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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 OSTI2026

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Esaulov, Vladislav [Georgia State University, Atla↗
DOE OSTI2020

BioGeoChemistry of Actinides (LLNL SFA OBER - SBR FY20 Program Management and Performance Report)

The focus of the BioGeoChemistry of Actinides SFA is to identify and quantify the biogeochemical processes and the underlying mechanisms that control actinide mobility in an effort to reliably predict and control the cycling and migration of actinides in the environment. The research approach includes: (1) Field Studies (Research Thrust 1) that capture actinide behavior on the timescale of decades and (2) Fundamental Laboratory Studies (Research Thrust 2) that isolate specific biogeochemical processes observed in the field. These research thrusts are underpinned by the unique capabilities and staff expertise at Lawrence Livermore National Laboratory (LLNL), allowing the BioGeoChemistry of Actinides SFA to advance our understanding of actinide migration behavior in the environment, and serve as an international resource for environmental radiochemistry research (Figure 1). Research Thrusts 1 and 2 are guided by broad central hypotheses: Thrust 1 Hypothesis: Biogeochemical processes occurring on the timescale of years to decades lead to greater actinide recalcitrance in sediments and limit their migration in surface and groundwater. Thrust 2 Hypothesis: Long-term biogeochemical processes include mineral and surface alteration, which leads to stabilization of actinide surface associations or incorporation into mineral precipitates. Our strategic goal is to use the knowledge gained from our Science Plan to advance our understanding of the behavior of actinides, providing DOE with the scientific basis for remediation and long-term stewardship of DOE’s legacy sites and, more broadly, increasing our understanding of transport phenomena in environmental systems sciences with a particular emphasis on environmentally relevant (long-term) timescales. Although our focus is on actinide biogeochemistry, the increased focus on biogeochemistry at unique Test Bed locations associated with this SFA is providing fundamental information on redox processes and associated microbiological processes that control the cycling of redox sensitive metals under dynamic and transient biogeochemical conditions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

BioGeoChemistry at Interfaces (LLNL SFA OBER FY21 Program Management and Performance Report)

The focus of the BioGeoChemistry at Interfaces (formerly BioGeoChemistry of Actinides) SFA is to identify and quantify the biogeochemical processes and the underlying mechanisms that control actinide mobility in an effort to reliably predict and control the cycling and migration of actinides in the environment. The research approach includes: (1) Field Studies (Research Thrust 1) that capture actinide behavior on the timescale of decades and (2) Fundamental Laboratory Studies (Research Thrust 2) that isolate specific biogeochemical processes observed in the field. These Research Thrusts are underpinned by the unique capabilities and staff expertise at Lawrence Livermore National Laboratory, allowing the BioGeoChemistry at Interfaces SFA to advance our understanding of actinide migration behavior in the environment, and serve as an international resource for environmental radiochemistry research (Figure 1).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2024

An Adaptive Dynamic Agrivoltaic Production Tool

In the pursuit of sustainable and land-use-efficient solutions to mitigate climate change, the concept of agrivoltaic systems, which integrate renewable solar energy into conventional agriculture, has emerged. By deploying solar arrays above the crop field, these systems are designed to maximize the land use efficiency or, in other words, the value of generated electricity and crop yield per unit area. While power generation has been extensively studied and modeled, research gaps persist in simulating crop performance [1], [2]. Early studies utilized generalized relationships between photosynthetically active radiation (PAR) and crop production. However, significant variations in crop performance due to other factors, such as weather and soil, can compromise the reliability of the results [2]. Current studies focus on comprehensive process-based crop models, such as DSSAT [3] and STICS [4]. Nevertheless, the validation status varies greatly across different species, and the validation process requires well-designed experiments to adjust specific processes. For less common shade-tolerant crops with limited validation, such as cabbage, physiological behaviors can be difficult to predict [5]. This study investigates an adaptive dynamic agrivoltaic production tool (ADAPT) with few data requirements and minimal on-site calibration that can be easily applied in real applications.

Long, Qirui↗
DOE OSTI2025

5G Communications in Nuclear: Potential Use Cases and Security Considerations

As fifth-generation (5G) communications continues to revolutionize the future of wireless technology, there is growing demand to utilize its benefits for critical infrastructures such as nuclear power plants (NPPs). In regard to achieving full automation and control in the operation of existing and future nuclear reactors, the unique capabilities of 5G can bring several potential advantages over other wireless technologies. However, a deep investigation is needed for the availability and security of 5G communications under various NPP operational scenarios. This article examines how 5G security capabilities can be architecturally deployed in nuclear applications so as to replace existing communication infrastructures. We discuss the current use of all wireless technologies in NPPs with their key features. Consequently, we investigated several NPP use cases in which 5G offers potential advantages but entails specific security considerations. The present article covers the characteristics of 5G communications, general challenges to its application in nuclear, and the security gaps that need to be addressed. We also highlight certain 5G security-by-design features that can help addressing current stringent NPP requirements. In addition, we discuss some future research direction that can facilitate the implementation of 5G in a nuclear facility. The findings presented herein can help foster 5G deployment in NPPs, thus enabling secured data transmission, cost savings, and increased operational efficiency with enhanced reliability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗
DOE OSTI2020

Hierarchical Phenomena in Multicomponent Liquids: Simulation Methods, Analysis, Chemistry

Complex, multicomponent, solutions have often been studied solely through the lens of specific applications of interest. Yet advances to both simulation methodologies (enhanced sampling, etc.) and analysis techniques (network analysis algorithms and others), are creating a trove of data that reveal transcending characteristics across vast compositional phase space. This perspective discusses technical considerations of the reliable and accurate simulations of complex solutions, followed by the advances to analysis algorithms that elucidate coupling of different length and timescale behavior (hierarchical phenomena). The different manifestations of hierarchical phenomena are presented across an array of solution environments, emphasizing fundamental and ongoing science questions. With a more advanced molecular understanding in hand, a quintessential application (solvent extraction) is discussed, where significant opportunities exist to re-imagine the technical scope of an established technology.

Servis, Michael J.↗
DOE OSTI2023

A new approach to the design of surface subsystems of polymeric insulators for HV and MV apparatus under AC voltage

The design of high voltage and medium voltage AC and DC standoff insulators, and, in general, of insulation systems where surface and interface properties are predominant factors, is based on specifications and standards that rely upon long-term experiences and lab testing. Surface insulating properties depend on electrical and thermal stresses, besides environmental factors, and the inception of extrinsic ageing phenomena, such as partial discharges, can be already a cause of reduced reliability and premature failure (for organic insulating materials). An innovative approach, based on field simulation, discharge modelling, and partial discharge inception measurements is presented by the authors, which can establish a solid basis to optimise insulation design for any type of supply voltage waveform. This method, the three-leg approach, is applied here to a cable system under AC sinusoidal voltage, but it can work for any type of insulation system, such as bushings, switch gears, transformers, rotating machines, and power electronics boards.

42 ENGINEERING↗
DOE OSTI2026

Exploring the fusion power plant design space: comparative analysis of positive and negative triangularity tokamaks through optimization

The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance metrics. This study presents a systematic comparison using constrained multi-objective optimization with the integrated FUsion Synthesis Engine (FUSE) framework. Over 200 000 integrated design evaluations were performed exploring the trade-offs between capital cost minimization and operational reliability (maximizing q 95 ) while satisfying engineering constraints including 250 ± 50 MW net electric power, tritium breeding ratio > 1.1, power exhaust limits and an hour flattop time. Both configurations achieve similar cost-performance Pareto fronts through contrasting design philosophies. PT, while demonstrating resilience to pedestal degradation (compensating for up to 40% reduction), are constrained to larger machines (R 0 > 6.5 m) by the narrow operational window between L–H threshold requirements and the research-established power exhaust limit (P sol /R < 15 MW m –1 ). This forces optimization through comparatively reduced magnetic field (∼8 T). NT configurations exploit their freedom from these constraints to access compact, high-field designs (R 0 ~ 5.5 m, B 0 >12 T), creating natural synergy with advancing HTS technology. Sensitivity analyses reveal that PT’s economic viability depends critically on uncertainties in L–H threshold scaling and power handling limits. Notably, a 50% variation in either could eliminate viable designs or enable access to the compact design space. These results suggest configuration selection should be risk-informed: PT offers the lowest-cost path when operational constraints can be confidently predicted, while NT is robust to large variations in constraints and physics uncertainties.

FUSE framework↗
DOE OSTI2025

Automating Testing of DUNE Electronics via a Finite State Machine

The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six total institutions performing testing. The outcomes of this internship contribute to Fermilab s mission to advance high-energy physics and support the DOE s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.

Kang, Caleb [William Rainey Harper Coll.]↗
DOE OSTI2024

Integrated Traction Drive Thermal Management: Keystone Project 3

The presentation represents a report of accomplishments during FY 2024 and is prepared for 2024 DOE VTO Annual Merit Review. The project goal is identifying pathways enabling high-performance, compact, and reliable integrated electric drives and facilitation of achieving DOE 2025 target of 33 kW/L system power density for an electric traction drive through support of other EDT consortium members in design and development of thermal management systems for their respective integrated drive concepts. Specific objectives are: 1) research and evaluation of motor-integrated power electronics packaging technologies and thermal management approaches; 2) explore novel materials and manufacturing options (3D printing with thermally enhanced polymers or ceramic materials) for key thermal management system components; 3) develop thermal management system and its sub-components to enable integrated electric drive DOE power density targets in collaboration with project partners; 4) support activities of DOE's Electric Drive Technologies (EDT) consortium members, Oak Ridge National Laboratory (ORNL), University of Wisconsin and Georgia Tech research teams in thermal management component design and thermal modeling of their integrated traction drives.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Automating Testing of DUNE Electronics via a Finite State Machine

The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos—tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab’s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE’s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six institutions performing testing. The outcomes of this internship contribute to Fermilab’s mission to advance high-energy physics and support the DOE’s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.

Kang, Caleb [Fermilab]↗
DOE OSTI2022

Inferring small-sample-reactivity worth from oscillation experiment with auto-corrected local flux

To estimate reactivity changes as a function of time from the signal of detectors count rates, one usually uses point kinetic equations. However, when detectors are located inside the core, it is often necessary to compute a correction factor to account for the flux-shape deformation (i.e. the change of detector efficiency that may have been induced by neutron absorbers movements). Various simulation methods have been developed depending on transients observed. They range from the resolution of static equations up to full-time-dependent neutron transport in 3D. However, those methods require high computational-cost calculations of local reaction rates that rely on nuclear data evaluations. Therefore, a 'measured' reactivity is never determined through pure measurements. Taking advantage of the known oscillation movement of a small sample at the center of the MINERVE reactor, the innovative algorithm presented in this paper derives the correction factor needed to get no time drift on reactivity steps. Derived correction factors are in good agreement with those obtained independently with TRIPOLI-4 through sensitivity analysis. Low sensitivity to the sample composition and moderate sensitivity to the detectors position reinforce the reliability of those results. This application paves the way to almost pure nuclear data reactivity measurements. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Process Control Plan to Monitor Acceptable Levels of Flux and other residues

IPC J-STD –001G, Amendment 1 requires that the assembler have a sampling plan to assure that the process remains in control once qualified and validated. It is well documented that electrochemical failures occur at component sites where flux residues are not fully activated. It is also well known that specific components have a higher risk of electrochemical failure. Selecting a control plan that monitors the process and its performance on challenging components provides assurance that the process maintains control once established during process qualification. The test board used to qualify the process establishes an upper and lower spec limit for each of the component types on the test board. Surface Insulation Resistance upper and lower control limits, using challenging components that are representative of production hardware, represent a golden process condition. The golden image is a measure of process deviations that represents the performance of a resistance curve within a specific period. The objective is to judge whether the process in or out of control.

42 ENGINEERING↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2021

The incidence of X-ray selected AGN in nearby galaxies

ABSTRACT We present the identification and analysis of an unbiased sample of active galactic nuclei (AGN) that lie within the local galaxy population. Using the MPA-JHU catalogue (based on SDSS DR8) and 3XMM DR7 we define a parent sample of 25 949 local galaxies (z ≤ 0.33). After confirming that there was strictly no AGN light contaminating stellar mass and star-formation rate calculations, we identified 917 galaxies with central, excess X-ray emission likely originating from an AGN. We analysed their optical emission lines using the BPT diagnostic and confirmed that such techniques are more effective at reliably identifying sources as AGN in higher mass galaxies: rising from 30 per cent agreement in the lowest mass bin to 93 per cent in the highest. We then calculated the growth rates of the black holes powering these AGN in terms of their specific accretion rates (∝LX/M*). Our sample exhibits a wide range of accretion rates, with the majority accreting at rates $\le 0.5\ \mathrm{ per \, cent}$ of their Eddington luminosity. Finally, we used our sample to calculate the incidence of AGN as a function of stellar mass and redshift. After correcting for the varying sensitivity of 3XMM, we split the galaxy sample by stellar mass and redshift and investigated the AGN fraction as a function of X-ray luminosity and specific black hole accretion rate. From this we found the fraction of galaxies hosting AGN above a fixed specific accretion rate limit of 10−3.5 is constant (at $\approx 1\ \mathrm{ per \, cent}$) over stellar masses of 8 < log M*/M⊙ < 12 and increases (from $\approx 1\ \mathrm{ per \, cent}$ to 10 per cent) with redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Data-Driven Understanding of Low-to-Moderate Income Customers’ Adoption and Financial Qualification in Community Solar (Final Technical Report)

When Solstice began working in the community solar industry, it quickly became clear that there was a wide disparity along race and class lines in terms of what kind of household traditional solar offerings benefited. Solstice is an organization that aligns itself to the principles of a Just Transition, or the concept that a transition to a green economy should benefit and prioritize historically marginalized groups of people. Given that low-income communities are disproportionately burdened by our current polluting fossil fuel economy, Solstice set out to investigate the root causes of the exclusion of low to moderate income (LMI) households in the community solar market. As we worked more and more with financiers, developers, and LMI households, we recognized that significant barriers to entry in a developer-owned project were credit threshold requirements. A FICO score of 750 and higher discriminates against LMI households that are not financially stable enough to take on multiple lines of credit (or who may not have been deemed credit worthy enough to access a line of credit) yet who may have been reliable utility customers. As Solstice began looking further into preliminary data and speaking to community-based organizations (CBOs) that serve LMI households, we found our hypothesis to be worth investigating; there was indeed a subset of LMI households with poor FICO scores (or no FICO score at all) that had perfect bill payment history. Though initially Solstice was intent on gathering a body of data to prove or disprove this hypothesis, we were also concerned that all this proof of a financially stable and reliable customer was not being incorporated into FICO, and wondered if such a score would be possible. After preliminary research into the existence of alternative scores, Solstice realized that many mission-driven lending institutions rely on alternative metrics that are formulated for their specific industry. However, we also realized that community solar did not have an alternative credit underwriting mechanism, though there was great need from solar financiers for one. We anticipated that the creation of an EnergyScore would contribute to financiers’ need to qualify more people, as these entities are desperately looking for ways to lower the cost of customer acquisition. FICO turns away nearly 50% of potential customers (according to our own acquisition experience), and much of the community solar industry is realizing that FICO is not the perfect qualifying mechanism. Additionally, with a grounding in energy justice, just transitions, and climate justice, Solstice recognizes the need for a solution that addresses a more urgent need for LMI households to access renewable energy savings and relieve energy burdens. With funding from the Department of Energy, we gathered the data necessary to build the EnergyScore, reached out to mission-driven developers willing to test the metric, and secured several demonstration projects to pilot the EnergyScore. After acquiring customers for these demo projects using the EnergyScore, we will be continuously collecting customer payment behavior data. Though it goes beyond the scope of this project, we plan to disseminate this de-identified data with the wider community solar industry, which includes not only financiers and developers, but other mission-driven nonprofits, solar cooperatives, community-based organizations, environmental justice activists, and academics. While we intend to disprove the notion that LMI households cannot be included in projects without acutely increasing the risk to project finances, we also hope the data can be used to negotiate better pricing of systems and terms of ownership for community groups seeking to build inclusive projects.

14 SOLAR ENERGY↗