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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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DOE OSTI2024

Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Zaki, Mohammed Tamim↗
DOE OSTI2026

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2022

The Third Assessment of the Effects of Climate Change on Federal Hydropower

Understanding the future changes in projected water supplies is a vital objective for federal hydropower facilities tasked with providing low-cost, reliable electricity across a large regional footprint that encompasses a growing customer base, alternative market structures for marketing the electricity, and a more diverse generation asset mix than was historically present when a majority of federal hydropower facilities were built. This study, The Third Assessment of the Effects of Climate Change on Federal Hydropower, directed by Section 9505 of the SECURE Water Act of 2009 (SWA), is the third quinquennial report on evaluating the effects of climate change on hydroelectric energy generated from 132 US federal hydropower plants marketed by four US Department of Energy (DOE) Power Marketing Administrations (PMAs). The technical assessment is conducted by DOE’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Texas A&M University under the guidance of DOE’s Water Power Technologies Office. This study is the result of extensive consultation with the four federal PMAs (Bonneville Power Administration [BPA], Western Area Power Administration [WAPA], Southwestern Power Administration [SWPA], and Southeastern Power Administration [SEPA]), as well as other agencies, including federal hydropower owners/operators (the US Army Corps of Engineers, US Bureau of Reclamation [Reclamation]), US Geological Survey, and National Oceanic and Atmospheric Administration). The main findings of this assessment, along with the PMA administrators’ recommendations, will be included in a subsequent DOE report to Congress. The assessment method and the technical findings are described in this report.

13 HYDRO ENERGY↗
DOE OSTI2025

Pricing Strategy of Electric Vehicle Aggregators Based on Locational Marginal Price to Minimize Photovoltaic (PV) Curtailment

The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Integrated Hydrogen Energy Storage System (IHESS) for Power Generation (Final Report)

GTI Energy partnered with Praxair Inc., a subsidiary of Linde PLC (Praxair), the Center for Electromechanics at the University of Texas at Austin (CEM), Entergy Corporation (Entergy), Quanta Services (Quanta) and Mitsubishi Heavy Industries (MHI) to perform a feasibility study that determines the economic and technical feasibility of low-carbon hydrogen energy storage integrated with a site-specific fossil fuel asset to decarbonize electricity production using the Integrated Hydrogen Energy Storage System (IHESS). The proposed IHESS concept is novel, as it sought to leverage the existing natural gas infrastructure, hydrogen production and pipelines, existing underground hydrogen storage system and power plants in the Gulf Coast region to reduce carbon emissions and generate lowcarbon electricity. This study has shown that IHESS has a promising commercial outlook as the concept leverages existing natural gas infrastructure assets and hydrogen production and storage that are unique to the Texas gulf coast region. A key hurdle with hydrogen supply and transportation has been capital investment. By storing hydrogen in underground storage and delivering it as a fuel source during peak demand using the existing infrastructure minimizes the capital investment. Regional petrochemical producers who are actively seeking opportunities to reduce costs and lower emissions should find the IHESS concept very appealing.

03 NATURAL GAS↗
DOE OSTI2026

Adaptive Reinforcement Learning Control for Power Distribution in Multi-Output Resonant Converters

This paper presents an adaptive reinforcement learning (ARL)-based control framework for efficient power distribution in a multi-output resonant converter for UAV applications. The proposed system is based on a high-frequency isolated resonant architecture, where a single energy source supplies multiple propulsion loads through independently controlled output rectifiers, addressing the need for coordinated multi-motor power management. The ARL framework dynamically allocates output power by learning optimal phase-shift control actions under varying load demands and operating conditions. The agent autonomously determines control parameters that maximize conversion efficiency while ensuring accurate power sharing among multiple outputs. In addition, the proposed approach enables adaptive operation without requiring detailed system modeling or manual tuning. Experimental results demonstrate stable and efficient performance over a wide range of operating conditions, confirming the effectiveness and robustness of the learning-based control strategy for multi-output resonant converter system.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2022

A Simple Condition for Sustained Super-Eddington Black Hole Growth

One of the most pressing questions in cosmology is how the black holes (BHs) powering quasars at high redshift grow to supermassive scales within a billion years of the Big Bang. Here we show that sustained super-Eddington accretion can be achieved for BHs with Eddington fractions f Edd ≳ 2/ϵ, where ϵ is the efficiency with which radiation is generated in the accretion process. In this regime, the radiation carries too little momentum to halt the accretion flow and the infalling gas traps the radiation. The BH growth then proceeds unimpeded until the gas supply is exhausted, in contrast to accretion at lower rates, which is limited by the radiation generated in the accretion process. The large gas supply available in massive high-redshift quasar host galaxies may be readily accreted onto seed BHs via this supply-limited mode of accretion, providing an explanation for how such supermassive BHs are assembled in the early universe. This sustained super-Eddington growth may also explain the short lifetimes inferred for the H ii regions surrounding high-redshift quasars, if the bulk of the BH growth occurs without the associated radiation escaping to ionize the intergalactic medium. It furthermore implies that a population of obscured rapidly growing BHs may be difficult to detect, perhaps explaining why so few quasars with Eddington fractions higher than a few have been observed. Finally, this simple condition for sustained super-Eddington growth can easily be implemented in cosmological simulations that can be used to assess in which environments it occurs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Flexibility Options: A Proposed Product for Managing Imbalance Risk

The presence of variable renewable energy resources with uncertain outputs in day-ahead electricity markets results in additional balancing needs in real-time. Addressing those needs cost-effectively and reliably within a competitive market with unbundled products is challenging as both the demand for and the availability of flexibility depends on day-ahead energy schedules. Existing approaches for reserve procurement usually rely either on oversimplified demand curves that do not consider how system conditions that particular day affect the value of flexibility, or on bilateral trading of hedging instruments that are not co-optimized with day-ahead schedules. This article proposes a new product, ‘Flexibility Options', to address these two limitations. The demand for this product is endogenously determined in the day-ahead market and it is met cost-effectively by considering real-time supply curves for product providers, which are co-optimized with the energy supply. As we illustrate with numerical examples and mathematical analysis, the product addresses the hedging needs of participants with imbalances cost-effectively, provides a less intermittent revenue stream for participants with flexible outputs, promotes value-driven pricing of flexibility, and ensures that the system operator is revenue-neutral. This article provides a comprehensive design that can be further tested and applied in large-scale systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint

The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

NH 4 OH Looping with Membrane CO 2 Absorber and Distributed Stripper for Enhanced Algae Growth

The University of Kentucky Center for Applied Energy (UK CAER) has devised a unique, integrated CO2 capture and utilization technology. CO2 from coal-fired power generation flue gas is first captured at half the operating cost of a typical aqueous CO2 capture system (CCS), distributed in an aqueous stream and then fixed by algae in bioreactors where the algae production is increased by 50% over that with a typical intermittent nutrient feeding system. Lower CCS operating cost is achieved by eliminating the flue gas pretreatment step for cooling and SO2 removal, eliminating steam extraction from the power generation steam cycle for solvent regeneration, and eliminating CO2 compression. Higher algae production is achieved by continuous, just-in-time nutrient feed to the bioreactors directly from a distributed solvent regenerator, which maintains the bioreactor pH for optimum growth. The process starts with a uniquely configured membrane absorber, where the flue gas is indirectly contacted with an ammonium hydroxide (NH4OH) solvent. Dissolved NH3 is attractive for both CO2 capture and as an algae nutrient. For CO2 capture it is inexpensive, has a low regeneration energy, is thermally- and oxidatively-stable and has a viscosity near that of water, which makes is easy to transport. Numerous studies have shown that the scrubbing capacity of NH3 is approximately 0.9-1.2 kg of CO2/kg of NH3, with a CO2 removal efficiency of ~99% and half the solvent regeneration energy than that of 30 wt% MEA[1, 2, 3]. NH3 is attractive as an algae nutrient due to its low cost. The rich NH4OH solvent is pumped to a set of distributed regenerators which are co-located with the algae bioreactors. Solvent pumping, transport and distribution reduces the balance of plant (BOP) cost compared to a typical aqueous CCS related to the flue gas duct and boost fan required to transport the flue gas. The energy required for the distributed solvent regeneration is supplied by solar-thermal panels eliminating the need for steam extraction from the power generation steam cycle. After solvent regeneration, the product stream contains both the CO2 captured from the flue gas and volatized NH3 from the solvent. This product stream is fed directly to the bioreactors, eliminating the need for compression of the CO2 stream. The relative amounts of CO2 and NH3 in the product stream are adjusted and controlled by a controlling the regeneration conditions (pressure and temperature). The continuous feed of the right ratio of nutrients overcomes the typical inhibition of algae growth resulting from frequent pH swings in the bioreactor due to unbalanced (intermittent) feeding systems for CO2 and N. Also, because the regenerators will operate at pressure and be located in close proximity to the bioreactors, there is no worry about pressure drop when sparging the gas into the algae. Sparging produces small bubbles which is beneficial for mass transfer efficiency. One known challenge when using an NH4OH solvent is high NH3 emission. Hydrophobic membranes are used for CO2 capture using an aqueous NH3 solution[4, 5] without the direct contact between flue gas and aqueous solution. Additionally, UK CAER CO2 capture and utilization process manages NH3 slip in three extra measures. First, NH3 slip is minimized by working with minimal species partial pressure, which is proportional to the concentration in the liquid. Hence, lowering the capture solvent concentration will lower the NH3 partial pressure. Second, UK CAER’s previous work has demonstrated that the addition of Zn2+ into NH3 solutions to chelate the NH3 can reduce NH3 volatility. Third, the configuration of the membrane CO2 absorber utilizes condensed water from the flue gas to continually wash the gas-side of the membrane to reduce fouling and recapture NH3 slip. Additional details about the UK CAER unique, integrated CO2 capture and utilization technology will be presented along with technology development plans. Diao, N., Q. Li, and Z. Fang. 2004. Heat transfer in ground heat exchangers with groundwater advection. International Journal of Thermal Sciences. 43: 1203-1211, He, Q., M. Chen, L. Meng, K. Liu, and W. Pan. 2004. Study on Carbon Dioxide Removal from Flue Gas by Absorption of Aqueous Ammonia. Western Kentucky University. Yeh, A.C., and H. Bai. 1999. Comparison of ammonia and monoethanolamine solvents to reduce CO2 greenhouse gas emissions. The Science of the Total Environment. 228: 121-133, Villeneuve, K., D. Roizard, J.C. Remigy, M. Iacono, and S. Rode. 2018. CO2 capture by aqueous ammonia with hollow fiber membrane contactors: Gas phase reactions and performance stability. Separation and Purification Technology, 199: 189-197, Toro Molina, C., and C. Bouallou. 2016. Carbon dioxide absorption by ammonia intensified with membrane contactors. Clean Techn Environ Policy 18, 2133–2146 (2016)

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Engineering Escherichia coli to produce medium chain oleochemicals from C 2 substrates

Many strategies to create a circular bioeconomy have been proposed. To be successful, CO 2 must be reduced with renewable energy into chemical building blocks, from which the chemical industry can be supported. Circular strategies include leveraging photosynthesis to produce sugar and lipid intermediates or renewable electricity to produce hydrogen or other electron carriers to support CO 2 reduction. Acetogens can anaerobically reduce CO 2 with H 2 to produce mixtures of small organic molecules in gas fermentations. We previously demonstrated that acetate, a common product of gas fermentation, can be converted to the model oleochemical dodecanol in engineered Escherichia coli. Here, we explored the conversion of ethanol and mixtures of ethanol and acetate to the same model oleochemicals. Co-feeding ethanol can supply both carbon and additional reducing power relative to acetate alone. In this work, we engineered E. coli to catabolize ethanol and expressed two distinct ethanol metabolism pathways in different operons and combined them with improved engineered acetate activation. We evaluated the performance of these operons in dodecanol-producing strains when fed ethanol or acetate and found ethanol to be a better carbon source when judged by product titers. The engineered strains fed ethanol produced about two-fold more dodecanol than the strains fed acetate. This increase was in part, due to change in product distribution. Cells fed ethanol produced predominantly dodecanol, whereas cells fed acetate generated a mixture of dodecanol and dodecanoic acid. Dodecanol titers were further improved by employing feeding strategies in controlled bioreactors.

Escherichia coli↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2025

Land-based resources for engineered carbon dioxide removal in the United States exceed the expected needs

Gigatonne-scale atmospheric carbon dioxide removal (CDR), alongside deep emission cuts, is critical to stabilizing the climate. However, some of the most scalable CDR technologies are also the most land intensive. Here, we examine whether adequate land resources exist in the contiguous United States to meet CDR targets when prioritizing grid emissions reduction, food production, and the protection of sensitive ecosystems. We focus on biomass carbon removal and storage (BiCRS) and direct air capture and storage (DACS) and show that suitable lands exceed the expected needs: 37.6 million hectares of land are available for BiCRS, resulting in 0.26 GtCO 2 of CDR/year, and 34 million hectares are suitable for wind- and solar-powered DACS, resulting in 4.8 GtCO 2 of CDR/year if facilities are co-located with geologic CO 2 storage. We identify biomass and energy supply hotspots to meet CDR targets while ensuring land protection and minimizing land competition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Algorithmic Advancements for High-Order Self-Gravitating Hydrodynamics

Self-gravity plays a key role in the formation and evolution of many astronomical objects. Though gravity is often dominant at large scales, other forces (e.g., gas pressure gradients, radiation, and/or magnetic fields) often compete. It is therefore essential for numerical simulations to evaluate their interplay accurately and robustly. Hanawa & Mullen derived a 4th-order accurate finite volume scheme to solve the equations of self-gravitating hydrodynamics on a uniform Cartesian grid. In this work, we supply improvements to the algorithm that (1) mitigate spurious gravitational circulation and (2) greatly simplify the evaluation of the high order corrections. The proposed algorithm provides the gravitational acceleration (ρg) and the gravitational energy release (ρv · g) as source terms for the hydrodynamic equations, all while preserving conservation of linear momentum. Spurious heating and/or cooling associated with truncation error in the numerical evaluation of the gravitational energy release decreases in proportion to the fourth power of the cell width. We demonstrate fourth order convergence on smooth problems (e.g., 3D inclined sound wave propagation and 3D equilibria). An application test tracks the spherical collapse of a polytrope by an imposed, sudden decrease of the central gas pressure; a bounce and second collapse (associated with a spherical accretion shock) are robustly captured by the high order algorithm.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Harnessing photoenzymatic reactions for unnatural biosynthesis in microorganisms

Photobiocatalysis provides a powerful strategy for integrating light and biological catalysts to drive abiological transformations. However, its scalability is hindered by high enzyme loading, reliance on costly cofactors and instability under radical-generating conditions. Here we report the integration of light-driven enzymatic reactions into the cellular metabolism of Escherichia coli, bridging flavin-based photobiocatalysis with biosynthesis. Using synthetic biology strategies, we engineered microbial cells to continuously produce olefin substrates and ene-reductase while regenerating cofactors directly from glucose. By externally supplying radical precursors or introducing synthetic pathways for their in situ production, we enabled fermentation-based microbial photobiosynthesis, achieving high titres and demonstrating feasibility for scale-up in a bioreactor. This approach extends photobiocatalysis from in vitro applications to in vivo semi- and complete biosynthesis, revealing its full potential for integrating light-driven reactions into cellular metabolism.

Biocatalysis↗
DOE OSTI2020

Demand Driven Cycamore Archetypes

Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

System Analysis of an Internal Combustion Engine (ICE) - Solid Oxide Fuel Cell (SOFC) Hybrid Cycle

The variability of renewable energy sources poses challenges for reliable grid operation. Conventional thermal power sources, though reliable, often lack operational flexibility. Hybrid energy systems that integrate Solid Oxide Fuel cells (SOFC) with Internal Combustion Engines (ICE) offer a promising solution by achieving relatively higher efficiency and grid-following capability. This study investigates performance of a 100-kW pressurized SOFC-ICE hybrid cycle. In this configuration, unutilized SOFC fuel is used to drive the engine, with a turbocharger providing air supply and an external reformer generating syngas. System components were numerically modeled using MATLAB/SIMULINK for the SOFC and reformer, and EBSILON® for the ICE and balance of plant. Parametric studies varied fuel utilization (70-90%), reformer temperature (600 – 1000K), anode off-gas recirculation (0 – 70%), and current density (0.2 – 0.55 A/cm2). Results show that the SOFC and ICE operate as thermally independent topping and bottoming cycles, achieving peak efficiency of 62% under optimized conditions.

hybrid↗