vapor (PWV) measurements derived from ground and airborne lidars and sun photometers during the Tropospheric Aerosol Radiative Forcing Observational Experiment. Such comparisons are important to verify the consistency between various remote sensing measurements before employing them in any assessment of the impact of aerosols on the global radiation balance. Total scattering ratio and extinction profiles measured by the ground-based NASA Goddard Space Flight Center scanning Raman lidar system, which operated from Wallops Island, Virginia (37.86 deg N, 75.51 deg W); are compared with those measured by the Lidar Atmospheric Sensing Experiment (LASE) airborne lidar system aboard the NASA ER-2 aircraft. Bias and root-mean-square differences indicate that these measurements generally agreed within about 10%. Aerosol extinction profiles and estimates of AOT are derived from both lidar measurements using a value for the aerosol extinction/backscattering ratio S(sub a) = 60 sr for the aerosol extinction/backscattering ratio, which was determined from the Raman lidar measurements. The lidar measurements of AOT are found to be generally within 25% of the AOT measured by the NASA Ames Airborne Tracking Sun Photometer (AATS-6). However, during certain periods the lidar and Sun photometer measurements of AOT differed significantly, possibly because of variations in the aerosol physical characteristics (e.g., size, composition) which affect S(sub a). Estimates of PWV, derived from water vapor mixing ratio profiles measured by LASE, are within 5-10% of PWV derived from the airborne Sun photometer. Aerosol extinction profiles measured by both lidars show that aerosols were generally concentrated in the lowest 2-3 km.

Ferrare, R.↗
NASA NTRS2013

VEG-01: Veggie Hardware Verification Testing

The Veggie plant/vegetable production system is scheduled to fly on ISS at the end of2013. Since much of the technology associated with Veggie has not been previously tested in microgravity, a hardware validation flight was initiated. This test will allow data to be collected about Veggie hardware functionality on ISS, allow crew interactions to be vetted for future improvements, validate the ability of the hardware to grow and sustain plants, and collect data that will be helpful to future Veggie investigators as they develop their payloads. Additionally, food safety data on the lettuce plants grown will be collected to help support the development of a pathway for the crew to safely consume produce grown on orbit. Significant background research has been performed on the Veggie plant growth system, with early tests focusing on the development of the rooting pillow concept, and the selection of fertilizer, rooting medium and plant species. More recent testing has been conducted to integrate the pillow concept into the Veggie hardware and to ensure that adequate water is provided throughout the growth cycle. Seed sanitation protocols have been established for flight, and hardware sanitation between experiments has been studied. Methods for shipping and storage of rooting pillows and the development of crew procedures and crew training videos for plant activities on-orbit have been established. Science verification testing was conducted and lettuce plants were successfully grown in prototype Veggie hardware, microbial samples were taken, plant were harvested, frozen, stored and later analyzed for microbial growth, nutrients, and A TP levels. An additional verification test, prior to the final payload verification testing, is desired to demonstrate similar growth in the flight hardware and also to test a second set of pillows containing zinnia seeds. Issues with root mat water supply are being resolved, with final testing and flight scheduled for later in 2013.

Massa, Gioia↗
NASA NTRS1981

Bifurcations from stationary to periodic solutions in a low-order model of forced, dissipative barotropic flow

The considered investigation is concerned with periodic solutions in the context of a forced, dissipative, barotropic spectral model truncated to three complex coefficients with constant forcing on only the intermediate scale. It is found that determining a periodic solution of this three-coefficient model also reduces to finding the algebraic roots of a real polynomial. In the derivation of this polynomial, a class of hydrodynamic spectral systems is described for which a periodic solution might be similarly specified. The existence of periodic solutions of the three-coefficient model is controlled by the roots of the stability polynomial of the basic stationary solution, which represents the simplest response to the constant forcing. When the forcing exceeds a critical value, the basic solution becomes unstable. Owing to the nature of the roots of the stability polynomial at critical forcing, bifurcation theory guarantees the existence of a periodic solution.

Mitchell, K. E.↗
NASA NTRS1980

Flexible body stability analysis of Space Shuttle ascent flight control system by using lambda matrix solution techniques

A lambda matrix solution technique is being developed to perform an open loop frequency analysis of a high order dynamic system. The procedure evaluates the right and left latent vectors corresponding to the respective latent roots. The latent vectors are used to evaluate the partial fraction expansion formulation required to compute the flexible body open loop feedback gains for the Space Shuttle Digital Ascent Flight Control System. The algorithm is in the final stages of development and will be used to insure that the feedback gains meet the design specification.

Bown, R. L.↗