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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

Rodiles Delgado, Brian G.↗
DOE OSTI2024

Additive Manufacturing of Thermal Energy Storage Composites with Microencapsulated Phase Change Materials Supported in a Multi-Polymer Matrix

Advanced manufacturing techniques, such as additive manufacturing (AM), that can directly integrate phase change materials (PCMs) have garnered interest in recent years due to their potential for development of highly efficient thermal energy storage architectures. Complex, high surface area geometries embedded with PCMs that are only feasible with AM can improve thermal management with reduced material waste. Our work focuses on developing composite filaments with microencapsulated phase change materials (MEPCM) bound within a single or dual polymer matrix that can be processed through standard filament extruders and additively manufactured using off-the-shelf 3D printers. Polymer powders, rather than polymer pellets, were key to homogenously mixed filaments achieving high MEPCM loadings with no deterioration in thermal energy storage (TES) capability during extrusion. Composite filaments contain upwards of 60 wt% MEPCM and were printed without loss in feature resolution, print speed, or layer adhesion. Storage enthalpies of printed composites range from 100 - 130 kJ/kg, which were within 5% of the theoretical enthalpy based on weight fraction of MEPCM and maintained enthalpies within 1% over 500 thermal cycles. We can reliably manufacture low density, high surface area structures like 15% gyroid infill, along with dense, compact pucks at a 100% concentric infill. Prints were also scalable to a 900 cm3 honeycomb infill heat exchanger model that has an estimated energy storage capacity of 9 Wh.

3D printing↗
DOE OSTI2026

Optics and Systems Design of the Ring-to-Second Target Transport Beam-Line for the SNS Second Target Station

The Second Target Station (STS) project at the Spallation Neutron Source (SNS) is being developed to provide world-leading cold neutron brightness for next-generation neutron scattering experiments. The STS Accelerator Systems (AS) scope includes the design and implementation of the Ring-to-Second Target (RTST) proton beam transport line, which extracts 1.3 GeV proton beam pulses from the existing Ring-to-Beam Transport (RTBT) system and delivers them to the STS target. The RTST design emphasizes operational reliability [high reliability], low activation [minimum activation of components and the tunnel], maintainability, and compatibility with existing SNS infrastructure through extensive reuse of proven RTBT systems and components. The beamline includes a new extraction region, a transport lattice consisting of dipole, quadrupole, and corrector magnets, beam instrumentation systems, vacuum systems, personnel protection systems, and radiation shielding systems. Beam optics and particle tracking studies were performed using PyORBIT to validate extraction trajectories, beam transport, and target beam spot requirements [60–90 cm² beam spot area]. This paper presents the optics design philosophy, extraction system architecture, transport lattice design, instrumentation strategy, vacuum system approach, and radiation protection integration for the RTST beamline. Particle tracking simulations indicate successful beam transport without beam loss under nominal operating conditions. The RTST is designed to transport 1.3 GeV proton beam pulses at repetition rates up to 15 Hz, delivering nominal beam power of 700 kW to the Second Target Station.

Baron, Alex [ORNL]↗
DOE OSTI2024

Development of an immersion fiber optic Raman probe for real-time analysis of molten materials

This study presents an advancement in high-temperature Raman spectroscopy, specifically for analyzing molten materials. It introduces an approach by integrating a fiber-optic Raman probe with a copper block protection system designed to endure extreme thermal conditions. The copper block features an open port designed to accommodate an external telescope with a 3 cm focal length, enabling Raman spectra collection in challenging high-temperature environments. A built-in gas channel ensures a continuous flow of argon gas to prevent flux intrusion. The robust copper block acts as a reliable shield, safeguarding the fiber-optic Raman probe within molten materials. This enhancement maintains the probe's integrity and significantly improves its resilience, making it ideal for rigorous investigations of molten substances. This advancement is particularly relevant in metallurgy, where flux materials impact production quality and efficiency. The ability to acquire Raman signals under elevated thermal conditions offers opportunities for studying molecular dynamics, compositional changes, and chemical interactions within molten substances. This introduced direct immersion probing technique has implications, benefiting both scientific and industrial fields. It holds promise for advancing research and exploration in various contexts, from fundamental scientific inquiries to practical applications in metallurgical processes, where flux materials are critical for optimizing production quality and efficiency. Furthermore, this approach enhances the capabilities of high-temperature Raman spectroscopy, making it a valuable tool for investigating molten materials and their properties in diverse settings.

Argon↗
DOE OSTI2026

Ultra-low thermal resistance and pressure drop copper and copper-tungsten diamond-shaped pin fin cold plates for liquid cooling of electronics

Modern and future data centers face increasing cooling challenges due to increasing chip thermal design power and die size, along with the need to reduce energy consumption used for cooling. High performance cooling solutions that maintain a low chip junction temperature are needed to ensure electronics reliability. This work develops an ultra-low thermal resistance and low pressure drop 75 mm × 75 mm cold plate, intended for next-generation electronics cooling. The cold plate features an array of diamond-shaped pin fins and integrated copper tungsten heat spreader, selected for its low coefficient of thermal expansion which reduces thermomechanical deformation and allows for closer integration of the cold plate with silicon dies. Starting with 300 candidate designs, three-dimensional computational fluid dynamics simulations predict the thermal-hydraulic performance of cold plate subsections. The highest performing geometries are evaluated with high fidelity simulations. Four cold plates are manufactured for experiments: three with diamond-shaped pin fins and one with straights fins for comparison purposes. The cold plates are fabricated from copper-tungsten (CuW), copper (Cu), or aluminum-silicon-magnesium alloy (AlSi10Mg). The diamond-shaped pin fins achieve a roughly 15 % lower thermal resistance compared to the conventional straight fin microchannel. The highest performing design achieves a chip-to-coolant (including thermal interface material) thermal resistance of 9.0 K/kW in CuW and 6.9 K/kW in Cu under a 1 kW heat load with an inlet-to-outlet pressure drop of 9.0 kPa and water as the working fluid. This work demonstrates ultra-low thermal resistance and pressure drop cold plates for large die, high heat load applications, and shows that CuW is an attractive cold plate material for improved reliability in next generation data center cooling.

Coefficient of thermal expansion↗
DOE OSTI2025

Development of interatomic potential and effect of ordering on defect properties in CrMnV

Developing materials that can withstand extreme environments, such as high radiation doses and elevated temperatures, is crucial for next-generation particle accelerators, including the 2.4 MW Long-Baseline Neutrino Facility. High-Entropy Alloys have emerged as promising candidates for beam window materials due to their superior mechanical strength, corrosion resistance, and radiation tolerance. In this study, we focus on the Cr–Mn–V alloy system, developing and employing machine-learning interatomic potentials (MLIPs) to investigate the formation of an ordered phase and its influence on defect properties. Using hybrid Monte Carlo-Molecular Dynamics simulations, we observe the formation of a B2-ordered phase at lower temperatures, consistent with Density Functional Theory (DFT) predictions. Ordered structures display a bimodal distribution of migration energies and reduced mean square displacement values, indicating suppressed vacancy diffusion. Our results also show that the migration energy barrier varies based on the atomic species, with Mn and V exhibiting the highest and lowest average barriers, respectively. These findings suggest that atomic ordering inhibits defect mobility, potentially enhancing the radiation resistance of CrMnV alloys. The validated MLIP provides a reliable framework for simulations that are faster than traditional DFT while maintaining the accuracy required to study defect and ordering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Application of Electrochemical Methods to Molten Salt Reactors: Draft TLR Documenting Assessment of Electrochemical Monitoring

Nuclear Regulatory Commission (NRC) is developing the regulatory framework and technical expertise to support regulatory review of advanced non-water reactors, including molten salt reactors (MSRs). In an MSR, it is essential that the salt chemistry be maintained in a desired range in terms of redox potential for reliable operation and mitigation of corrosion to structural materials in the reactor, particularly the reactor vessel and heater exchanger. Measuring the redox potential of the salt in the reactor would also allow for the material lifetimes to be predicted more accurately, and chemical issues to be diagnosed more quickly. In addition to the chemical composition analysis by ICP-MS, electrochemical methods including potentiometry and linear wave scanning (LSC) were also used in molten salt reactor experiment (MSRE) for redox potential monitoring purposes. Electrochemcial methods offers unique advantages such as quick turnaround in results and unique capability of in-line monitoring of redox potential, and are considered popular electroanalytical techniques that can be used to monitor redox potentials and salt chemistry including impurities. The last two decades have seen significant advances in science and engineering of electrochemical methods for potential application to molten salts. The primary goal of this report is to assist NRC in understanding the monitoring of the salt chemistry by electrochemical methods and provide NRC reviewers with necessary information and tools to support regulatory review of MSR designs. This reports consists of two majors parts—Part 1, chemical potential of molten salts and effects by fission process in MSR; Part 2, assessment of electrochemical methods for application to MSRs. The TRLs of the typical relevant electrochemical methods for molten salts were evaluated based on the DOE TRL guidelines and upon a review of the current status of the electrochemical methods for two typical salt systems, fluoride and chloride, for MSRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The git based ATLAS data acquisition configuration service in LHC Run 3

The ATLAS experiment at the LHC at CERN uses a large, distributed trigger and data acquisition system composed of many computing nodes, networks, and hardware modules. Its configuration service is used to provide descriptions of control, monitoring, diagnostic, recovery, dataflow and data quality configurations, interconnections, and parameters for modules, chips, and channels of various online systems, detectors, and the whole ATLAS experiment. Those descriptions have historically been stored in more than one thousand interconnected XML files, which are updated by various experts many times per day. Maintaining error-free and consistent sets of such files and providing reliable and fast access to current and historical configurations is a major challenge. This paper gives details of the configuration service upgrade on the modern Git version control system backend for LHC Run 3 and its exploitation experience. It may be interesting for developers using human-readable file formats, where consistency of the files, performance, access control, traceability of modifications, and effective archiving are key requirements.

Soloviev, Igor [Univ. of California, Irvine, CA (U↗
DOE OSTI2025

Resilient Entanglement Distribution in a Multihop Quantum Network

The evolution of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions. We introduce multihop quantum networks to improve network reach and resilience by enabling quantum communications across intermediate nodes, thus broadening network connectivity and increasing scalability. We present multihop two-qubit polarization-entanglement distribution within a quantum network at the Oak Ridge National Laboratory campus. Our system uses wavelength-selective switches for adaptive bandwidth management on a software-defined quantum network that integrates a quantum data plane with classical data and control planes, creating a flexible, reconfigurable mesh. Our network distributes entanglement across six nodes within three subnetworks, each located in a separate building, optimizing quantum state fidelity and transmission rate through adaptive resource management. Additionally, we demonstrate the network's resilience by implementing a link recovery approach that monitors and reroutes quantum resources to maintain service continuity despite link failures—paving the way for scalable and reliable quantum networking infrastructures.

Alshowkan, Muneer [Oak Ridge National Laboratory (↗
DOE OSTI2025

Development of Transformative Preparation Methods to Push up High Q&G Performance of FRIB Spare HWR Cryomodule Cavities

The FRIB accelerator project construction, a top priority of US nuclear science, was completed in January 2022, and is now moving to user operation. The stable and reliable operation of the accelerating cryomodules is essential in achieving/fulfilling DOE and user expectations. So far, FRIB cryomodules meet all FRIB specifications for cavity performance. However, during the lifetime of machine operation, degradation of cryomodule performance is possible, as reported in similar operating facilities (CEBAF, SNS). If cryomodule degradation is observed at FRIB, the under-performing cryomodule will require replacement/maintenance. In effort to manage operational reliability, FRIB plans to construct a 0.53 half-wave cryomodule to serve as an active spare. In a parallel effort, FRIB will also work toward increasing operational Q and gradient of spare cryomodule cavities to gain an overall performance margin to support future operational reliability. The current FRIB cavity designs have a potential to operate at gradients higher than 8 MV/m, but are currently limited by field emission (FE) and/or high field Q slope (HFQS); known issue in buffered chemical polished (BCP) treated cavities. The proposal looks to develop transformative surface preparation treatments to improve the operational gradient of spare cryomodules higher than 10 MV/m while maintaining high Q. Thus, increasing operational margin by 30 - 50%. With the goal to improve operational reliability set, the proposal will investigate multiple objectives as possible paths forward to achieve an overall increase in cavity performance and gain a better understanding of SRF limiting mechanisms. The proposal will study the application of different chemical surface treatments to 0.53 half-wave cavities, with the addition of low temperature bakes (LTB), and measure their effects on accelerating performance. Proposed chemical treatments to be explored in this proposal include conventional EP acid mixtures, as well as innovated EP and BCP acid mixtures designed to simplify processing paths in migrating FE and HFQS. The proposed transformative treatment wet N-doping also has the potential to replicate recent advancements in SRF technology relating to nitrogen doping and high Q operation without the requirement for an ultra-high vacuum annealing furnace; currently being developed at FNAL and JLAB. In parallel, high Q performance relating to flux trapping will be investigated with the installation of a second layer of magnetic shielding in the vertical test Dewar. The research objectives presented in the proposal, and their corresponding effects on cavity performance, will provide essential knowledge and future guidance to the SRF community and provide possible paths for future SRF based projects and applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Optimization and active stabilization of a far-infrared laser for NSTX-U high poloidal wavenumber scattering diagnostics

The far-infrared (FIR) laser output beam power and profile are important parameters in the laser-aided diagnostics, directly influencing the spatial resolution and signal-to-noise ratio of measurements. Here, this work focuses on developing a systematic control method to enhance FIR laser beam quality through optimized mirror alignment and real-time feedback-based precision cavity length tuning. A 150 W CO 2 laser, aligned with the waveguide axis using a HeNe reference laser, serves as the pump source. The sensitivity of FIR beam intensity to pump gas pressure and thermal expansion is investigated, revealing that even a 1 µm cavity expansion can significantly degrade output power stability to about two-thirds of its original value. To address this, a feedback control module has been designed and implemented for active cavity length adjustment, stabilizing the output power at ∼30 mW. In addition, maintaining a high formic acid gas pressure ($>$190 mTorr) within the cavity ensures reliable operation. The optimized FIR laser will be deployed on the National Spherical Torus eXperiment-U high poloidal wavenumber scattering system for studying electron-scale turbulence in tokamak plasmas.

Xu, Xinhang [Univ. of California, Davis, CA (Unite↗
DOE OSTI2025

Weather-Driven Resilience for Distribution Systems

With increasing billion dollar disasters due to weather events, it is of utmost priority for utility and grid operators to maintain and enhance the resilience of distribution grid. Traditionally, such analysis were conducted with reliability metrics alone. However, with increasing complexity of distribution grid and advancements in grid edge resources, evaluating grid resilience is challenging and involves multi-faceted analysis of the impact of weather events on the distribution grid. This presentation provides background, details, and future works in the field of weather-driven resilience for power distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2026

Experimental Evaluation of a High-Performance Cold-Climate Heat Pump Using Tandem Vapor-Injection Compressors

A high-performance cold-climate heat pump (CCHP) was developed and experimentally validated through comprehensive laboratory and field testing. The system utilized two equal-size tandem vapor-injection (VI) compressors with an inter-stage flash tank to improve efficiency and capacity retention under subfreezing conditions. Laboratory testing was conducted on a 3-ton prototype in a controlled environmental chamber equipped with calibrated thermocouples, refrigerant-side mass flow and pressure transducers, and precision airflow measurement for energy balance verification. The prototype achieved heating coefficients of performance (COPs) of 4.4 at 47 °F, 3.1 at 17 °F, and 2.0 at –13 °F, while maintaining 88% of its rated heating capacity at –13°F. These results confirm strong low-temperature performance and indicate the potential for significant reductions in electric resistance backup use. A field prototype was installed and monitored in a residential building in Fairbanks, Alaska, during a heating season. Instrumentation included real-time power, temperature, and refrigerant state measurements to evaluate performance under dynamic outdoor conditions. The heat pump operated reliably down to –30 °F, delivering 75% of its rated heating capacity with a COP of 1.8, while maintaining stable operation, effective defrost control, and indoor comfort without auxiliary heating. The combined laboratory and field results demonstrate that the tandem VI compressor configuration provides a practical and energy-efficient approach for residential heat pumps designed for cold and very cold climate regions.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗