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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Efficient Hydrogen Delivery for Microbial Electrosynthesis via 3D-Printed Cathodes

The efficient delivery of electrochemically in situ produced H 2 can be a key advantage of microbial electrosynthesis over traditional gas fermentation. However, the technical details of how to supply large amounts of electric current per volume in a biocompatible manner remain unresolved. Here, we explored for the first time the flexibility of complex 3D-printed custom electrodes to fine tune H 2 delivery during microbial electrosynthesis. Using a model system for H 2 -mediated electromethanogenesis comprised of 3D fabricated carbon aerogel cathodes plated with nickel-molybdenum and Methanococcus maripaludis, we showed that novel 3D-printed cathodes facilitated sustained and efficient electromethanogenesis from electricity and CO 2 at an unprecedented volumetric production rate of 2.2 L CH4 /L catholyte /day and at a coulombic efficiency of 99%. Importantly, our experiments revealed that the efficiency of this process strongly depends on the current density. At identical total current supplied, larger surface area cathodes enabled higher methane production and minimized escape of H 2 . Specifically, low current density (<1 mA/cm 2 ) enabled by high surface area cathodes was found to be critical for fast start-up times of the microbial culture, stable steady state performance, and high coulombic efficiencies. Our data demonstrate that 3D-printing of electrodes presents a promising design tool to mitigate effects of bubble formation and local pH gradients within the boundary layer and, thus, resolve key critical limitations for in situ electron delivery in microbial electrosynthesis.

03 NATURAL GAS↗
DOE OSTI2022

Effect of lot microstructure variations on detonation performance of the triaminotrinitrobenzene (TATB)-Based insensitive high explosive PBX 9502

PBX 9502 is an important insensitive high explosive due to its combination of safety properties and detonation performance. It is a polymer-bonded formulation consisting of 95 wt.% 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as the high explosive crystal, bound with Kel F-800 (FK-800), a co-polymer of chlorotrifluoroethylene and vinylidene-fluoride. Two different types are used, one known as virgin that uses only pristine manufactured TATB, and a second known as recycled that has 50 wt.% of its TATB reclaimed from machining scraps of previously pressed virgin PBX 9502. Recycled lots have a higher percentage of fine particles compared to virgin lots, due to the fracturing and damage sustained by TATB crystals during pressing. We examine the influence that this TATB microstructure difference between virgin and recycled PBX 9502 has on detonation performance properties. New rate-stick geometry diameter effect, detonation front shape and cylinder expansion test data are obtained for two previously uncharacterized virgin lots and one recycled lot of PBX 9502. This is combined with previously published data for one virgin and one recycled lot for evaluation purposes. Detonation shock dynamics model calibrations are conducted on each of the five lots to provide an assessment of the detonation timing characteristics of virgin versus recycled PBX 9502 lots. For two of the virgin lots, detonations propagate slower in the rate-stick geometry than those in the two recycled lots. However, the other virgin lot tested has propagation rates comparable to that of the recycled lots for larger diameter rate-sticks. The latter result, though, is shown to be geometry dependent and depends on the range of detonation curvatures accessed in different geometries. New copper-confined, cylinder expansion tests are conducted on each of the three virgin and two recycled lots to obtain detonation product Jones-Wilkins-Lee equations of state, enabling an assessment of the metal push capabilities for each of the five lots. We find that the metal push capabilities, characterized by the evolution of the heat of detonation with volume, are similar between the virgin and recycled lots. Thus, changes in microstructure between different PBX 9502 lots seemingly affect the rate of reaction, but not the overall energy content.

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

Observational constraints on early dark energy

In this paper, we review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam and KiDS+VIKING-450, as well as BOSS/eBOSS galaxy clustering and Lyman-[Formula: see text] forest data. We detail the fit to CMB data, and perform the first analyses of EDE using the CAMSPEC and Hillipop likelihoods for Planck CMB data, rather than Plik, both of which yield a tighter upper bound on the allowed EDE fraction than that found with Plik. We then supplement CMB data with LSS data in a series of new analyses. All these analyses are concordant in their Bayesian preference for [Formula: see text]CDM over EDE, as indicated by marginalized posterior distributions. We perform a series of tests of the impact of priors in these results, and compare with frequentist analyses based on the profile likelihood, finding qualitative agreement with the Bayesian results. All these tests suggest prior volume effects are not a determining factor in analyses of EDE. This work provides both a review of existing constraints and several new analyses.

Astronomy & Astrophysics↗
DOE OSTI2022

The High Latitude Spectroscopic Survey on the Nancy Grace Roman Space Telescope

The Nancy Grace Roman Space Telescope will conduct a High Latitude Spectroscopic Survey (HLSS) over a large volume at high redshift, using the near-IR grism (1.0–1.93 μm, R = 435–865) and the 0.28 deg 2 wide-field camera. We present a reference HLSS that maps 2000 deg 2 and achieves an emission-line flux limit of 10 -16 erg s -1 cm -2 at 6.5σ, requiring ~0.6 yr of observing time. We summarize the flowdown of the Roman science objectives to the science and technical requirements of the HLSS. We construct a mock redshift survey over the full HLSS volume by applying a semianalytic galaxy formation model to a cosmological N-body simulation and use this mock survey to create pixel-level simulations of 4 deg 2 of HLSS grism spectroscopy. We find that the reference HLSS would measure ~10 million H α galaxy redshifts that densely map large-scale structure at z = 1–2 and 2 million [O III] galaxy redshifts that sparsely map structures at z = 2–3. We forecast the performance of this survey for measurements of the cosmic expansion history with baryon acoustic oscillations and the growth of large-scale structure with redshift-space distortions. We also study possible deviations from the reference design and find that a deep HLSS at f line > 7 × 10 -17 erg s -1 cm -2 over 4000 deg 2 (requiring ~1.5 yr of observing time) provides the most compelling stand-alone constraints on dark energy from Roman alone. This provides a useful reference for future optimizations. The reference survey, simulated data sets, and forecasts presented here will inform community decisions on the final scope and design of the Roman HLSS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. Further, to understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reaction Rate Distribution Measurements in ZPPR-15

Small metal foils were irradiated in ZPPR-15 to measure distributions for 239 Pu(n,f), 235 U(n,f), 238 U(n,f), 238 U(n,γ), 115 In(n,n’) and 115 In(n,γ) reaction rates in ZPPR-15. The measured data were reported as basic reaction rate distributions for individual foils, cell-averaged reaction rate distributions and spectral indices or reaction rate ratios. All of the measured data were analyzed and tabulated in such a manner that the data can be readily compared with computed values. The published uncertainties for ZPPR-15 the reaction rate measurements include the statistical uncertainties in the measurements, reproducibility uncertainty and small uncertainties related to corrections for items such as foil compositions and post-irradiation decay between reactor shutdown and foil measurement. There are additional uncertainty components related to detector calibration that are not included in the published uncertainties. A full uncertainty analysis was performed for each foil measurement and for quantities derived from the basic foil measurements. The significant uncertainties were quantified, and a total uncertainty was derived for each measured value. The ZPPR-15 experimental records were used to create detailed as-built Monte Carlo models for the foil reaction rate measurements in ZPPR-15 loadings 123, 134 and 203 and for the fission chamber measurements of 237 Np and 242 Pu fission rates in ZPPR-15 loading 141. Because there were many foils distributed in the ZPPR-15 assembly and because an individual foil occupies a negligible volume in the ZPPR-15 assembly, direct Monte Carlo calculations of the measured foil reaction rates would require an impractical computational effort. The as-built Monte Carlo models and related files were prepared for conversion to deterministic models that can be calculated with realistic computational effort.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Non-destructive simulation of node defects in additively manufactured lattice structures

Additive Manufacturing (AM), commonly referred to as 3D printing, offers the ability to not only fabricate geometrically complex lattice structures but parts in which lattice topologies in-fill volumes bounded by complex surface geometries. However, current AM processes produce defects on the strut and node elements which make up the lattice structure. This creates an inherent difference between the as-designed and as-fabricated geometries, which negatively affects predictions (via numerical simulation) of the lattice’s mechanical performance. Although experimental and numerical analysis of an AM lattice’s bulk structure, unit cell and struts have been performed, there exists almost no research data on the mechanical response of the individual as-manufactured lattice node elements. Here we propose a methodology that, for the first time, allows non-destructive quantification of the mechanical response of node elements within an as-manufactured lattice structure. A custom-developed tool is used to extract and classify each individual node geometry from micro-computed tomography scans of an AM fabricated lattice. Voxel-based finite element meshes are generated for numerical simulation and the mechanical response distribution is compared to that of the idealised computer-aided design model. The method demonstrates compatibility with Uncertainty Quantification methods that provide opportunities for efficient prediction of a population of nodal responses from sampled data. Overall, the non-destructive and automated nature of the node extraction and response evaluation is promising for its application in qualification and certification of additively manufactured lattice structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detrital U-Pb zircon and 40 Ar/ 39 Ar muscovite geochronology from Middle Pennsylvanian strata in the Anadarko Basin, Texas Panhandle, USA

The Late Mississippian-Permian Anadarko Basin formed in Texas and Oklahoma, USA as the result of inversion of Neoproterozic and Cambrian rift structures. Subsidence was driven by flexural loading of the Amarillo-Wichita Uplift, and this uplift may represent the easternmost element of the Ancestral Rocky Mountains system. The northwestern part of this basin has generally been interpreted to have been filled by sediment derived from the Ancestral Front Range Uplift, ~475 km to the northwest during the early stages of basin filling. We test this model using U-Pb detrital zircon and 40 Ar/ 39 Ar detrital muscovite results from three subsurface samples of the Morrow B sandstone in the northwestern part of the Anadarko Basin. We provide a new maximum depositional age of 310.9 ± 4.9 Ma that indicates the age of the Morrow B to be no older than late Atokan to early Desmoinesian Age (Moscovian), ~10 Myr younger than previously interpreted. In contrast to some previous interpretations, we propose that the most likely source for the sediment in the Morrow B is the Amarillo Uplift to the south. Detrital zircon and detrital muscovite data have age peaks at 900–1300 Ma, 1370 Ma and 1600–1800 Ma corresponding to derivation from Grenville, Granite-Rhyolite and Yavapai-Mazatzal basement provinces, respectively. A dominant detrital zircon peak at 1370 Ma suggests that Mesoproterozoic granites in the Amarillo Uplift were exposed by Middle Pennsylvanian time, and detritus eroded from the Amarillo Uplift dominated the lowstand sediment budget of the Texas Panhandle during this time; small volumes of sediment were likely sourced from the Ancestral Front Range. In conclusion, this study presents the first detrital geochronology data from the subsurface Anadarko Basin and the first detrital muscovite data from late Paleozoic southwestern Laurentia. The results presented here highlight the interpretive power of combined detrital zircon and muscovite datasets.

58 GEOSCIENCES↗
DOE OSTI2021

Development of NDE/NDT Tools for High-Volume & High-Speed Inspection of CFRP Structures in Automotive Manufacturing

Main advantages of the air-coupled ultrasound testing (ACUT) and electromagnetic testing (EMT) techniques for NDE of CFRP composites were non-contact sensing, scalability for high-speed inspection, cost-effectiveness, and non-hazardous operation. Despite these advantages, no systems that would satisfy the project requirements were commercially available. Hence, one of the major efforts of the Michigan State University (MSU) team at the initial stage of the project was to close this technological gap by developing, optimizing, and validating array sensors that would provide sufficient sensitivity, spatial coverage, and resolution for robust defect detection. Optimization of the ACUT and EMT sensor designs was performed using experimentally validated finite element models. Initial experiments using array probes were conducted on relatively flat CFRP samples. In parallel, the MSU team designed and assembled a portable platform with two robotic arms. The robots were equipped with newly designed sensors that enabled high-speed NDE of curved CFRP parts. Presently, the developed robotic platform can be used as a demo/template NDE system, which is easily adaptable to manufacturing environments and in-line NDE. The ACUT NDE system developed by the MSU team used a high-power 4-channel pulser receiver for parallel data acquisition. The array probes were designed by stacking commercially available ACUT transducers, which operated in the frequency range between 100 kHz and 500 kHz. MSU optimized the excitation procedure and developed wave focusing cones so as to reduce the crosstalk between the transducers and to provide higher pulse repletion frequency (PRF). The through-transmission (TT) and single-side access (SSA) inspection modes were successfully implemented. In the TT-ACUT, structural defects in CFRP were detected by passing ultrasonic waves through the test part. Hence, the ACUT transmitters and receivers needed to be placed on the opposite sides of the test part. In the SSA-ACUT, guided waves (GW) were excited in the test part using the transmitters and were sensed by the receivers from the same side. Multi-channel TT-ACUT and SSA-ACUT provided high-speed NDE, and were successfully validated on CFRP test samples with interlaminar delaminations and other embedded defects The EM techniques developed by the MSU team included: 1) eddy current testing (ECT), 2) capacitive imaging (CI) and hybrid dual-mode imaging. In ECT, structural damage was detected in CFRP using coils sensor arrays. In ECT, the excitation magnetic field is generated by passing an alternating current through a coil, which is placed above the test sample. The excitation field penetrates the conductive sample and induces the eddy currents in its transect. In turn, the eddy currents generate the reaction field, which affects the total field sensed by a coil. Hence, the presence of structural flaws will alter the eddy current flow and the picked-up signal. ECT is mostly sensitive to local changes of the electric conductivity of the test sample, and CFRPs are mostly conductive in the direction of carbon fibers. Hence, ECT was well suited for the detection of fiber damage/fiber irregularities. The MSU team developed printed circuit boards (PCB) with coil sensor arrays optimized for NDE of CFRP. Unlike most commercial probes designed for ECT of metallic structures, the MSU array probes were designed for operation in [1-10] MHz frequency range, which was optimal for low-conductive CFRP. Multiple sensing topologies (coil groups excitation/sensing arrangements) were implemented and successfully validated. Capacitive Imaging (CI) technique developed by MSU was complementary to ECT. In contrast to ECT, which was sensitive to local changes of the electrical conductivity, the CI was sensitive to local changes of the dielectric constant. Therefore, CI could provide information about matrix damage/matrix irregularities in CFRP. The MSU CI sensor arrays were made of multiple circular or rectangular open-plate capacitors printed on PCB. Sensors of this type are not commercially available. In addition to ECT and CI, the MSU team developed a hybrid (dual-mode) inductive/capacitive measurement technique that synergistically combined the benefits of inductive and capacitive sensing for rapid NDE of fiber reinforced polymer (FRP) composite structures. Fiber damage and fiber irregularities in FRPs were detected by configuring hybrid sensors as coil sensors. Similarly, matrix damage, matrix irregularities and interlaminar delaminations were detected by configuring hybrid sensors as capacitive sensors. ECT and CI were performed sequentially by means of electronic switching. Hence, eliminating the need for mounting two separate sensor arrays on the probe. Portable robotic platform was developed by MSU for multi-technique high-speed NDE of CFRP test parts. The platform had two 6-axis robots, which enabled inspection of curved parts in approximately a 6×6×6 ft 3 active scan area. On the software side, the MSU team integrated scripts for NDE hardware control with scripts for robot motion control. MSU also implemented automated path planning for the robots, reconstruction of part’s surfaces via stereovision, 3D rendering of inspection data, and image processing algorithms for enhanced defect detection. Automotive composite parts manufactured by Plasan Composites from Phase I were used to validate the ACUT and EMT techniques on representative testbeds. Among those parts were three X-braces for a Dodge Viper, one composite calibration plaque with known defects at known locations, and four other test sections, including sections from a front splitter, a corner section from a composite hood, and a high-pressure RTM panel made using non crimp fabric. Other test samples included CFRP and GFRP calibration plates with fiber/matrix defects fabricated at MSU/CVRC.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coincident learning for beam-based rf station fault identification using phase information at the SLAC linac coherent light source

Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2021

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗
DOE OSTI2023

High-Fidelity Modeling and Experiments to Inform Safety Analysis Codes for Heat Pipe Microreactors

Heat pipe microreactors are reactor designs that primarily use liquid-metal heat pipes to cool the core. The main interest in heat pipes is the fact that they can remove heat passively. This, along with the use of liquid metal, allows the reactor to operate at higher temperatures. Although the use of heat pipes in nuclear reactors is new, liquid-metal heat pipe technology is mature. Nevertheless, experimental data on heat pipes are scarce, and very little is known about their behavior during abnormal operations and close to their thermal limits. Therefore, new experiments and accurate heat pipe simulations are needed to develop reliable closure models. This work describes a joint experimental and numerical investigation into heat pipes that attempts an initial closure of this gap. Further, the numerical and experimental efforts are currently proceeding in parallel, aimed at different aspects of heat pipes. The numerical part is focused on gaps in local closures, and the experiments capture the overall heat pipe behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Synthesis and characterization of Pt(Cu 0.67 Sn 0.33 )

Pt(Cu 0.67 Sn 0.33 ) has recently been found in a natural sample. In order to be able to characterize this new ternary compound, we synthesized it from the elements. Samples were characterized by X-ray powder diffraction, differential scanning calorimetry, thermal relaxation calorimetry, and scanning electron microscopy studies. Density functional theory-based model calculations complemented the experimental studies. Pt(Cu 0.67 Sn 0.33 )was already formed at a relatively low temperature of 773 K. Rietveld refinement of Pt(Cu 0.67 Sn 0.33 ) has been carried out in CuAu-type or L1 0 -type structure, space group P4/mmm, with Pt on 0,0,0 and disordered Cu and Sn on 1/2, 1/2, 1/2 and Z = 1. The lattice parameters are a = 2.823(1) Å, c = 3.64(1) Å, and V = 29.00(4) Å which are in good agreement with values obtained earlier on the natural sample and with the results of DFT calculations. The vibrational entropy for Pt(Cu 0.67 Sn 0.33 ) is $S_{298.15}^{vib}$ = 79.9(7) J mol -1 K -1 . The pressure dependence up to 36(2) GPa of the unit-cell volume and the lattice parameters and unit-cell volume have been obtained by synchrotron based powder diffraction using a diamond anvil cell. A fit of a 3rd-order Birch–Murnaghan equation of state to the Pt(Cu 0.67 Sn 0.33 )) (p,V)-data results in a bulk modulus of B 0 = 215(27) GPa and B' = 5(2).

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗