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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Battery Energy Storage System (BESS) Licensing and Permitting [Slides]

This presentation provides an overview of the licensing, permitting, and regulatory frameworks governing Battery Energy Storage Systems (BESS) across multiple international contexts. It outlines foundational permitting considerations - including safety standards, siting requirements, regulatory and environmental obligations, and interconnection processes - alongside typical pathways for installation, approval, and inspection. The presentation summarizes key technical standards such as NFPA 855 and IEEE guidelines, and emphasizes best practices for safe and effective deployment. A series of detailed case studies from South Africa, Chile, Mexico, Brazil, Costa Rica, Honduras, and the Dominican Republic illustrates how different countries structure approval authority, evaluate environmental impacts, and integrate BESS into national grid systems. The deck concludes with insights on emerging challenges, including evolving regulatory frameworks, financial viability, and the need for pricing mechanisms that accurately value storage services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Advances in GDSA Framework Development and Process Model Integration

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2020 advances of the Geologic Disposal Safety Assessment (GDSA) Framework and PFLOTRAN development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of disposal options and generic sites. The capability is a framework called GDSA Framework that employs high-performance computing (HPC) capable codes PFLOTRAN and Dakota.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Theoretical model of dielectrophoretic water vapor condensation during multiphase flow of electrically charged water droplets in air

Water droplet growth in air, influenced by vapor diffusion and electrical charging, has significant implications for indoor air quality, cloud formation, and aerosol dynamics. Here, this study presents a comprehensive theoretical model that describes the growth dynamics of electrically charged water droplets under external electric fields, incorporating diffusion, electrostatic, and dielectrophoretic forces to enhance understanding and practical applications in air dehumidification and environmental control. A theoretical model was established to analyze the growth dynamics of the charged water droplets. The approach extended the thermodynamic framework of the Modified Kelvin-Thomson equation by integrating electrostatic and dielectrophoretic effects, enabling a comprehensive evaluation of vapor concentration near the surface of charged droplets. A sensitivity analysis was performed to assess the roles of droplet size, electric field intensity, surface charge, and vapor concentration on condensation rates. The study progressed beyond the classical diffusion-limited growth of neutral droplets, addressing the behavior of charged droplets in electrohydrodynamic environments. The model highlighted the significance of dielectrophoretic drift flux for dipolar vapor molecules, especially in micron-sized droplets ranging from 10 to 100 µm. Findings demonstrated that, for smaller droplets subjected to elevated electrical charge, average growth rates varied from 0.3 to 1.6 µm/s if the air relative humidity increased from 50 to 100 %. Analytical results are corroborated by numerical simulations, which exhibited strong concordance, thereby validating the proposed theoretical approach. This research introduced an innovative and scalable analytical methodology for electric field-enhanced droplet growth, offering valuable insights into the advancement of energy-efficient air dehumidification, water desalination, and atmospheric water harvesting technologies.

42 ENGINEERING↗
DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗
DOE OSTI2020

First-Principles Approach for Assessing Cold Electron Injection Efficiency of Dye-Sensitized Solar Cell: Elucidation of Mechanism of Charge Injection and Recombination

The adequacy of the inclusion of spacer units in the metal-free D-π–A organic dyes pertaining to the augmentation of dye-sensitized solar cell (DSSC) efficiency has been examined through the excited-state simulations of the charge injection and recombination processes at the dye–semiconductor interface. Within the framework of the time-dependent density functional theory, the proposed computational studies focus on the precise evaluation of pivotal factors controlling the rates of photoinduced charge-transfer and energy-transfer processes, including electronic coupling, reorganization energy, and threshold energy barrier in the semiclassical Marcus formalism. The estimation of the fluorescent state appears to be the crucial step while explaining the ultrafast electron injection process and the charge recombination at the Marcus inverted region, as revealed by the obtained results. The retardation of charge recombination is facilitated by the insertion of a thiophene moiety between the π-bridge and the acceptor units. The estimated cold electron injection efficiencies deploying the Onsager–Braun theory, that rely on the computations of cold electron injection lifetime and cold electron lifetime, show a linear correlation with the experimental photovoltaic parameters of the DSSC comprising short-circuit current density, open-circuit voltage, and power conversion efficiency. The outcomes of the present investigation establish a basis for unraveling the mechanism of intricate dynamical processes upon photoexcitation of the sensitizers, as well as devising plausible routes for functional DSSC materials.

14 SOLAR ENERGY↗
DOE OSTI2024

Two-Way Option Contracts That Facilitate Adaptive Water Reallocation in the Western United States

Many water markets in the western United States (U.S.) have the ability to reallocate water temporarily during drought, often as short-term water rights leases from lower value irrigated activities to higher value urban uses. Regulatory approval of water transfers, however, typically takes time and involves high transaction costs that arise from technical and legal analyses, discouraging short-term leasing. This leads municipalities to protect against drought-related shortfalls by purchasing large volumes of infrequently used permanent water rights. High transaction costs also result in municipal water rights rarely being leased back to irrigators in wet or normal years, reducing agricultural productivity. This research explores the development of a multi-year two-way option (TWO) contract that facilitates leasing from agricultural-to-urban users during drought and leasing from urban-to agricultural users during wet periods. The modeling framework developed to assess performance of the TWO contracts includes consideration of the hydrologic, engineered, and institutional systems governing the South Platte River Basin in Colorado where there is growing competition for water between municipalities (e.g., the city of Boulder) and irrigators. The modeling framework is built around StateMod, a network-based water allocation model used by state regulators to evaluate water rights allocations and potential rights transfers. Results suggest that the TWO contracts could allow municipalities to maintain supply reliability with significantly reduced rights holdings at lower cost, while increasing agricultural productivity in wet and normal years. Additionally, the TWO contracts provide irrigators with additional revenues via net payments of option fees from municipalities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mathematical solutions in internal dose assessment: A comparison of Python-based differential equation solvers in biokinetic modeling

Abstract In biokinetic modeling systems employed for radiation protection, biological retention and excretion have been modeled as a series of discretized compartments representing the organs and tissues of the human body. Fractional retention and excretion in these organ and tissue systems have been mathematically governed by a series of coupled first-order ordinary differential equations (ODEs). The coupled ODE systems comprising the biokinetic models are usually stiff due to the severe difference between rapid and slow transfers between compartments. In this study, the capabilities of solving a complex coupled system of ODEs for biokinetic modeling were evaluated by comparing different Python programming language solvers and solving methods with the motivation of establishing a framework that enables multi-level analysis. The stability of the solvers was analyzed to select the best performers for solving the biokinetic problems. A Python-based linear algebraic method was also explored to examine how the numerical methods deviated from an analytical or semi-analytical method. Results demonstrated that customized implicit methods resulted in an enhanced stable solution for the inhaled 60 Co (Type M) and 131 I (Type F) exposure scenarios for the inhalation pathway of the International Commission on Radiological Protection (ICRP) Publication 130 Human Respiratory Tract Model (HRTM). The customized implementation of the Python-based implicit solvers resulted in approximately consistent solutions with the Python-based matrix exponential method ( expm ). The differences generally observed between the implicit solvers and expm are attributable to numerical precision and the order of numerical approximation of the numerical solvers. This study provides the first analysis of a list of Python ODE solvers and methods by comparing their usage for solving biokinetic models using the ICRP Publication 130 HRTM and provides a framework for the selection of the most appropriate ODE solvers and methods in Python language to implement for modeling the distribution of internal radioactivity.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Technoeconomic Analysis of Photovoltaics Module Manufacturing with Thin Silicon Wafers

Reducing silicon usage by adopting thinner wafers can significantly reduce capital expenditure (capex) and cost, and thus accelerate the growth of manufacturing capacity and deployment. In this work, we evaluated potential benefits of thin Si wafers for current and future PV modules. We apply a technoeconomic framework that couples bottom-up cost model, and a cash-flow growth model to analyze PV modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules. modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules.

14 SOLAR ENERGY↗
DOE OSTI2023

VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Brogan, Joel↗
DOE OSTI2023

Building water resilience in the face of cascading wildfire risks

Severe wildfire is altering the natural and the built environment and posing risks to environmental and societal health and well-being, including cascading impacts to water systems and built water infrastructure. Research on wildfire-resilient water systems is growing but not keeping pace with the scale and severity of wildfire impacts, despite their intensifying threat. In this study, we evaluate the state of knowledge regarding wildfire-related hazards to water systems. We propose a holistic framework to assess interactions and feedback loops between water quality, quantity, and infrastructure hazards as determinants of post-fire water availability and access. Efforts to address the evolving threat of wildfires to water systems will require more interdisciplinary research on the complex relationships shaping wildfire’s threat to water availability and access. To support this, we need reliable long-term data availability, consistent metrics, greater research in shared contexts, more extensive research beyond the burn area, and multistakeholder collaboration on wildfire risks to water systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toadstool Deep Learning Framework

SAND2025-11741O Toadstool is a deep learning framework and support library that provides PyTorch boiler plate training and testing loops. This enables the user to remember parts and customize a callback interface. Toadstool also provides useful callbacks and other methods for deep learning experimentation. The framework also implements publicly available temperature and calibration methods, model initialization methods, learning rate schedulers, and model evaluation methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Heidbrink, Scott↗
DOE OSTI2021

Shockley-Queisser analysis of the temperature-efficiency correlation of solar cells in the presence of non-radiative heat transfer

An empirical negative T cell − η correlation has been widely applied, without careful justifications, to evaluate the efficiency ( η ) increase of solar cells due to their temperature ( T cell ) decrease. Here, a framework is introduced to simultaneously compute T cell and η . This model confirms the negative T cell − η correlation if the above-bandgap absorptivity ( α I ) is not suppressed in the optimization of the sub-bandgap reflectivity ( ρ II ) or the infrared emissivity, otherwise it may suggest a positive correlation. It further gives a guideline to enhance η of silicon cells under typical conditions: every 1% increase of α I is equivalent to a 57% increase of ρ II .

14 SOLAR ENERGY↗
DOE OSTI2023

JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics

JetNet is a Python package that aims to increase accessibility and reproducibility for machinelearning (ML) research in high energy physics (HEP), primarily related to particle jets. Basedon the popular PyTorch ML framework, it provides easy-to-access and standardized interfacesfor multiple heterogeneous HEP datasets and implementations of evaluation metrics, lossfunctions, and more general utilities relevant to HEP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating the Interactions Between Variable Renewable Energy and Diurnal Storage

Cost declines and growing deployment of photovoltaic (PV), wind, and storage have led to increasing interest in the potential interactions of these three technologies as their role in the power system grows. In this work we enhance a national-scale capacity expansion model to evaluate how PV, wind, and storage interact in the evolution of the power system. Importantly, the modeling framework captures interactions in both investments and operations. Through this work we identify significant synergies between PV and storage. Scenarios with more PV always have more storage, and scenarios with more storage always have more PV. This synergy is due to the diurnal alignment of PV generation with 4-8 hour storage, and to the ability of PV to narrow system peaks to allow shorter-duration storage to serve as a peaking resource. Interactions between wind and storage are less pronounced, though we do observe that longer-duration storage resources appear to provide greater value for wind.

14 SOLAR ENERGY↗
DOE OSTI2023

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PROACTIVE Focus Area 4: Structured Decision Metrics Analysis (Final Report)

A structured decision metrics analysis was developed as one of the tasks under PROACTIVE’s Focus Area 4 (FA4) and used structured decision metrics for processes, items, and facilities (PIF) to determine the efficacy of an M&V system in meeting treaty goals and technical objectives. The method starts with the functional decomposition of a “treaty” with the M&V system overlaid on it. A Functional Decomposition Rubric (FDR) for each PIF is used to determine a score ranging from 0 (weak) to 4 (strong) for assurance, security, and burden. The individual scores are combined to provide an overall Verification System Score (VSS) which assesses the overall verification system’s suitability given goals; scores for each step of the process are also given. The outcome of the evaluation is to identify needs or modifications to the enterprise model, verification system, or proposed testbed capabilities. VeriScore was used to evaluate FA4’s Spiral 0 exercise; those results are included in this report. The VeriScore and FDR framework can also be used in a non-treaty environment, as any goal/objective/method structure will work, thus expanding its applicability beyond traditional arms control structures.

99 GENERAL AND MISCELLANEOUS↗