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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 OSTI2025

Grid Communications: Cybersecurity and Supply Chain Challenges and Emerging Regulation Session Three

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem. This is Session 3 of 3 (Full Version).

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Enabling Nuclear Systems Through Demonstrations

DOE program launched in October 2019 Authorized by the Nuclear Energy Innovation Capabilities Act (NEICA) DOE-Office of Nuclear Energy; INL Nuclear Science & Tech Partner with industry to bridge the gap between research and commercial deployment Leverage national lab expertise and infrastructure

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Provable Security and Resilience in Critical Infrastructure – Next Steps

With the conclusion of the Laboratory Directed Research and Development (LDRD) project on Provable Security and Resilience (PSaR) in Critical Infrastructure, we present forward-looking technical concepts and strategies that build on the project’s outcomes and INL’s long-standing expertise in infrastructure protection. The challenge is to protect critical infrastructure and functions much more efficiently at scale than capable adversaries can attack at scale. After summarizing progress and ongoing work we’ll discuss what are the challenges that remain and what are new/emerging technologies, strategies, and processes to meet those challenges. Finally, we’ll layout concepts that integrate with other protection work in the coming year and beyond. For example, building secure function-specific platforms based on the seL4 microkernel, and considering the successes of Cyber-Informed Engineering as a model for engage, collaboration, and adoption. We look forward to your feedback and collaboration as we refine and expand this vision.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗

DOE OSTI2026

Affordable and Scalable Modular Multifamily Housing: A Case Study on Cost, Construction Time Savings, and Waste Reduction in California: Preprint

Modular construction can significantly reduce waste when compared to traditional methods. This case study evaluates cost, construction time, and waste metrics for a 195-unit stick-built project and for a 66-unit modular project both based in Los Angeles, CA. We partner with SoLa Impact (real estate developer) and Model/Z (modular manufacturer) to evaluate the impact of Model/Z's 1-bedroom modular unit which is produced in a 160,000 sq ft local factory in South Los Angeles. We compare them to similar stick-built/site-built multifamily construction by the same developer in Los Angeles. This study finds that modular production reduced construction waste through precise prefabrication, concentrated workforce expertise, and streamlined logistics while cutting transportation needs, improving project efficiency and lowering associated timelines and costs. Economies of scale are being realized as Model/Z has produced over 500 affordable housing units and supplied for projects up to 188 units, shortening schedules and lowering per-unit costs and supporting affordable housing goals in income-challenged South Los Angeles. Modular methods offer a scalable, resource-efficient pathway to increasing affordable housing supply while reducing total development costs by 10-15% and project timelines by 50%, while simultaneously creating local jobs and training opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

GOOML (Geothermal Operational Optimization with Machine Learning) [SWR-23-01]

The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.

Buster, Grant↗

DOE OSTI2020

Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts

The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Innovative laser-based methods for monitoring fuel retention in ITER

This paper addresses the challenge of tritium inventory management in ITER and future fusion reactors, highlighting the importance of accurate tritium measurement and its spatial distribution within the vacuum vessel. Given ITER’s operational constraints, especially the limit on tritium retention, precise measurement is essential for both safety and regulatory compliance. To tackle these questions, the paper presents the T-monitor diagnostic system developed by Forschungszentrum Jülich, which uses Laser-Induced Desorption (LID) in combination with Diagnostic Residual Gas Analysis (DRGA) to measure hydrogen isotope concentrations on the surface of divertor tiles. The system integrates a high-power laser, advanced optical components, and a Fast Scanning Mirror Unit (FSMU) for accurate laser spot positioning with rapid response. Designed to measure in situ tritium retention, the diagnostic provides high-resolution spatial mapping, vital for evaluating detritiation strategies. The laser heating process increases the divertor surface temperature to 1600 K within the laser spot, promoting hydrogen isotope desorption. Accurate measurements require the precise control of laser parameters, including pulse duration and spot size, with a target relative accuracy of 20%. The optical design includes both in-vessel and ex-vessel components, such as durable high-reflectivity mirrors made of gold and copper, selected not only for their infrared performance but also for their transmission of visible wavelengths for observation purposes. To protect optical components from contamination, a pneumatic shutter is used.

Hydrogen isotopes↗

DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗

DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗

DOE OSTI2020

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Nuclear Weapons Theater Experience_V1

The theater experience is driven by the big message of nuclear deterrence through successful stockpile stewardship. This job enables other national and global security work with roots going back to the capabilities developed during the Manhattan Project and honed through the history of this successor scientific laboratory. The story that unfolds uses a narrative (traditional film-type approach), although the theater setup would offer immersion and decision-making (audience participation). The idea is to demonstrate how and why the Laboratory continues its mission in maintaining the nation’s stockpile while concurrently enabling the ever-evolving multi-disciplinary science and technology innovations developed for that purpose to take other scientists along vibrant paths.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

Hydrodynamic Analysis and Optimization of Aquantis Marine Turbine: Cooperative Research and Development (Final Report)

The primary aim of this proposal is to improve the accurate prediction of hydrodynamic performance and dynamic load responses of the AQ10 floating axial-flow tidal turbine with a tri-cat mooring configuration. The validation of reduced-order modeling approaches with high-fidelity model will be implemented. Additionally, the frequency response domain, Response Amplitude Floating Wind (RAFT) toolbox plus an optimizer expanded for marine hydrokinetic turbines under the Submarine Hydrokinetic And Riverine Kilo-megawatt. Systems (SHARKS) program will be used for designing and exploring different key design parameters (platform dimension, mooring layout and its parameters) of next marine hydrokinetic (MHK) turbine generation.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

The Net Zero World Initiative

The U.S. is committed to working with countries all over the globe to accelerate clean climate goals from ambition to action, and the Net Zero World Initiative is the latest example of our dedication. The Net Zero World Initiative is uniquely positioned to achieve rapid global energy decarbonization. It will partner with countries to help them implement climate ambition pledges and accelerate global transitions to net zero, resilient, and inclusive energy systems. The Net Zero World Initiative Will Harness Unique USG and Lab Assets in Partnership with Philanthropies. Scale-up and goals listed.

accelerate↗