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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

14 SOLAR ENERGY↗
DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Standardized Protocol for Real-Time APIs as Required by Title 23 CFR 680.116(c)

Improving the ability of drivers to easily locate working and available chargers is key to improving the public charging experience. Electric vehicle charging providers who are recipients of federal funds through the National Electric Vehicle Infrastructure (NEVI) Formula Program, Charging and Fueling Infrastructure (CFI) Discretionary Grant Program, and other funding programs as identified under Title 23 of the U.S. Code must deploy and maintain an application programming interface (API) to access information about charging stations they operate.1 This includes information about individual charging ports, pricing, and availability in accordance with the Federal Highway Administration’s National Electric Vehicle Infrastructure Standards and Requirements, 23 CFR 680.116(c), herein referred to as the minimum standards (Federal Highway Administration 2023). Specifically outlined in the minimum standards, states and other designated recipients are required to ensure that charging station information including location, connector type, power level, real-time status, and real-time price to charge are available free of charge to third-party software developers through an API. These requirements are intended to enable effective communication with consumers about available charging stations and help consumers make informed decisions about trip planning, including when and where to charge. This document provides a standardized protocol for how to structure data, data update frequency, and practices for making the data required to be shared via API usable for improving public transparency and the customer experience. These are recommendations only and do not modify the Federal Highway Administration’s minimum standards in any way.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

An Efficient 1-D Thermal Stratification Model for Pool-Type Sodium-Cooled Fast Reactors

Investigating thermal stratification in the upper plenum of a sodium fast reactor (SFR) is presently a technology gap in SFR safety analysis. Understanding thermal stratification will promote safe operation of the SFR before its commercial deployment. Stratified layers of liquid sodium with a large vertical temperature gradient could be established in the upper plenum of an SFR during a down-power or a loss-of-flow transient. These stratified layers are unstable and could result in uncertainties for the core safety of an SFR. In order to predict the occurrence of the thermal stratification efficiently, we developed a one-dimensional (1-D) transport model to estimate the temperature profile of the ambient fluid in the upper plenum. This model demands much less computational effort than computational fluid dynamics (CFD) codes and provides calculations with higher fidelity than historical system-level codes. Two flow conditions were considered separately in the current study depending on if in-vessel components are presented in the upper plenum. For the condition where in-vessel components, specifically the upper internal structure, are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the distance between the bottom of the in-vessel component and the jet inlet surface. For the condition where no in-vessel components are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the jet length, which was determined through data-driven trainings. The newly developed 1-D model showed similar performance with the CFD model in both cases. However, due to the assumption of flat profiles of the impinging jet axial dispersion rate, nonnegligible discrepancies between the 1-D prediction and the measured data were observed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Improving robustness for model discerning synthesis process of uranium oxide with unsupervised domain adaptation

The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.

scanning electron microscopy↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Structure-Dependent Lithium Metal Reactivity of Lithium Lanthanum Titanium Oxide Solid Electrolytes

Ongoing efforts to design stable, ionically conductive solid-state electrolytes (SSEs) for next-generation solid-state batteries make it clear that both long and short-range structural order strongly influence materials performance. However, clear structure-property relationships are generally lacking, making it difficult to develop design rules for improving the (electro) chemical stability of SSEs. Here, in this work, we synthesize epitaxial, single-crystal lithium lanthanum titanium oxide (LLTO) films and demonstrate that the kinetics of Ti 4+ reduction and lithium intercalation depend sensitively on the crystal orientation, with electrochemical stability increasing as LLTO (001) < (110) similar to (112) < (100). However, thermodynamic stability is ultimately unaffected-all orientations fully reduce after extended contact with Li metal. In contrast, amorphous LLTO films exhibit minimal, self-limiting reactivity that results in an interface that is stable to extended contact with Li metal. The results demonstrate the potential to engineer crystal lattice strain and long-range order to differentially tune the stability of the solid electrolyte toward reactive lithium metal and cathode materials, suggesting strategies for enabling the wider deployment of LLTO and other ionically conductive ceramic films in advanced energy storage technologies

Ketter, Benjamin [Argonne National Laboratory (ANL↗
DOE OSTI2021

Development of a Unified Taxonomy for HVAC System Faults

Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.

Science & Technology - Other Topics↗
DOE OSTI2023

Addendum to Capability Needs for Irradiated and Radioactive Materials Research (Ad Hoc Committee Summary Report)

Nuclear materials and fuels studies are challenging research and engineering targets because of their inherent radioactivity as well as their heterogeneous microstructure. Advanced light sources have made significant underpinning scientific contributions to the understanding of structural and fuel cladding material microstructure and properties and the development of structure-property relationships. These facilities have the potential to help advance the Department of Energy (DOE) Office of Nuclear Energy (DOE-NE) mission priorities providing underpinning understanding of technically important challenges, including: irradiation induced embrittlement and swelling; stress corrosion cracking and corrosion in extreme environments; ageing of reactor pressure vessels steels; nuclear fuel characterization; and nuclear waste form optimization as well as playing a major role in the development of a materials performance matrix to aid in the design of new materials to meet the needs of advanced reactor concepts. However, the delivery of underpinning understanding is the mission of DOE Office of Science (DOE-SC). While filling such knowledge gaps is important, efforts in this direction should not be confused with the strategic technology focused mission goals of DOE-NE to enable the continued operation of existing U.S. nuclear reactors, to enable the deployment of advanced nuclear reactors, to develop advanced nuclear fuel cycles, and to maintain U.S. leadership in nuclear energy technology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Multiplex Editing of the Nucleoredoxin1 Tandem Array in Poplar: From Small Indels to Translocations and Complex Inversions

The CRISPR-Cas9 system has been deployed for precision mutagenesis in an ever-growing number of species, including agricultural crops and forest trees. Its application to closely linked genes with extremely high sequence similarities has been less explored. In this study, we used CRISPR-Cas9 to mutagenize a tandem array of seven Nucleoredoxin1 (NRX1) genes spanning ~100 kb in Populus tremula × Populus alba. We demonstrated efficient multiplex editing with one single guide RNA in 42 transgenic lines. The mutation profiles ranged from small insertions and deletions and local deletions in individual genes to large genomic dropouts and rearrangements spanning tandem genes. We also detected complex rearrangements including translocations and inversions resulting from multiple cleavage and repair events. Target capture sequencing was instrumental for unbiased assessments of repair outcomes to reconstruct unusual mutant alleles. The work highlights the power of CRISPR-Cas9 for multiplex editing of tandemly duplicated genes to generate diverse mutants with structural and copy number variations to aid future functional characterization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Decomposition and Algorithmic Approaches for Solving Large-Scale Process Family Design Problems

Our most recent work expands the water desalination case study from 76 variants to 10,897 variants using the equation-oriented model built in Pyomo as part of the PARETO project. Using the discretization formulation presented in Stinchfield (2024a), rather than solving for all 10,897 variants simultaneously, we decompose the formulation into subproblems containing subsets of variants from the process family. We solve the overall problem with Progressive Hedging (PH) deployed in parallel on a distributed HPC cluster using the open-source Python package mpi-sppy (Knueven et al., 2023). This approach allowed us to solve this process family design problem to ~1.5% relative optimality gap in about 5 hours; in comparison, Gurobi reached ~50% relative optimality gap in about 6 hours (Stinchfield et al., 2024b). However, this approach still requires discretization of the common unit module design ranges; additionally, PH acts as a heuristic for MILP’s with gap-closing capabilities. Ideally, we would not have to use ML surrogates or discretization to solve this problem, instead solving the process family design problem with the equation-oriented model directly to achieve the most accurate results. However, recall that we did not consider solving the MINLP directly due to complexity and size. In this work, we aim to decompose and solve this large-scale MINLP using a Structured Nonlinear Global Optimization algorithm presented by Cao and Zavala (2019).

Stinchfield, Georgia↗
DOE OSTI2021

Valuation of Surface Coatings in High-Energy Density Lithium-ion Battery Cathode Materials

Artificial barriers, usually with either electrochemically active or inactive coating materials, are deployed on cathode material surfaces to mitigate detrimental side reactions by suppressing direct contact of cathode and electrolyte called surface coatings. These surface coatings are commonly known to increase the wettability of liquid electrolyte and reduce the interfacial charge transfer resistance. An important caveat is the selection of appropriate coating material with appropriate thickness for achieving enhanced electrochemical performance. As modern battery materials are increasingly developed with some type of surface coating, a careful and thorough examination of their role in mitigating the cycle life issues of cathode materials is paramount. Here, this comprehensive review article extensively covers the selection criteria of coating materials based on their chemical and physical properties and electrochemical functionalities. Additionally, the article discusses the concept of critical coating thickness and methods of achieving homogeneous coating architectures that deliver desired performance benefits. Furthermore, this comprehensive article summarizes the recent advancements, effectiveness, necessity of cathode surface coatings and identifies the key aspect of structure-property correlation between coating type/thickness and lithium-ion diffusion through coating layers as the linchpin that validates surface coating approaches especially for high capacity nickel-rich cathodes.

25 ENERGY STORAGE↗
DOE OSTI2026

From Modular ADMS to Plug-and-Play Ops: Distribution Grid Operations with Platform-Level Orchestration to Enable Ambitious App Hosting

The core function of the distribution grid is to provide electricity to consumers affordably, reliably, and securely. In pursuing these core objectives, distribution utilities are accountable to customers, regulators, and in some cases, shareholders. Other third parties such as aggregators and microgrids can also have a stake in the smooth operation of the grid. Each of these stakeholders has economic, business, and/or governance objectives that inform their expectations of the distribution grid. This multi-objective, multi-stakeholder environment creates tension that must be reconciled to successfully design and operate the distribution grid. Innovative companies are competing to bring high-tech solutions to electric utilities and their customers that address each of these objectives. Many developers of advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS) have adopted a modular architecture that allows grid operators to select functions and features according to their individual system needs. A modular platform also allows the solution provider to develop and integrate specific new product modules; however, the need to pursue multiple objectives with a fixed set of controllable devices makes integration expensive whether it is done at the product development stage or the deployment stage. This cost creates a significant barrier to adoption and can lengthen the product to market time of new solutions. To fundamentally address the complexity of system integration for distribution grid operations, the U.S. Department of Energy Office of Electricity has funded the GridAPPS-D project at PNNL, which streamlines integration by contributing to standards development, defining system architecture, applying advanced mathematics, and developing open-source software to demonstrate the concept of an open data-integration platform for distribution operations. The open data-integration platform concept enables system operators and solution providers to deploy ambitious, best-of-breed applications (or apps) without continually reengineering for integration. Ambitious apps developed by different solution providers will inevitably attempt to achieve different control objectives with the same set of controllable devices. If the open platform itself can resolve these conflicts in a way that achieves the best available outcomes for all apps, doesn’t restrict the ambitious design of apps, and ensures safe and secure operations, apps will be able to plug-and-play with the platform at the same time as other ambitious apps. In this paper, we describe a framework called App Deconfliction that empowers a platform to assign setpoints to controllable devices based on the values preferred by different apps (and even external stakeholder entities like customers or aggregators). The App Deconfliction framework is compatible with several methods for determining setpoint values. We present two methods based on game theory that provide a subtle built-in incentive structure for developers to adapt their apps to the fact that they will be operating in a moderated multi-app environment and to favor device setpoints that have the most effect on their objectives over those that have the least effect. Our simulation-based demonstrations have shown that game-theory-based deconfliction can lead to a 7% improvement in control space utilization compared to design-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗