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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

The Doubly Labeled Water Method for Measuring Human Energy Expenditure: Adaptations for Spaceflight

It is essential to determine human energy requirements in space, and the doubly labeled water method has been identified as the most appropriate means of indirect calorimetry to meet this need. The method employs naturally occurring, stable isotopes of hydrogen (H-2, deuterium) and oxygen (O-18) which, after dosing, mix with body water. The deuterium is lost from the body as water while the O-18 is eliminated as both water and CO2. The difference between the two isotope elimination rates is therefore a measure of CO2 production and hence energy expenditure. Spaceflight will present a unique challenge to the application of the doubly labeled water method. Specifically, interpretation of doubly labeled water results assumes that the natural abundance or 'background' levels of the isotopes remain constant during the measurement interval. To address this issue, an equilibration model will be developed in an ongoing ground-based study. As energy requirements of women matched to counterparts in the Astronauts Corps are being determined by doubly labeled water, the baseline isotope concentration will be changed by consumption of 'simulated Shuttle water' which is artificially enriched. One group of subjects will be equilibrated on simulated Shuttle water prior to energy determinations by doubly labeled water while the others will consume simulated Shuttle water after dosing. This process will allow us to derive a prediction equation to mathematically model the effect of changing background isotope concentrations.

Schulz, Leslie O.↗
DOE OSTI2021

Method of controlling a vehicle to adjust perception system energy usage

A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.

Hu, Yuxiao↗
DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗
NASA NTRS2007

A Semi-Analytical Method for Determining the Energy Release Rate of Cracks in Adhesively-Bonded Single-Lap Composite Joints

A semi-analytical method for determining the strain energy release rate due to a prescribed interface crack in an adhesively-bonded, single-lap composite joint subjected to axial tension is presented. The field equations in terms of displacements within the joint are formulated by using first-order shear deformable, laminated plate theory together with kinematic relations and force equilibrium conditions. The stress distributions for the adherends and adhesive are determined after the appropriate boundary and loading conditions are applied and the equations for the field displacements are solved. Based on the adhesive stress distributions, the forces at the crack tip are obtained and the strain energy release rate of the crack is determined by using the virtual crack closure technique (VCCT). Additionally, the test specimen geometry from both the ASTM D3165 and D1002 test standards are utilized during the derivation of the field equations in order to correlate analytical models with future test results. The system of second-order differential field equations is solved to provide the adherend and adhesive stress response using the symbolic computation tool, Maple 9. Finite element analyses using J-integral as well as VCCT were performed to verify the developed analytical model. The finite element analyses were conducted using the commercial finite element analysis software ABAQUS. The results determined using the analytical method correlated well with the results from the finite element analyses.

Yang, Charles↗
DOE OSTI2020

Thermal Energy Storage Using Solid Particles for Long-Duration Energy Storage

The rapid growth of renewable energy increases the importance of economically firming the electricity supply from variable solar photovoltaic- and wind-power generators. Energy storage will be the key to manage variability and to bridge the generation gap over time scales of hours or days for high renewable grid integration. The integration of renewable power and storage of excess electricity has several significant and positive impacts including: 1) expanding the renewable energy portion of total electricity generation, 2) improving the peak-load response, and 3) coordinating the electricity supply and demand over the grid. Long-duration energy storage can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Several energy storage methods are deployed or under development, including mechanical, chemical or electrochemical, and thermal energy storage (TES). Comparing their economic potential for different scales and applications helps identify suitable technology to support high renewable grid integration. Despite the progress of TES technologies developed and deployed with concentrating solar power (CSP) systems, TES has been undervalued for its potential role in electric energy storage. This paper introduces TES methods applicable to grid energy storage and particularly focuses on solid-particle-based TES to serve the purpose of long-duration energy storage (LDES). The objective of this paper is to present a standalone particle-based TES system for electric storage and to show the potential of TES systems for LDES applications over other energy storage methods such as batteries, compressed-air energy storage, or pumped-storage hydropower.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Charge Transfer in 2D Halide Perovskites and 2D/3D Heterostructures

Halide perovskites have emerged as a versatile class of materials, with applications spanning photovoltaics, light-emitting diodes, transistors, and photodetectors. The introduction of semiconducting ligands to form two-dimensional (2D) perovskites on the surface of three-dimensional (3D) perovskites in perovskite solar cells has been shown to enhance the performance and stability. To improve the interface properties between the 3D perovskite layer and charge carrier transport layers, understanding the charge transfer (CT) process in 2D perovskites is crucial. In this Perspective, we address common terminological inaccuracies in energy level descriptions, delineate methods for energy alignment characterization, and present practical instances of CT in 2D perovskite-incorporated solar cells. Furthermore, we emphasize the significance of precise terminology, appropriate measurement techniques, and rational design of 2D ligands to harness the full potential of 2D perovskites in optoelectronic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multielectron Organic Redoxmers for Energy-Dense Redox Flow Batteries

Redox flow battery is a highly promising stationary energy storage method but the limited energy density and high chemical cost are among the main barriers for commercialization. Multi-electron organic redoxmers represent a family of structurally tailorable candidates that can achieve multiplied energy density with decreased materials consumption, potentially resulting in a viable solution to address these challenges. In this work, the recent development of organic molecules with reversible multi-redox activities in both aqueous and nonaqueous electrolytes is reviewed. The major focus is on the fundamental correlation between the chemical structures and the functional properties of reported multi-electron organic molecules. Valuable insights are offered on rational structural design strategies for improving the relevant physico- and electro-chemical properties. Finally, the current challenges are discussed to suggest future research needs along the avenue of using the multi-electron approach to achieve energy-dense, stable, cost-effective redox flow batteries.

25 ENERGY STORAGE↗
NASA NTRS1973

Analysis of temperature-dependent neutron transmission and self-indication measurements on tantalum at 2-keV neutron energy

The method of pseudo-resonance cross sections is used to analyze published temperature-dependent neutron transmission and self-indication measurements on tantalum in the unresolved region. In the energy region analyzed, 1825.0 to 2017.0 eV, a direct application of the pseudo-resonance approach using a customary average strength function will not provide effective cross sections which fit the measured cross section behavior. Rather a local value of the strength function is required, and a set of resonances which model the measured behavior of the effective cross sections is derived. This derived set of resonance parameters adequately represents the observed resonance hehavior in this local energy region. Similar analyses for the measurements in other unresolved energy regions are necessary to obtain local resonance parameters for improved reactor calculations. This study suggests that Doppler coefficients calculated by sampling from grand average statistical distributions over the entire unresolved resonance region can be in error, since significant local variations in the statistical distributions are not taken into consideration.

Semler, T. T.↗
DOE OSTI2022

Surrogate Hessian accelerated structural optimization for stochastic electronic structure theories

In this work, we present an efficient energy-based method for structural optimization with stochastic electronic structure theories, such as diffusion quantum Monte Carlo (DMC). This method is based on robust line-search energy minimization in reduced parameter space, exploiting approximate but accurate Hessian information from a surrogate theory, such as density functional theory. The surrogate theory is also used to characterize the potential energy surface, allowing for simple but reliable ways to maximize statistical efficiency while retaining controllable accuracy. We demonstrate the method by finding the minimum DMC energy structures of the selected flake-like aromatic molecules, such as benzene, coronene, and ovalene, represented by 2, 6, and 19 structural parameters, respectively. In each case, the energy minimum is found within two parallel line-search iterations. The method is near-optimal for a line-search technique and suitable for a broad range of applications. It is easily generalized to any electronic structure method where forces and stresses are still under active development and implementation, such as diffusion Monte Carlo, auxiliary-field Monte Carlo, and stochastic configuration interaction, as well as deterministic approaches such as the random-phase approximation. Accurate and efficient means of geometry optimization could shed light on a broad class of materials and molecules, showing high sensitivity of induced properties to structural variables.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

A method of predicting the energy-absorption capability of composite subfloor beams

A simple method of predicting the energy-absorption capability of composite subfloor beam structure was developed. The method is based upon the weighted sum of the energy-absorption capability of constituent elements of a subfloor beam. An empirical data base of energy absorption results from circular and square cross section tube specimens were used in the prediction capability. The procedure is applicable to a wide range of subfloor beam structure. The procedure was demonstrated on three subfloor beam concepts. Agreement between test and prediction was within seven percent for all three cases.

Farley, Gary L.↗
DOE OSTI2021

Systems and methods of long-duration energy storage and regeneration of energy-bearing redox pairs

Described herein are systems and methods of storing and delivering electrical using hydrogen at low-cost and for long-durations. The systems and methods use energy-bearing redox pairs that electrochemically bear energy through decoupled hydrogen and oxygen consumption and/or evolution reactions, which are typically associated with fuel cells. Each species of the energy-bearing redox pair is associated with a standard electrode potential within a water electrolysis voltage window for the electrolyte solution. Electrical energy delivery, hydrogen generation, electrolyte regeneration, or combinations thereof can be performed by logically or physically separated unit operations in a continuous manner, batch manner, or semi-batch manner facilitated by the energy-bearing redox pair.

08 HYDROGEN↗
NASA NTRS1987

A method of predicting the energy-absorption capability of composite subfloor beams

A simple method of predicting the energy-absorption capability of composite subfloor beam structure has been developed. The method is based upon the weighted sum of the energy-absorption capability of constituent elements of a subfloor beam. An empirical data base of energy absorption results from circular and square cross section tube specimens were used in the prediction capability. The procedure is applicable to a wide range of subfloor beam structure. The procedure was demonstrated on three subfloor beam concepts. Agreement between test and prediction was within seven percent for all three cases.

Farley, Gary L.↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Measurement of Beam Energy in the Fermilab's Linac taken at the transfer Line

Linac is the first machine in the Accelerator chain at Fermilab where 𝐻− particles are accelerated from 35 keV to 400 MeV and travel to the Booster where they are stripped. of the extra electrons to become protons. The Booster requires stable beam energy at injection. The Linac extraction energy tuning has been performed using two Beam Velocity Monitors (a.k.a Griffin detectors) to maintain stable relative energy between the Booster and the Linac. Our goal is to control the Linac energy parameters using Machine Learning techniques to increase reliability and quality of beam delivered from Linac. The first step is to understand the data from the Linac extraction diagnostics; and develop reliable, accurate methods for energy measurement and control before implementing machine learning. There were two methods of energy measurement implemented, and the results of these are compared. The first method was time of flight measurements using the Phase probes inside B eam Position Monitors (BPM) that provide the beam’s phase of arrival data. The second method used the relation between beam position data from the BPMs and dispersion values from the 400MeV MAD-X model to calculate momentum variation. The results of these two energy measurement methods compare well as the Linac energy is varied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1995

Predictions of thermal buckling strengths of hypersonic aircraft sandwich panels using minimum potential energy and finite element methods

Thermal buckling characteristics of hypersonic aircraft sandwich panels of various aspect ratios were investigated. The panel is fastened at its four edges to the substructures under four different edge conditions and is subjected to uniform temperature loading. Minimum potential energy theory and finite element methods were used to calculate the panel buckling temperatures. The two methods gave fairly close buckling temperatures. However, the finite element method gave slightly lower buckling temperatures than those given by the minimum potential energy theory. The reasons for this slight discrepancy in eigensolutions are discussed in detail. In addition, the effect of eigenshifting on the eigenvalue convergence rate is discussed.

Ko, William L.↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗