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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Energizer Bunny: Dual-Use Photovoltaic and Pasture-Raised Rabbit Farms (Final Report)

This case study project lays the groundwork for the broader realization of agrivoltaics by considering the techno-economic, environmental, and social aspects of system development and assessing a novel rabbit-based concept. Research on the social and political barriers to agrivoltaic development is sparse, which emphasizes the importance of this project as it provides key insight into the consequential socio-political challenges that may hinder the diffusion of agrivoltaic technology. Additionally, the rabbit agrivoltaic concept is novel and has not been applied in experimental or commercial applications to date. This project advances our understanding of the viability of this co-location scheme through empirical assessment of integrating grazing rabbits with solar. A total of five manuscripts (3 published in peer-reviewed journals, 2 under review) have resulted from this project. The techno-economic component of the project included a) a pilot test study investigating the feasibility of the rabbit agrivoltaic concept and b) an economic analysis of the advantages available to solar operators who leverage grazing animals for lawn maintenance and a dual-revenue stream. Our calculations indicate that the co-location of solar and rabbit farms is a viable form of agrivoltaics, increasing overall site revenue by 2.5%-24.0% above projected electricity revenue. The environmental component of the project entailed an ISO 14040 and 14044 compliant life cycle assessment to quantify the impacts of the rabbit agrivoltaic concept. The results indicate pasture-based agrivoltaic systems provide a dual synergy that produces 69.3% less emissions and demands 82.9% less fossil energy per functional unit of cumulative MWh output of electricity and cumulative kg of meat compared to non-integrated production. The social component of the project included conducting two separate interview studies with agriculture and solar industry professionals and the distribution of a public survey. The interview findings identify the key socio-political opportunities for agrivoltaics include: the retention of agricultural land and rural interests, and increased local level acceptance of solar development. The key barriers to agrivoltaics include: ensuring long term agricultural productivity is not compromised, and subnational zoning strategies. The survey results indicate that support for local solar projects increase by 10% when an agricultural function is incorporated. Survey respondents prefer agrivoltaic projects that are a) designed to provide economic opportunities for farmers and the local community b) located on private property or existing agricultural land, and c) do not threaten local interests. Cumulatively, this project can inform agrivoltaic decision making, solar development practices, rabbit-based applications, land use management, and policy making in a way that supports the furtherance of the renewable energy transition, conserves arable land, and utilizes innovative solar photovoltaic technologies.

14 SOLAR ENERGY↗
DOE OSTI2021

Evidence for Coupling Between the Subseasonal Oscillations in the Southern Hemisphere Midlatitude Ocean and Atmosphere

Abstract A regional perspective is taken to investigate the intriguing ∼25‐day periodicity in the Southern Hemisphere (SH) baroclinic annular mode (BAM) and the possible underlying mechanisms. Results show that the 25‐day periodicity exists not only in the zonal average but also in the regional average of eddy activity as long as the domain of the average is wide enough to accommodate a wave packet. The favorable domain for the ∼25 days periodicity is located downstream of the largest baroclinicity in midlatitudes. The wave activity developed there can travel circumglobally several times with a typical group velocity of 25 m s −1 , and reenter the domain every 12.5 days, setting the base periodicity. A slower oceanic oscillation at a time scale of ∼100 days in the same region appears to select and reinforce the multiples of 12.5 days, giving rise to a pronounced ∼25‐day periodicity in the wave activity.

Xue, Daokai↗
DOE OSTI2024

Low-cost sensor-based damage localization for large-area monitoring of FRP composites

Abstract In recent years, there has been growing interest in self-sensing structural materials across research and industry sectors. Detecting and locating structural damage typically requires numerous sensors wired to a data acquisition (DAQ) circuit, rendering implementation impractical in real structures. This paper proposes an innovative, cost-effective sensor network for damage detection and localization in fiber-reinforced polymer composites. The innovation encompasses three key elements: (1) utilizing carbon fiber tows within the composite as piezoresistive sensors, eliminating the need for additional foreign sensor devices; (2) introducing a novel sensor layout wherein sensor tow branches with varied resistance values are connected in parallel, reducing the number of connections to the DAQ circuit and cutting manufacturing costs significantly; (3) developing a practical sensor terminal fabrication technique to minimize manufacturing expenses. The proposed design methodology for the branch resistance values is first validated using a demonstration panel. Subsequently, the overall strategy is assessed by conducting impact tests on carbon and glass fiber-reinforced composite specimens. Results validate the sensor’s ability to accurately detect and locate structural damage.

Demo, Luke B. (ORCID:0000000208806522)↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy

The advantages offered by additive manufacturing over traditional processes has driven a great deal of industrial and academic interest in recent years. However, the process is relatively new and requires additional investigation to become sufficiently mature for wide scale industrial adoption. Electron beam melting powder bed fusion is one technology that has shown promise for fabricating high temperature resistant materials such as nickel based superalloys. The resulting microstructures typically exhibit a strong fiber texture in the build direction giving rise to anisotropic time-dependent deformation behavior. In order to accelerate the qualification of these materials for industrial adoption accurate numerical models are needed for simulating their behavior. In this work a crystal plasticity model including non-Schmid effects is presented for capturing creep anisotropy observed in additively manufactured IN738LC. The model is calibrated via a probabilistic framework where model parameters are treated as random variables. An iterative sequential design strategy is utilized to efficiently identify the probability density of the unknown model parameters. As a case study the model is utilized to investigate the behavior of randomly oriented equiaxed grain clusters sometimes observed embedded in the additively manufactured columnar structure. A synthetic realization is simulated and uncertainty is propagated through to the full-field response. Results indicate that these features are the source of significant creep relaxation and strain accumulation which partially explains observed grain boundary decohesion at these locations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2022

Residual stress and microstructure in IN718-René41 graded superalloy fabricated by laser blown directed energy deposition

Additively printed Ni-based superalloy with a compositionally graded transition from IN718 to René41 was fabricated by laser blown-powder directed energy deposition (DED/LB-M), with the goals of meeting location-specific temperature capability and reducing component cost for hot gas path turbine components. Residual stress distribution in thin wall specimens with three sets of DED build parameters in the as-built and stress-relieved states was measured by neutron diffraction. For calculating residual stress, the calculated d0 method was found to be more appropriate as stress-free reference than using the lattice spacing measured from the stress-relief heat treated specimens. Longer dwell time (lower interpass temperature), higher energy input, smaller layer thickness resulted in a higher magnitude of tensile residual stresses at edges and compressive residual stresses at center of the specimens. The residual stress results did not show a strong dependence on graded compositions, indicating that the residual stress build-up was more geometry and process dependent. Non-destructive neutron imaging based on the attenuation coefficient qualitatively visualized the compositional variation in the bulk and showed good agreement with quantitative Electron Probe Micro-Analysis (EPMA) measurement. Grain structure, texture, and residual plastic strain along the build direction were characterized by Electron Backscatter Diffraction (EBSD). Long columnar grains with (001) preferred grain orientation were dominant along the build direction. Compositional change did not show an obvious effect on the epitaxial growth of dendrites and the continuation of the columnar grains. Residual plastic strain was relatively low in the as-built specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The atomic-level structure and stability of interfaces of Pt nanoparticles in alumina: An experimental and computational evaluation

The atomic-level structure of interfaces between Pt and a transition form of Al2O3 were studied using a combination of electron microscopy and first principles calculations. A model system of Pt nanoprecipitates in Al2O3 were formed in sapphire wafers via high-energy ion implantation of Pt followed by thermal annealing at 1000 °C in air. The Pt nanoparticles took the form of tetrahedra and truncated tetrahedra primarily bound by {111}Pt facets. The high prevalence of these facets motivated the development of density functional theory (DFT) based models of (111)Pt interfaces with six different chemical terminations of ( 2 ¯ 01 ) θ-alumina. The atomic-level structure of the Pt/Al2O3 interfaces was characterized with aberration-corrected scanning transmission electron microscopy (STEM) and the experimental images were compared to STEM image simulations of the DFT models. The model interface with Pt bonded to oxygen-terminated θ-Al2O3, with the Pt located on top of the O and with an underlying layer of octahedral Al, provided the best match to the experimental images. This interfacial termination is also the most stable for the thermal annealing conditions used based on thermodynamic calculations of the interfacial energy as a function of temperature and oxygen partial pressure. This experimentally verified model provides a basis for improving models of Pt/γ-alumina interfaces.

Clauser, A L↗
DOE OSTI2022

WBS 1.2.3.405 - Life Cycle Assessment of Storage Technologies

Recent commitments by the Biden administration have established targets to achieve a net-zero energy system by 2050. Meeting these targets will spur a rapid transition to clean energy technologies and a commensurate need to develop and deploy energy storage technologies at scale. Pumped Storage Hydro (PSH) is expected to be part of this solution because its ability to provide grid flexibility and stability and enable the dispatching of disparate variable renewable energy technologies. Despite PSH being a mature technology with a history of deployment dating back several decades, there is very little information on the greenhouse gas (GHG) implications of PSH as compared to other storage technologies. The objective of this project is to perform a full lifecycle assessment (LCA) of new PSH projects in the U.S. This LCA includes all project phases (resource extraction, construction, operation, maintenance, end-of-life). The functional unit for this study is 1 kWh electricity delivered by system to grid substation connection point and the estimated lifetime for our base case is 80 years. Data used in this study are based on over 30 potential PSH projects that are in preliminary planning phases and are represent a wide range of potential closed-loop PSH systems in terms of location, technology, and capacity. The project approach, data sources, and modeling assumptions have been informed by a technical review committee of stakeholders that include experts from academia, national and international government, industry, and utilities. The GHGs and energy return on investment (EROI) from PSH will be compared to other storage technologies (e.g., stationary battery storage). Results from this project will improve the PSH community's understanding of the environmental impacts and sustainability of new PSH projects and how PSH compares to other storage technologies. The approach used in this project relies on open-source programming. The analysis framework (source code and data) and will be made publicly available at the end of the project. In addition to reporting results for the base case, we will perform rigorous sensitivity analysis to identify the major drivers, understand impacts of different configurations, and future energy markets. Results from this project will be published in a suitable journal.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2023

Cascadia Subduction Zone Fault Heterogeneities From Newly Detected Small Magnitude Earthquakes

The Cascadia subduction zone (CSZ) is known to host M9 megathrust ruptures; however, no such event has occurred in historical observation. The distribution and characteristics of small- to moderate-sized earthquakes can be used to determine the behavior of the megathrust fault but are notably absent offshore the CSZ due to the distance from onshore seismometers. We use automated subspace detection coupled with an onshore-offshore seismic deployment to find small-magnitude earthquakes in the offshore seismogenic zone and analyze their locations in the context of interseismic locking and seismogenic zone extent. Here we detected and located 5,282 earthquakes, 4,096 of which had been previously undetected. We find that the downdip extent of the seismogenic zone as defined by interplate seismicity agrees with the 20% locking contour of the Schmalzle et al. (2014, https://doi.org/10.1002/2013GC005172) geodetic model and extends deeper than predicted by previous thermal models. We cannot determine the updip extent of the seismogenic zone; this may be due to a lack of templates for detection in the updip source area, stress shadows updip of asperity loading, and/or strong locking to the trench. We present a map of possible asperities determined by the small earthquakes in this study. Our asperity locations and extents show some, but not complete, agreement with the asperities modeled from the 1700 M9 rupture and geodetic locking models, and good agreement with the paleo-rupture extents determined from offshore turbidites and forearc basin-based asperity estimates. This highlights the need of continued offshore observations over time, and to elucidate fine-scale variation in locking.

58 GEOSCIENCES↗
DOE OSTI2019

Direct measurements of slip irreversibility in a nickel-based superalloy using high resolution digital image correlation

Fatigue crack nucleation in crystalline materials typically develops due to highly localized cyclic slip. During a fatigue cycle, reverse slip differs locally from slip in the forward direction particularly in precipitate-containing materials such as superalloys. In this paper we report the first direct measurements of irreversibility at the scale of individual slip bands by high-resolution digital image correlation (DIC) in a polycrystalline nickel-based superalloy. Quantitative measurements of the slip irreversibility are challenging for regions of material that have a size that captures the microstructure and its variability. High spatial resolution at the nanometer scale during experimental measurements is needed to observe slip localization during deformation. Moreover, large fields are also needed to obtain the material response over statistically representative populations of microstructural configurations. Recently, high resolution scanning electron microscope (SEM) digital image correlation (DIC) has been extended for quantitative analysis of discontinuities induced by slip events using the Heaviside-DIC method. Therefore, this novel method provides quantitative measurements of slip localization at the specimen surface. In this paper, the Heaviside-DIC method is used to measure slip irreversibility and plastic strain accumulation in a nickel-based superalloy. The method detects bands with high levels of irreversibility early in cycling that ultimately form fatigue cracks upon further cycling. The local microstructural configurations that induce large amounts of plasticity and slip irreversibility are correlated to crack nucleation locations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Demonstrating SolarPILOT's Python API Through Heliostat Optimal Aimpoint Strategy Use Case: Preprint

SolarPILOT is a software package that generates heliostat field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL's System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application's graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called "LK." This paper presents a new, full-featured Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT's capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instances with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layout; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying heliostat field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating heliostat field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed tower CSP analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot's functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize CSP tower research.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Climate Impact on CO2 Capture Efficiency and Levelized Cost of Liquid solvent-based DAC (Direct Air Capture) System

The transition towards net-zero emission poses economic, technical, and political challenges for the commercial-scale deployment of CO2 removal technologies by 2050. The opportunities for large-scale deployment will largely depend on the distinctive conditions found in the different locations of the world such as energy cost, carbon intensity of energy source, construction, transportation, and weather conditions. In this work, we focus on one of the promising negative emission technology, DAC (Direct Air Capture), and its response to different weather conditions. This work presents two potential operation scenarios: (i) natural gas standalone, (ii) grid electricity connected DAC plant. For the first time, we investigated the influence of temperature and RH (relative humidity) of the air on liquid-based DAC system and its response to carbon capture efficiency and levelized cost of carbon capture with operational sensitivity analysis. It is observed that the overall energy demand decreases from 11.1 to 8.3 GJ/ton-CO2 as the CO2 capture rate increases from 40 to 85%. We observed that a CO2 capture rate of 75% is only possible above 17°C even at 90% RH and this drops tremendously at lower temperatures. It is also observed that water evaporation in the air contactor is highest at dry and low RH as expected. The sensitivity analysis showed that weather conditions are insensitive to CO2 capture efficiency for the liquid-solvent based DAC plant for both scenarios. In addition, higher CO2 capture efficiency is achievable from lower upstream methane emissions and carbon intensity of grid electricity (for grid-connected scenario). Lastly, the levelized cost of natural gas standalone scenario varies from 239.7 to 408.8 $/t-CO2 is sensitive to temperature conditions more than relative humidity and compared to electricity grid-connected scenario, from 265.3 to 440.1 $/t-CO2.

An, Keju↗
DOE OSTI2023

Real-Time, Simultaneous Soil Water Content and Meteorological Data Measurement to Support TRACER over Harris County, Texas (Field Campaign Report: Part I)

The main purpose of this project was to provide ground-truth and satellite-based soil water content data in the Houston, Texas, area to support the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s 2022 field campaign, the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This involved installing four soil monitoring stations at different locations in the Houston area, two of which were part of the ARM facility and two were ancillary. The two stations associated directly with ARM were installed alongside other facilities managed and maintained by the TRACER research team during the intensive operational period (IOP), one located at the La Porte, Texas airport and the other near Guy, Texas. Data from these stations were assimilated with similar data collected by the Harris County Flood Control District (HCFCD) to improve the spatial coverage of the real-time monitoring network. The ground-truth data were compared to the satellite-based (National Aeronautics and Space Administration [NASA]’s Soil Moisture Active Passive [SMAP] mission) data in the TRACER area of interest, and analyzed further to nowcast gridded data over the Houston area. The nowcasted, gridded data product was made available to TRACER researchers for use in climate and land-atmosphere interaction modeling that can help understand formation and persistence of convective storms in dense urban areas, and predict possible environmental events such as floods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying preferential flow from soil moisture time series: Review of methodologies

Abstract Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones. Core Ideas Soil moisture data can be used to identify the occurrence of preferential flow (PF) and its initiating conditions. Various data‐analysis methods to identify PF differ in susceptibility to error, bias, and subjectivity. These methods can utilize vast amounts of data from soil moisture monitoring networks to develop understanding of when, where, and under what conditions PF occurs. Newly developed methods may lead to better accuracy and reliability, and reduce the need for subjective judgments. Plain Language Summary Preferential flow through soil occurs when a large amount of water is suddenly available, as during an intense storm. This type of flow moves rapidly through the soil in distinct narrow pathways rather than moving evenly throughout the body of soil, with major consequences for groundwater resources, ecosystems, spreading of contaminants, and other vital concerns. Methods of detecting preferential flow have been developed that utilize measurements of soil water content made by sensors installed at various depths. This measurement technology has been widely implemented, many locations now having datasets years in length, and various methods have been developed for using these to identify preferential flow. The various methods are based on different features in the soil moisture records and vary in their advantages and shortcomings. In this review, we explain and evaluate these methods, highlighting important information for their implementation to identify preferential flow from soil moisture data and for efforts to develop new methods or improve existing ones.

Nimmo, John R↗