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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Identification of the threshold security applied for fuel under reactivity-initiated conditions by self-organizing maps and neural networks - 14386

In recent times, we have 439 reactors distributed in 31 countries that give about 377 GW, covering around 12% of world capacity of energy. Currently, it accounts for an estimated 40 serious accidents reported in nuclear power plants. In this article, turns out the behavior of fuel rod in light-water reactors under accident conditions. Besides, we investigate deeply about reactivity-initiated accidents, by introducing Nordheim-Fuchs model as state equations. The early research showed that pre-irradiated fuels and afterward subjected to a rapid pulse of energy. It adopts the key items necessary to check the threshold of failure. These items are few limits as burning the fuel; the value applied to the peak of energy, pulse width, oxidation layer, and cladding. The kinetic equations of nuclear reactors modeled as a non-linear system. In this way, it can apply the paradigm of artificial intelligence using the self-organized maps. Where the goal is to find the cluster of failure for irradiated fuels until 75 GWd/MTU. The second model based on statistical is the logistic regression theory. Hence, the tests carried out and compared against the results found by neural networks for classification of clusters. It proofs that both approaches present capacity to find the flaws accurately of about 80% of cases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Techno-Economic Impact of Partial String Failure in Multi-string Energy Storage Systems

Electric utilities and large industrial/commercial customers, which integrate multi-MW multi-string energy storage systems (ESS), are typically at a significant risk of string failure. This results in the loss of system performance and capacity. In order to evaluate the accurate costs and benefits associated with a particular battery system, it is essential to incorporate the costs associated with battery string failure and, as a result, reduced capacity. This report analyzes how the economic losses can be estimated based on market rules and ESS availability. A framework for calculation of economic losses is developed and implemented with an illustrative string failure case for Independent System Operator (ISO-NE) forward capacity market. Further scenarios and case studies are analyzed by varying the probability of string failure as well as the capacity bid into the market. These scenarios are used to assess the variability of economic losses and the impact of string failure on the final realized benefits from the battery's market participation.

Fotedar, Vanshika↗
DOE OSTI2021

Techno-Economic Impact of Partial String Failurein Multi-string Energy Storage Systems

Electric utilities and large industrial/commercial customers, which integrate multi-MW multi-string energy storage systems (ESS), are typically at a significant risk of string failure. This results in loss of system performance and capacity. In order to evaluate the accurate costs and benefits associated with a particular battery system, it is essential to incorporate the costs associated with battery string failure and, as a result, reduced capacity. This report analyzes how the economic losses can be estimated based on market rules and ESS availability. A framework for calculation of economic losses is developed and implemented with an illustrative string failure case for Independent System Operator (ISO-NE) forward capacity market. Further scenarios and case studies are analyzed by varying the probability of string failure as well as the capacity bid into the market. These scenarios are used to assess the variability of economic losses and the impact of string failure on final realized benefits from the battery's market participation.

Fotedar, Vanshika↗
NASA NTRS1992

Lunar resource evaluation and mine site selection

Two scenarios in this evaluation of lunar mineral resources and the selection of possible mining and processing sites are considered. The first scenario assumes that no new surface or near-surface data will be available before site selection (presumably one of the Apollo sites). The second scenario assumes that additional surface geology data will have been obtained by a lunar orbiter mission, an unmanned sample return mission (or missions), and followup manned missions. Regardless of the scenario, once a potentially favorable mine site has been identified, a minimum amount of fundamental data is needed to assess the resources at that site and to evaluate its suitability for mining and downstream processing. Since much of the required data depends on the target mineral(s), information on the resource, its beneficiation, and the refining, smelting, and fabricating processes must be factored into the evaluation. The annual capacity and producing lifetime of the mine and its associated processing plant must be estimated before the resource reserves can be assessed. The available market for the product largely determines the capacity and lifetime of the mine. The Apollo 17 site is described as a possible mining site. The use of new sites is briefly addressed.

Bence, A. Edward↗
DOE OSTI2018

Prediction of Carbon Dioxide Adsorption via Deep Learning

Abstract Porous carbons with different textural properties exhibit great differences in CO 2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO 2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO 2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO 2 adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO 2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO 2 adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next‐generation carbons.

Zhang, Zihao↗
DOE OSTI2018

Prediction of Carbon Dioxide Adsorption via Deep Learning

Porous carbons with different textural properties exhibit great differences in CO 2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO 2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO 2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO 2 adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO 2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO 2 adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next-generation carbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules

Abstract A basic tenet of microeconomics is that for a competitive industry in equilibrium the market price of a product will be equal to its marginal cost. This paper develops a model framework and a corresponding empirical inference procedure for estimating long‐run marginal cost in industries where production costs decline over time. In the context of the solar photovoltaic (PV) module industry, we rely primarily on firm‐level financial accounting data to estimate the long‐run marginal cost of PV modules for the years 2008–2013. During those years, the industry experienced both unprecedented price declines and significant expansions of manufacturing capacity. We compare the trajectory of average sales prices with the estimated long‐run marginal costs in order to quantify the extent to which actual price declines were attributable to reductions in production costs. The trajectory of estimated product costs is then extrapolated to forecast an equilibrium trend line for future PV module prices.

Reichelstein, Stefan↗
DOE OSTI2016

ENVI-PV: An Interactive Web Client for Multi-Criteria Life Cycle Assessment of Photovoltaic Systems Worldwide

Solar photovoltaics (PV) is the second largest source of new capacity among renewable energies. The worldwide capacity encompassed 135 GW in 2013 and is estimated to increase to 1721 GW in 2030 and 4674 GW in 2050, according to a prospective high-renewable scenario. To achieve this production level while minimizing environmental impacts, decision makers must have access to environmental performance data that reflect their high spatial variability accurately. We propose ENVI-PV (http://viewer.webservice-energy.org/project_iea), a new interactive tool that provides maps and screening level data, based on weighted average supply chains, for the environmental performance of common PV technologies. Environmental impacts of PV systems are evaluated according to a life cycle assessment approach. ENVI-PV was developed using a state-of-the-art interoperable and open standard Web Service framework from the Open Geospatial Consortium (OGC). It combines the latest life cycle inventories, published in 2015 by the International Energy Agency (IEA) under the Photovoltaic Power Systems Program (PVPS) Task 12, and some inventories previously published from Ecoinvent v2.2 database with solar irradiation estimates computed from the worldwide NASA SSE database. ENVI-PV is the first tool to propose a worldwide coverage of environmental performance of PV systems using a multi-criteria assessment. The user can compare the PV environmental performance to the environmental footprint of country electricity mixes. ENVI-PV is designed as an environmental interactive tool to generate PV technological options and evaluate their performance in different spatial and techno-economic contexts. Its potential applications are illustrated in this paper with several examples.

14 SOLAR ENERGY↗
DOE OSTI2020

Complementary relationship between small-hydropower and increasing penetration of solar photovoltaics: Evidence from CAISO

To achieve the 100% green electricity goal, we need to understand the relationship between resources in the market and identify the flexible clean resources (i.e., hydropower) to integrate power from wind and photovoltaic (PV). This paper reveals a complementary relationship between small hydropower plants and solar PVs in the California Independent System Operator (CAISO) based on the system-wide hourly generation data from 2013 to 2017. When the solar PV increases its portion in the generation mix by 1%, small hydro will increase its portion by 0.01–0.06%. Such response is obvious in the net demand peak hours, both morning and evening. The low operation cost, flexibility, and dispatchability of small hydro in CAISO explain this complementarity. Due to its benefit in emission and low Levelized Cost of Electricity (LCOE), it is suggested to consider more small hydro projects to accommodate additional PV capacity for the 100% green electricity goal. Our estimation indicates that the current feasible potential of small hydro is sufficient, if the relation stays the same over years. If developers can mitigate the environmental impact, more technical potential will become feasible. Thus, small hydro could integrate more solar PV and reduce the demand for natural gas plants and batteries.

13 HYDRO ENERGY↗
DOE OSTI2020

The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations

Geothermal power plants have typically been operated as baseload plants. The recent expansion of wind and solar power generation creates a potential opportunity to increase the value of geothermal generation through flexible operations. In recent years, California’s wholesale electricity markets have exhibited frequent, short-term periods of negative pricing, indicating that additional flexibility would be valued in the market. Here, we examine local nodal hourly price records at all geothermal plants located in the western United States. We describe how the frequency and temporal characteristics of negative pricing episodes have changed over recent years. Based on these price series, we calculate the value of multiple strategies of flexible operations. Additionally, we use the estimates of future prices, developed through a capacity-expansion model and a dispatch model, to explore how the value of such flexible operations might change along with further penetration of variable renewable power sources. Based on the historical pricing records, we find that simple curtailment of operations during negative pricing episodes could increase the average energy value by 1–2 dollars per MWh and that allowing for increased production during limited high-priced hours, in addition to curtailment during negative priced hours, could potentially double the increase in value (up to 4 dollars per MWh). The forward-looking simulations indicate reduced values of flexibility from geothermal power despite higher penetrations of variable renewable energy. This result highlights the possibility that increasing flexibility options throughout the system may counteract the influence of increased variable renewable energy deployment.

15 GEOTHERMAL ENERGY↗
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 OSTI2016

Development of Cloud and Precipitation Property Retrieval Algorithms and Measurement Simulators from ASR Data

What has made the ASR program unique is the amount of information that is available. The suite of recently deployed instruments significantly expands the scope of the program (Mather and Voyles, 2013). The breadth of this information allows us to pose sophisticated process-level questions. Our ASR project, now entering its third year, has been about developing algorithms that use this information in ways that fully exploit the new capacity of the ARM data streams. Using optimal estimation (OE) and Markov Chain Monte Carlo (MCMC) inversion techniques, we have developed methodologies that allow us to use multiple radar frequency Doppler spectra along with lidar and passive constraints where data streams can be added or subtracted efficiently and algorithms can be reformulated for various combinations of hydrometeors by exchanging sets of empirical coefficients. These methodologies have been applied to boundary layer clouds, mixed phase snow cloud systems, and cirrus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Probabilistic Assembly of a Ball Screw

The assembly of ball screws is considered theoretically and experimentally in the case where the screw and nut are combined with an optimal combination of systematic and random errors in the screw pitch (optimal pitch error ratio).

42 ENGINEERING↗
DOE OSTI2019

Analysis on Part Distortion and Residual Stress in Big Area Additive Manufacturing with Carbon Fiber-Reinforced Thermoplastic using Dehomogenization Technique

Part distortion and bottom surface warpage often occur in extrusion-based additive manufacturing. The undesirable deformations during the printing process are due to residual stresses caused by material shrinkage. Analysis on residual stress and deformation requires accurate thermo-mechanical material properties. Polymer composites reinforced with short fibers have intrinsic inhomogeneities with nonuniform fiber orientation. Therefore, homogenized macro properties may not accurately represent the progressive damage behavior or distortion. In this study, fiber orientations in Acrylonitrile Butadiene Styrene (ABS) reinforced with 20%wt carbon fiber are calculated using a de-homogenization technique. Thermal expansion coefficients in multiple directions are obtained from Thermo-Mechanical Analysis (TMA) tests. Temperature dependent stiffness is measured, and temperature dependent strength is estimated. Thermal conductivities in multiple directions and thermal capacity are measured. The calibrated thermo-mechanical properties of the composite with de-homogenized technique are used to analyze the residual stress and distortion of a 4 ft-wide wall printed in the Big Area Additive Manufacturing (BAAM) system. For experimental measurement on the wall printing, Infra-Red (IR) camera captures the temperature field, and Linear Variable Differential Transformer (LVDT) is installed to measure the warpage at the bottom surface. The experimental data are compared to the numerical analysis results. The temperature profile and the distortion profile from experiment are close to the simulation results. Damages due to residual stress and distortion are analyzed.

Kim, Pum↗
DOE OSTI2015

Changes in the firn structure of the western Greenland Ice Sheet caused by recent warming

Atmospheric warming over the Greenland Ice Sheet during the last 2 decades has increased the amount of surface meltwater production, resulting in the migration of melt and percolation regimes to higher altitudes and an increase in the amount of ice content from refrozen meltwater found in the firn above the superimposed ice zone. Here we present field and airborne radar observations of buried ice layers within the near-surface (0–20 m) firn in western Greenland, obtained from campaigns between 1998 and 2014. We find a sharp increase in firn-ice content in the form of thick widespread layers in the percolation zone, which decreases the capacity of the firn to store meltwater. The estimated total annual ice content retained in the near-surface firn in areas with positive surface mass balance west of the ice divide in Greenland reached a maximum of 74 ± 25 Gt in 2012, compared to the 1958–1999 average of 13 ± 2 Gt, while the percolation zone area more than doubled between 2003 and 2012. Increased melt and column densification resulted in surface lowering averaging –0.80 ± 0.39 m yr⁻¹ between 1800 and 2800 m in the accumulation zone of western Greenland. Since 2007, modeled annual melt and refreezing rates in the percolation zone at elevations below 2100 m surpass the annual snowfall from the previous year, implying that mass gain in the region is retained after melt in the form of refrozen meltwater. If current melt trends over high elevation regions continue, subsequent changes in firn structure will have implications for the hydrology of the ice sheet and related abrupt seasonal densification could become increasingly significant for altimetry-derived ice sheet mass balance estimates.

58 GEOSCIENCES↗
NASA NTRS1981

Solar heating for a restaurant--North Little Rock, Arkansas

Hot water consumption of large building affects solar-energy system design. Continual demand for hot water at restaurant makes storage less important than at other sites. Storage capacity of system installed in December 1979 equals estimated daily hot-water requirement. Report describes equipment specifications and modifications to existing building heating and hot water systems.

Source record↗
NASA NTRS1999

Dimensionless Numbers Expressed in Terms of Common CVD Process Parameters

A variety of dimensionless numbers related to momentum and heat transfer are useful in Chemical Vapor Deposition (CVD) analysis. These numbers are not traditionally calculated by directly using reactor operating parameters, such as temperature and pressure. In this paper, these numbers have been expressed in a form that explicitly shows their dependence upon the carrier gas, reactor geometry, and reactor operation conditions. These expressions were derived for both monatomic and diatomic gases using estimation techniques for viscosity, thermal conductivity, and heat capacity. Values calculated from these expressions compared well to previously published values. These expressions provide a relatively quick method for predicting changes in the flow patterns resulting from changes in the reactor operating conditions.

Kuczmarski, Maria A.↗