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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Exploring the Future of Mobility: Transportation Energy and Mobility Pathway Options (TEMPO) Model

The Transportation Energy and Mobility Pathway Options (TEMPO) modeling framework is intended to generate future pathways to achieve system-level goals, estimate implications of different scenarios and decisions, estimate affordability and infrastructure use impacts, assess energy use and emissions implications, inform decision makers, and assess integration opportunities between transportation and energy infrastructure and supply systems.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Thermal Ratcheting Analysis of TEDS Packed-bed Thermocline Energy Storage Tank - Modeling Methodology and Data Validation

This report investigates numerical modeling methods for thermal ratcheting analysis of packed-bed thermal energy storage (TES) tank and discusses the validation results via comparison with experimental data. The experimental data obtained from various design characteristics of packed-bed thermocline tanks, including the Thermal Energy Distribution System (TEDS) TES tank at Idaho National Laboratory (INL), were used to validate thermal and mechanical models developed in this study to evaluate the thermal ratcheting potential. The thermal model was shown to predict the transient thermal propagation through the packed-bed thermocline tanks generally well. However, a larger discrepancy was observed during the comparison with the data from TEDS, presumably due to the uncertainty of boundary conditions given from the experiment. Based on the comparative study between the thermal model predictions and experimental data of various packed-bed thermocline tanks, potential improvements were suggested for the future TEDS experiments for more precise validation study. For mechanical (thermally induced stress) analysis, two different modeling approaches were tested to evaluate hoop stress applied to the packed-bed TES tank wall, which is a major cause of thermal ratcheting process: (i) infinite rigidity model and (ii) Drucker-Prager (DP) model. The ‘model (i)’ is a conservative method with infinite rigidity assumption of granular filler inside a TES tank, whereas the ‘model (ii)’ is a method that takes into account more realistic processes such as thermal expansion of filler and tank wall as well as inter-particle interactions during the cyclic operation of a packed-bed TES tank. The validity of each modeling method was examined by comparing the numerical simulation with the experimental data obtained from the packed-bed TES tank for Solar One pilot plant. Then, the effects of various model parameters were discussed to evaluate the thermal ratcheting potential of the TEDS TES tank. The preliminary thermal ratcheting analysis implies that the TEDS TES tank will hold its structural integrity during the normal operation cycles.

25 ENERGY STORAGE↗
NASA NTRS1992

The simulated features of heliospheric cosmic-ray modulation with a time-dependent drift model. III - General energy dependence

The time-dependent cosmic-ray transport equation is solved numerically in an axially symmetric heliosphere. Gradient and curvature drifts are incorporated, together with an emulated wavy neutral sheet. This model is used to simulate heliospheric cosmic-ray modulation for the period 1985-1989 during which drifts are considered to be important. The general energy dependence of the modulation of Galactic protons is studied as predicted by the model for the energy range 1 MeV to 10 GeV. The corresponding instantaneous radial and latitudinal gradients are calculated, and it is found that, whereas the latitudinal gradients follow the trends in the waviness of the neutral sheet to a large extent for all energies, the radial gradients below about 200 MeV deviate from this general pattern. In particular, these gradients increase when the waviness decreases for the simulated period 1985-1987.3, after which they again follow the neutral sheet by increasing rapidly.

Potgieter, M. S.↗
DOE OSTI2023

2023 Roadmap on molecular modelling of electrochemical energy materials

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO 2 reduction reaction, and fuel cell applications.

25 ENERGY STORAGE↗
NASA NTRS1980

A thermalized ion explosion model for high energy sputtering and track registration

A velocity spectrum of neutral sputtered particles as well as a low resolution mass spectrum of sputtered molecular ions was measured for 4.74 MeV F-19(+2) incident of UF4. The velocity spectrum is dramatically different from spectra taken with low energy (keV) bombarding ions, and is shown to be consistent with a hot plasma of atoms in thermal equilibrium inside the target. A thermalized ion explosion model is proposed for high energy sputtering which is expected to describe track formation in dielectric materials. The model is shown to be consistent with the observed total sputtering yield and the dependence of the yield on the primary ionization rate of the incident ion.

Seiberling, L. E.↗
DOE OSTI2021

Coupled Ocean Wave-Atmosphere Models for Offshore Wind Energy Applications

Coupling ocean wave models to mesoscale atmospheric models is necessary to represent the effect of waves on wind turbine hub-height winds. In this report we provide a review of the most widely used ocean waves models and the phased-averaged spectral wave modeling paradigm that they are based on. Methodologies used to couple these wave models to mesoscale atmospheric models are described along with details of existing coupled modeling systems. We summarize impacts on offshore wind resource assessments that have been investigated with such coupled modeling systems to-date. Specifically, coupling in the North and Baltic Seas was shown to have a small negative effect on the offshore wind energy resource at weak-to-moderate wind speeds, with little impact at higher wind speeds. Finally, limitations of these existing coupled modeling systems and impact assessments are discussed, including examples of and the potential for improved parameterizations of the air-sea fluxes, and the use of fine-scaled simulations using phased-resolved wave models.

17 WIND ENERGY↗
NASA NTRS1994

A simple hydrologically based model of land surface water and energy fluxes for general circulation models

A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data from three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.

Liang, XU↗
DOE OSTI2020

High-Performance Computing for Earth System Modeling

High-performance computing (HPC) plays an important role during the development of Earth system models. This chapter reviews HPC efforts related to Earth system models, including community Earth system models and energy exascale Earth system models. Specifically, this chapter evaluates computational and software design issues, analyzes several current HPC-related model developments, and provides an outlook for some promising areas within Earth system modeling in the era of exascale computing.

Wang, Dali↗
NASA NTRS1997

Correspondence between theoretical models and dual energy x-ray absorptiometry measurements of femoral cross-sectional growth during adolescence

We have developed an analytical model of long bone cross-sectional ontogeny in which appositional growth of the diaphysis is primarily driven by mechanical stimuli associated with increasing body mass during growth and development. In this study, our goal was to compare theoretical predictions of femoral diaphyseal structure from this model with measurements of femoral bone mineral and geometry by dual energy x-ray absorptiometry. Measurements of mid-diaphyseal femoral geometry and structure were made previously in 101 Caucasian adolescents and young adults 9-26 years of age. The data on measured bone mineral content and calculated section modulus were compared with the results of our analytical model of cross-sectional development of the human femur over the same age range. Both bone mineral content and section modulus showed good correspondence with experimental measurements when the relationships with age and body mass were examined. Strong linear relationships were evident for both parameters when examined as a function of body mass.

NASA Discipline Musculoskeletal↗
DOE OSTI2019

A Consistent Model of Ultrafast Energy Transfer in Peridinin Chlorophyll-a Protein Using Two-Dimensional Electronic Spectroscopy and Förster Theory

Solar light harvesting begins with electronic energy transfer in structurally complex light-harvesting antennae such as the peridinin chlorophyll-a protein from dinoflagellate algae. Peridinin chlorophyll-a protein is composed of a unique combination of chlorophylls sensitized by carotenoids in a 4:1 ratio, and ultrafast spectroscopic methods have previously been utilized in elucidating their energy transfer pathways and timescales. However, due to overlapping signals from various chromophores and competing pathways and timescales, a consistent model of intra-protein electronic energy transfer has been elusive. Here, we used broadband two-dimensional electronic spectroscopy, which alleviates the spectral congestion by dispersing excitation and detection wavelengths. Inter-chromophoric couplings appear as cross peaks in two-dimensional electronic spectra, and these spectral features were observed between the peridinin S2 states and chlorophyll-a Qx and Qy states. In addition, the inherently high time- and frequency-resolution of 2DES enabled accurate determination of the ultrafast energy transfer dynamics. Kinetic analysis near the peridinin S1 excited state absorption, which forms in 24 fs after optical excitation, reveals an ultrafast energy transfer pathway from the peridinin S2 state to the chlorophyll-a Qx state—a hitherto unconfirmed pathway critical for fast inter-chromophoric transfer. Lastly, we propose a model of ultrafast peridinin chlorophyll-a protein photophysics that (1) includes a conical intersection between peridinin S2 and S1 states in order to explain both the ultrafast peridinin S1 formation and the residual peridinin S2 population for energy transfer to chlorophyll-a, and (2) computationally- and experimentally-derived peridinin S2 site energies that support the observed ultrafast peridinin S2 to chlorophyll-a Qx energy transfer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2018

The Regional Energy Deployment System (ReEDS) model is a capacity expansion and dispatch model for the contiguous U.S. electric power sector that relies on system-wide least cost optimization to estimate the type and location of future generation and transmission capacity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

An Overview of the Waste-to-Energy System Simulation (WESyS) Model

The leveraging of waste streams for energy and chemical production could add revenue to waste disposal operations, and it presents opportunities for addressing a variety of economic and environmental objectives at the local, state, and national levels. The Waste-to-Energy System Simulation (WESyS) model is a system dynamics model that was created to simulate the development of the U.S. waste-to-energy industry over time. For each of the three primary waste resources modeled (landfills, concentrated animal feeding operations, and publicly owned treatment works), WESyS simulates technically feasible scenarios for use of the waste, including direct conversion to fuels, and anaerobic digestion followed by flaring, electricity generation, combined heat and power, cleanup and compression to compressed natural gas, and cleanup and injection into an existing pipeline. The model allows users to explore numerous plausible future scenarios for the development of the U.S. waste-to-energy industry. This report provides an overview of the WESyS model and documents the key assumptions, equations, and data sources used to create the model.

09 BIOMASS FUELS↗
NASA NTRS1977

A simulation model for wind energy storage systems. Volume 2: Operation manual

A comprehensive computer program (SIMWEST) developed for the modeling of wind energy/storage systems utilizing any combination of five types of storage (pumped hydro, battery, thermal, flywheel, and pneumatic) is described. Features of the program include: a precompiler which generates computer models (in FORTRAN) of complex wind source/storage/application systems, from user specifications using the respective library components; a program which provides the techno-economic system analysis with the respective I/O the integration of system dynamics, and the iteration for conveyance of variables; and capability to evaluate economic feasibility as well as general performance of wind energy systems. The SIMWEST operation manual is presented and the usage of the SIMWEST program and the design of the library components are described. A number of example simulations intended to familiarize the user with the program's operation is given along with a listing of each SIMWEST library subroutine.

Warren, A. W.↗
DOE OSTI2022

Modeling antiphase boundary energies of Ni 3 Al-based alloys using automated density functional theory and machine learning

Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni 3 Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m –2 . We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.

36 MATERIALS SCIENCE↗