Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model based development”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Monolithic Heat Pipe Microreactor Reference Plant Model

This work introduces a reference plant model for a generic monolithic heat-pipe-cooled microreactor. The model will serve as a springboard to develop future evaluation models in the licensing process of similar microreactor designs at the U.S. Nuclear Regulatory Commission. This model has been developed with the Comprehensive Reactor Analysis Bundle and its specifications are based on open literature publications for the eVinci TM design. BlueCRAB is the U.S. Nu- clear Regulatory Commission non-light-water reactor analysis system based on the Multiphysics Object-Oriented Simulation Environment framework, which can couple the Griffin, BISON, and Sockeye applications to resolve the various physics that are essential for the safety analysis of this type of reactor system. The core specifications includes tristructural isotropic fuel, graphite monolith, graphite reflectors, and drums composed of graphite and B 4 C. No moderator or burnable poison pins are used in the design. The fuel enrichment is reduced to control excess reactivity in the core. This core design is not optimized and only serves for testing purposes, since the primary objective of this work is to exercise the multiphysics coupling for this type of reactor system. A three dimensional (3D) core heterogeneous Griffin discrete ordinates (SN) transport model allows the precise calculation of the flux distribution and pin powers. Griffin transfers the power density distribution and obtains a temperature distribution to and from BISON. The BISON model com- putes the 3D core temperature distribution and is coupled to 876 Sockeye subapplications running a heat pipe model. This 3D conduction model is coupled to the various heat pipes via heat flux boundary conditions. The model includes a small gap between the heat pipe and the monolith. Convective heat transfer boundaries with either ambient temperature or condenser temperature as heat sinks are imposed at the model boundaries. The 2D Sockeye heat pipe model uses a vapor- only methodology, which provides the needed resolution for transient calculations and allows the determination of various heat pipe limits. This approach is superior to the superconductor model traditionally used in steady-state calculations. BlueCRAB computes steady-state power and temperature distributions that serve as the initial condition for a loss-of-heat-sink transient simulation. The steady-state results show significant peaking due to the position of the control drum, but this is a characteristic of the particular design used, which is not optimized at this stage. The transient results show the reactor power slowly stabilizing towards a 3% power level after the partial loss of secondary heat removal. Several recriticalities are observed due to cooling through the secondary system but the reactor is self-stabilizing and behaves as expected.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Comparison of ML-Based Proxy Modeling Strategies: Lessons Learned from the SMART Initiative

Teams of researchers on Task 5 of the SMART project have developed a variety of modeling architectures to predict subsurface behavior during carbon injection and post-injection periods. One important part of this task was to compare the candidate approaches in terms of accuracy, reliability, speed, and memory use, all using a common set of metrics and visualizations for an “apples to apples” comparison. Dr. Jared Schuetter will share the details of this task, the results that were obtained, and the lessons learned.

Schuetter, Jared↗
DOE OSTI2025

Site Identification framework and environmental compliance for floating ocean turbines in US Waters (Abstract)

Like all potential marine energy deployment sites, characterizing the site, understanding the environmental interactions with floating tidal technology, and mapping a pathway towards regulatory acceptance will be needed to site and permit ocean current development within the Florida Current. The proposed work will be based on a modeling analysis, a desk-top review and analysis, focusing on the characteristics of the ocean current technology and environmental monitoring information on the marine animals and habitats that are specific to the appropriate latitude, particularly those that are endemic to the offshore Florida area. The analysis will also describe the state and federal authorizations required for deploying a floating tidal energy technology, with an emphasis on how they differ from those in Washington, Alaska, and Maine. Environmentally compliant sites within Florida Current will be selected based on environmental, logistical, and regulatory criteria. Recommendations on monitoring needs and adaptive management practices will be provided. The need to acquire social acceptance for the Orbital Marine Power technology will also be explored and general recommendations prepared to encourage the best acceptance in the communities of interest.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Larval connectivity for European green crab management in the Salish Sea and surrounding waters

The presence of invasive species is a growing concern in coastal marine ecosystems because of their adverse effects on biodiversity. The European green crab Carcinus maenas (EGC) is a small crab inhabiting inshore areas. Although it is native to the Northeast Atlantic Ocean and Baltic Sea, its distribution has expanded to North America, where it is an invasive species. Its main food sources are small invertebrate species that support valuable fisheries in the USA. The first presence of EGC in northern Washington was observed around 20 yr ago, along the Pacific Coast of the USA. Recently, EGC has been detected throughout the Salish Sea (Washington, USA, and British Columbia, Canada) wherein spread dynamics are unknown. The overall distribution of EGC is mainly driven by larval dispersal and, in the Salish Sea and surrounding waters, the assessment of EGC population dynamics is essential to understand its migration patterns and prevent its future expansion. To investigate the dispersal patterns of EGC larvae, a larval dispersal model was developed which couples a regional model of hydrodynamic circulation with an individual-based model of ichthyoplankton dynamics. Simulations were performed over 9 yr (2013-2022) to analyze average larval transport trends in the Salish Sea and surrounding waters, interannual variability of EGC larval connectivity, and the influence of larval behavior on connectivity patterns. Lastly, areas were identified to inform invasive species management moving forward. The prediction of likely sources and settlement locations of EGC larvae from the model will help improve the management of the population in the Salish Sea and surrounding waters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Representing Fine‐Scale Topographic Effects on Surface Radiation Balance in Hyper‐Resolution Land Surface Models

Land surface models are increasingly used to simulate land surface processes at hyper-spatial resolutions (e.g., ∼1 km). As model resolution increases, grid-scale topographic effects on surface radiation fluxes and their interactions between adjacent grids become more pronounced. However, current land surface models routinely neglect the fine-scale topographic effects on surface radiation balance. This study developed physically-based and computationally-efficient parameterizations (fineTOP) that explicitly resolve fine-scale topographic effects on downward shortwave and longwave radiation as well as land surface radiative properties. The newly developed parameterizations were implemented and tested in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Multi-decadal km-resolution ELM simulations over the California Sierra Nevada show that fine-scale topography significantly impacts the surface energy balance and snow processes across seasons. Slope determines the magnitude of topographic effects, while aspect controls their sign. For slopes larger than 30°, topography-induced change in annual surface temperature can be as large as 3.3 K. Regionally, the mean value and standard deviation of topography-induced changes in annual surface temperature are −0.22 ± 0.38 K and +0.25 ± 0.37 K over north-facing and south-facing slopes, respectively. Topography-induced changes in surface radiative properties account for 3.5% ± 13.8% of total topographic effects on annual net radiation. With fineTOP, ELM captures the aspect-dependence of snow cover fraction, snow water equivalent, and land surface temperature found in MODIS satellite observations and a snow reanalysis data set, while the default ELM fails to capture this phenomenon. The enhanced capability to represent fine-scale topographic effects on surface radiation balance can be used to advance understanding of the role of fine-scale topography in land surface processes and land-atmosphere interactions over mountainous regions.

Hao, Dalei [Pacific Northwest National Laboratory ↗
DOE OSTI2026

Thermomechanical analysis and modeling of a high-temperature light trapping planar cavity solar receiver

The development of durable particle-based high-temperature solar receivers is critical for advancing concentrating solar-thermal (CST) technologies to enable high-efficiency power generation and industrial process heat. Here, this study presents a computational framework to evaluate the thermomechanical performance of a proposed enclosed light-trapping planar cavity receiver designed for particle-based thermal energy systems. The receiver incorporates absorptive cavities and fluidized particle-bed channels to enhance heat capture and reduce thermal losses. Finite element analysis (FEA) is employed to assess stress, strain, and creep-fatigue behavior under concentrated solar flux using realistic thermal boundary conditions derived from coupled system models and experimental assembly parameters. The analysis investigates the influence of particle-to-wall heat transfer coefficients (HTC) ranging from 800 to 1800 W/m 2 .K on the thermomechanical response of six candidate high-temperature alloys: Alloy 740H, Alloy 282, Alloy 617, 316H, Alloy 230, and 800H. Results show that increasing HTC reduces thermal gradients, leading to lower stresses and strains and extended minimum predicted creep life. While all materials satisfy fatigue life requirements under the investigated conditions, significant differences in creep resistance are observed. Alloy 740H consistently exhibits the longest minimum predicted creep life and the most favorable durability margins, followed by Alloy 282, with the remaining materials showing reduced creep resistance under identical loading. The reported creep lives are conservative lower-bound estimates intended for comparative material evaluation. This framework highlights the critical roles of material selection and geometry optimization in improving mechanical durability and reliability of solar-thermal receivers, forming a foundation for future experimental validation and design optimization.

14 SOLAR ENERGY↗
DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

The impact of kinetic and global effects on ideal ballooning 2nd stable pedestals of conventional and low aspect-ratio tokamaks

The EPED model (Snyder et al 2011 Nucl. Fusion 51 103016) has had success in describing the pedestal structure of Type-I ELM and QH-mode plasmas in tokamaks, by combining kinetic ballooning mode (KBM) and peeling–ballooning (PB) constraints. Within EPED, the KBM constraint is often approximated by a technique using calculated ideal ballooning mode (IBM) thresholds, fit to a functional form designed to capture non-local effects. It has been noted that quantitative differences between local ideal MHD and gyro-kinetic (GK) ballooning stability can be larger at low aspect ratio. KBM critical pedestals are consistent with observations in initial studies on conventional and spherical tokamaks. In this work, the application of a reduced model for the calculation of the kinetic ballooning stability boundary is presented based on a novel and newly developed gyro-fluid system (GFS) code (Staebler et al 2023 Phys. Plasmas 30 102501). GFS is observed to capture KBMs in DIII-D as well as the NSTX pedestals, opening a route to integrating this model into EPED. Finally, high but finite $n$ global ballooning modes are observed to limit the access to the local 2nd stability and thus provide a transport mechanism that constrains the pedestal evolution with $β_{p,\textrm{ped}}$. The high $n$ global ballooning stability is approximated by its ideal MHD analog using ELITE. It is shown that nearly-local high $n$ modes with $k_yρ_s$ ~ $0.25 - 0.5$ can provide a proxy for the critical $β_{p,\textrm{ped}}$ when a 2nd stable access exists on DIII-D plasmas. The use of GFS and ELITE scaling in EPED provides improved agreement to EPED1 in an initial comparison with DIII-D pedestal data.

Anastopoulos Tzanis, Michail Savvas [Oak Ridge Nat↗
DOE OSTI2024

Evaluation of Global Fire Simulations in CMIP6 Earth System Models

Fire is the primary form of terrestrial ecosystem disturbance on a global scale and an important Earth system process. Most Earth system models (ESMs) have incorporated fire modeling, with 19 of them submitting model outputs of fire-related variables to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study provides the first comprehensive evaluation of CMIP6 historical fire simulations by comparing them with multiple satellite-based products and charcoal-based historical reconstructions. Our results show that most CMIP6 models simulate the present-day global burned area and fire carbon emissions within the range of satellite-based products. They also capture the major features of observed spatial patterns and seasonal cycles, the relationship of fires with precipitation and population density, and the influence of the El Niño–Southern Oscillation (ENSO) on the interannual variability of tropical fires. Regional fire carbon emissions simulated by the CMIP6 models from 1850 to 2010 generally align with the charcoal-based reconstructions, although there are regional mismatches, such as in southern South America and eastern temperate North America prior to the 1910s and in temperate North America, eastern boreal North America, Europe, and boreal Asia since the 1980s. The CMIP6 simulations have addressed three critical issues identified in CMIP5: (1) the simulated global burned area being less than half of that of the observations, (2) the failure to reproduce the high burned area fraction observed in Africa, and (3) the weak fire seasonal variability. Furthermore, the CMIP6 models exhibit improved accuracy in capturing the observed relationship between fires and both climatic and socioeconomic drivers and better align with the historical long-term trends indicated by charcoal-based reconstructions in most regions worldwide. However, the CMIP6 models still fail to reproduce the decline in global burned area and fire carbon emissions observed over the past 2 decades, mainly attributed to an underestimation of anthropogenic fire suppression, and the spring peak in fires in the Northern Hemisphere midlatitudes, mainly due to an underestimation of crop fires. In addition, the model underestimates the fire sensitivity to wet–dry conditions, indicating the need to improve fuel wet-ness estimation. Based on these findings, we present specific guidance for fire scheme development and suggest a postprocessing methodology for using CMIP6 multi-model outputs to generate reliable fire projection products.

Wildfire, Earth system models↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients in Consequence Management: Propagated Dose Uncertainty in ICRP 66 Human Respiratory Tract Model

Reference inhalation dose models rely on deterministic biokinetics and reference computational phantoms, limiting their applicability to the variability present in population-specific exposures encountered in emergency response scenarios. Here, this study introduces REDCAL, a Python-based computational framework developed to propagate uncertainty in inhalation dose coefficients using the International Commission on Radiological Protection (ICRP) Publication 66 Human Respiratory Tract Model. REDCAL integrates ICRP deposition and clearance models, systemic biokinetics, and governing physics principles, and leverages Sandia National Laboratories’ Dakota toolkit for uncertainty quantification via Latin Hypercube Sampling. REDCAL was validated against DCAL, with biokinetic retention results differing by less than 1% and effective dose coefficients by less than 2% across all tested radionuclides. Stochastic sampling introduced variability in dose coefficients, with geometric standard deviations (GSD) in committed effective dose coefficients (CEDC) ranging from 1.0 to 1.5, based on lognormal distribution fits. Analysis demonstrated that variations in the activity median aerodynamic diameter (AMAD) notably influenced the computed CEDC values. Smaller particles (<1 µm) increased doses by 20–30% due to deeper lung deposition and prolonged retention for alpha emitting radionuclides, such as 241 Am and 239 Pu. Radionuclides with fast clearance, such as 133 I, demonstrated a dose reduction exceeding 50%, as AMAD increased beyond 5 µm due to upper airway deposition and rapid mucociliary clearance. The greatest GSD among the radionuclides reported in this study was for 241 Am. In most cases, the largest GSDs in the CEDC were associated with larger particle sizes, an expected outcome, as ICRP Publication 66 defines GSD in particle size as a function of AMAD, resulting in an extended tail of the lognormal distribution. The findings support improved inhalation dose assessments and enhance consequence management strategies for the U.S. Federal Radiological Monitoring and Assessment Center by quantifying uncertainty in dose coefficients and strengthening decision-making for emergency response scenarios.

Biokinetic Modeling↗
DOE OSTI2025

Assessing Dynamic Behaviors in Converter- Dominated Power Systems via RMS and EMT Simulations: A Study of Hawaii’s NELHA Microgrid

The transition from conventional power systems to converter-based microgrids has significantly advanced sustainability, clean energy integration, and operational reliability. However, this paradigm shift introduces operational challenges due to the intermittent nature of renewable energy sources and the non-linear characteristics of power electronic loads, inducing voltage fluctuations and harmonic distortions that complicate voltage and frequency regulation. Accurate dynamic modeling is hypothesized to be critical for capturing such effects, enabling reliable simulation and control strategy development. This study introduces an innovative dynamic modeling framework for a real-world converter-based microgrid, utilizing both root mean square (RMS) and electromagnetic transient (EMT) simulation methods. The microgrid was modeled in DIgSILENT PowerFactory, with simulations calibrated against high-resolution field measurements from SEL-735 power quality meters. Results show that RMS simulations effectively characterize steady-state dynamics, while EMT simulations are essential for capturing high-frequency transients and non-linear effects from photovoltaic inverters and variable frequency drives (VFDs). This complementary approach provides a comprehensive understanding of microgrid behavior, providing critical insights for improving simulation accuracy, advancing protection schemes, and improving resilience in future low-inertia power networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A novel peridynamics-based approach to predict pharmaceutical tablet robustness

The pharmaceutical drug product development process can be greatly accelerated through the use of modeling and simulation techniques to predict the manufacturability and performance of a given formulation. The anticipation and possible mitigation of tablet damage due to manufacturing stresses represents a specific area of interest in the pharmaceutical industry for predicting formulation and tableting performance. While the finite element method (FEM) has been extensively used for predicting the mechanical behavior of powder material in the compaction processes, a shortcoming of the approach is the inherent difficulty to predict discontinuities (e.g., damage or cracking) within a tablet as FEM is a continuum-based approach. In this work, we propose a novel method utilizing peridynamics (PD), a numerical method that can capture discontinuities such as tablet fracture, to predict the evolution of damage and breakage in pharmaceutical tablets. The approach links (1) the finite element method – to elucidate the behavior of powders during die compaction – with (2) the peridynamics modeling technique – to model the discontinuous nature of damage and predict tablet breakage during the critical stages of unloading and ejection from the compression die. This short communication presents a proof of concept including a workflow to calibrate the linked FEM-PD simulation models. Further, it demonstrates promising results from a preliminary experimental validation of the approach. Following further development, this approach could be used to guide the optimization of compression processes through targeted changes to formulation material properties, compression process conditions, and/or tooling geometries to deliver improved process efficiency and tablet robustness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗
DOE OSTI2025

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan details the strategy, mission, scope, and goals—both near-term and long-term—along with the structure and organization of nuclear fuels and materials research, development, and demonstration (RD&D) activities within the Fuel Cycle Technologies (FCT) program. The FCT program, tasked by the U.S. Department of Energy (DOE), employs a science-based approach to advance fuel technologies. This approach integrates theory, experiments, and multi-scale modeling and simulation (M&S) to develop a predictive understanding of fuel fabrication processes and fuel/cladding performance under irradiation, moving beyond traditional empirical methods. The long-term goals of the AFC are guided by the AFC Strategic Plan and align with the DOE Office of Nuclear Energy (NE) Roadmap [1], which outlines a multi-decade vision for demonstrating and qualifying advanced fuel forms to support diverse fuel cycle options. Near-term goals focus on enhancing accident tolerant fuels (ATF) for Light Water Reactors (LWR), a significant challenge that demands balancing immediate objectives with ongoing progress toward advanced reactor missions. Accelerating the traditional fuel qualification process to meet ATF objectives is another critical challenge. A detailed set of 5-year goals, summarized below, has been developed in line with the overarching science-based fuel development approach: • Advanced LWR Fuel Technologies: By 2027, support the development of advanced LWR fuel technologies with improved performance and enhanced accident tolerance. This includes high burnup (HBu), low enriched uranium (LEU)+, coated cladding, and doped fuel, aimed at complementing industry-led significant LWR uprates and plant refurbishments. • Tristructural Isotropic (TRISO) Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Metal Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Molten Salt Fuel: By 2027, deploy a robust program that enables fuel salt qualification technologies needed to support fuel salt research and development (R&D), focusing on emergent needs to derisk fuel salt production and utilization in advanced reactors. • Long-Term ATF: Develop fuel technologies that enable significant power uprates (~50%) in refurbished or new LWRs while optimizing fissile material utilization and waste disposal. The 5-year milestones in the AFC Execution Plan are contingent on an assumed budget. This Execution Plan will be updated annually to reflect actual funding profiles as budget guidance becomes available, ensuring milestones are adjusted accordingly. In summary, the AFC Execution Plan presents a comprehensive strategy to advance nuclear fuel technologies through a science-based approach, addressing both near-term and long-term goals while adapting to funding realities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗
DOE OSTI2024

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

Abstract Can the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Duncan, James P. C.↗