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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

17 WIND ENERGY↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗
DOE OSTI2020

Fallout Cloud Regimes

The U.S. Department of Defense (DOD), Department of Energy (DOE), and other organizations maintain operational nuclear explosion and atmospheric dispersion models to provide critical guidance on the expected effects of an accidental or deliberate explosion of a nuclear weapon (in this paper simply referred to as “device”). To be effective, these models must represent, as accurately as possible, the complex interactions of the blast, fire, and residual radiological hazards with the environment and population. One hundred atmospheric nuclear tests that form the basis for many models were conducted at the Nevada Test Site (NTS) (now referred to as the Nevada Nuclear Security Site, NNSS) in a dry desert environment. Other environments should be studied, but have less data available and are beyond the scope of the work presented in this paper. The debris clouds produced by the NTS tests, frequently called “mushroom clouds,” are familiar, with common structural elements such as a buoyant cap connected to a skirt of raised dust at the desert surface by a thin, dirt-filled stem. The film scanning project at LLNL has investigated historical film records of nuclear weapons tests. Here, we summarize findings showing that the mushroom cloud behavior for historic U.S. tests conducted in Nevada, has similar characteristics based on the distance of the device from the ground surface or Height of Burst (HOB), scaled by the energy release, or yield, of the device. This scaled height is referred to as the scaled-height-of-burst (SHOB). The findings discussed below show that mushroom clouds look and behave similarly when detonated at the same SHOB. The amount of residual radiation that is produced by a nuclear detonation is proportional to the yield. But, the amount of that residual radiation that actually becomes local fallout is strongly dependent on the SHOB and the type of surface over which the detonation occurs. In order to develop a more comprehensive model that predicts the fraction of the residual radiation that becomes local fallout, it is convenient to define a series of regimes based on SHOB values in which all detonations that occur within a given regime can be modeled using the same algorithms. The purpose of this paper is to provide a framework for defining different regimes, and, in a qualitative way, a basic understanding of the fundamental characteristics of each of these regimes.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.

machine learning↗
DOE OSTI2023

Heat flux micro coolers having multi-stepped features and fluid wicking

A cooler device includes a cold plate and a manifold with fluid wicking structure. The cold plate includes an array of bonding posts and an array of fluid channels. Each bonding post of the array of bonding posts has a first height and is in contact with the manifold with fluid wicking structure. Each fluid channel of the array of fluid channels has a second height that is less than the first height. The array of fluid channels include a MIO secondary wick structure. The array of bonding posts is orthogonal to the array of fluid channels. The manifold with fluid wicking structure includes a plurality of spacer elements and a plurality of mesh layers. Each one of the plurality of spacer elements alternate with each one of the plurality of mesh layers in a stacked arrangement.

Zhang, Chi↗
DOE OSTI2020

Utilizing Commercial Submersible Mixer Pumps for Sludge Removal in Savannah River Site's Tank 26 - 20289

The Savannah River Site (SRS) Liquid Waste System (LWS) safely manages, stores, treats, and dispositions liquid radioactive waste. The LWS consists of 51 underground waste storage tanks (eight of which are operationally closed and filled with grout), waste evaporators, treatment facilities, and solidification facilities, known as the Defense Waste Processing Facility (DWPF) and Saltstone Production Facility (SPF). One of the waste storage tanks, Tank 26, was placed into service in 1980 as an F Tank Farm (FTF) Evaporator Feed Tank. From 1980 to 2013, Tank 26 received F Canyon receipts and dilute supernate that was then transferred to the 242-16F FTF Evaporator for volume reduction. In 2013, the steam tube bundle failed in the FTF Evaporator, and the decision was made not to repair/resume evaporator operation. During operation of Tank 26, solids built up to a level of 2.03 m in the tank with a volume of 1062.9 kL. Due to the evaporator failure and subsequent shutdown, the solids in Tank 26 were selected to feed Sludge Batch 10, which is collected and prepped prior to being sent to DWPF for final disposition. To accomplish solids removal, Commercial Submersible Mixer Pumps (CSMPs) were selected to slurry the solids in preparation to be sent to Tank 51. Four CSMPs were installed in Tank 26 with each one installed in a separate quadrant of Tank 26. This work would mark the first deployment of CSMPs in an SRS waste tank. The CSMPs were developed in response to operational issues from previous mixing pumps and budgetary constraints. The CSMPs use the concept of modifying commercially available equipment for nuclear waste applications. The CSMPs consist of a 230-horsepower submersible mixing pump (manufactured by GPM, Inc.) joined to a mast fabricated at SRS. For this application, Savannah River Remediation (SRR) design services was challenged to provide a mast design that required no steel superstructure support system on top of Tank 26. Also, SRR design services provided a simplified Tank 26 riser interface that did not require extensive machine parts to adjust height of the suction screen of the CSMP within the tank. Other design improvements include no requirements for tank top radiation monitors and no requirements for flushing of the CSMPs during startup/shutdown operations. The CSMPs were operated at an initial height of 2.29 m above the Tank 26 bottom for 10 days. After sludge sounding, the CSMPs were lowered to a height of 1.52 m with no issues due to proper work planning and the simplified riser interface. With the CSMPs lowered to a level of 1.52 meters above the tank bottom, the CSMPs were operated for 12 days and another sounding was performed. The sounding level came back matching the disturbance depth results of the first pump run. Chemistry samples were analyzed while the tank was left to settle. The analysis revealed enough weight percent solids to provide good feed to Sludge Batch 10. Also, the results revealed the supernate within the mixture would provide good salt solution feed to Salt Waste Processing Facility (SWPF) Salt Batch 3. So, the plan was modified to let the disturbed solids settle and decant the salt solution for addition to SWPF Salt Batch 3. Then water will be added back to Tank 26, and the CSMPs run in order to wash the solids to decrease settling time prior to being sent to Tank 51 for Sludge Batch 10. Utilizing CSMPs for waste removal in an SRS Tank has provided a cost-effective means for further waste removal efforts. In addition, the CSMPs are easier to operate by utilizing a robust and simplified design. The CSMPs performed quite well with no process shutdown or delays during operation. As a result, CSMPs are integral to the future of removing radioactive waste from storage tanks at SRS. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons’ RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tree architectural characteristics and stem and leaf functional traits for 17 individuals in the Central Amazon

Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Research was conducted at the ZF2 Research Station located north or Manaus, Brazil, managed by the Instituto Nacional de Pesquisas da Amazônia (INPA). Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Wood logs were collected at DBH, 50% stem length and at 100% stem length (at the base of the canopy). For wood anatomy samples, n=3-6 subsamples from each height. For leaf samples, 30 leaves were collected from the upper sunlit canopy. The methodology is detailed in the accompanying manuscript. The trait data are summarized in this file: "Trait_Summary.CSV". Summary Trait code abbreviations and units are described in this file: "Sample_Info_Traits_Summary.CSV". Stem traits measured along the bole from the base of the tree (DBH, diameter breast height), mid-stem, and base of the canopy are described in these files: "Sapwood_Area_height.CSV"; "Species_Info_height.CSV"; "Sample_Info_height.CSV"

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Compiled TBS data from Oliktok Point from ICARUS, AVPOP, and POPEYE

The dataset encompasses QA/QC TBS data, including TBS altitude, atmospheric pressure, dew point, POPs number concentrations and mean aerosol size, relative humidity, temperature, equivalent potential temperature, equivalent temperature, and date and time. These data are provided per day for at least one TBS flight. Supporting data from the AMF3 aligned with the timestamps of the TBS data include cloud base distance from TBS, if the time corresponds to cloud base transit of the TBS, surface-based pressure, surface-based precipitation rate, surface-based UHSAS data, ceilometer cloud base height, KAZR cloud top height, and MWR liquid water content. Both POPS and UHSAS aerosol number concentrations are provided as: (1) absolute (i.e. concentrations for each instrument's total size range) and (2) the overlapping size range (i.e. the same size bins for both instruments).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

KAZR Hydrometeor and Insect Masks

The Ka-band ARM zenith pointing radar (aka, KAZR) is so sensitive that it detects cloud particles and individual insects. While detecting insects with Ka-band radar is beneficial and desirable to advance radar entomology, this sensitivity can be detrimental to radar meteorology because insects could be interpreted as clouds or precipitation. For example, misclassifying insects as clouds has been a problem for the ARM Active Remote Sensing of Clouds (ARSCL) Value Added Product since its inception (Clothiaux et al. 2000). Based on cloud particle and insect radar scattering properties, an algorithm was developed that identifies clouds, raindrops, ice particles, and insects in KAZR co- and cross-polarimeteric Doppler velocity spectra. The algorithm produces affirmative masks in the KAZR native time and height resolution indicating time-height locations of hydrometeors and insects. The hydrometeor mask contains binary information (e.g., yes/no hydrometeor presence), and the insect mask includes a proxy for insect activity that increases when more insects are detected in the Doppler velocity spectra. The algorithm was developed using KAZR medium sensitivity mode (MD) observations and was applied to two summer seasons of KAZR observations at the Southern Great Plains (SGP) Central Facility: May-October 2018 and 2019. In the future, this data set will be expanded to include other KAZR operating modes and observations from other ARM field sites. Details of the algorithm and data set can be found in: Williams, C.R., K.L. Johnson, S.E. Giangrande, J. C. Hardin, R. Oktem, and D. M. Romps, 2021: Identifying Insects, Clouds, and Precipitation using Vertically Pointing Polarimetric Radar Doppler Velocity Spectra. Atmospheric Measurement Techniques, submitted 6-Feb-2021. https://amt.copernicus.org/preprints/amt-2021-27/#discussion.For more information on the ARSCL VAP, see Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000; J. Appl. Meteor., 39, 645-665.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Investigation on cloud vertical structures based on Ka-band cloud radar observations at Wuhan in Central China

Based on vertically pointing observations of Ka-band frequency-modulated continuous millimeter wave cloud radar (MMCR) in 2020, we investigate cloud vertical structures at Wuhan in central China. Case comparison indicates that the cloud top locations in the MMCR observation are largely consistent with those in satellite data. The precipitation has the occurrence frequency of 9.5% over the year, with the maximum value of 16.3% in July and the submaximal value of 14.7% in January, showing the significant influences of monsoons on precipitation at Wuhan. As the humid, cloudy and rainy climate, the probability of precipitation and non-precipitation clouds increases gradually from 53.9% in January to a large value of 71.5% in July. The non-precipitation clouds have the probability of 44.5% in the year, with two prominent peaks at the reflectivities of -35 to -30 dBZ around 12 km in summer and - 37– -32 dBZ around 10 km in spring. The cloud top can reach the heights of 14.49, 16.62, 13.38 and 11.73 km from spring to winter, respectively. The monthly mean cloud layer thickness is the largest (2.18 km) in May and September, and the smallest (1.36 km) in November. The seasonally averaged base and top heights and thickness of clouds have small diurnal variations. The high cloud is predominant in the warm months from May to September, and in the other cool months, the middle cloud has the most fraction. The quantitative investigation of clouds is helpful in improving the cloud parameterization in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Streamflow measurements from four sites on the Tuolumne River in Yosemite National Park from Water Years 2002 to 2021

Regions with remote and complex terrain experience spatially varying streamflow patterns, but are often poorly sampled due to difficult access. This data package includes streamflow measurements collected using low-visibility and low-impact installations at four sites on the Tuolumne River in Yosemite National Park, for water years 2002 to 2021. The resulting data set offers a unique opportunity to explore hydrologic processes in complex terrain.This data package contains half-hourly recordings of unvented pressure, vented pressure, and water temperature are measured and used to estimate discharge and stage height. Discharge flags provide insight into data anomalies. This dataset is formatted in accordance with ESS-Dive's Hydrologic Monitoring and File Level Metadata Formats. It contains the following files:1) Folder containing four csv files of time series streamflow measurements (unvented pressure, vented pressure, estimated discharge, water temperature, stage height, and discharge flag) from four locations on the Tuolumne River2) Data dictionary (dd.csv) containing units, definitions, human readable column names, and data type for all column headers throughout the dataset3) File-level metadata (FLMD.csv) containing metadata for files contained in the dataset4) Installation methods (InstallationMethods.csv) containing metadata on sensor installation

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗
DOE OSTI2024

SAIL Storm Boards 2022-2023

“Storm Boards” are simple installations comprised of a horizontal white plastic board that is placed in situ on the surface of the snowpack with a 1 m vertical marker pole for identifying the board center and approximate new snow height. Storm boards allow newly fallen snow to accumulate on them while staying separated from moisture and energy exchange with the snowpack, thus creating a discrete sample of newly fallen snow depth & SWE (snow water equivalent) that can be measured to calculate the net moisture input of individual or multiple storm events to the seasonal snowpack. Samples are measured with a 2” diameter plastic tube that allows the observer to non destructively take a snow core of the newly deposited snow; this core is weighed with a small hand scale - then the mass of the sample is converted to SWE. Depth of the snow sample is obtained with a snow depth probe or from graduated marks on the plastic tube. 3 or more replicate samples are taken for each board measurement and averaged. Total snow height (HS) of the snowpack is measured with a snow depth probe for each site & timepoint to track snowpack accumulation/ablation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ARM SGP PBLH and MLH datasets from Raman lidar and Doppler lidar

The planetary boundary layer (PBL) plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including the daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary for studying PBL characterization and processes. Here, new approaches are developed to provide reliable PBLH and MLH estimates to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) User Facility. Compared to widely used lidar aerosol measurements for PBLH, WVMR is a better tracer for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to reconstruct 2D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm-season RL and ML measurements at the SGP site for five years to study warm-season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in a warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable data set of PBLH and MLH values will be valuable for studying PBL processes, model evolution, and PBL parameterization improvements. The MLH dataset includes the MLH in values of km above ground level. The PBLH dataset includes the PBLH in values of km above ground level, along with a flag ("situation_PBLH") to determine the state of the PBL (1 = Cloudy Condition, 2 = Stable Layer, 3 = Multi-layer WVMR structure, 4 = Well-Mixed PBL, 5 = A de-coupled layer, 6 = Other).

mixing layer height↗
DOE OSTI2022

Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing

Abstract The binary fluidization of Geldart D type nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in a laboratory‐scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using particle image velocimetry (PIV). The wood particles orientation and velocity are measured using particle tracking velocimetry (PTV). A machine learning pixel‐wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.

Li, Cheng↗
DOE OSTI2022

Additively manufactured cryogenic microchannel distillation device for air separation

Abstract The efficiency of air separation is tested using three different small‐scale cryogenic distillation columns. The performance of a random packed column is compared to the performance of two microchannel distillation (MCD) columns that use thin wicking structures and gas flow channels to achieve process intensification. The MCD columns tested include a plate‐type layered column and an additively manufactured porous honeycomb (AMPH) column. For columns with 25.4 cm of active height and run under similar conditions, the packed, plate‐type layering, and AMPH columns achieved approximate height equivalent of a theoretical plate (HETP) values of 5.5, 3.7, and 3.2 cm for nitrogen, and 5.9, 4.9, and 3.3 cm for argon. The AMPH column can produce up to 0.4 SLM of more than 90% purity oxygen with 12 W of cooling lift. These results demonstrate the feasibility of using additive manufacturing to construct MCD devices and pave a way for constructing novel MCD designs.

36 MATERIALS SCIENCE↗