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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Metal–organic frameworks as O 2 -selective adsorbents for air separations

Oxygen is a critical gas in numerous industries and is produced globally on a gigatonne scale, primarily through energy-intensive cryogenic distillation of air. The realization of large-scale adsorption-based air separations could enable a significant reduction in associated worldwide energy consumption and would constitute an important component of broader efforts to combat climate change. Certain small-scale air separations are carried out using N 2 -selective adsorbents, although the low capacities, poor selectivities, and high regeneration energies associated with these materials limit the extent of their usage. In contrast, the realization of O 2 -selective adsorbents may facilitate more widespread adoption of adsorptive air separations, which could enable the decentralization of O 2 production and utilization and advance new uses for O 2 . Here, we present a detailed evaluation of the potential of metal–organic frameworks (MOFs) to serve as O 2 -selective adsorbents for air separations. Drawing insights from biological and molecular systems that selectively bind O 2 , we survey the field of O 2 -selective MOFs, highlighting progress and identifying promising areas for future exploration. As a guide for further research, the importance of moving beyond the traditional evaluation of O 2 adsorption enthalpy, ΔH, is emphasized, and the free energy of O 2 adsorption, ΔG, is discussed as the key metric for understanding and predicting MOF performance under practical conditions. Based on a proof-of-concept assessment of O 2 binding carried out for eight different MOFs using experimentally derived capacities and thermodynamic parameters, we identify two existing materials and one proposed framework with nearly optimal ΔG values for operation under user-defined conditions. While enhancements are still needed in other material properties, the insights from the assessments herein serve as a guide for future materials design and evaluation. Computational approaches based on density functional theory with periodic boundary conditions are also discussed as complementary to experimental efforts, and new predictions enable identification of additional promising MOF systems for investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microwave-Assisted Synthesis of Cu@IrO 2 Core-Shell Nanowires for Low-Temperature Methane Conversion

A facile microwave-assisted synthesis was developed for the tunable fabrication of a novel Cu@IrO 2 core@shell nanowire motif. Experimental parameters, such as (i) reaction time, (ii) the method of addition of the Ir precursor, (iii) capping agent, (iv) reducing agent, as well as (v) the capping agent-to-reducing agent ratio, were subsequently optimized. The viability of other methods based on previously reported literature, such as refluxing, stirring, and physical sonication, was studied and compared with our optimized microwave-assisted protocol in creating our as-prepared materials. It should be noted that the magnitude of the IrO 2 shell could be tailored based on varying the Cu: Ir ratio coupled with judicious variations in the amounts of capping agent and reducing agent. Structural characterization techniques, such as XRD, XPS, and HRTEM (including HRTEM-EDS), were used to analyze our Cu@IrO 2 motifs. Specifically, the shell could be reliably tailored from sizes of 10 nm, 8 nm, 6 nm, and 3.5 nm with corresponding Cu: Ir ratios of 10:1, 15:1, 20:1, and 25:1, respectively. Moreover, the structural integrity of the motifs was probed and found to have been maintained after not only heat treatment but also the post-methane conversion process, indicative of an intrinsically high stability. Both components within the CuO-IrO 2 interface were able to activate methane at temperatures between 400 to 500 K with a reduction of the associated metal cations (Cu 2+ → Cu 1+ ; Ir 4+ → Ir 3+ ) and the deposition of CH x fragments on the surface, as clearly observed in the ambient-pressure XPS results. Thus, on the basis of their stability and chemical activity, these core-shell materials could be very useful for the catalytic conversion of methane into 'higher value" chemicals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Autoclave treatment of the classical scrapie agent US No. 13-7 and experimental inoculation to susceptible VRQ/ARQ sheep via the oral route results in decreased transmission efficiency

Scrapie, a prion disease of sheep, is highly resistant to conventional deactivation. Numerous methods to deactivate scrapie have been tested in laboratory animal models, and adequate autoclave treatment can reduce or remove the infectivity of some classical scrapie strains depending on the heating parameters used. In this study, we autoclaved brain homogenate from a sheep with US scrapie strain 13–7 for 30 minutes at 121°C. Genetically susceptible VRQ/ARQ sheep were orally inoculated with 3 grams of the autoclaved brain homogenate. For comparison, a second group of sheep was inoculated with a non-autoclaved brain homogenate. Rectal biopsies were used to assess antemortem scrapie disease progression throughout the study. Five out of ten (5/10) sheep that received autoclaved inoculum ultimately developed scrapie after an experimental endpoint of 72 months. These sheep had a mean incubation period of 26.99 months. Two out of five (2/5) positive sheep had detectable PrP Sc in antemortem rectal biopsies, and two (2/5) other sheep had PrP Sc in postmortem rectal tissue. A single sheep (1/5) was positive for scrapie in the CNS, small intestine, and retropharyngeal lymph node but had negative rectal tissue. All of the sheep (10/10) that received non-autoclaved inoculum developed scrapie with a mean incubation period of 20.2 months and had positive rectal biopsies at the earliest timepoint (14.7 months post-inoculation). These results demonstrate that sheep are orally susceptible to US derived classical scrapie strain 13–7 after autoclave treatment at 121°C for 30 minutes. Differences in incubation periods and time interval to first positive rectal biopsies indicate a partial reduction in infectivity titers for the autoclaved inoculum group.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Correlation-aware binning for small-angle neutron scattering via Gaussian-process inference

Binning in small-angle neutron scattering (SANS) is typically performed empirically, with fixed parameters chosen for convenience rather than statistical optimality. Such practices often fail to balance statistical precision and spatial resolution, leading to inconsistencies across instruments and datasets. Here we establish a correlation-aware framework that determines the optimal bin width from first principles by extending the classical Freedman–Diaconis (FD) rule to account for inter-bin correlations with a Gaussian process. In this formulation, the scattering intensity is treated as a smooth stochastic field whose statistical coherence is described by a covariance matrix. Analytical expressions of errors derived from this model yield closed-form criteria that separate the total deviation into contributions from counting noise, aliasing distortion and curvature-dependent correlation effects. Expressed in reduced variables, the resulting dimensionless error surface reveals a continuous transition from the uncorrelated FD regime to the correlation-dominated limit, providing a unified description of noise suppression and resolution control. Because the formulation depends only on the profile characteristics of scattering intensity I(Q), specifically its average intensity and first- and second-order derivatives, it applies generally to any SANS measurement regardless of sample, instrument or geometry. Experimental validation using small- and ultra-small-angle neutron scattering data confirms the predicted scaling behavior, demonstrating that correlation-aware inference systematically reduces mean-squared error and enables information-efficient reproducible data reduction across materials and instruments.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2024

Robust Dissimilar Metal Friction Welded Spool for Enhanced Capability for Steam Power Components (Final Program Report)

This project successfully demonstrated the feasibility and advantages of producing durable, friction-welded dissimilar-metal spools, both with and without transition pieces, as well as the use of advanced oxidation-protective coatings for steam power applications. The work encompassed the optimization and full-scale production of NFA tubing, overcoming manufacturing and processing challenges to achieve enhanced strength, creep, and fatigue properties validated by extensive microstructural characterization. Optimized welding techniques were developed and refined for joining dissimilar materials, resulting in defect-free welds optimized for both low- and high-pressure boiler circuits. Systematic evaluation of welding parameters, coupled with heat treatments, ensured reliable performance and minimized weld defects such as banding and cracking. Comprehensive mechanical and non-destructive testing, including tensile, bend, fatigue, and CT scanning, confirmed the superior integrity and durability of the friction-welded joints under representative service conditions. To address oxidation-related degradation, a range of protective alloy coatings—such as IN625, APMT, Duplex 2507, and FeCrAlY—were selected and evaluated for their ability to counteract oxide notch formation at dissimilar metal interfaces. These coatings demonstrated improved oxidation resistance and long-term stability under thermal cycling, thereby extending weld service life. Thermal cyclic testing and long-term studies captured the phase stability and diffusion behavior of coated and uncoated configurations. Validated models for low-cycle fatigue (LCF), creep, and thermal mechanical fatigue (TMF) were developed to predict component life and failure modes. These models confirmed that friction-welded configurations exhibited significantly enhanced performance compared to conventional gas-tungsten arc welding (GTAW), with up to 7× improvement in creep resistance and at least 2× enhancement in fatigue properties. The introduction of a tailored transition piece further reduced thermally induced strains, enhancing fatigue life. The results demonstrate a robust pathway for retrofitting existing steam fleet components and upgrading future equipment to deliver substantially improved cold-start cycling behavior and reduction of unplanned outages due to premature weld failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Pythia8 Quark and Gluon Jets (float8 e4m3FN)

A float8 (e4m3FN) quantized version of the quark and gluon jet dataset originally published by Komiske, Metodiev, and Thaler (Zenodo record 3164691). Only the 20-file subset without charm and bottom quark jets is included here. All simulation parameters and jet selection criteria are identical to the original: Pythia 8.226, √s = 14 TeV Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos FastJet 3.3.0, anti-k_t jets with R = 0.4 p_T^jet ∈ [500, 550] GeV, |y^jet| < 1.7 There are 20 files, each in compressed NumPy format (QG_jets_fp8e4m3fn_0.npz through QG_jets_fp8e4m3fn_19.npz). Each file contains two arrays: X: (100000, M, 4) — 50k quark and 50k gluon jets, randomly sorted, padded to max multiplicity M, with particle features (pt, rapidity, azimuthal angle, pdgid) y: (100000,) — jet labels, gluon = 0, quark = 1 Since NumPy has no native fp8 dtype, X is stored as float32, but the values have been quantized through TensorFlow's float8_e4m3fn type and carry only fp8 precision. The quantization procedure is as follows: a global per-channel scale factor is computed from the absolute maximum value across all 20 chunks (with FP8_MAX = 448.0, the maximum representable value of e4m3FN). Each chunk is then scaled into the fp8 dynamic range, round-tripped through tf.experimental.float8_e4m3fn, and scaled back. This global scaling ensures a consistent quantization grid across the full dataset. The y labels are unchanged. Users should be aware that e4m3FN has limited dynamic range and precision. We recommend verifying this format is appropriate for your application; for a less aggressive reduction see the float16 and float32 versions linked below. If you use this dataset, please cite the original Zenodo record and its associated paper: Komiske, Metodiev, Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165

DiLullo, Nicholas [Brown University] (ORCID:000000↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Phylogenetics-based identification and characterization of a superior 2,3-butanediol dehydrogenase for Zymomonas mobilis expression

Abstract Background Zymomonas mobilis has recently been shown to be capable of producing the valuable platform biochemical, 2,3-butanediol (2,3-BDO). Despite this capability, the production of high titers of 2,3-BDO is restricted by several physiological parameters. One such bottleneck involves the conversion of acetoin to 2,3-BDO, a step catalyzed by 2,3-butanediol dehydrogenase (Bdh). Several Bdh enzymes have been successfully expressed in Z. mobilis, although a highly active enzyme is yet to be identified for expression in this host. Here, we report the application of a phylogenetic approach to identify and characterize a superior Bdh, followed by validation of its structural attributes using a mutagenesis approach. Results Of the 11 distinct bdh genes that were expressed in Z. mobilis, crude extracts expressing Serratia marcescens Bdh ( Sm Bdh) were found to have the highest activity (8.89 µmol/min/mg), when compared to other Bdh enzymes (0.34–2.87 µmol/min/mg). The Sm Bdh crystal structure was determined through crystallization with cofactor (NAD + ) and substrate (acetoin) molecules bound in the active site. Active Sm Bdh was shown to be a tetramer with the active site populated by a Gln247 residue contributed by the diagonally opposite subunit. Sm Bdh showed a more extensive supporting hydrogen-bond network in comparison to the other well-studied Bdh enzymes, which enables improved substrate positioning and substrate specificity. This protein also contains a short α6 helix, which provides more efficient entry and exit of molecules from the active site, thereby contributing to enhanced substrate turnover. Extending the α6 helix to mimic the lower activity Enterobacter cloacae ( Ec Bdh) enzyme resulted in reduction of Sm Bdh function to nearly 3% of the total activity. In great contrast, reduction of the corresponding α6 helix of the Ec Bdh to mimic the Sm Bdh structure resulted in ~ 70% increase in its activity. Conclusions This study has demonstrated that Sm Bdh is superior to other Bdhs for expression in Z. mobilis for 2,3-BDO production. Sm Bdh possesses unique structural features that confer biochemical advantage to this protein. While coordinated active site formation is a unique structural characteristic of this tetrameric complex, the smaller α6 helix and extended hydrogen network contribute towards improved activity and substrate promiscuity of the enzyme.

09 BIOMASS FUELS↗
DOE OSTI2022

Production of Fischer-Tropsch Synfuels at Nuclear Plants

A case study analysis was performed to evaluate nuclear-powered synthetic fuel production in the midwestern United States (U.S.). A Fischer-Tropsch (FT) fuel synthesis plant design was used as the basis for the analysis. The FT plant design was configured to produce a product slate consisting of diesel fuel, jet fuel, and motor gasoline blend stocks from carbon dioxide (CO 2 ) and hydrogen (H 2 ) feedstocks. The CO 2 feedstock for the FT plant was assumed to be sourced from biorefineries in the region around a Midwest light water reactor (LWR) nuclear power plant (NPP). The analysis specifies that power from the LWR is used to produce H 2 via high-temperature steam electrolysis and to operate the FT synfuel production plant. Capital costs were estimated for the FT plant while capital costs for the electrolysis plant were based on previous Idaho National Laboratory (INL) studies. In addition to labor and maintenance costs for the FT and electrolysis plants, operating costs also include the costs for CO 2 feedstock transport. An analysis was performed to determine the cost of transporting CO 2 from the distributed biorefinery sources to the centralized fuel synthesis plant as a function of the synfuel plant capacity and corresponding CO 2 demand. The primary revenue streams are associated with sales of the synthetic fuel products. The synthetic fuel products will likely follow the same market trends as the conventional fuel products. The synfuel price data was thus based on projections made by the U.S. Energy Information Administration (EIA) 2021 Annual Energy Outlook (AEO) for conventional fuel products minus federal and state taxes, as well as marketing and distribution costs. The economic analysis also considered cases that included and excluded revenues from the 2022 Inflation Reduction Act (IRA) clean hydrogen production tax credit (PTC) of $\$ $3.00/kg for the first ten years of operation. The economic analysis calculated the net present value (NPV) for cases involving steady-state synfuel production for comparison with the NPV for a business-as-usual case in which NPP continues to sell only electric power to the grid. A synfuel production “Reference Case” was considered in addition to sensitivity cases in which the plant capacity, electricity price, and synthetic fuel product prices were perturbed. The synfuel production Reference Case considered a scenario in which the electrolysis and synfuel plants utilized a combined electrical load of 1000 megawatt electrical (MWe) from the LWR with the balance of the LWR power output being sold to the electric grid. The economic analysis suggests that the synfuel production Reference Case evaluated in this analysis would lead to considerable economic potential for near-term deployment of a nuclear-based synfuel production plant. Specifically, the economic analysis suggests that the deployment of a 1000 megawatt (MW) nuclear-powered synfuel plant could result in a NPV increase of approximately $\$ $1.7 billion for a case with no clean synfuel price premium relative to conventional petroleum fuels when accounting for the additional revenues from the 2022 IRA clean hydrogen PTCs of $\$ $3/kg. Sensitivity analysis was performed to evaluate the effect of perturbation of selected model input parameters on the NPV for the synfuel production Reference Case. The sensitivity analysis indicates that the plant capacity has the largest impact on the differential NPV, with a smaller synfuel production capacity resulting in a decrease in revenue when a larger fraction of the power from the NPP is sold to the grid and a smaller fraction of the power is used to produce synthetic fuel products. The synfuel product pricing has the next largest impact on the differential NPV, with lower synfuel prices resulting in decreased NPV from decreased synfuel sales revenue while higher synfuel prices result in increased NPV from increased synfuel sales revenue. Electricity pricing has a smaller effect on the NPV than the fuel sales price since, in the Reference Case, most of the energy from the NPP is used for synfuel production and a smaller amount of the system revenues are associated with electrical power sales. However, the electricity price sensitivity does indicate that the Synfuel Integrated Energy System (IES) would have a greater NPV than the business-as-usual case (e.g., grid power sales only) when electricity market prices are low, suggesting that synfuel production could provide a strategy for decreasing the economic risks to NPPs posed by a loss of revenues attributed to falling electricity market prices.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Tuning the Quantum Dot (QD)/Mediator Interface for Optimal Efficiency of QD-Sensitized Near-Infrared-to-Visible Photon Upconversion Systems

Lead sulfide (PbS) quantum dots (QDs) have shown promising performance as a sensitizer in infrared-to-visible photon upconversion systems. To investigate the key design rules, we compare three PbS-sensitized upconversion systems using three mediator molecules with the same tetracene triplet acceptor at different distances from the QD. Using transient absorption spectroscopy, we directly measure the triplet energy-transfer rates and efficiencies from the QD to the mediator and from the mediator to the emitter. With increasing distance between the mediator and PbS QD, the efficiency of the first triplet energy transfer from the QD to the mediator decreases because of a decrease in the rate of this triplet energy-transfer step, while the efficiency of the second triplet energy transfer from the mediator to the emitter increases because of a reduction in the QD-induced mediator triplet state decay. The latter effect is a result of the slow rate constant of the second triplet energy-transfer process, which is 3 orders of magnitude slower than the diffusion-limited value. The combined results lead to a net decrease of the steady-state upconversion quantum yield with distance, which could be predicted by our kinetic model. Our result shows that the QD/mediator interface affects both the first and second triplet energy transfer processes in the photon upconversion system, and the QD/mediator distance has an opposite effect on the efficiencies of the first and second triplet energy transfer. Lastly, these findings provide important insight for the further rational improvement of the overall efficiency of QD-based upconversion systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Temperature-induced degradation of GaN HEMT: An in situ heating study

High-power electronics, such as GaN high electron mobility transistors (HEMTs), are expected to perform reliably in high-temperature conditions. This study aims to gain an understanding of the microscopic origin of both material and device vulnerabilities to high temperatures by real-time monitoring of the onset of structural degradation under varying temperature conditions. This is achieved by operating GaN HEMT devices in situ inside a transmission electron microscope (TEM). Electron-transparent specimens are prepared from a bulk device and heated up to 800 °C. High-resolution TEM (HRTEM), scanning TEM (STEM), energy-dispersive x-ray spectroscopy (EDS), and geometric phase analysis (GPA) are performed to evaluate crystal quality, material diffusion, and strain propagation in the sample before and after heating. Gate contact area reduction is visible from 470 °C accompanied by Ni/Au intermixing near the gate/AlGaN interface. Elevated temperatures induce significant out-of-plane lattice expansion at the SiNx/GaN/AlGaN interface, as revealed by geometry-phase GPA strain maps, while in-plane strains remain relatively consistent. Exposure to temperatures exceeding 500 °C leads to almost two orders of magnitude increase in leakage current in bulk devices in this study, which complements the results from our TEM experiment. The findings of this study offer real-time visual insights into identifying the initial location of degradation and highlight the impact of temperature on the bulk device’s structure, electrical properties, and material degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Characterization of high thermal conductivity fuel surrogates before and after ion irradiation

High thermal conductivity nuclear fuels offer important potential advantages over traditional oxide-based fuels such as higher burnup, reduction in fission gas release, and better overall safety of the system. One proposed approach to high thermal conductivity fuels utilizes high thermal conductivity nonfissile additives with UO 2 fuel to lower the fuel operating temperature and thereby take advantage of the highly favorable radiation resistance of UO 2 at lower operating temperatures. However, differential swelling in the matrix and high conductivity additive phases during high dose irradiation could lead to internal cracking and poor performance. In the current study, ceria (CeO 2 ) and zirconia (ZrO 2 ) surrogate matrices were used to model UO2 behavior. Additives of 10 vol. % Al 2 O 3 or SiC in the form of short fibers or platelets were used for the high conductivity second phase. The nuclear fuel surrogates were sintered to achieve densities greater than 93% of the ideal values. Scanning electron microscopy (SEM) imaging and X-ray diffraction confirmed the uniform distribution of the second phase and that no intermetallic second phase was formed during sintering. The thermal conductivity of the sintered samples was measured from 50 °C to 900 °C and confirmed the desirable increase compared to pure CeO 2 /ZrO 2 pellets. Samples were irradiated with 20 MeV Ni 6+ ions at midrange doses ranging from 1 to 15 displacements per atom (dpa) and temperatures from 300 °C to 700 °C. Post irradiation characterization revealed a good stability of the samples at low to medium doses with matrix lattice parameter swelling of < 0.14 % but showed a significant microstructural deterioration and decrease of the mechanical properties at 15 dpa.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Contactless Production Testing of Silicon Solar Cells

Critical cost reductions in silicon solar cells are made by minimizing silver usage in their metal contacts. The reason why is both clear and unavoidable—silver is not only expensive, but also a potential limiting resource for scale-up (the PV industry alone used 10.3 % of the global silver supply in 2020 [1]). The result is solar cells with vanishing electrical contact area that are extremely difficult to test prior to module manufacture, when they must be sorted for quality to maximize power output and reliability of the subsequent modules. This project developed a new measurement instrument that enables the transition to solar cells with very low silver content, including cell designs with both minimal busbars and no busbars at all to electrically contact. The resulting instrument at project completion demonstrated the ability to sort cells with comparable—or better—results than existing technology. Compared to existing cell-test instruments, the new tool has minimal contacting requirements, which lowers the maintenance costs and use of consumable parts. We demonstrated innovative, yet pragmatic, solutions for reporting a comprehensive set of measurement results useful for both cell sorting (going forward in the line for module manufacture) and process control (looking backward in the line to identify cell manufacturing issues from wafer to cell-test). In addition to the traditional current-voltage characterization at cell test—short circuit current, open-circuit voltage, power, efficiency and fill factor—the new tool maintains all the advanced parameter characterization of our existing product line such as a substrate doping measurement, carrier recombination (lifetime) analysis, and surface recombination analysis.

14 SOLAR ENERGY↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2021

A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction

Here, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of digital twins in aviation, the proposed approach allows for constructing a realtime predictive tool for wake-vortex transport and decay systems. We build on the fact that in realistic application, there are uncertainties in initial and boundary conditions, model parameters, as well as measurements. Moreover, conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer from imperfection and solution instabilities, especially for advection-dominated flows with slow decay in the Kolmogorov n-width. In the presented LSTM nudging (LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparse Eulerian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework. We illustrate our concept by solving the two-dimensional vorticity transport equation. We investigate the effects of measurements noise and state estimate uncertainty on the performance of the LSTM-N behavior. We also demonstrate that it can sufficiently handle different levels of temporal and spatial measurement sparsity, and offer a huge potential in developing next-generation digital twin technologies for aerospace applications.

42 ENGINEERING↗