Search NASA⌕ Search

SEARCH · Search NASA

Results for “Power to explore”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A Two-Stage Approach for PV Inverter Engagement in Power Factor Correction and Voltage Regulation

The rapid integration of distributed energy resources, like solar photovoltaics (PVs), can lead to overvolt-age challenges due to reverse power flow and a noticeable decrease in power factor at the substation interface. While existing literature extensively explores utilizing smart inverter capabilities for reactive power flexibility using a volt-var curve (VVC), obtaining time-varying operating points of such curves in real-time is challenging due to computational demands and communication requirements. Similarly, employing optimization-based approaches for reactive power control and active voltage regulation in large-scale distribution feeders is difficult due to the complexity of the problem and the challenges in effectively engaging customer-owned resources. This paper proposes a two-stage strategy to harness smart inverters for reactive power support. The first stage formulates short-term planning by optimally designing VVCs (on a daily or hourly basis) for large-scale solar PVs based on projected system needs and communicating optimal curves to smart inverters in advance. Subsequently, the second stage employs a transactive-based method to involve customer-owned PVs for reactive power support, effectively enhancing overall system performance and addressing real-time demands. In conclusion, the efficacy of this approach will be demonstrated using real-world distribution circuits provided by Vermont Electric Power Company (VELCO) and Vermont Electric Cooperative (VEC).

Poudel, Shiva [Pacific Northwest National Laborato↗
DOE OSTI2024

Exploring mounting solutions for cryogenically cooled thin crystal optics in high power density x-ray free electron lasers

This study investigates three mounting methods—clamping, soldering, and a hybrid clamping–soldering approach—for cryogenically cooled thin diamond crystals crucial to stable operation of X-ray Free Electron Laser (XFEL) systems. While clamping methods exhibit temperature resilience and flexibility, meticulous design is required to prevent stress-induced warping and reduce thermal contact area. Soldering methods offer reliable mechanical and thermal bonding but encounter challenges due to the coefficient of thermal expansion mismatch at cryogenic temperatures. The hybrid method, integrating clamping and soldering with strain relief cuts, effectively mitigates overall distortion caused by mounting and XFEL thermal loads. These findings offer a novel mounting solution for high-performance x-ray optics in XFEL research and applications, ensuring stability and optimal functionality in cryogenic conditions.

Instruments & Instrumentation↗
DOE OSTI2025

Application of Ocean Thermal Energy Conversion (OTEC) Systems for Powering Safety Monitoring Systems of Offshore Oil and Gas Operations, OESI 2.0 M-1 T-1-P1.1 – Objective 3 Report: Collocation of OTEC with Offshore Oil and Gas Activities

This report is a component of a comprehensive research initiative aimed at evaluating the technoeconomic feasibility of deploying Ocean Thermal Energy Conversion (OTEC) systems to power safety monitoring systems for offshore oil and gas operations in the Gulf of America (GOA). The overarching study has three primary objectives: summarizing existing oil and gas equipment monitoring systems, modeling OTEC as a renewable marine energy source, and exploring the collocation of OTEC power sources with offshore oil and gas activities to offset power demand. This report specifically addresses Objective 3, focusing on the integration of OTEC systems with floating oil and gas facilities.

02 PETROLEUM↗
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

Data and scripts associated with “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA"

This data package is associated with the publication “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA” submitted to JGR-Biogeosciences (Regier et al. 2025).This study used reach-scale modeled estimates of hyporheic aerobic respiration made by the River Corridor Model (Fang et al. 2020) and watershed characteristics across the Willamette and Yakima River basins to explore potential allometric scaling (i.e., power-law relationships between size and function) of cumulative hyporheic respiration across catchment-to-basin scales. Scaling was explored quantitatively via the R2, slope, and y-intercept of relationships between cumulative hyporheic respiration and watershed area, divided into hyporheic exchange flux (HEF) quantiles. We also explored relationships between allometric scaling and other watershed characteristics through linear regression, spatial patterns, and mutual information analyses. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales. This data package is associated with the GitHub repository found at https://github.com/peterregier/rc_wrb_yrb_scaling. The data package is organized into several key directories. The “data” folder contains multiple CSV files, including landscape heterogeneity, scaling analysis, and watershed boundary data. The “figures” folder has all figure files in both PDF and PNG formats. Core analysis scripts and figure generation scripts are in the “scripts” directory, systematically numbered for sequential execution. The root directory includes essential project files; please see the file ending in “flmd.csv” for a list and description of all files contained in this data package and the file ending in “dd.csv” for data dictionaries used to describe tabular column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Site Integration and Regulatory Considerations for an NPP Colocated with a Petroleum Refinery, Methanol Plant, and Wood Pulp Plant

This research explores the colocation of nuclear power plants (NPPs) with industrial applications. Three existing industrial sites were considered to demonstrate the siting process and illuminate technological gaps for future work. The three applications demonstrated for colocation here are a petroleum refinery, a methanol production plant, and a pulp and paper plant. This study uses a modified version of the EPRI siting criteria to explore the geological and demographic characteristics of the location of the current industrial site, as well as exploring external hazards from the industrial plant and its surrounding land use. Data was collected from public databases to estimate site characteristics. We then discuss how the site characteristics may impact the ability to colocate an NPP with an industrial application. The application site and 5 additional sites were explored for each application to give a general indication of the siting implications for an NPP in each area. The hazards for each industrial application was also explored to determine how colocation may impact reactor safety. The following gaps have been identified and should be explored in future research on colocation of NPPs with petroleum refineries, methanol plants, and pulp and paper plants: - There is a variety of industrial use, hazards, and pipelines in the surrounding area. A more thorough review of these hazards should be considered for colocation. - In general, the whole region around some applications seems to have softer soil, with implications for large site preparation costs. Further site investigations should prioritize looking into the geotechnical conditions. - Applications along coastlines are susceptible to flooding and hurricanes. The benefits of colocation should be weighed against the potential design implications. - The benefits of natural gas pipeline infrastructure in place should be explored further. If heat supply from the NPP is not required or not feasible due to the distance between the NPP and the application, there may be an opportunity to supply hydrogen to the plant through an existing pipeline. - Because there are several collocated industrial plants in the regions for the refinery and methanol plant, the benefits of sharing resources from the NPP should be explored further. This may open up additional sites for colocation. The following knowledge gaps were identified for the colocation of NPPs with these three industries, and industrial applications in general. These gaps are: - While the STAND tool contains many important characteristics for the reactor siting process, it is not calibrated for the colocation of NPPs with industrial facilities. - There are aspects of both the NPP and industrial application that need to be quantified for a siting analysis. Particularly, we need to understand the water intake requirements for NPPs and each application. - Further work may focus on adapting the STAND site comparison methodology to comparison of sites for co-location. This will involve using the data documented in this report as a starting point and performing a comprehensive and quantitative comparison. - Without spending significant resources, it would be impossible to gather data for each site to evaluate all aspects of siting. One approach to finding data and understanding its implications to siting is looking at FSARs for existing plants. For example, most sites considered in this study have small Vs30 values, indicating soft soil. However, there are NPPs located in the vicinity of most of the sites (e.g., Waterford Steam Electric Station near New Orleans) and reviewing available site characteristics and geotechnical data for these NPPs, might provide further information for siting. - The siting analysis in this study indicates that colocation of the NPP with the industrial site could be difficult based on external hazards, cooling requirements, weather, or population. We need to determine the impact of distance between the two facilities on cost and quality of energy transport. - This study did not touch on socioeconomic impacts for NPP colocation with industrial facilities. The input-output analysis methodology could be applied to the communities referenced in this study to determine the socioeconomic impact of these projects. - Similarly, the impacts of colocation on emergency planning was not explored in this study. The impacts on emergency planning infrastructure are somewhat related to the socioeconomic impacts, and could be explored using a similar methodology. - This study also did not address physical and cybersecurity, which will be important aspects of co-location [ref] . Cybersecurity will be important, regardless of the distance, but physical security will be important if the facilities are located very closely. Physical security might also be important for the steam lines between the plants, unless they are determined to be non-safety significant. - In many site l

08 - HYDROGEN↗
DOE OSTI2026

Reliable Power for Remote Applications

Optical power beaming - using lasers to transmit energy across distances - could change how remote devices receive power. The National Laboratory of the Rockies (NLR) seeks partners to explore the next stage in long-distance power beaming for terrestrial and space-based applications.

14 SOLAR ENERGY↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2025

Open Architecture for Cost Savings in Advanced Nuclear Reactors

Recently, nuclear power plant build projects in the West have run over budget due to high capital costs and schedule overruns. Compared to other sources of energy, nuclear power plants have higher capital costs. Reactors are often different at every site, resulting in a lack of standardization. Nuclear is expected to compete with other low carbon sources of energy which have lower capital costs making it essential for nuclear to develop ways of reducing costs. Strategies such as standardization, learning rates, modularization, and schedule reduction in advanced reactors can reduce nuclear costs by about 40%. Standardization as a way of cutting capital costs has been explored even in large nuclear power plants. Standardization of certain plant components can result in lower component and installation costs and higher learning from experience. Standardization can be achieved by adopting a criterion of key performance indicators and general design principles for a specific system or component such as the balance of plant. Modularization allows the construction of certain components of SMRs in a factory, which saves time, increases productivity, and encourages higher learning rates. Production learning decreases the time and the cost related to an activity. The potential for modularized components of advanced reactors to be manufactured in factories makes it conducive to achieving higher learning rates. Developing large-capacity nuclear programs through sequential builds cultivates a higher learning rate, which in effect may reduce schedule overruns. Open architecture has been identified as a way to drive standardization among advanced reactor designs and result in cost savings. Open architecture (OA) is defined as a design enabling a diverse supply chain by defining and publishing requirements of systems or equipment in functional and/or interface terms, utilizing technical standards in widespread use. Currently, the nuclear industry’s approach is to use closed architecture, making most designs proprietary. However, collaboration between various advanced reactor vendors and suppliers utilizing the concept of open architecture can result in modular and standardized architecture of subsystems or subcomponents of a nuclear power plant. Completely standardizing nuclear power plants may be impossible, however, certain common subsystems amongst the various reactor designs could be standardized and/or access a wider supply chain and leverage existing learning from other sectors. Open architecture will save time and allocate resources to the parts of the plants that have the most unique features. A key advantage of open architecture is its ability to improve production learning across advanced reactors (AR) types in the industry, by providing and utilizing the same kind of component. Sodium fast reactor (SFR), High Temperature Gas Reactor (HTGR) and Molten Salt Reactor (MSR) are the advanced reactors considered for this project. This paper aims to determine the cost savings in advanced reactor programs due to open architecture learning rate. This work is an extension of work done on light water reactor small modular reactors; the cost methodology was utilized to investigate the impact of open architecture on advanced reactors with a particular focus on sodium fast reactors. The cost data on sodium fast reactors used in the model presented the most adequate information required for the analysis.

Advanced Nuclear Reactors↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2024

Light fields during inflation from BOSS and future galaxy surveys

Abstract Primordial non-Gaussianity generated by additional fields present during inflation offers a compelling observational target for galaxy surveys. These fields are of significant theoretical interest since they offer a window into particle physics in the inflaton sector. They also violate the single-field consistency conditions and induce a scale-dependent bias in the galaxy power spectrum. In this paper, we explore this particular signal for light scalar fields and study the prospects for measuring it with galaxy surveys. We find that the sensitivities of current and future surveys are remarkably stable for different configurations, including between spectroscopic and photometric redshift measurements. This is even the case at non-zero masses where the signal is not obviously localized on large scales. For realistic galaxy number densities, we demonstrate that the redshift range and galaxy bias of the sample have the largest impact on the sensitivity in the power spectrum. These results additionally motivated us to explore the potentially enhanced sensitivity of Vera Rubin Observatory's LSST through multi-tracer analyses. Finally, we apply this understanding to current data from the last data release of the Baryon Oscillation Spectroscopic Survey (BOSS DR12) and place new constraints on light fields coupled to the inflaton.

Astronomy & Astrophysics↗
DOE OSTI2024

Exploration of Rayleigh-Taylor seeding mechanisms in laser- and pulsed-power-driven inertial fusion

The ubiquitous Rayleigh-Taylor instability (RTI) has been observed in fluids and plasmas in a wide range of parameter regimes from astrophysical to laboratory plasmas. In high-energy-density (HED) laboratory plasmas, such as laser-driven inertial confinement fusion (ICF) plasmas, the RTI can have a detrimental impact on achieving fusion ignition through the generation of hydrodynamic mix. Hence, an understanding of the seeding mechanisms that produce the RTI and identifying ways to mitigate hydrodynamic mix is of critical importance. Appropriately aligned magnetic fields have been known to stabilize short-wavelength RTI. A number of numerical and experimental studies have demonstrated the benefits of using imposed magnetic fields in laser-driven ICF to achieve higher ion temeratures, higher neutron yields, and a relaxation of the ignition criteria. This work addresses seeding mechanisms in laser-driven implosions to understand critical early-stage physics that ultimately leads to substantial growth of the RTI along with mechanisms for mitigation of this growth. Surface perturbations due to machining tolerances and single-feature seeds (for example due to fill tubes) can produce substantial RTI growth in the ignition-relevant high-convergence ratio targets for laser- driven ICF implosions. RTI growth from single-feature seeding in laser-based implosions has the potential to be mitigated through appropriately aligned externally applied magnetic fields and the goal of this work has been to quantify that numerically and experimentally. Resistive-magnetohydrodynamic (MHD) simu- lations are used to study the seeding and evolution of the RTI leveraging previous support from the DOE HEDLP program. Furthermore, experimental data has been obtained and applied towards code validation of unmagnetized and magnetized evolution of single-feature seeded RTI growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Early Research in Load-Following Management for HPC-Nuclear Integration

With the rising demand for high performance computing (HPC) and artificial intelligence (AI) systems, maintaining a stable and efficient power supply is increasingly critical. The HPC team at Idaho National Laboratory is spearheading efforts to seamlessly integrate HPC systems with nuclear reactors. This lightning talk explores one early strategy for managing power fluctuations using software-defined controls. To effectively harness nuclear reactors for power generation, control mechanisms are essential to address the slow load-following capabilities of reactors, which are typically around 5% per minute. While this rate is sufficient for many uses, large HPC systems can experience rapid power consumption changes by tens of megawatts when jobs start or stop running. A reactor could overproduce power and match the peak power rating for the HPC system, however when the system is not running a job or a job unexpectedly stops, the load-following of the system would be affected leading to power being wasted and the likelihood of power transient occurrences increases. Controlling the increase or decrease of power consumption on these systems at the same rate as the load-following of reactors is one piece of the puzzle to properly utilizing nuclear reactors as a power source for HPC systems.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rugged WBG Devices and Advanced Electric Machines for High Power Density Automotive Electric Vehicles

This work explored two very important approaches for supporting transportation electrification and reducing dependence on imports of critical materials. In the first task, several novel electric machine architectures with low rare earth metal content were compared analytically, then experimentally to verify their performance. Rare earth metals are imported largely from China and are widely used in many clean energy systems such as wind turbines and EV motors. Reducing our dependence on this critical material is an important objective for ensuring our independence and continued economic prosperity. In the second task, a GaN based inverter for EV inverters was developed to demonstrate the suitability of that wide bandgap semiconductor device in this important application.

42 ENGINEERING↗
DOE OSTI2026

Neutrino and Electromagnetic Signatures from Superluminous Supernovae: A Case Study for SN 2017egm

Superluminous supernovae (SLSNe) are rare transients that are ∼10–100 times more luminous than ordinary stellar explosions, reaching peak optical luminosities ∼10 44 –10 45 erg s −1 . The energy source powering SLSNe remains uncertain. In this work, we explore the multiwavelength and multimessenger signatures of the scenario in which SLSNe are powered by a newly born millisecond magnetar. We model the dynamical evolution and emission from the coupled system comprised of the magnetar, wind, nebula, and supernova ejecta, consistently evaluating the pair multiplicity of the wind and nebula regions, and the bulk wind Lorentz factor governing the e + − e − injection spectra in the nebula. We compute the thermal and nonthermal electromagnetic signatures, neutrino signatures, and investigate their detection prospects. For SN 2017egm, the nearest observed SLSNe, our prediction for high-energy gamma rays matches the recent detection by the Fermi Large Area Telescope. For neutrinos, using SN 2017egm a canonical SLSNe, we find that in the era of the Vera C. Rubin Observatory, a stacking analysis with upcoming neutrino observatories can lead to 3σ detection significance of neutrino events from a population of SLSNe within a decade of operation.

Mukhopadhyay, Mainak [Fermi National Accelerator L↗
DOE OSTI2026

Investigation of Drift Effects in UEDGE Simulations of NSTX-U Edge Plasma With Lithium Divertors

Lithium is a low-Z material, and lithium-based plasma-facing components (PFCs) are planned for the National Spherical Torus Experiment Upgrade (NSTX-U) to explore potential benefits for divertor power exhaust and core plasma management. NSTX-U is a medium-sized spherical tokamak with up to 12 MW of auxiliary heating, capable of generating reactor-relevant plasma conditions. This work presents boundary plasma simulations for NSTX-U with lithium PFCs using the UEDGE code, incorporating full magnetic and 𝐄 ×𝐁 drift physics. The simulations show that drifts strongly influence heat and particle transport: they enhance convective transport, broaden the scrape-off layer heat-flux width 𝜆 𝑞 , and reduce the anomalous heat diffusivity 𝜒 required to reproduce predicted SOL heat-flux width. 𝐄 ×𝐁 drifts provide poloidal transport, while ∇𝐵 (which includes both gradB and curvature) drifts provide radial heat and particle transport. Lithium transport is also affected by drifts, with lithium ions migrating from the outer divertor to the inner divertor through the private flux region (PFR) following the 𝐄 ×𝐁 drifts flow, lowering upstream impurity lithium densities. UEDGE is self-consistently coupled with the Wall-Li model to study plasma lithium PFC interactions depending on the local lithium sourcing based on local plasma conditions and lithium surface temperature. In these simulations, lithium evaporation shows a vapor-shielding effect that reduces divertor heat flux and increases radiative losses once surface temperatures exceed 450°C. This research work provides a first step toward self-consistent modeling of lithium PFCs in NSTX-U, demonstrating the impact of drift-driven plasma transport in SOL and divertor regions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗