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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Long-term multi-wavelength study of 1ES 0647+250

The BL Lac object 1ES 0647+250 is one of the few distant γ-ray emitting blazars detected at very high energies (VHEs; &100 GeV) during a non-flaring state. It was detected with the MAGIC telescopes during a period of low activity in the years 2009-2011 as well as during three flaring activities in the years 2014, 2019, and 2020, with the highest VHE flux in the last epoch. An extensive multi-instrument data set was collected as part of several coordinated observing campaigns over these years. We aim to characterise the long-term multi-band flux variability of 1ES 0647+250, as well as its broadband spectral energy distribution (SED) during four distinct activity states selected in four different epochs, in order to constrain the physical parameters of the blazar emission region under certain assumptions. We evaluated the variability and correlation of the emission in the different energy bands with the fractional variability and the Z transformed discrete correlation function, as well as its spectral evolution in X-rays and γ rays. Owing to the controversy in the redshift measurements of 1ES 0647+250 reported in the literature, we also estimated its distance in an indirect manner through a comparison of the GeV and TeV spectra from simultaneous observations with Fermi-LAT and MAGIC during the strongest flaring activity detected to date. Moreover, we interpret the SEDs from the four distinct activity states within the framework of one-component and two-component leptonic models, proposing specific scenarios that are able to reproduce the available multi-instrument data. We find significant long-term variability, especially in X-rays and VHE γ rays. Furthermore, significant (3-4σ) correlations were found between the radio, optical, and high-energy (HE) γ-ray fluxes, with the radio emission delayed by about ~400 days with respect to the optical and γ-ray bands. The spectral analysis reveals a harder-when-brighter trend during the non-flaring state in the X-ray domain. However, no clear patterns were observed for either the enhanced states or the HE (30 MeV < E < 100 GeV) and VHE γ-ray emission of the source. The indirect estimation of the redshift yielded a value of z = 0.45 ± 0.05, which is compatible with some of the values reported in the literature. The SEDs related to the low-activity state and the three flaring states of 1ES 0647+250 can be described reasonably well with the both one-component and two-component leptonic scenarios. However, the long-term correlations indicate the need for an additional radio-producing region located about 3.6 pc downstream from the gamma-ray producing region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗
DOE OSTI2022

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders

The anomalies in the high voltage converter modulator (HVCM) remain a major down time for the spallation neutron source facility, that delivers the most intense neutron beam in the world for scientific materials research. In this work, we propose neural network architectures based on Recurrent AutoEncoders (RAE) to detect anomalies ahead of time in the power signals coming from the HVCM. Bi-directional gated recurrent unit, bi-directional long-short term memory (LSTM), and convolutional LSTM (ConvLSTM) are developed, trained, and tested using real experimental signals from the HVCM module. The results show a good performance of the proposed RAE models, achieving precision up to 91%, recall up to 88%, false omission rate as low as 20% (i.e. 80% of the anomalies were detected), and area under the ROC curve up to 0.9. The three RAE models provide very comparable performance, with LSTM showing slightly better performance than GRU and ConvLSTM. The RAE models are benchmarked against other anomaly detection methods, including isolation forest, support vector machine, local outlier factor, feedforward and convolutional autoencoders, and others; showing a better performance. Here, the results of this study demonstrate the promising potential of RAE in anomaly detection for real-world power systems, and for increasing the reliability of the HVCM modules in the spallation neutron source.

42 ENGINEERING↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Doppler effect in rotating systems and Mössbauer rotor experiments

We address once more the problem of the physical interpretation of the Mössbauer effect in a rotating system in the view of the recently discovered extra energy shift (EES) between emitted and absorbed resonant radiation, which emerges in addition to the usual second order Doppler shift for an orbiting absorber. We highlight that both fractional components of the total energy shift have the same sign and correspond to the blueshift of the resonant radiation propagating from the rotational axis to the rotor rim. We indicate a possible way of understanding the origin of the EES in terms of combining metric effects in rotating systems with the quantum mechanical description of resonant nuclei confined in crystal cells by taking into account the recoil-free essence of the Mössbauer effect, where no energy is transmitted to the source (absorber) during the emission (absorption) of resonant radiation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The root-zone soil moisture spectrum in a mediterranean ecosystem

Storage of water within soil pores of the root zone introduce memory effects in the dynamics of soil moisture that are considerably longer than the integral timescale of many atmospheric processes. Thus, hydro-climatic states can be “sustained” through land-surface heat and water vapor fluxes primarily because they can “feed off” on this long-term soil moisture memory. Root-zone soil moisture memory is only but one feature characterizing the spectrum of soil moisture dynamics, which is analyzed here using a combination of long-term measurements and models. In particular, the spectrum of root-zone soil moisture content in a Mediterranean ecosystem is examined using 14-years of half-hourly measurements. A distinguishing hydro-climatic feature in such ecosystems is that sources (mainly rainfall) and sinks (mainly evapotranspiration) of soil moisture are roughly out of phase with each other. For over 4 decades of time scales and 7 decades of energy, the canonical shape of the measured soil moisture spectrum is shown to be approximately Lorentzian determined by the soil moisture variance and its memory but with two exceptions: the occurrences of a peak at diurnal-to-daily time scales and a weaker peak at near annual time scales. Model calculations and spectral analysis demonstrate that diurnal and seasonal variations in hydroclimate forcing responsible for variability in evapotranspiration had minor impact on the normalized shape of the soil moisture spectrum. However, their impact was captured by adjustments in the temporal variance. These findings indicate that precipitation and not evapotranspiration variability dominates the multi-scaling properties of soil moisture variability consistent with prior climate model simulations. Furthermore, the soil moisture memory inferred by the annual peak of soil moisture (340 d) is consistent with climate model simulations, while the memory evaluated from the loss function of a linearized mass balance approach leads to a smaller value (50 d), highlighting the effect of weak non-stationarity on soil moisture variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<10 3 km 2 ) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. Furthermore, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

13 HYDRO ENERGY↗
DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Can a Nuclear-Assisted Biofuels System Enable Liquid Biofuels as the Economic Low-carbon Replacement for All Liquid Fossil Fuels and Hydrocarbon Feedstocks and Enable Negative Carbon Emissions?

This report integrates the results of a series of studies and workshops to address the question: Can a nuclear-assisted biofuels system enable liquid biofuels as the economic low-carbon replacement for all liquid fossil fuels and hydrocarbon feedstocks and simultaneously enable negative carbon emissions? “Economic” is defined as economically competitive relative to other low-carbon replacement options for crude oil. “All” refers to the capability to replace the 18 million barrels of oil per day used by the United States. “Nuclear-assisted” refers to the provision of massive quantities of low-carbon heat and hydrogen at the refinery to fully utilize the carbon content of the cellulosic biomass feedstocks for liquid hydrocarbon production. A system is proposed that decarbonizes about half of the U.S. economy while improving long-term soil productivity and sequestering carbon from the atmosphere. In the U.S. almost half the energy consumed by the final customer is in the form of liquid hydrocarbons produced from crude oil. Liquid hydrocarbons are used as (1) an energy source, (2) a method for daily-to-seasonal energy storage, (3) a chemical feedstock, (4) a chemical reducing agent, (5) a method to enhance high-temperature heat transfer in many furnaces and industrial processes and (6) other purposes. As a consequence, the U.S. consumes ~18 million barrels of crude oil per day to produce liquid hydrocarbons. While there are substitutes for liquid hydrocarbons for some applications, our assessment is that the costs and difficulty will dramatically increase if liquid hydrocarbon use goes much below the equivalent of 10 million barrels per day of crude oil. New uses of liquid hydrocarbons to partly replace coal and natural gas could increase demand beyond the equivalent of 10 million barrels of oil per day. One can produce and burn liquid hydrocarbons from biomass without any net addition of carbon dioxide to the atmosphere. Plants grow by removing carbon dioxide from the atmosphere; thus, burning biomass does not result in any net addition of carbon dioxide to the atmosphere. Biomass is typically 40% oxygen. To remove this oxygen to create hydrocarbon liquids, there are two options. The first option is to use biomass as (1) a feedstock, (2) an energy source to operate the process and (3) a supply of carbon to remove the biomass oxygen as carbon dioxide. The second option is to use external heat and hydrogen to remove the oxygen as water and produce liquid hydrocarbons. The use of massive quantities of external heat and hydrogen for hydrocarbon liquid fuels production reduces the biomass feedstock per unit of liquid hydrocarbon product by more than a factor of two reducing land use by more than a factor of two. Many cellulosic feedstocks unsuitable for liquid hydrocarbon production are viable feedstocks with external heat and hydrogen inputs. As a result, there is sufficient cellulosic feedstocks to meet U.S. and global liquid fuels hydrocarbon demand without significant impacts on food and fiber prices. The heat and hydrogen (heat plus electricity) are produced using base-load nuclear power plants—the most economic form of nuclear energy. If low-price natural gas, there is also the option of producing hydrogen from natural gas with sequestration of the carbon dioxide. The biomass is locally processed in depots to produce commodity feedstocks that can be shipped long distances to large biorefineries (250,000 barrels per day, oil equivalent) similar to existing refineries except modifications of front-end processes. The depot system enables local recycle of nutrients back to the soil. The biorefinery can produce variable quantities of liquid hydrocarbon fuels and carbon dioxide for sequestration enabling removal of carbon dioxide from the atmosphere. Preliminary estimates are that the liquid hydrocarbon costs are equivalent to crude oil at between $60 and $70 per barrel. The largest cost is for hydrogen with biomass feedstocks and refinery costs a smaller fraction of the total cost.

09 BIOMASS FUELS↗
DOE OSTI2024

Tierra del Fuego Case Study Capacity Expansion Analysis

This case study, developed by Net Zero World Initiative and the Government of Argentina, examines least-cost decarbonization pathways for Tierra del Fuego, Argentina, utilizing renewable energy, energy storage, hydrogen, and other decarbonization technologies. Being the second largest natural gas producing province in Argentina, Tierra del Fuego has historically relied on natural gas for their energy sector needs. As they look at possible decarbonization pathways, they face challenges due to extreme weather conditions, isolation from the mainland, and low population density. The study utilizes the Engage web application for capacity expansion modeling, addressing both business-as-usual (BAU) and accelerated decarbonization scenarios, with varying degrees of electrification and carbon emission constraints. Key findings reveal that an interconnection with the mainland, high contribution of wind energy development on Tierra del Fuego, energy storage, and hydrogen, coupled with energy-efficient electrification technologies (such as heat pumps and electric vehicles), emerge as the most cost-effective solutions to decarbonize, significantly reducing carbon emissions and total system energy costs. The study explores self-generation and interconnection alternatives, demonstrating the economic advantage of an interconnection of Tierra del Fuego with the mainland, as an alternative to 100% local generation. Sensitivity analyses on wind data sources and temporal resolutions, as well as projected natural gas prices, highlight the influence of external factors on the feasibility of decarbonization pathways. Challenges identified include the practicality of phasing out natural gas, economic uncertainty, cost implications of long-term storage technologies as wind energy increases, and geographical limitations for wind generation. The case study concludes that while substantial emissions reductions can be achieved by 2050, and be competitive with conventional pathways, achieving a full 100% decarbonization by 2050 would entail higher costs, particularly due to the significant reliance on storage solutions with higher contribution of wind energy. The analysis offers valuable insights for policymakers and stakeholders in Argentina's energy sector, emphasizing the importance of strategic planning, investment in renewable energy and storage technologies, and careful consideration of local conditions in the transition towards Net Zero targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Down-selection of Innovative Fusion Materials

The goal of this proposal is to develop and fundamentally understand the microstructure of refractory multi-component alloys (MCA) as fusion-relevant plasma facing and structural materials, a step to down-select innovative fusion materials prior to the process of advancing their technology readiness level (TRL). Developing materials is paramount to enable fusion as energy source since no existing material is capable of coping with such extreme conditions. We will aim at understanding the role of chemistry and microstructure in these complex multi-component alloys systematically comparing their performance with pure tungsten materials in terms of mechanical properties and manufacturability. Such understanding will open opportunities to optimize MCAs for fusion applications and has a potential of attracting industry partnership.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Numerical and Experimental Investigations on the Ignition Behavior of OME

On the path towards climate-neutral future mobility, the usage of synthetic fuels derived from renewable power sources, so-called e-fuels, will be necessary. Oxygenated e-fuels, which contain oxygen in their chemical structure, not only have the potential to realize a climate-neutral powertrain, but also to burn more cleanly in terms of soot formation. Polyoxymethylene dimethyl ethers (PODE or OMEs) are a frequently discussed representative of such combustibles. However, to operate compression ignition engines with these fuels achieving maximum efficiency and minimum emissions, the physical-chemical behavior of OMEs needs to be understood and quantified. Especially the detailed characterization of physical and chemical properties of the spray is of utmost importance for the optimization of the injection and the mixture formation process. The presented work aimed to develop a comprehensive CFD model to specify the differences between OMEs and dodecane, which served as a reference diesel-like fuel, with regards to spray atomization, mixing and auto-ignition for single- and multi-injection patterns. The simulation results were validated against experimental data from a high-temperature and high-pressure combustion vessel. The sprays’ liquid and vapor phase penetration were measured with Mie-scattering and schlieren-imaging as well as diffuse back illumination and Rayleigh-scattering for both fuels. To characterize the ignition process and the flame propagation, measurements of the OH* chemiluminescence of the flame were carried out. Significant differences in the ignition behavior between OMEs and dodecane could be identified in both experiments and CFD simulations. Liquid penetration as well as flame lift-off length are shown to be consistently longer for OMEs. Zones of high reaction activity differ substantially for the two fuels: Along the spray center axis for OMEs and at the shear boundary layers of fuel and ambient air for dodecane. Additionally, the transient behavior of high temperature reactions for OME is predicted to be much faster.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone

As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease—namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗
DOE OSTI2021

Lipid production from non-sugar compounds in pretreated lignocellulose hydrolysates by Rhodococcus jostii RHA1

Valorization of all carbon sources from biorefinery wastes to biofuels and bioproducts is an attractive but challenging strategy to improve the carbon efficiency of the entire biorefinery process for economic competitiveness. Rhodococcus jostii RHA1 is considered as a promising candidate for conversion of non-sugar compounds in pretreated lignocellulose hydrolysate, which contain aromatic monomers, furans and acids derived from biomass. However, the insufficient understanding of the strain’s complex metabolism network limits its application. In this study, furans (furfural, 5-hydroxymethylfurfural), phenols (vanillin, vanillate), and organic acids (acetic acid), which mimic the complex components of the non-sugar compounds in pretreated biomass hydrolysate, were mixed with benzoate or used individually as carbon sources to investigate the growth and lipid accumulation of R. jostii RHA1. Fermentation kinetics, specific growth rates (h-1) and lipid yield (g(g) -1, g(mmol)-1), for each selected biomass-derived compound were compared for lipid production. The order of the specific growth rates (h-1) was benzoate > acetic acid > vanillate > vanillin. Higher consumption rate of benzoate than that of vanillate, as well as different lipid yield from them, suggested the preference of strain employing two branches of ß-ketoadipate pathway and plausibly distinct carbon routing to lipid biosynthesis. And acetic acid showed the lowest lipid yield (g(mmol)-1) indicating less carbon contributed to lipid production. The highest lipid content (0.46 g lipid/g CDW) was achieved using benzoate as a main carbon source in the presence of 5-hydroxymethylfurfural (5-HMF). The generated lipids predominantly contained C16:0 and C17:0 fatty acids. The concentration of furfural and 5-HMF in fermentation broth decreased individually or simultaneously with benzoate catabolism. NMR results revealed the oxidation of furfural and 5-HMF by R. jostii RHA1. This study provides new insights into the catabolism and metabolism of non-sugar compounds derived from biomass by Rhodococcus in terms of growth and lipid production, facilitating its application to 2nd generation biofuel production.

Li, Xiaolu↗
DOE OSTI2025

R&D Effort of Geologic Hydrogen Production at the National Renewable Energy Lab (NREL)

Geologic hydrogen (geoH2) is an emerging technology with massive current market interest and distinct potential to change the paradigm of hydrogen production. Two major subsurface processes influence the amount of geoH2 that are available for energy extraction: 1) geochemical reactions of H2O and Fe2+-bearing rocks which can produce hydrogen in the subsurface environment, where 2) various active microbial communities consume hydrogen as an energy source before the hydrogen reaches the surface. The net gain of hydrogen from these two competing processes dictates the production rate of geoH2. A recent study (Templeton et al., 2024) suggested that for most natural geoH2 systems, five orders of magnitude of production rate enhancement are needed to make geoH2 production economical in the near term. Effective enhancement of the production rate requires an in-depth understanding of the two geoH2 processes, in order to promote the H2-generating geochemical processes and suppress the H2-consuming microbial processes. However, current significant knowledge gaps in these two processes hinders the efforts to formulate strategies to enhance geoH2 production. The National Renewable Energy Laboratory (NREL) is a U.S. Department of Energy National Laboratory with the core mission of leading research, innovation, and strategic partnership to deliver solutions for a clean energy based economy. NREL's extensive research portfolio in hydrogen, bioenergy, geothermal, industrial decarbonization, and energy analysis makes us well positioned to conduct interdisciplinary research and facilitate technology deployment in the geoH2 space. In this presentation, we will discuss ongoing geoH2 research and engagement efforts at NREL, including: 1) geochemical investigation to understand the reaction mechanisms and production rate and potential of different source minerals and rocks, 2) microbiological investigation to understand methanogenesis and acetogenesis in the subsurface geoH2 environment, and identify effective inhibitors for these microbial processes, and 3) preliminary analysis for geoH2 production in the State of Minnesota, where abundant Fe-rich rocks for stimulated geoH2 production and ample opportunity to utilize geoH2 in transforming iron and steel industries are currently available.

08 HYDROGEN↗