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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Development and Demonstration of Sustainable Surface Infrastructure for Moon/Mars Exploration

For long-term human exploration of the Moon and Mars to be practical, affordable, and sustainable, future missions must be able to identify and utilize resources at the site of exploration. The ability to characterize, extract, processes, and separate products from local material, known as In-Situ Resource Utilization (ISRU), can provide significant reductions in launch mass, logistics, and development costs while reducing risk through increased mission flexibility and protection as well as increased mission capabilities in the areas of power and transportation. Making mission critical consumables like propellants, fuel cell reagents and life support gases, as well as in-situ crew/hardware protection and energy storage capabilities can significantly enhance robotic and human science and exploration missions, however other mission systems need to be designed to interface with and utilize these in-situ developed products and services from the start or the benefits will be minimized or eliminated. This requires a level of surface and transportation system development coordination not typically utilized during early technology and system development activities. An approach being utilized by the US National Aeronautics and Space Administration and the Canadian Space Agency has been to utilize joint analogue field demonstrations to focus technology development activities to demonstrate and integrate new and potentially game changing. mission critical capabilities that would enable an affordable and sustainable surface infrastructure for lunar and Mars robotic and human exploration. Two analogue field tests performed in November 2008 and February 2010 demonstrated first generation capabilities for lunar resource prospecting, exploration site preparation, and oxygen extraction from regolith while initiating integration with mobility, science, fuel cell power, and propulsion disciplines. A third analogue field test currently planned for June 2012 will continue and expand the fidelity and integration of these surface exploration and infrastructure capabilities while adding Mars exploration technologies, improving remote operations and control of hardware, and promoting the use of common software, interfaces, & standards for control and operation for surface exploration and science. The next field test will also attempt to include greater involvement by industry, academia, and other countries/space agencies. This paper will provide an overview of the development and demonstration approach utilized to date, the results of the previous two ISRU-focused field analogue tests in Hawaii, and the current objectives and plans for the 3rd international Hawaii analogue field test.

Sanders, Gerald B.↗
DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

PRIME

SAND2021-0565 O PRIME is a modeling framework designed for the real-time characterization and forecasting of partially observed epidemics. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. Examples are provided using publicly available COVID-19 data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗
DOE OSTI2026

Analyzing the Free States of one Quantum Resource Theory as Resource States of Another

In the context of quantum resource theories (QRTs), free states are defined as those that can be obtained at no cost under a certain restricted set of conditions. However, when taking a free state from one QRT and evaluating it through the optics of another QRT, it might well turn out that the state is now extremely resourceful. Such realization has recently prompted numerous works characterizing states across several QRTs. Here, in this work, we contribute to this body of knowledge by analyzing the resourcefulness in free states for—and across witnesses of—the QRTs of multipartite entanglement, fermionic non-Gaussianity, imaginarity, realness, spin coherence, Clifford non-stabilizerness, $S_n$-equivariance, and non-uniform entanglement. We provide rigorous theoretical results as well as present numerical studies that showcase the rich and complex behavior that arises in this type of cross-examination.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1988

Learning from examples - Generation and evaluation of decision trees for software resource analysis

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

Selby, Richard W.↗
DOE OSTI2021

Oilwell Conversion (Well API 121913310501) to Geothermal Heat Storage Well for Flexible Electricity Storage

Geothermal growth is limited by a lack of geographically dispersed high-temperature thermal resources and high initial upfront investment in characterization and well construction. This project intended to address the challenges of energy supply intermittency and enhance grid resilience, reliability, and energy security by storing energy provided from currently available renewable resources in the subsurface to harvest it a later time during at-peak energy demand. This project intended to improve geothermal adoption, reduce initial project risk, and improve price competitiveness through utilizing existing oil and gas infrastructure such as non-productive wells, non-economic fields, dry holes, and orphaned wells. The project also intended to address the lack of geographically dispersed thermal resources and enhance grid resilience, reliability, and energy security by introducing an economical method for storing energy from currently available renewable resources in the subsurface for usage during at-peak energy demand. During this research, the project furthered the understanding of the feasibility of utilizing abandoned oil and gas wells as geothermal heat storage wells. The project team investigated the heat storage and hydrogeological characteristics of subsurface reservoirs in the Illinois Basin to evaluate their response to heat injection for determining the evolution of temperature profiles and heat losses over time using existing and available data sets. The project team then performed modeling and simulation to evaluate the heat losses of returning fluids during heat extraction. The outputs were used to select an optimal candidate reservoir and location in Southern Illinois. The team designed and performed a small-scale field test in an existing oil well to refine the model and to demonstrate the permitting and regulatory pathways necessary for the conversion of oil and gas assets to geothermal use. The field test also serves as a proof of concept and can guide the procedures for future research and implementation. Additionally, the project team, conducted initial market research and customer discovery to develop a go to market strategy for an Advanced Geothermal Energy Storage (AGES) system. The project team in this research also identified the parameters to be refined in future research, to improve the current go to market strategy economic model. To this end several subject matter experts were also identified to assist in future research with geothermal infrastructure setup, energy storage policy and law, energy storage market demand, potential siting based on demand etc. Future research will involve further sophistication of the site commercial modeling, implementing a larger-scale test, and further refinement of the thermodynamic modeling/simulation process. The output will be lifecycle costs and economics suitable for comparison to alternative approaches from a validated full-scale demonstration for venture capital investment into this technology. The project successfully demonstrated the ability to leverage existing oilfield infrastructure, permits, and land access and leasing agreements, to enable geothermal storage projects to come online faster and cheaper than a greenfield development could. This technology could allow for greater energy independence and security through long-term energy storage solutions. The longer duration allows for greater storage for renewables currently limited by hours-long storage durations of lithium-ion. The AGES system would support the growth of renewable energy farms, and provide greater opportunities for a cleaner energy infrastructure.

15 GEOTHERMAL ENERGY↗
NASA NTRS1993

Population-based learning of load balancing policies for a distributed computer system

Effective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. We present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtained prior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our load-balancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterization, is effective in exploiting idle resources in a distributed computer system.

Mehra, Pankaj↗
DOE OSTI2021

A National Roadmap for Grid-Interactive Efficient Buildings

The way electricity is generated and consumed in the US is quickly changing, including in terms of the rapid growth in variable power generation resources and the need for large-scale investments to replace aging infrastructure and modernize the grid. Buildings that coordinate electricity use with grid conditions are a flexible and cost-effective resource to address the evolving power system challenges. Outfitted with smart technologies, GEBs are energy-efficient buildings with smart technologies characterized by the active use of distributed energy resources to optimize energy use for grid services, occupant needs and preferences, and cost reductions in a continuous and integrated way. In doing so, GEBs can play a key role in promoting greater affordability, resilience, environmental performance, and reliability. The report finds that, over the next two decades, GEBs could deliver between $100 and $200 billion in savings to the US power system and cut CO 2 emissions by 80 million tons per year by 2030, or 6% of total power sector CO 2 emissions. The report also provides 14 recommendations for addressing the top barriers to overcome barriers to GEB adoption and deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗
NASA NTRS1973

Identification and Mapping of Soils, Vegetation, and Water Resources of Lynn County, Texas, by Computer Analysis of ERTS MSS Data

Results of the analysis and interpretation of ERTS multispectral data obtained over Lynn County, Texas, are presented. The test site was chosen because it embodies a variety of problems associated with the development and management of agricultural resources in the Southern Great Plains. Lynn County is one of ten counties in a larger test site centering around Lubbock, Texas. The purpose of this study is to examine the utility of ERTS data in identifying, characterizing, and mapping soils, vegetation, and water resources in this semiarid region. Successful application of multispectral remote sensing and machine-processing techniques to arid and seminarid land-management problems will provide valuable new tools for the more than one-third of the world's lands lying in arid-semiarid regions.

Baumgardner, M. F.↗
NASA NTRS2013

Neutron Characterization for Additive Manufacturing

Oak Ridge National Laboratory (ORNL) is leveraging decades of experience in neutron characterization of advanced materials together with resources such as the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) shown in Fig. 1 to solve challenging problems in additive manufacturing (AM). Additive manufacturing, or three-dimensional (3-D) printing, is a rapidly maturing technology wherein components are built by selectively adding feedstock material at locations specified by a computer model. The majority of these technologies use thermally driven phase change mechanisms to convert the feedstock into functioning material. As the molten material cools and solidifies, the component is subjected to significant thermal gradients, generating significant internal stresses throughout the part (Fig. 2). As layers are added, inherent residual stresses cause warping and distortions that lead to geometrical differences between the final part and the original computer generated design. This effect also limits geometries that can be fabricated using AM, such as thin-walled, high-aspect- ratio, and overhanging structures. Distortion may be minimized by intelligent toolpath planning or strategic placement of support structures, but these approaches are not well understood and often "Edisonian" in nature. Residual stresses can also impact component performance during operation. For example, in a thermally cycled environment such as a high-pressure turbine engine, residual stresses can cause components to distort unpredictably. Different thermal treatments on as-fabricated AM components have been used to minimize residual stress, but components still retain a nonhomogeneous stress state and/or demonstrate a relaxation-derived geometric distortion. Industry, federal laboratory, and university collaboration is needed to address these challenges and enable the U.S. to compete in the global market. Work is currently being conducted on AM technologies at the ORNL Manufacturing Demonstration Facility (MDF) sponsored by the DOE's Advanced Manufacturing Office. The MDF is focusing on R&D of both metal and polymer AM pertaining to in-situ process monitoring and closed-loop controls; implementation of advanced materials in AM technologies; and demonstration, characterization, and optimization of next-generation technologies. ORNL is working directly with industry partners to leverage world-leading facilities in fields such as high performance computing, advanced materials characterization, and neutron sciences to solve fundamental challenges in advanced manufacturing. Specifically, MDF is leveraging two of the world's most advanced neutron facilities, the HFIR and SNS, to characterize additive manufactured components.

Watkins, Thomas↗
DOE OSTI2021

Guest Editorial: Special Issue on recent advancements in electric power system planning with high-penetration of renewable energy resources and dynamic loads

The goal of this Special Issue is to present the state-of-the-art methodologies developed for expansion planning of all segments of the modern power systems, characterized by separated businesses, high penetration of renewable resources and new load types, as well as by application of technologically advanced solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Genome‐wide association study of multiple yield traits in a diversity panel of polyploid sugarcane ( Saccharum spp.)

Abstract Sugarcane ( Saccharum spp.) is an important economic crop, contributing up to 80% of sugar and approximately 60% of biofuel globally. To meet the increased demand for sugar and biofuel supplies, it is critical to breed sugarcane cultivars with robust performance in yield traits. Therefore, dissection of causal DNA sequence variants is of great importance, as it provides genetic resources and fundamental information for crop improvement. In this study, we analyzed nine yield traits in a sugarcane diversity panel consisting of 308 accessions primarily selected from the World Collection of Sugarcane and Related Grasses. By genotyping the diversity panel via target enrichment sequencing, we identified a large number of sequence variants. Genome‐wide association studies between the markers and traits were conducted, taking dosages and gene actions into consideration. In total, 217 nonredundant markers and 225 candidate genes were identified to be significantly associated with the yield traits, which can serve as a comprehensive genetic resource database for future gene identification, characterization, and selection for sugarcane improvement. We further investigated runs of homozygosity (ROH) in the sugarcane diversity panel. We characterized 282 ROHs and found that the occurrence of ROHs in the genome were nonrandom and probably under selection. The ROHs were associated with total weight and dry weight, and high ROHs resulted in a decrease in the two traits. This study suggests that genomic inbreeding has led to negative impacts on sugarcane yield.

Yang, Xiping↗
DOE OSTI2022

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys

Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Validation of RU-WRF, the Custom Atmospheric Mesoscale Model of the Rutgers Center for Ocean Observing Leadership

The Rutgers University Center for Ocean Observing Leadership (RU-COOL) contracted the National Renewable Energy Laboratory (NREL) to evaluate RU-COOL's atmospheric observation and modeling capabilities for characterizing the New Jersey offshore wind resource. The observational network used by RU-COOL consists of mostly public but some private coastal and offshore buoy-based stations. The core wind resource modeling capability of RU-COOL is a custom setup of the Weather Research and Forecasting (WRF) mesoscale model, referred to in this report as RU-WRF. The most unique feature of RU-WRF and not found in other WRF model setups is the use of custom sea surface temperature (SST) product generated by RU-COOL. This custom product was designed to better capture the unique coastal upwelling and strong storm mixing in the Mid-Atlantic Bight, which other typical SST products are not designed to capture. Funding for the development, maintenance, and use of RU-WRF by RU-COOL is provided by the New Jersey Board of Public Utilities, who also funded the validation work presented in this report. In this validation study, NREL was specifically tasked to: 1) Assess the observational network used by RU-COOL to validate RU-WRF and make recommendations for improvement, 2) Assess methods used by RU-COOL to validate RU-WRF and make recommendations for improvement, 3) Examine the inputs to and setup within RU-WRF, compare against available NREL data sets, and make recommendations for improvement.

17 WIND ENERGY↗
DOE OSTI2025

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗