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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Thermally Stable Co@C3N4 Single-Atom Catalysts for CO Oxidation: Atomic-Level Insights into Structure and Activity

Single Co atoms supported on C3N4 (Co@C3N4) have demonstrated high activity and selectivity in photocatalysis. However, the investigation of structure–function relationships and reaction mechanisms under photocatalytic conditions is very challenging due to the complex conditions of light absorption, charge transfer, and catalysis. In this study, we employed thermal CO oxidation as a prototypical probe reaction to benchmark the intrinsic catalytic performance and track the active-site evolution of Co@C3N4. Single Co atoms were identified and shown to be the catalytically active sites for CO oxidation based on control experiments and isotope-labeling experiments. The Co sites remained atomically dispersed before, during, and after the reaction with temperatures up to 400 °C, as established by in situ X-ray absorption fine structure (XAFS) combined with density functional theory (DFT), FDMNES simulations, and dynamic-time-warping (DTW)-assisted X-ray absorption near edge structure (XANES) matching. Together with theoretical calculations, the integrated analysis reveals a stable coordination environment under reaction conditions, which correlates with sustained activity, establishing Co@C3N4 single-atom catalysts as thermally stable CO oxidation catalysts. Beyond these findings, the current study provides a workflow for unambiguously assigning active sites in Co@C3N4 for thermal CO oxidation. This workflow will aid the understanding of their behavior in photocatalysis in the future, where light-driven dynamics obscure direct structure–function links. Notably, this study provides fundamental insights for the rational design of robust single-atom catalysts and a foundation for the broader application of Co@C3N4 catalysts in oxidation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Design Trade-Offs in Composite Fuel Cell Membranes: Effects of Reinforcement and Chemical Additives

Perfluorosulfonic acid (PFSA) membranes are critical components in proton exchange membrane fuel cells, where performance depends on balancing ionic conductivity, mechanical durability, and chemical stability. This study characterizes a composite membrane (NC700) featuring PFSA-impregnated expanded polytetrafluoroethylene (ePTFE) reinforcement and cerium-based radical scavengers, benchmarked against unreinforced NR211. Complementary techniques, including electron microscopy, X-ray scattering, infrared spectroscopy, thermogravimetric analysis, and dynamic mechanical analysis, identify the structural and compositional strategies employed in NC700. Water sorption isotherms reveal lower water uptake for NC700 across all conditions, attributed to reinforcement and cerium incorporation. Reinforcement reduces in-plane swelling from 11% to 2.1% at 90% RH, confirming strong swelling anisotropy, while maintaining mechanical properties at elevated temperatures. While the ionic conductivity of NC700 is approximately 10% lower than that of NR211, the reduced thickness yields a 40% decrease in calculated area-specific resistance, suggesting the composite architecture can favorably shift the conductivity-stability trade-off. The composite structure also reduces gas permeability, indicating potential for improved separator function alongside favorable transport properties. Systematic deconvolution of reinforcement and additive contributions shows that conductivity losses from cerium incorporation are largely offset by gains from the lower equivalent-weight polymer, providing quantitative relationships that may guide composite membrane design for fuel cells and other electrochemical applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Control Room of the Future Testbed Workshop – After-Action Report

The U.S. Department of Energy’s Office of Electricity is supporting a one-year, multi-laboratory effort to define the needs and requirements for a Control Room of the Future testbed, or CROFT. The effort responds to increasing grid complexity driven by large new loads, dynamic generation resources, and the growing adoption of advanced technologies and tools, including artificial intelligence (AI) and machine learning (ML). To support safe, secure, and effective grid modernization, CROFT will focus on how emerging technologies and tools can be rigorously evaluated in realistic operational settings, with attention to human-machine interaction, cognitive load, and workforce readiness. The project team includes Argonne National Laboratory, Idaho National Laboratory, National Laboratory of the Rockies, and Pacific Northwest National Laboratory. As part of the scoping effort, the team conducted two industry-focused workshops: one at DTECH on February 5, 2026, informed by prior industry interviews, and a second on May 4, 2026, adjacent to IEEE T&D. These engagements brought together utilities, vendors, consultants, national laboratories, academia, and government stakeholders to identify and prioritize use cases, barriers, validation needs, data-sharing constraints, and near- and longer-term requirements. This feedback will directly inform CROFT’s architecture and research focus areas, ensuring the testbed is grounded in real-world operational needs and designed to evaluate emerging technologies and tools in realistic control-room environments.

artificial intelligence↗
DOE OSTI2026

Adapter Signaling Evaluations on EVs [Slides]

In case J3400 and J3400/1 define a basic analog signaling approach for DC charging adapters to communicate over-temperature events to both the EV and EVSE. They also require the EV to implement mitigation and corrective actions in cases where the EVSE does not respond to adapter thermal signals. In this study, multiple production vehicles were evaluated for responsiveness to assess field readiness and ensure consistent performance in accordance with the SAE J1772, J3400 and J3400/1 standards. A series of test cases were developed and executed across different vehicles, using an adapter thermal breakout fixture designed by NLR for evaluations. The results revealed notable variations in behavior: some vehicles expected to comply with J1772 were found to be non-responsive under certain conditions. Additionally, while some EV OEMs appear to align with the J3400 standard, discrepancies exist due to its evolving nature, resulting in inconsistent implementation of the latest requirements. These findings highlight the need for alignment within standards organizations to ensure consistent interpretation, encourage compliance, and reduce potential confusion across implementations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

A Sensitivity-driven Wide Area Protection (SWAP) Coordination Tool for High Penetration of Inverter-based Resources (IBR)

Traditionally, power system generation sources have been composed of synchronous generators, of which the fault current behavior is understood with minimal differences between generation size and types due to the physics of their construction. Present protection schemes and modeling methods are based upon these understood characteristics. Most renewable generation is composed of inverter-based resources (IBR), in which fault current is determined by switching control software and hardware limitations, each of which can vary between manufacturers and even between models of the same manufacturer. The resulting fault current is low in magnitude, low in negative-sequence current, unpredictable phase angles, and is a challenge to model. These characteristics also result in a challenge to traditional protection schemes and fault simulation software. To address several of these concerns, the project has the following goals: 1. Improve IBR models: Improve IBR models used in short circuit (SC) programs to accurately capture the response of IBRs at the bulk power system (BPS) level for fault and protection studies. 2. Develop automation tool: Develop an automation tool that allows engineers to identify protection coordination and sensitivity issues by performing SC and protection coordination studies in a high IBR-penetrated grid by applying variations to the IBR models, faults, contingencies, etc. 3. Develop schemes: Develop new protection mitigation solution schemes that complement the existing protection systems to ensure safe operation of the BPS with higher IBR penetration levels. The project team did not achieve this final goal, as the Department of Energy (DOE) stopped the project early due to changes in DOE funding priorities. The termination notice came at the beginning of the final project phase, while the team was identifying and beginning to investigate protection issues. It should be noted that the team discussed a 100% penetration scenario. However, this scenario would require the use of grid-forming IBR models that are not presently available. Since developing these models requires additional effort, the 100% penetration scenario was not pursued during this project. In the future, developing the methodology and models for the 100% scenario could benefit the industry.

14 SOLAR ENERGY↗
DOE OSTI2023

Temperature Dependence of Low‐Frequency Noise Characteristics of NiO x /β‐Ga 2 O 3 p–n Heterojunction Diodes

Temperature dependence of the low-frequency electronic noise in NiO x /β-Ga 2 O 3 p–n heterojunction diodes is reported. The noise spectral density is of the 1/f-type near room temperature but shows signatures of Lorentzian components at elevated temperatures and at higher current levels (f is the frequency). It is observed that there is an intriguing non-monotonic dependence of the noise on temperature near T = 380 K. The Raman spectroscopy of the device structure suggests material changes, which results in reduced noise above this temperature. The normalized noise spectral density in such diodes is determined to be on the order of 10 −14 cm 2 Hz −1 (f = 10 Hz) at 0.1 A cm −2 current density. In terms of the noise level, NiO x /β-Ga 2 O 3 p–n diodes perform excellently for new technology and occupy an intermediate position among devices of various designs implemented with different ultra-wide-bandgap semiconductors. The obtained results are important for understanding the electronic properties of NiO x /β-Ga 2 O 3 heterojunctions and contribute to the development of noise spectroscopy as the quality assessment tool for new electronic materials and device technologies.

1/f noise↗
DOE OSTI2026

Modeling a generic TRISO-fueled heat pipe microreactor using SCALE: Depletion, transportation criticality, and shielding

This paper demonstrates the applicability of the SCALE code system to tristructural-isotropic (TRISO)-fueled heat pipe microreactors through depletion, transportation criticality, and shielding analyses of a generic reference design. The study conducted supports US Nuclear Regulatory Commission code readiness efforts for advanced non–light-water reactor technologies and is intended as a code capability demonstration rather than as an optimization of a specific microreactor design. The modeled reactor employs high-assay low-enriched uranium (HALEU) uranium oxycarbide (UCO) TRISO fuel and beryllium oxide (BeO) reflectors and operates at 7.5 MWth with a nominal lifetime of about 3 effective full power years. Representative cases for fresh and irradiated cores were selected to exercise SCALE methods relevant to reactor operation and post-irradiation transport. The discharged-core decay heat is approximately 6% of operating power immediately after shutdown. Transportation criticality calculations show that internal water ingress is the dominant reactivity effect, with fully flooded fresh core and irradiated core configurations remain above the subcriticality criterion, even with the available control mechanisms. Shielding calculations for a simplified transportation package indicate that normal-condition dose rates are governed mainly by shielding thickness and cooling time, whereas the breached hypothetical accident case is governed primarily by cooling time. Overall, the study shows that SCALE supports depletion, transportation criticality, and shielding evaluations efficiently for TRISO-fueled heat pipe microreactors within a single code system.

Criticality↗
DOE OSTI2024

Estradiol associations with brain functional connectivity in postmenopausal women

Abstract Objective Previous studies have found that estrogens play a role in functional connectivity in the brain; however, little research has been done regarding how estradiol is associated with functional connectivity in postmenopausal women. The purpose of this study was to examine the relationship between estradiol and functional connectivity in postmenopausal women. Methods Structural and blood oxygenation level–dependent resting-state magnetic resonance imaging scans of 88 cognitively healthy postmenopausal individuals were obtained along with blood samples collected the same day as the magnetic resonance imaging to assess hormone levels. We generated connectivity values in CONN toolbox version 20.b, an SPM-based software. Results A regression analysis was run using estradiol level and regions of interest (ROI), including the hippocampus, parahippocampus, dorsolateral prefrontal cortex, and precuneus. Estradiol level was found to enhance parahippocampal gyrus anterior division left functional connectivity during ROI-to-ROI regression analysis. Estradiol enhanced functional connectivity between the parahippocampal gyrus anterior division left and the precuneus as well as the parahippocampal gyrus anterior division left and parahippocampal gyrus posterior division right. An exploratory analysis showed that years since the final menstrual period was related to enhanced connectivity between regions within the frontoparietal network. Conclusions These results illustrated the relationship between estradiol level and functional connectivity in postmenopausal women. They have implications for understanding how the functioning of the brain changes for individuals after menopause that may eventually lead to changes in cognition and behavior in older ages.

Obstetrics & Gynecology↗
DOE OSTI2021

Tritium Betavoltaic Powered Sensor Platforms: Power Augmentation with Scintillating Particles

Betavoltaics (BV) are long-life power sources that typically convert beta particle radiation into electricity. Largely, the radioactive decays within the source go unharvested by the device. This work seeks to augment the power generation of BV devices by integration of scintillating particles within the radiative getter to convert beta emission which would otherwise not leave the getter into usable light for power generation. Silica-covered barium fluoride scintillating particles were integrated into a tritiated water getter. Power generation was increased from 100s of nW to µW levels with the addition of 0.2 wt. % particles into the getter. Nanowatt-scale sensor platforms were demonstrated with the µW BV devices and the maximum possible lifetime of such platforms was estimated. As this technique enables higher power density BV devices from conventional Si semiconductors (compared to wider bandgap BVs), the further implementation may lower the barrier-to-deployment of these long-life power/sensor platforms.

42 ENGINEERING↗
DOE OSTI2025

Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation

Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.

Science & Technology - Other Topics↗
DOE OSTI2026

Electrochemical Deconstruction of Ortho-Phthalate Plasticizers and Recovery of Plasticizing Moieties

Electrochemical methods for the direct reduction of alkyl esters have been understudied but provide a potential advantage for addressing specific waste streams given that such strategies often require only a minimal chemical profile. One such waste stream that could benefit from electrochemical remediation is poly(vinyl chloride) (PVC) plastics. PVC recycling often faces challenges due to the complexity of such waste. Solvent-based recycling methods pose several advantages for addressing post-use PVC; however, such methods are complicated by the high amounts of toxic legacy plasticizers (ortho-phthalates) present in PVC. A potential solution to addressing such phthalates is to address them electrochemically, coupled with recovery of the resulting valuable products. Presented herein is an optimized electrochemical method for ester activation that is leveraged to separate and recover the aromatic and alkyl components of ortho-phthalate plasticizers. The resulting aliphatic alcohols may be reused to prepare other non-toxic plasticizers. This electro-degradation method is demonstrated on six phthalates that have been identified to pose health concerns in addition to plasticizers recovered from commercial samples of PVC.

di-(2-ethylhexyl) phthalate↗
DOE OSTI2026

Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES)

The C2SAGES project evaluated the feasibility of a community geothermal system for the planned Windy Ridge affordable housing development in Hinesburg, Vermont. Led by GTI Energy with Vermont Gas Systems, LN Consulting, NREL, and Frontier Energy, the work assessed technical design, energy performance, costs, business models, community engagement, maintenance, workforce development, and permitting. The proposed system was designed to serve 100% of the development’s heating, cooling, and domestic hot water loads. Compared with a baseline using air-source heat pumps and natural gas water heating, the geothermal system was estimated to reduce HVAC and domestic hot water energy use by about 45% to 48%, lower operating and maintenance costs, and reduce 30-year life-cycle costs by 37% for Phase 1 and 10% for Phase 2. Technical testing and modeling indicated that the Windy Ridge site is suitable for a community-scale geothermal system. The project also developed borehole field layouts, piping concepts, pump house designs, controls, maintenance plans, and supporting engineering drawings. The business model analysis found that first cost, ownership structure, and customer affordability remain major deployment challenges. Utility-led maintenance and operation were viewed favorably, but traditional utility cost-recovery models may require subsidy or revised financing structures to be practical for affordable housing. Community engagement highlighted the need for clear public education, transparent financing, reliable long-term maintenance, trained technicians, and the potential to pair geothermal systems with weatherization. Overall, the report concludes that community geothermal is technically feasible and offers meaningful energy, emissions, and life-cycle cost benefits, but broader deployment will depend on workable financing models and workforce readiness.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
DOE OSTI2025

The role of fast and slow dynamics in nonlinear resonant ultrasound spectroscopy of consolidated granular materials

Abstract Elastic nonlinearity observed in consolidated granular media can be attributed to the combination of slow and fast effects, which give rise to hysteresis and relaxation of both modulus and damping after the sample is perturbed. A consequence is a high level of complexity in the measurements of the sample linear and nonlinear elastic parameters. The results of experiments are dependent on the experimental protocol that is adopted to measure the relevant quantities and it is hard to quantify parameters with accuracy and repeatability. Here we focus on examining Nonlinear Resonant Ultrasound Spectroscopy, showing experimentally the role of slow dynamics in the process and quantifying/discussing its influence on the quantification of nonlinearity. We also propose a model to describe the process, which shows that different contributions to nonlinearity (e.g., classical and hysteretic) could be due to physical features (defects) relaxing with different relaxation times.

Science & Technology - Other Topics↗
DOE OSTI2024

Making Plasticized Polymer Electrolytes Stable Against Sodium Metal for High‐Energy Solid‐State Sodium Batteries

Solid polymer electrolytes based on plastic crystals are promising for solid-state sodium metal (Na 0 ) batteries, yet their practicality has been hindered by the notorious Na 0 -electrolyte interface instability issue, the underlying cause of which remains poorly understood. Here, in this study, by leveraging a model plasticized polymer electrolyte based on conventional succinonitrile plastic crystals, we uncover its failure origin in Na 0 batteries is associated with the formation of a thick and non-uniform solid electrolyte interphase (SEI) and whiskery Na 0 nucleation/growth. Furthermore, we design a new additive-embedded plasticized polymer electrolyte to manipulate the Na 0 deposition and SEI formulation. For the first time, we demonstrate that introducing fluoroethylene carbonate (FEC) additive into the succinonitrile-plasticized polymer electrolyte can effectively protect Na 0 against interfacial corrosion by facilitating the growth of dome-like Na 0 with thin, amorphous, and fluorine-rich SEIs, thus enabling significantly improved performances of Na//Na symmetric cells (1,800 h at 0.5 mA cm −2 ) and Na//Na 3 V 2 (PO 4 ) 3 full cells (93.0 % capacity retention after 1,200 cycles at 1 C rate in coin cells and 93.1 % capacity retention after 250 cycles at C/3 in pouch cells at room temperature). Our work provides valuable insights into the interfacial failure of plasticized polymer electrolytes and offers a promising solution to resolving the interfacial instability issue.

25 ENERGY STORAGE↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗