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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗
DOE OSTI2024

Evaluating the Impact of Off-Design CHP Performance on the Optimal Sizing and Dispatch on Hybrid Renewable-CHP Distributed Energy Resources

The maturation of distributed energy resources (DER) has prompted the exploration of their deployment in commercial building applications due to their potential to supply energy at lower costs and emissions rates compared to centralized generation. While several software tools exist for evaluating the techno-economic potential of integrated renewable energy and combined heat and power (CHP) systems for distributed generation applications, many suffer from poor accuracy in capturing off-design (part load and changes in ambient air temperature and pressure) performance characteristics of microturbines, combustion turbines, or internal combustion engines. Thus, this paper presents a methodology for integrating these off-design characteristics in the mixed-integer linear program within REopt, a hybrid DER screening tool. The economic impact of the CHP off-design performance is observed through several application studies of various hybrid system configurations in different climates. Each study indicates how CHP off-design performance influences optimal sizing and dispatch decisions and therefore overall system economic value. We observe through case studies that modeling without the off-design effects, depending on the CHP prime mover and site, can result in Net Present Value predictions of hybrid systems that can be overoptimistic in frequently hot climates (up to 52%), too conservative in frequently cold climates (up to 11%), or unaffected (+/-1%) in temperate climates. Cases also highlight several advantages of hybrid systems relative to non-hybrid systems such as total economic value and the systems' ability to mitigate potentially negative consequences attributed to off-design performance.

ambient de-rate↗
DOE OSTI2025

Vanadium-enhanced Na 2 FePO 4 F cathodes for high-performance sodium-ion batteries

Sodium fluorophosphate Na 2 FePO 4 F holds great potential for sodium-ion batteries due to its high theoretical capacity, excellent structural stability, abundant resources, and affordability. However, its poor electronic and ionic conductivities limit its practical applications. Therefore, ion doping and carbon coating have been employed as synergistic strategies in this study to overcome these limitations. A one-step, energy-efficient solid-state method using sucrose as a carbon coating source was used to synthesize Na 2 FePO 4 F/C (NFPF/C) and its doped variant, Na 2 Fe 0.85 V 0.1 PO 4 F/C (NFVPF/C). 23 Na-MAS-NMR spectra confirm the existence of two distinct sites for sodium (Na1/Na2). The ex-situ 23 Na-MAS-NMR performed at different states-of-charge reveals the activity of only one sodium. The scanning electron microscopy findings reveal a reduction in the particle size with V-introduction, enhancing the energetic performances. NFVPF/C delivers higher specific capacity of 122 mAh g −1 compared to 116 mAh g −1 for NFPF/C at 0.1C. It also demonstrates improved cycling stability, retaining 81 % of its initial capacity after 120 cycles, in contrast to 46 % for the pristine material. The doped phase outperforms the pristine at higher current rates, delivering specific capacities of 81 and 55 mAh g −1 at 2C and 3C, respectively, compared to 35 and 17 mAh g −1 for NFPF/C.

25 ENERGY STORAGE↗
DOE OSTI2020

Impacts of directed evolution and soil management legacy on the maize rhizobiome

Domestication and agricultural intensification dramatically altered maize and its cultivation environment. Changes in maize genetics (G) and environmental (E) conditions increased productivity under high-synthetic-input conditions. However, novel selective pressures on the rhizobiome may have incurred undesirable tradeoffs in organic agroecosystems, where plants obtain nutrients via microbially mediated processes including mineralization of organic matter. Using twelve maize genotypes representing an evolutionary transect (teosintes, landraces, inbred parents of modern elite germplasm, and modern hybrids) and two agricultural soils with contrasting long-term management, here we integrated analyses of rhizobiome community structure, potential microbe-microbe interactions, and N-cycling functional genes to better understand the impacts of maize evolution and soil management legacy on rhizobiome recruitment. We show complex shifts in rhizobiome communities during directed evolution of maize (defined as the transition from teosinte to modern hybrids), with a larger effect of domestication (teosinte to landraces) than modern breeding (inbreds to hybrids) on rhizobiome structure and greater impacts of modern breeding on potential microbe-microbe interactions. Rhizobiome structure was significantly correlated with plant nutrient composition. Furthermore, plant biomass and nutrient content were affected by G x E interactions in which teosinte and landrace genotypes had better relative performance in the organic legacy soil than inbred and modern genotypes. The abundance of six N-cycling genes of relevance for plant nutrition and N loss pathways did not significantly differ between teosinte and modern rhizospheres in either soil management legacy. These results provide insight into the potential for improving maize adaptation to organic systems and contribute to interdisciplinary efforts toward developing resource-efficient, biologically based agroecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum Computing for Neutrino-Nucleus Scattering

Neutrino-nucleus cross section uncertainties are expected to be a dominant systematic in future accelerator neutrino experiments. The cross sections are determined by the linear response of the nucleus to the weak interactions of the neutrino, and are dominated by energy and distance scales of the order of the separation between nucleons in the nucleus. These response functions are potentially an important early physics application of quantum computers. Here we present an analysis of the resources required and their expected scaling for scattering cross section calculations. The current estimates of Trotter steps needed to achieve an energy resolution of 10 MeV and the number of CNOT gates for analyzing Ar40 highlights the need for significant improvements in algorithms. We also examine simple small-scale neutrino-nucleus models on modern quantum hardware. In this paper, we use variational methods to obtain the ground state of a three nucleon system (the triton) and then implement the relevant time evolution. To tame the errors in present-day NISQ devices, we explore the use of different error-mitigation techniques to increase the fidelity of the calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Grid Value Investigation of Medium-Voltage Back-to-Back Converters

This paper presents the results of an investigation into the value that medium voltage back-to-back (MVB2B) converters can be realized through two potential applications: voltage regulation via the provision of reactive power supply or absorption, and distributed energy resource (DER) hosting capacity improvement via the controlled transfer of real power within the distribution system. Two groups of distribution systems have been prepared for the value investigation: the IEEE 123-bus system for the voltage regulation study and two utility-provided, realistic distribution systems for the DER hosting capacity improvement study. The results demonstrate that the MVB2B converter can realize significant value for the grid through these two applications. In particular, with the MVB2B converter transferring power between two feeders, DER-based power can be routed to nearby loads instead of feed through the distribution substation. Additionally, the size of energy storage elements used to enable higher levels of DERs can be significantly reduced.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2022

Lithologies and Depositional Environments for the NATCARB Saline Reservoirs

This dataset summarize the results of a literature review performed to define the primary lithologies and depositional environments for the target reservoir units defined by the National Carbon Sequestration Database (NATCARB) Saline reservoir 10km Grid data layer, accessible at https://edx.netl.doe.gov/dataset/natcarb. The potential saline storage reservoirs, defined in the Saline 10km Grid layer and in this sheet as "RESOURCE_NAME", were summarized and a literature review was completed to define the primary lithology and depositional environment, and sometimes a secondary lithology and depositional environment for each rock unit. If no verifiable resources were found during the literature review, the primary and secondary lithology and depositional environments were left blank. This file is organized by sheets for each of the 7 Regional Carbon Sequestration Partnerships (RCSP): Big Sky Carbon Sequestration Partnership (BSCSP), the Midwest Geological Sequestration Consortium (MGSC), the Midwest Regional Carbon Sequestration Partnership (MRCSP), the Plains CO₂ Reduction Partnership (PCOR), the Southeast Regional Carbon Sequestration Partnership (SECARB), the Southwest Regional Partnership on Carbon Storage (SWP), and the West Coast Regional Carbon Sequestration Partnership (WESTCARB). The partnerships last updated the NATCARB database in 2015.

Basin↗
DOE OSTI2020

Using Machine Learning to Identify Optimum Prospects for Offshore Geologic CO 2 Storage and Enhanced Oil Recovery in the Gulf of Mexico

The SECARB Offshore Partnership is a government-industry partnership focused on assembling the knowledge base required for secure, long-term, large-scale CO 2 subsea storage in saline formations and mature oil reservoirs. The SECARB Offshore Partnership is evaluating potential storage opportunities in the Central and Eastern planning areas of the outer continental shelf (OCS) of the Gulf of Mexico. The evaluation focuses on active and depleted oil and gas fields and potentially associated CO 2 -enhanced oil recovery (CO 2 -EOR), as well as, deep saline storage resources. As part of Subtask 3.0 (Offshore Storage Resources Characterization) and Subtask 4.0 (Risk Assessment, Simulation, and Modeling), Oklahoma State University (OSU) is developing a machine learning system that employs the SAS Institute’s Viya platform with visual data mining and machine learning to identify and characterize optimum prospects for enhanced oil recovery and stacked storage in saline reservoirs. This report summarizes this effort to date and discusses the many variables that can be used to characterize these storage objectives, as well as, the design vision of the Viya machine learning system.

02 PETROLEUM↗
DOE OSTI2023

Federal Aviation Administration Vertiport Electrical Infrastructure Study

In this detailed analysis, the authors assess the charging infrastructure needed for the deployment of advanced air mobility involving electrified vertical take-off and landing technologies. The report covers four research areas: (1) Identifying charging infrastructure requirements for existing facilities based on flight operational parameters, potential use cases, charging strategy, and other constraints. (2) Assessing sites on power availability to meet charging demand, the impact on grid infrastructure, potential hazards, and cybersecurity needs, and using technoeconomic analysis to identify opportunities for onsite distributed energy resources, primarily solar photovoltaics and battery energy storage systems. (3) Calculating greenhouse gas emission based on total energy consumption attributable to each site. (4) Analyzing the job and economic development impact for sites adopting new infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The potential for New Mexico basalts to sequester CO 2 with a focus on critical element mobility

This study evaluates the potential of New Mexico basaltic rocks and mafic mine waste for CO₂ sequestration via mineral carbonation. Batch experiments up to 200 days at 40°C simulated CO₂-bearing fluid interactions with various crushed basaltic materials at different grain sizes. Results showed that 70–80% of injected CO₂ was mineralized, with the finest grain sizes showing the most rapid and effective drawdown. The experiments also assessed the mobility of critical metals (Cu, Ni, Zn, Co), revealing trends tied to mineral dissolution and secondary mineral formation. The findings support the dual potential of mine waste for CO₂ sequestration and critical element recovery, informing future carbon capture and resource upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Design of a Multiphase CO2 Turbine Test Facility

As the energy market continues to evolve with increased renewables, such as solar and wind energy, the need to balance energy demand with its availability has become increasingly more challenging. The country’s aggressive decarbonization goals in the upcoming decades will increase renewable energy market penetration, thus exacerbating the disparity between energy demand and renewable resource availability. Long-duration energy storage technologies, such as pumped thermal energy storage (PTES), are potential solutions to this challenge with high potential round-trip efficiency (RTE), no geological or geographical constraints, and low safety risks. The charge mode of a PTES system operates a heat pump cycle, converting input power to thermal energy in hot and cold reservoirs. Fundamentally, heat pumps are commonly used in daily life, but the operating conditions required by high RTE PTES systems are outside the design experience of these systems. The low-pressure side of the cycle for a supercritical CO2 (sCO2) PTES operates just above the vapor-dome and potentially drives the turbine to expand into the dome, resulting in multiphase operation in the rear stages of the turbine. This paper presents the design of the test facility for an advanced multiphase-tolerant sCO2 turbine for the heat pump of a PTES system. Heat rejection and pressure loss estimations for the multiphase sections of the test loop presented unforeseen challenges.

25 ENERGY STORAGE↗
DOE OSTI2024

Nationwide cost and capacity estimates for sedimentary basin geothermal power and implications for geologic CO 2 storage

Sedimentary basins are naturally porous and permeable subsurface formations that underlie approximately half of the United States. In addition to being targets for geologic CO 2 storage, these resources could supply geothermal power: sedimentary basin geothermal heat can be extracted with water or CO 2 and used to generate electricity. The geothermal power potential of these basins and the accompanying implication for geologic CO 2 storage are, however, understudied. Here, we use the Sequestration of CO 2 Tool (SCO2T PRO ) and the generalizable GEOthermal techno-economic simulator (genGEO) to address this gap by a) estimating the cost and capacity of sedimentary basin geothermal power plants across the United States and b) comparing those results to nationwide CO 2 sequestration cost and storage potential estimates. We find that across the United States, using CO 2 as a geothermal heat extraction fluid reduces the cost of sedimentary basin power compared to using water, and some of the lowest cost capacity occurs in locations not typically considered for their geothermal resources (e.g., Louisiana, South Dakota). Additionally, using CO 2 effectively doubles the sedimentary basin geothermal resource base, equating to hundreds of gigawatts of new capacity, by enabling electricity generation in geologies that are otherwise (with water) too impermeable, too thin, too cold, or not deep enough. We find there is competition for the best sedimentary basin resources between water- and CO 2 -based power, but no overlap between the lowest-cost resources for CO 2 storage and CO 2 -based power. In this way, our results suggest that deploying CO 2 -based power may increase the cost of water based systems (by using the best resources) and the cost of CO 2 storage (by storing CO 2 in locations that otherwise may not be targeted). As such, our findings demonstrate that determining the best role for sedimentary basins within the energy transition may require balancing tradeoffs between competing priorities.

CPG↗
DOE OSTI2020

Herbaceous Feedstock 2019 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the State of Technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014)) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2019 Herbaceous SOT incorporates several technology changes in feedstock preprocessing and introduces opportunities from the integrated landscape management (ILM) strategy and increased grower participation to reduce biomass access costs, while maintaining or improving grower profitability. During FY18 uneven flow from the horizontal bale grinder was identified as a significant issue limiting preprocessing system throughput. Based on FSL-funded research at INL, the 2019 Herbaceous SOT replaces the horizontal bale grinder used in the first stage size reduction with a bale processor. The improved uniformity of biomass flow entering the PDU eliminated slugging flow from the first stage size reduction and improved the throughput of downstream operations. In order to achieve moisture reduction through frictional heating during grinding (which allowed elimination of the costly rotary drum dryer in previous SOTs), the second stage grinder was changed from a rotary shear, which does not remove moisture, back to a hammer mill. Finally, the 2019 Herbaceous SOT introduces modified three-pass and two-pass corn stover supply curves derived from the BT16 resource assessment, based on FY19 modeling results (WBS 4.2.1.20) quantifying economic benefits of ILM in the supply area, together with modeling results (WBS 1.2.1.5) identifying ILM strategies to increase grower participation. The 2019 Herbaceous SOT report documents the current modeled cost of an herbaceous feedstock supply system from harvest to the pretreatment reactor throat for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2019 Herbaceous SOT predicts a modeled delivered feedstock cost of $81.37 /dry ton (2016$); this is a $2.30/dry ton (2016$) decrease from the 2018 Herbaceous SOT. Technology improvements that contributed to this modeled cost reduction include reduced cost for the new preprocessing design and quantification of the opportunities of the integrated landscape management (ILM) strategy and an increased grower participation rate to reduce the grower payment portion of biomass access costs, while maintaining or improving grower profitability. A greenhouse gas emissions (GHG) assessment was completed by Argonne National Laboratory using the 2019 Greenhouse Gases, Regulated Emissions, and Energy use in Transportation model, estimating an increase of 14.89 kg CO2e/ton from the 2018 SOT (69.27 kg CO2e/ton in 2018 to 84.16 kg CO2e/ton in 2019). The increase of energy consumption during preprocessing along with higher transportation distance to access low cost biomass from further distance contributed to the increase of GHG emissions in the 2019 Herbaceous SOT. The reason for the increased transportation distances was the cost tradeoff of going farther from the biorefinery to access the cheaper ILM-derived counties (the cheaper price outweighed the cost of increased supply radius).

09 BIOMASS FUELS↗
DOE OSTI2023

Cataloging and Quantifying Value Streams from Non-Powered Dam Conversion

Hydropower is a key resource in the United States’ renewable energy transition; however, only 3% of existing dams in the United States currently generate electricity. While many non-powered dams (NPDs) have the potential for energy generation, hydropower addition to these dams has been slow. This is likely due, in part, to perceptions that hydropower development has prohibitively long payback periods, especially in comparison to other renewable energy resources that have shorter development processes. On the other hand, powering some NPDs may unlock new value streams in addition to the value obtained from selling generated energy. There is a need to understand and quantify a wider range of additional value streams to help increase the feasibility of such hydropower projects. In this work, we catalog indirect value streams and difficult-to-quantify direct value streams from NPD retrofitting, specifying which stakeholders are affected, and what kind of impacts (positive or negative) might occur. We identify appropriate quantification methodologies for these indirect value streams and describe the best practices around their implementation for NPD site-level analysis. We also demonstrate the quantification of a few indirect value streams for a selected NPD site, allowing for easier integration of these value streams into future studies by providing basis and background for quantifying these elements.

13 HYDRO ENERGY↗
DOE OSTI2023

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

15 GEOTHERMAL ENERGY↗