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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling In-Reactor Temperature Gradients in Lithium Aluminate Ceramic Pellets

In reactor thermal performance of lithium aluminate ceramic pellets are calculated to determine operating temperatures if internal components shift during irradiation. A 2D conduction model was setup to model this scenario with additional physics, including thermal expansion of components to reactor temperatures, creep down of the cladding due to differential pressure between the inside of the rod and reactor coolant system, and net radiation exchange between components. The non-linear coupling of the governing equations presented convergence challenges due to the very different effects from a small temperature change on the radiation vs radial and circumferential conduction equations. Initial temperature guesses for the multidimensional solution were provided from conservative 1D models simulating bounding scenarios. Convergence was greatly improved by taking small steps towards the root solutions and averaging with initial guesses. The final method robustly converged all 132 points at an axial plane after a few dozen iterations. The temperature results show that the additional physics have a secondary effect as the dominant heat transfer mechanism is radial conduction across the gas gap followed by circumferential conduction around components. Circumferential conduction in the lithium aluminate pellets is enhanced by the relatively high thermal conductivity of this ceramic.

Carstens, Nathan A.↗
DOE OSTI2020

Evaporation Dataset from a Temperate Closed-basin Lake in Minnesota 2014-2016

This data set contains observed and modeled evaporation data, eddy covariance data, and supporting information measured at White Bear Lake in the Twin Cities Metropolitan Area near St. Paul, Minnesota, USA. The data package includes csv files containing half-hourly latent heat flux, sensible heat flux, and meteorological data from 2014 to 2016. It also includes csv files containing half-hourly observed evaporation measurements, along with supporting data for validation and retrospective modeling of evaporation. This data was collected to examine current, past, and future lake evaporation to better understand how climate will impact the water balance of lakes within this region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Duke Forest FACE (FACTS-I): Meteorological and Soil Data

This dataset, collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment, includes variables describing the meteorological conditions above canopy, within canopy, and soil depending on the variable. The Duke FACE experiment was located in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. The CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plot 1-6 with a common protocol of N-fertilization in 2005. N-fertilization continued until 2012. The data range varied by sensor availability. A summary of information about variable name and data range can be found in the ‘FileDescription_[variable_name].txt’ files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Selected micrometeorological and soil data from the Manaus ZF2 K34 eddy covariance tower for the 2015/16 El Niño event

The K34 tower site (2.609 S, 60.209 W, 130 masl), located within the Tropical Silviculture Experimental Station (EEST, also known as ZF2) research station, in an area of undisturbed tropical forest (Araújo et al. 2002). It features long term measurements using micrometeorological, eddy covariance, and soil instrumentation, among others. This dataset covers January 2012 to December 2017, and includes air temperature (TA, ℃) and air relative humidity (RH, %), measured with thermohygrometers (HC2S3, Campbell Scientific, Logan, UT, USA) deployed above the canopy at the top of the tower (at 51.1 m height), and net radiation (NETRAD, W m-2) using an NR-LITE sensor (Kipp & Zonen, Delft, Netherlands), also at the top of the tower (at 35.0 m above the canopy). The soil moisture data included here is from a soil profile located approximately 12 m from the tower, and covers the period between February 2015 and September 2016. Volumetric soil water content (SWC, m3 m-3) measurements used Water Content Reflectometers (CS655 Campbell Scientific, Logan, UT, USA) at six depths: 10 cm, 20 cm, 30 cm, 40 cm, 60 cm, and 100 cm. Data are included in .csv files, and the text file ZF2-K34_Tower_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Aerosol effects on the anvil characteristics, cold pool forcing and stratiform-convective precipitation partitioning and latent heating of mesoscale convective systems

The overarching science goal of this project has been to assess the impacts of aerosols on mesoscale convective systems (MCSs). More specifically we have investigated the impacts of (a) aerosol types, (b) aerosol concentrations, and (c) the vertical and horizontal location of aerosols on the (1) anvil cloud characteristics and feedbacks; (2) total precipitation and the partitioning between convective and stratiform precipitation; (3) the vertical heating structure; (4) hydrometeor size distributions; and (5) the strength and intensity of the cold pools and updrafts. These have been achieved both through observational analyses and high-resolution cloud-resolving model simulations of three MCSs that were observed during the MC3E field campaign (25 April, 20 May and 23 May 2011). The primary findings of these research are the following: Condensation and deposition are the primary contributors to MCS latent warming, when compared to riming and nucleation processes. In terms of MCS latent cooling, sublimation, melting, and evaporation all play significant roles. Throughout the MCS lifecycle, convective regions demonstrate an approximately linear decrease in the magnitudes of latent heating rates, while latent heating within stratiform regions is associated with transitions between MCS flow regimes including the rear inflow jet. Such information regarding the temporal evolution of latent heating within convective and stratiform MCS regions could be useful in developing parameterizations representing convective organization. Increasing aerosol concentration from a clean continental to a highly polluted state produces an increase in the riming rates of cloud water, which results in less lofted cloud water in polluted environments. Aerosol-induced trends in the cloud mixing ratio profiles are, however, nonmonotonic in the mixed phase region, such that a moderate increase in aerosol concentration produces the greatest reduction in cloud water. Less lofted cloud water due to enhanced riming rates leads to reduced anvil ice mixing ratios but to greater numbers of small ice crystals within the anvil. In spite of reduced anvil ice mixing ratio, the anvil clouds exhibit greater areal coverage, increased albedo, reduced cloud top cooling, and reduced net radiative flux, which produces an aerosol-induced warming (reduced cooling) effect in these MCSs. Lower-tropospheric aerosol particles largely influence the MCS precipitation response directly rearward of the leading cold pool boundary. However, further backward in the MCS, the relative impact of lower- versus mid-tropospheric aerosol particles is strongly dependent on the MCS structure and differences in the line-normal wind shear. Mid-tropospheric aerosol particles are able to activate new cloud droplets in the mid-tropospheric levels of convective updrafts and thereby enhance mixed-phase precipitation through increased cloud riming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment (FY2021--Part 1)

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Support for Advanced Non-Light Water Reactors.” The tasks completed for this report are as follows: First, Task 1d: The net radiation transfer method was implemented into MOOSE for modeling reactor cavity cooling system geometries. RCCS models for two experiments were created: (1) Natural Convection Shutdown Heat Removal Test Facility (NSTF) experiment R022, and (2) HTTR VCS mockup. For validation, computed temperature distributions were compared to measured temperatures. Next, Task 4c: An algorithm for computing the pebble bed reactor equilibrium core isotopic com-position was developed and an initial version is implemented into the reactor multi-physics code Griffin. Initial results for a simplified axisymmetric pebble bed reactor are presented. Finally, Task 7: generation of a reference plant model for molten salt cooled pebble bed reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The Early Career Research Program Annual Progress Report: Using ARM Data and Multiscale Models to Advance the Understanding of Liquid-Phase Cloud Response to Aerosol Perturbation over Ocean and Land

This project aims to study the response of liquid-phase clouds to aerosol perturbation and reduce the related uncertainty in the DOE Earth system model. Specific objectives include: Objective 1: Quantify the aerosol indirect effect on liquid-phase clouds using ARM observations and satellite retrievals while constraining the confounding large-scale conditions; Objective 2: Harness LES case studies, untangle the confounding meteorological impacts on the observed aerosol indirect effect, and identify key cloud processes controlling the net radiative forcing of aerosol-cloud interactions at different spatiotemporal scales; Objective 3: Evaluate the aerosol indirect effect on liquid-phase clouds with process-level understanding in E3SM multiscale model configurations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment

In temperate humid catchments, evapotranspiration returns more than half of the annual precipitation to the atmosphere, thereby determining the balance available to recharge groundwaters and support stream flow and lake levels. Changes in evapotranspiration rates and therefore catchment hydrology could be driven by changes in land use or climate. Here we examine the catchment water balance over the past 50 y for a catchment in southwest Michigan covered by cropland, grassland, forest, and wetlands. Over the study period about 27% of the catchment has been abandoned from row-crop agriculture to perennial vegetation and about 20% of the catchment has reverted to deciduous forest, and the climate has warmed by 1.14°C. Despite these changes in land use, precipitation and stream discharge, and by inference catchment-scale evapotranspiration, have been stable over the study period. The remarkably stable rates of evapotranspirative water loss from the catchment across a period of significant land cover change suggest that rainfed annual crops and perennial vegetation do not differ greatly in evapotranspiration rates, and this is supported by measurements of evapotranspiration from various vegetation types based on soil water monitoring in the same catchment. Compensating changes in the other meteorological drivers of evaporative water demand besides air temperature—wind speed, atmospheric humidity, and net radiation—are also possible, but cannot be evaluated due to insufficient local data across the 50-y period. Regardless of the explanation, this study shows that the water balance of this landscape has been resilient in the face of both land cover and climate change over the past 50 y.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2024

Diurnal evolution of non-precipitating marine stratocumuli in a large-eddy simulation ensemble

Abstract. We explore the cloud system evolution of non-precipitating marine stratocumuli with a focus on the impacts of the diurnal cycle and free-tropospheric (FT) humidity based on an ensemble of 244 large-eddy simulations generated by perturbing initial thermodynamic profiles and aerosol conditions. Cases are categorized based on their degree of decoupling and the cloud liquid water path (LWPc, based on model columns with cloud optical depths greater than one). A budget analysis method is proposed to analyze the evolution of cloud water in both coupled and decoupled boundary layers. More coupled clouds start with a relatively low LWPc and cloud fraction (fc) but experience the least decrease in LWPc and fc during the daytime. More decoupled clouds undergo greater daytime reduction in LWPc and fc, especially those with higher LWPc at sunrise because they suffer from faster weakening of net radiative cooling. During the nighttime, a positive correlation between FT humidity and the LWPc emerges, consistent with higher FT humidity reducing both radiative cooling and the humidity jump, both of which reduce entrainment and increase LWPc. The LWPc is more likely to decrease during the nighttime for a larger LWPc and greater inversion base height (zi), conditions under which entrainment dominates as turbulence develops. In the morning, the rate of the LWPc reduction depends on the LWPc at sunrise, zi, and the degree of decoupling, with distinct contributions from subsidence and radiation.

Chen, Yao-Sheng (ORCID:0000000208354132)↗
DOE OSTI2022

Spatial heterogeneity effects on land surface modeling of water and energy partitioning

Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NCAR/EOL ISFS Data for LASSO-CACTI Overview Paper

5 minute averages of surface meteorology and flux data collected by the NCAR/EOL Integrated Surface Flux System (ISFS) at 15 sites during the RELAMPAGO field campaign. These data have been quality-controlled and are available in NetCDF format. Winds reported by the sonic anemometers have been tilt corrected and rotated into geographic coordinates. Data providence, citation, and acknowledgement This ARM data set is a copy of v2.0 of the NCAR data set obtained on 6-Jun-2024 from https://doi.org/10.26023/ZPHJ-JW9W-2B0Y. The citation for the original data source is: NCAR/EOL In-situ Sensing Facility, Oncley, S. 2021. NCAR/EOL ISFS Surface Meteorology and Flux Products, 5-minute. Version 2.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/ZPHJ-JW9W-2B0Y Accessed 06 Jun 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: "Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/"

atmosphere: surface↗
DOE OSTI2021

Surface Meteorology at Teller Mile 47 Watershed, Seward Peninsula, Alaska, Ongoing from 2018

Meteorological data are currently being collected at one location at the Teller Mile 47 (TL_MM47) Research Basin Site, Seward Peninsula (N64 58' 36.918", W166 12' 32.67", 67 meters above sea level). The site was installed and initial measurements started in September 2018 and it has operated continuously since then. The meteorological station is co-located with a continuous snow depth sensor and two soil pits for subsurface temperature and moisture measurements. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements also at this location. The data could also be used as supporting measurements for other research and modeling activities. There are 35 comma separated value format (*.csv) files provided, where each file contains the full data for an individual parameter (e.g. air temperature at 1.5 meters above the ground surface (teller_m47_air_temperature_150cm_ags_Avg.csv) or soil temperature 20 centimeters below ground surface (teller_m47_dry_soil_pit_temperature_20cmbgs_Avg.csv)) plus the time in Universal Coordinated Time (UTC) and Alaska Standard Time (UTC time minus nine hours). The site was installed and initial measurements started in September 2018. It has been operated continuously since. Primary data gaps are due to battery failure or sensor failure. These data are being collected to better understand the surface energy dynamics above the active layer and permafrost. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗