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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Using Separation-Enhanced Isotope Ratio Mass Spectrometry to Enable Increased Renewable Carbon Content in Transportation Fuels (CRADA 525)

Stable isotope ratio measurements of carbon atoms using isotope ratio mass spectrometry (IRMS) can be an effective tool for quantifying biogenic carbon in co-processed fuels, with results approaching the precision and accuracy of accelerator mass spectrometry (AMS). The lower cost of an IRMS may enable deployment to refineries, improving access and analysis turnaround times (≤2 hours), and, by extension, provide data that can allow process optimization to maximize renewable carbon in desired refinery products. This project explored the integration of chemical separation with IRMS analyses to enable highly detailed tracking of biogenic carbon into fuel product streams separated by boiling point range, chemical class, or specific compound. Forty-nine fuels and fuel components of fossil and biogenic origin, spanning gasoline and diesel boiling point ranges, were received from three refiners and were analyzed for their δ 13 C values via IRMS. Results spanned a 13 C range from ca. 10‰ to 44‰ and reflect materials derived from sustainable sources (e.g., C4 or C3 plants, animal-based pathways, syngas) or from fossil-derived fuels. Common ranges are approximately 18‰ to 9‰ and approximately 30‰ to 20‰ for C4 and C3 plants, respectively, and approximately 34‰ to 24‰ and approximately 70‰ to 33‰ for petroleum-derived fuels and methane, respectively. Fuel-like standards were developed and tested using direct-injection elemental analyzer (EA) IRMS for liquid fuels. This method was compared with the published methods, yielding statistically similar results. Four blend curve sets were produced ranging from 0% to 100% of a fuel containing biogenic carbon, focusing on 0% to 10% biogenic carbon. Linear fits were the most applicable for two of the four blend curve sets; however, two sets were found to exhibit slightly quadratic behavior, which was more pronounced in low biogenic blend samples, necessitating second-order fits. The origin of the slight quadratic behavior remains unclear; however, the discussion points to possible interpretations. CanmetENERGY thoroughly characterized a majority of the samples using one- and two-dimensional gas chromatography (GC and GC×GC, respectively) and other analyses. Selected samples were subjected to solid phase extraction (SPE) for saturate, olefin, aromatic, and polar (SOAP) analysis, and the resulting solvent-diluted fractions containing saturates and aromatics were returned to Pacific Northwest National Laboratory (PNNL), where the solvent was removed via evaporation or physical separation using GC techniques. Characterization and separations provided an understanding of saturate and aromatic content, as well as boiling point ranges for each sample and sample fraction. Samples resulting from SPE were examined using EA-IRMS and gas chromatography combustion IRMS (GC-C-IRMS) analyses. Both approaches suggest that the range in values between end-members can be increased by selecting the paraffinic or aromatic fraction of the end-member or by selecting among individual compounds resulting from GC separation of the paraffinic fractions. Considerable work remains to put these approaches into practice and statistically validate the benefit for using a fraction or individual compound over bulk analysis of a sample. However, initial results suggest that separations provide advantages for samples having blend ratios of less than 10% biogenic blendstocks. 13 C results showed statistically similar biofuel blend results to those obtained at PNNL, although additional work is needed to obtain better reproducibility. Select samples were sent to Los Alamos National Laboratory (LANL) for IRMS measurements and Beta Analytics for AMS measurements. This work suggests that IRMS and AMS yield closely comparable results and in some circumstances, IRMS could serve as a surrogate for AMS. While additional work is needed to better resolve statistical advantages for separations and better show the comparable nature of IRMS and AMS in both the biogenic carbon analysis of bulk chemical classes, initial results from this study suggest that these should be pursued in order to proliferate this approach for quantifying biogenic carbon in transportation fuels to the refinery level, thereby potentially enabling process optimization in co-processing scenarios.

09 BIOMASS FUELS↗
DOE OSTI2025

Using low cost, bio-derived and recycled materials in advanced scalable small- and medium- wind turbine manufacturing

In partnership with Bergey Windpower, this project explored the use of recyclable and recycled materials in small- to medium-wind turbine blades (WTBs). A recyclable epoxy resin and infusible thermoplastic resin were investigated using identical fiber reinforcements and manufacturing practices used as Bergey, and the substitution of continuous virgin glass fiber (GF) layers with recycled nonwoven GF (rGF, NW) mats was trialed. The alternative resins both showed very promising performance in terms of static tensile and flexural behavior as well as thermal properties and fatigue behavior in comparison with that of the incumbent materials used at Bergey. The NW rGF materials, however, were found to be a poor substitute for the continuous GF materials for two primary reasons. Firstly, the NW mats took up significantly more resin than their continuous counterparts, which would increase both the weight and the cost of the WTBs. Additionally, an unexpected reaction occurred between the rGF and epoxy materials that resulted in a highly porous, foamed structure. The foaming phenomenon was not observed with the rGF NW and infusible thermoplastic material, but the resin uptake was still prohibitively high. Based on the results of this study, recyclable infusible resins could be viable candidate materials for Bergey in the future. The foaming reaction with the rGF NW mats could prove highly useful in other applications requiring structural foams and has been used to produce demonstrative sandwich panels.

17 WIND ENERGY↗
DOE OSTI2025

Demonstration of Monte Carlo Reference Equilibrium Core Solutions for Pebble Bed Reactors Using Shift in kugelpy

This report describes how to produce reference solutions for the equilibrium core search of pebble bed reactors using the pyrates Python library. The pyrates library uses the kugelpy methodology to perform running-in calculations to reach an equilibrium core. This approach relies on full-core Monte Carlo neutron transport calculations and the shifting of fuel pebble inventory through the reactor to simulate pebble movement in the core from one step to the next of a running-in scenario. In addition to the use of Serpent for the Monte Carlo neutron transport calculations, as part of this work, updates for the use of the Shift Monte Carlo code were integrated into the official pyrates GitHub repository. Comparisons of running-in simulations for a generic pebble bed reactor produced by using pyrates with Serpent and Shift are shown. Although consistent trends are shown between the use of the two Monte Carlo codes, the limiting factor in either calculation is the computation time due to the need to perform several hundred full-core neutron transport calculations before an equilibrium core is reached.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

NUM-DAT File Format Specification: Used in M-9 Gun Experiment Data Archiving

The M-9 Shock and Detonation Physics group executes experiments on gun and explosive platforms with large numbers of oscilloscopes used for data acquisition. The data acquisition from these oscilloscopes was automated many years ago using a custom piece of software called RunDig . The default save format from this software is a custom structure referred to as "NUM-DAT" format. This file format includes a text ".DAT" file which is a header file used to interpret the binary ".NUM" file which contains the oscilloscope data. The data save format was originally developed by John Vorthman and has been in use by M-9 personnel for over 20 years. This data format has been used for archiving data from experiments performed by M-9 personnel at TA-40, TA-39, and the TA-55 Impact Test Facility. Numerous custom analysis and visualization programs have also been developed, and continue to be used, that utilize this data format. This document describes the NUM-DAT format and provides code examples for reading the format and converting it to other formats.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Demonstration of Monte Carlo Reference Equilibrium Core Solutions for Pebble Bed Reactors Using Shift in kugelpy

This report describes how to produce reference solutions for the equilibrium core search of pebble bed reactors using the pyrates Python library. The pyrates library uses the kugelpy methodology to perform running-in calculations to reach an equilibrium core. This approach relies on full-core Monte Carlo neutron transport calculations and the shifting of fuel pebble inventory through the reactor to simulate pebble movement in the core from one step to the next of a running-in scenario. In addition to the use of Serpent for the Monte Carlo neutron transport calculations, as part of this work, updates for the use of the Shift Monte Carlo code were integrated into the official pyrates GitHub repository. Comparisons of running-in simulations for a generic pebble bed reactor produced by using pyrates with Serpent and Shift are shown. Although consistent trends are shown between the use of the two Monte Carlo codes, the limiting factor in either calculation is the computation time due to the need to perform several hundred full-core neutron transport calculations before an equilibrium core is reached.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Evaluating User Errors and Temporal Trends in Marine Fish Communities Using 360-Degree Underwater Photography

The use of environmental DNA (eDNA) sampling has been proposed as a complementary method to monitor fish species in marine environments, offering a non-invasive and potentially more efficient approach to marine species observations. eDNA monitoring could be especially useful in and around sites targeted for marine energy generation as these regions need regular monitoring that would be impractical with traditional techniques. Before we can fully rely upon eDNA, we must first verify its accuracy against other proven methods, such as the use of underwater photography. In this study, I deployed a 360-degree camera in the tidal channel of Sequim Bay once a month during several hours overlapping slack tide. I investigated how having multiple people identify and count fish on underwater images could affect the overall results. Using chi square tests in R, I compared my fish identifications and counts to those made by another intern on the same images recorded in August. I found significant differences in the number of species identified and the total individual counts between the two different datasets. I also tested the statistical differences in both Shannon diversity and Pielou evenness indices between the August, September, and November camera deployments using a Hutcheson t-test. Only one significant difference was found in the Shannon index comparisons, and none were found between the Pielou evenness comparisons. These findings show that if multiple identifiers are used to process underwater images, quality control checks must be made to reduce the potential for error. This also points toward the possibility to leverage more advanced image analysis processes, such as automated image analysis software. The findings from this study also show that the dynamics of marine fish communities can vary over a few months; however, further analysis is needed to determine the extent of the seasonal changes in Sequim Bay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗
DOE OSTI2026

Characterization of Oxidative Modifications to Short Peptides Using Low Dose Rate X-Rays

The method of X-ray footprinting and mass spectrometry (XFMS) using high flux synchrotron X-ray sources has become an established method in structural biology and is based on the radiolytic production of hydroxyl radicals, which oxidatively modify protein sidechains. While other methods of producing hydroxyl radicals are available, one benefit of using high flux density sources is that hydroxyl radical scavenging reactions can be minimized, and exposure times kept short to minimize secondary reactions. Here we present an application of the XFMS method using low dose rate X-rays from a commercial instrument. We demonstrate the feasibility of the approach using short peptides, characterizing the oxidative modifications +14, +16, and +32 Da under both aerobic and low oxygen conditions, and we additionally quantify the hydrogen peroxide production for various doses using the low dose rate source. These results provide fundamental information on the oxidative damage to peptides due to hydroxyl radicals using a low dose rate X-ray source.

X-ray methods↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2026

Evolution of the Antarctic Ice Sheet from 2000–2300 and beyond: model sensitivity and uncertainty analysis using MPAS-Albany Land Ice

We present a description of the Antarctic Ice Sheet model configuration submitted to the ISMIP6-Antarctica-2300 experiment using the MPAS-Albany Land Ice model, along with three new sets of simulations: (1) a set of extended simulations to 2500 for three forced experiments and to 2775 for the control experiment; (2) a sensitivity analysis of our model configuration to parameters controlling basal sliding and sub-shelf melt, and to model structural choices including the choice of the energy and stress balances; and (3) a 72-member ensemble run on graphics processing units (GPUs) and analysis of variance to determine the primary sources of uncertainty in our ice-sheet model projections. Our extended simulations predict rapid retreat beginning after 2300 for SSP1-2.6 forcing and after 2500 for present-day (control) forcing, primarily in the Amundsen Sea Embayment. We find that varying the sub-shelf melt parameter between the 5th to 95th percentile values for a mean-Antarctic calibration target results in an up to ∼ ± 40 % change in sea-level contribution relative to our baseline simulations that used the median value. Using a linear basal sliding law reduces sea-level contribution by 51 %–73 % relative to our baseline nonlinear sliding law with an exponent of 1/5. When using basal sliding law exponents of 1/3 and 1/10, the overall difference from our baseline simulations at 2300 is on the order of 10 %. The Amundsen Sea Embayment region displays a strongly non-linear dependence of mass loss on the sliding law exponent, with no discernible relationship between the sliding law exponent and the mass loss by 2300, while the sectors feeding the Ross and Filchner-Ronne ice shelves exhibit more mass loss with a more-plastic sliding law. Our model fidelity sensitivity experiments reveal a 9 %–31 % increase in sea-level contribution when using a depth-integrated stress balance approximation relative to our three-dimensional solver, while using a fixed-in-time temperature field increases sea-level contribution by 14 %–88 % relative to two thermomechanically coupled configurations. Our 72-member ensemble and analysis of variance show that the uncertainty in long-term projections is dominated by the choice of Earth system model forcing and the presence or absence of hydrofracture forcing, rather than uncertainty in sliding and sub-shelf melt parameters.

58 GEOSCIENCES↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗
DOE OSTI2024

A Measurement of νe Appearance and νµ Disappearance Using 10 Years of Data from the NOvA Experiment

NOvA is a long baseline neutrino experiment with an 810 km baseline, using the NuMIbeam at Fermilab, and a functionally identical near and far detector operating at an angle14 mrad off axis from the beam. NOvA jointly measures muon neutrino (and antineutrino)disappearance and electron neutrino (and antineutrino) appearance to make a measurementof sin2θ23, δCP, and ∆m232, including its sign, the mass ordering.This dissertation reports a new measurement from NOvA, using 10 years of data, with a totalexposure of 26.6 ×1020 POT of neutrino beam and 12.5 ×1020 POT of antineutrino beam.This represents 95.6% more neutrino beam exposure since the last NOvA analysis. Aspects ofthe analysis are discussed in detail, including neutrino energy estimation, analysis systematicuncertainties, including the implementation of systematics new to the NOvA analysis, andthe Bayesian fit infrastructure using Markov Chain Monte Carlo (MCMC).The analysis yields the following credible intervals for the oscillation parameters assumingthe normal ordering: δCP = 0.930+0.210−0.290 π ∪0.150+0.150−0.110 π,∆m232 = 2.424+0.035−0.040 × 10−3eV2,and sin2θ23 = 0.55+0.02−0.06, with a 76% preference for normal ordering using a prior for sin2(2θ13)using Daya Bay’s measurement. If the Daya Bay sin2(2θ13) vs ∆m232 constraint is used as aprior instead, an 87% preference for normal ordering emerges.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Use of Rig Parameter Data in Bit Constraint Models for Improved Drilling Performance at The Geysers

Surface parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. However, these measurements are of reduced value without a standard to aid in evaluation and decision making. A method is demonstrated whereby drill bit constraint models are used to interpret drilling response parameters. Drill rig parameter data for well GDC-36 at the Geysers Geothermal Field Power were acquired by Geysers Power Company and drilling contractor Kenai Drilling using Pason US DataHub and evaluated. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, monitor bit integrity, and detect the presence of drillstring vibrations and other conditions contributing to bit failure; comparisons are made to observations of bit wear and damage. The method will be applied in real-time to improve decision-making on subsequent wells and has applicability to development of advanced analytics on future geothermal wells using real-time electronic drilling recorder (EDR) data for improved performance and reduced drilling costs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Use of Captured CO2 for Production of Sustainable Polyurethane Foams in Automotive Applications

Captured CO2 has been investigated as a feedstock for the production of polyurethane (PU) foams for automotive applications. Previous work has shown successful incorporation of CO2 into polyols via three distinct reaction pathways. These CO2-derived polyols, as well as several commercially available polyols with varying CO2 content, were used to produce PU foams for automotive seating and NVH (noise, vibration, and harshness) applications. Foam formulations were optimized to maximize sustainable content while maintaining manufacturability constraints and performance requirements for each end use application. PU foams were first formulated at lab scale to confirm free rise and molded foam properties, then scaled up to pilot and industrial scales to evaluate production manufacturing feasibility. Finally, CO2-derived foams were used to produce molded components for end use product validation. Life cycle assessment (LCA) was used to quantify the environmental impact of using captured CO2 and bio-renewable content into polyols and PU foams through the metrics of global warming potential and embodied energy. Tradeoffs among environmental impacts, energy usage, manufacturability, and PU foam performance from the incorporation of CO2 and bio-renewable content will be discussed.

Lee, Ellen [Ford Motor Company]↗
DOE OSTI2024

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

GPS-supported smartphone apps↗
DOE OSTI2024

Multicriteria screening evaluation of geothermal resources on mine lands for direct use heating

Abstract Direct use of geothermal energy is the oldest and most versatile form of utilizing geothermal energy. In the last decade, this utilization has significantly increased, especially with the installation of geothermal (ground-source) heat pumps. Many current and inactive mine land sites across the U.S. could be redeveloped with clean energy technologies such as direct use geothermal, which would revitalize former mining communities, help with reducing greenhouse gas emissions, and accelerate the transition to a clean energy economy. We present a multicriteria screening framework to evaluate various aspects of direct-use geothermal projects on mine lands. The criteria are divided into three categories: (1) technical potential, (2) demand and benefits, and (3) regulatory and permitting. We demonstrate the framework using publicly available data on a national scale (continental U.S.). Then, using an example of abandoned coal mines in Illinois and focusing on resource potential, we illustrate how this evaluation can be applied at the state or more local scales when a region’s characteristics drive spatial variability estimates. The strength of this approach is the ability to combine seemingly disparate parameters and inputs from numerous sources. The framework is very flexible—additional criteria can be easily incorporated and weights modified if input data support them. Vice versa, the framework can also help identify additional data needed for evaluating those criteria. The multicriteria screening evaluation methodology provides a framework for identifying potential candidates for detailed site evaluation and characterization.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Data and Code for 'GHG Mitigation and Land Use Change Implications of Sustainable Aviation Fuel in the United States'

BEPAM, Biofuel and Environmental Policy Analysis Model, models the agricultural sector and determines economically optimal land-use and feedstock mix at the US scale by maximizing the sum of agricultural sector consumers’ and producers’ surplus subject to various resource balances, land availability, and technological constraints under a range of biomass prices, from zero to $140 Mg-1 over the 2016-2030 period. Here BEPAM is used to model SAF production using energy crops and crop residues. BEPAM uses the GAMS format and uses yield and GHG balance projections from the biogeochemical model, DayCent.

09 BIOMASS FUELS↗
DOE OSTI2024

Coal Ash Beneficial Use at Savannah River Site

The Savannah River Site (SRS) has over 1.4 million cubic meters of coal ash and coal fines left over from coal-burning power plants that operated on site. Currently, the coal ash must be disposed of in an approved landfill or the coal ash-containing basins must be closed in place (i.e. consolidation, appropriate cover and liner system). Potential beneficial uses of the coal ash include geotechnical fill, such as backfill needed in the closure cap of the Z-area Saltstone Disposal Units (SDU), and use in cementitious material applications like thermal beneficiation or cement kiln feed, thereby reducing the environmental footprint of SRS. In this study, samples of coal ash from SRS were obtained and characterized for chemical and physical properties. Coal ash samples did not leach sulfates or heavy metals, so the coal ash is a candidate for geotechnical fill use. The samples also did not increase the acidity of the leachate during leaching tests, so it would not be detrimental to use as geotechnical fill near cementitious materials. The composition and energy potential of the coal ash makes it favorable for use as feed for external/off-site cement kilns or thermal beneficiation plants

01 COAL, LIGNITE, AND PEAT↗