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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Oxygen isotope evidence for input of magmatic fluids and precipitation of Au-Ag-tellurides in an otherwise ordinary adularia-sericite epithermal system in NE China

Abstract Tellurium-rich (Te) adularia-sericite epithermal Au-Ag deposits are an important current and future source of precious and critical metals. However, the source and evolution of ore-forming fluids in these deposits are masked by traditional bulk analysis of quartz oxygen isotope ratios that homogenize fine-scale textures and growth zones. To advance understanding of the source of Te and precious metals, herein, we use petrographic and cathodoluminescence (CL) images of such textures and growth zones to guide high spatial resolution secondary ion mass spectroscopy (SIMS) oxygen isotope analyses (10 µm spot) and spatially correlated fluid inclusion microthermometric measurements on successive quartz bands in contemporary Te-rich and Te-poor adularia-sericite (-quartz) epithermal Au-Ag vein deposits in northeastern China. The results show that large positive oxygen isotope shifts from –7.1 to +7.7‰ in quartz rims are followed by precipitation of Au-Ag telluride minerals in the Te-rich deposit, whereas small oxygen isotope shifts of only 4‰ (–2.2 to +1.6‰) were detected in quartz associated with Au-Ag minerals in the Te-poor deposits. Moreover, fluid-inclusion homogenization temperatures are higher in comb quartz rims (avg. 266.4 to 277.5 °C) followed by Au-Ag telluride minerals than in previous stages (~250 °C) in the Te-rich deposit. The Te-poor deposit has a consistent temperature (~245 °C) in quartz that pre- and postdates Au-Ag minerals. Together, the coupled increase in oxygen isotope ratios and homogenization temperatures followed by precipitation of Au-Ag tellurides strongly supports that inputs of magmatic fluid containing Au, Ag, and Te into barren meteoric water-dominated flow systems are critical to the formation of Te-rich adularia-sericite epithermal Au-Ag deposits. In contrast, Te-poor adularia-sericite epithermal Au-Ag deposits show little or no oxygen isotope or fluid-inclusion evidence for inputs of magmatic fluid.

Geochemistry & Geophysics↗
DOE OSTI2023

Single and dual metal atom catalysts for enhanced singlet oxygen generation and oxygen reduction reaction

We demonstrate rational design of graphene-supported single and dual metal atom catalysts (SACs and DACs) for photocatalytic applications, such as singlet oxygen ( 1 O 2 ) sensitization and H 2 O 2 production. Here we combine density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations with experimental verifications. We found a synergistic effect between triplet sensitization and triplet–triplet (Dexter) energy transfer; both play a role in the photocatalytic activity through the volcano plot of 3d transition metal SACs. More specifically, FeN 4 -SAC exhibits a low ISC energy gap (ΔE ISC ) of 0.039 eV, compared with 0.108 eV for FeNiN8-DACs, both possessing a high Bader charge transfer of 0.366 e- and 0.405 e-, respectively. Guided by these computational results, we synthesized a series of SACs and a DAC and confirmed their structures with scanning transmission electron microscopy (STEM) along with the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We then confirm their band structures with low-energy inverse photoemission spectroscopy (LEIPS) and UV-vis-NIR. Subsequently, we synthesized the catalysts for the photooxygenation of anthracene and two-electron oxygen reduction reaction (ORR) to measure their photocatalytic activity. We found that H 2 O 2 production through the two-electron ORR competes with the 1 O 2 generation through Dexter energy transfer. FeN 4 -SAC demonstrates a high photooxygenation conversion of 86% and a high 1 O 2 quantum yield of 1.04, obtained from electron spin resonance (ESR) spectroscopy, with low H 2 O 2 production. In contrast, NiN 4 -SAC exhibits a low 1 O 2 generation and a high H 2 O 2 production mainly because of the high Gibbs free energy of the OOH* intermediate. In conclusion, this work proposes an effective DFT-guided strategy for designing SACs and DACs for various photocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phase II Field Demonstration at Lansing Smith Generating Plant, Southport, Florida (Final Report)

The Final Technical Report: Field Test Design and Pressure Management Strategies for Phase II Field Demonstration of Optimal Pressure Control, Plume Management, and Produced Water Strategies presents the culmination of multi-year efforts under the U.S. Department of Energy’s Brine Extraction and Storage Test (BEST) program, led by EPRI in partnership with Gulf Power at the Plant Smith site near Panama City, Florida. The project was designed to evaluate and demonstrate the technical feasibility of managing subsurface pressures and fluid movement associated with large-scale CO₂ injection, using low-salinity water as a proxy fluid. Through a combination of field injection testing, reservoir modeling, and optimization studies, the research team developed and refined active and passive brine extraction strategies aimed at controlling injection-induced pressure buildup, mitigating risks of fault activation, and managing plume migration. The field demonstration incorporated a new injection well (TIW-2), a new monitoring/extraction well (TEMW-A), and an existing well (TIW-1) repurposed for passive pressure relief. Complementary geophysical monitoring was designed to track plume development and evaluate the effectiveness of pressure management strategies. The report details the integrated workflow encompassing wellfield development, regulatory permitting, model calibration, and survey design. It includes a comprehensive electromagnetic (EM) modeling and inversion study used to develop a cost-effective, time-lapse geophysical monitoring plan capable of imaging the evolving low-salinity plume within the highly saline Lower Tuscaloosa formation. Reservoir simulation results guided the design of the 17-month injection program and the timing of active extraction to maintain formation pressures below a hypothetical fault reactivation threshold. Supporting analyses evaluated potential injectivity risks related to clay fines migration and geochemical incompatibility, identifying practical mitigation measures such as salinity and pH control. The final design recommends a series of crosswell EM surveys—conducted before, during, and after injection—as the most effective and economical approach for plume imaging, supplemented by continuous downhole pressure and flow monitoring. Collectively, the study provides a field-ready framework for cost-effective pressure management and monitoring in support of future CO₂ storage projects.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Engineering transformation pathways in an Al 0.3 CoFeNi complex concentrated alloy leads to excellent strength–ductility combination

Guided by thermodynamic modeling, engineering phase transformation pathways via thermo-mechanical processing, in a complex concentrated alloy/high entropy alloy (HEA) of composition Al 0.3 CoFeNi, lead to a novel multi-scale microstructure consisting of fine-scale FCC + L1 2 grains mixed with B2 + BCC grains. The two-step pathway comprises initial decomposition of the parent single-phase FCC to form a fine-grained FCC + B2 microstructure, which further decomposes in the second step into the complex four-phase mixture, exhibiting an excellent combination of tensile yield stress of ~1490 MPa, ultimate tensile strength of ~1663 MPa, with a good ductility of ~12% at room temperature. This paper reports a novel multi-phase microstructure in a HEA/CCA similar to the microstructure observed in dual-phase stainless steel. This report exemplifies the novelty of HEA/CCA compositional space.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Engineering the electronic and strained interface for high activity of PdM core @Pt monolayer electrocatalysts for oxygen reduction reaction

Alloyed nanoparticles with core-shell structures provide a favorable model to modulate interfacial interaction and surface structures at the atomic level, which is important for designing electrocatalysts with high activity and durability. Herein, core-shell structured Pd 3 M@Pt/C nanoparticles with binary PdM alloy cores (M = Fe, Ni, and Co) and a monolayer Pt shell were successfully synthesized with diverse interfaces. Among these, Pd 3 Fe@Pt/C exhibited the best oxygen reduction reaction catalytic performance, roughly 5.4 times more than that of the commercial Pt/C catalyst used as reference. The significantly enhanced activity is attributed to the combined effects of strain engineering, interfacial electron transfer, and improved Pt utilization. Density functional theory simulations and extended X-ray absorption fine structure analysis revealed that engineering the alloy core with moderate lattice mismatch and alloy composition (Pd 3 Fe) optimizes the surface oxygen adsorption energy, thereby rendering excellent electrocatalytic activity. Finally, future researches may use this study as a guide on the construction of highly effective core-shell electrocatalysts for various energy conversions and other applications.

25 ENERGY STORAGE↗
DOE OSTI2025

SPRUCE Measurements of Fine Root Production and Chemistry from Root Ingrowth Cores, Marcell Experimental Forest, Minnesota, 2022-2023

This dataset contains fine root production and tissue chemistry measurements from root ingrowth cores. Ingrowth cores were deployed in peat from June 28, 2022 to June 24, 2023 (2022-06-28 to 2023-06-24) inside SPRUCE Experiment plots at the Marcell Experimental Forest in northern Minnesota. The warming and elevated carbon dioxide (CO2) treatments in this dataset include +0 degrees Celsius (C) (+0 and +500 parts per million (ppm) elevated CO2), +4.5 degrees C (+0 and +500 ppm elevated CO2) and +9 degrees C (+0 and +500 ppm elevated CO2) for both hummocks and hollows, as well as +2.25 degrees C (+0 and +500 ppm) and +6.75 degrees C (+0 and +500 ppm) for hollows from minimum 10 cm depth from the peat surface. Measurements include root average diameter, root length, root biomass, and root tissue nitrogen (%N and δ15N) and carbon (%C and δ13C) concentration per plant functional type and microtopographical feature. Root length and biomass are standardized to 10 cm depth. These data were used to assess the warming and elevated CO2 response of fine roots across different peatland microtopographical features (hummocks and hollows) and plant functional types (shrub, spruce and larch). This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2021

SPRUCE S1 Bog Production of Newly-Grown Fine Roots Assessed Using Root Ingrowth Cores in 2013

This dataset contains data from root ingrowth cores from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Root ingrowth cores were used to capture newly-grown fine roots during the 2013 growing season (June - October) at 20 locations at the southern end of the S1-Bog. Rates of fine root production were measured with root ingrowth cores and fine root traits were measured on roots that grew into ingrowth cores. Traits are reported by plant species and root orders and include nitrogen (N) content, root tissue density, specific root length, average diameter. Ingrowth cores were used to compare species-specific depth distribution patterns with minirhizotron analyses and to obtain newly produced roots for chemical analysis and measurement of morphological traits prior to the construction of the SPRUCE experimental plots. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Computational thermodynamic study of SiC chemical vapor deposition from MTS-H 2

This study focuses on the computational thermodynamic analysis of the chemical vapor deposition (CVD) of SiC from the methyltrichlorosilane-hydrogen (MTS-H 2 ) using up-to-date thermodynamic databases. High-resolution computation has been performed with the fine intervals of temperature and pressure at the various H 2 /MTS ratios of interest to systematically investigate the deposition condition range (800 to 1600°C, 0 to 26 664 Pa, and H 2 /MTS ratios of 0.1 to 100) to guide experimental exploration. The influence of deposition parameters on the compositions and phase stabilities of the deposit and gas phase pertinent to vapor processing is elucidated. Low pressure and medium temperatures (1000 to 1400°C) are beneficial to reaching a higher SiC deposition efficiency and provide an optimal window for preparing a high-purity (>99 wt.% SiC) deposit. This optimal processing window expands significantly with an increasing H 2 /MTS ratio (<20). These results are supported by a number of previous theoretical and experimental observations. The mass fraction of SiC in deposit is proposed as an additional perspective to understand the discrepancy between thermodynamic calculation and experimental observation of pure CVD SiC at low H 2 /MTS ratios.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hollow fiber mid-ID spectrometer with UV laser ablation sampling for fine spatial reoslution of isotope ratios in solids

We describe a system that combines isotope ratio analysis via mid-infrared (Mid-IR) laser absorption spectroscopy with fine spatial resolution sampling using a UV pulsed laser. The UV laser ablates a pit in a solid on the order of 10 microns in diameter. The sample-derived particulates resulting from laser ablation pass through a micro-combustor, and the resulting gas is analyzed using Mid-IR laser absorption spectroscopy in a capillary absorption spectrometer (CAS). The CAS uses a hollow fiber optic waveguide with a reflective inner coating and a small internal volume on the order of 1ml. The hollow fiber both guides the laser light from source to detector and contains the gas sample at reduced pressure. Near unity overlap between the laser beam and sample enables sensitive analysis with ultra-small sample size. A prototype system has been demonstrated to enable stable carbon isotopic analysis (d13C) with 1 per mil precision using <1 picomole of carbon and is currently being used to study nutrient exchange in soil/root/microbial rhizosphere studies. The smaller sample size of this system is enabling fine spatial resolution analysis (on the order of 10 microns), which is roughly an order of magnitude smaller than was possible with an isotope ratio mass spectrometer (IRMS). In addition to rhizosphere studies, the system can provide a useful tool for fine scale isotope analysis with applications in biological, forensic, and environmental science.

capillary absorption spectroscopy, laser ablation,↗
DOE OSTI2022

Ligand-coordination effects on the selective hydrogenation of acetylene in single-site Pd-ligand supported catalysts

The selective hydrogenation of acetylene to ethylene is a critical step in the synthesis of polyethylenes. Achieving high conversion to ethylene without over-hydrogenation to ethane is a challenge that requires control of the transition metal site, which we achieve through a ligand-coordinated supported catalyst (LCSC) strategy. Using Pd catalysts coordinated to 1,10-phenanthroline-5,6-dione (PDO) ligands on CeO2 supports, we have discovered that the reaction selectivity depends strongly on the ligand:metal ratio with higher selectivity when more ligand is present in the catalyst. Catalyst structure was examined by extended X-ray absorption fine structure spectroscopy, transmission electron microscopy, and CO adsorption, which indicate single-atom character of the Pd. The ligand:metal ratio is determined by Xray photoelectron spectroscopy measurements and correlated with hydrogenation reactions under steady-state flow conditions to examine trends in hydrogenation activity and selectivity. Those trends can be better understood by density functional theory calculations that indicate hydrogen binding on the ligand to guide reaction selectivity toward the desired ethylene hydrogenation product. Here these results demonstrate the importance of considering the dynamic character of LCSCs and inform the design of future single-site heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Inhibition mechanisms of AcrF9, AcrF8, and AcrF6 against type I-F CRISPR–Cas complex revealed by cryo-EM

Prokaryotes and viruses have fought a long battle against each other. Prokaryotes use CRISPR–Cas-mediated adaptive immunity, while conversely, viruses evolve multiple anti-CRISPR (Acr) proteins to defeat these CRISPR–Cas systems. The type I-F CRISPR–Cas system in Pseudomonas aeruginosa requires the crRNA-guided surveillance complex (Csy complex) to recognize the invading DNA. Although some Acr proteins against the Csy complex have been reported, other relevant Acr proteins still need studies to understand their mechanisms. As such, here, we obtain three structures of previously unresolved Acr proteins (AcrF9, AcrF8, and AcrF6) bound to the Csy complex using electron cryo-microscopy (cryo-EM), with resolution at 2.57 Å, 3.42 Å, and 3.15 Å, respectively. The 2.57-Å structure reveals fine details for each molecular component within the Csy complex as well as the direct and water-mediated interactions between proteins and CRISPR RNA (crRNA). Our structures also show unambiguously how these Acr proteins bind differently to the Csy complex. AcrF9 binds to key DNA-binding sites on the Csy spiral backbone. AcrF6 binds at the junction between Cas7.6f and Cas8f, which is critical for DNA duplex splitting. AcrF8 binds to a distinct position on the Csy spiral backbone and forms interactions with crRNA, which has not been seen in other Acr proteins against the Csy complex. Our structure-guided mutagenesis and biochemistry experiments further support the anti-CRISPR mechanisms of these Acr proteins. Our findings support the convergent consequence of inhibiting degradation of invading DNA by these Acr proteins, albeit with different modes of interactions with the type I-F CRISPR–Cas system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SPRUCE Root Tip and Ectomycorrhizal Fungi Colonization Measurements from Ingrowth Cores, 2017

This data set contains root tip and ectomycorrhizal fungi colonization measurements taken from ingrowth cores from the SPRUCE experiment (Hanson et al. 2017) that were deployed during the 2017 growing season (2017-06 to 2017-10-01). This study explored the relationship between warming treatments and fine-root growth. Increased fine-root growth may increase root exudates and accelerate turnover, representing an underlying mechanism for peat decomposition through priming, as exudates provide a labile carbon source to the microbial community. Roots of two tree species were studied: an evergreen conifer Picea mariana (black spruce) and a deciduous conifer Larix laricina (tamarack). Measurements include root tips counts and densities by tree species and the abundance of ectomycorrhizal colonization on root tips. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

black spruce [Picea mariana]↗
DOE OSTI2022

Effect of oxidation on the release of multiple metals from industrially polluted sediments and synchrotron-based evidence of Cu–S dynamic association

Disposal operations for industrially polluted sediments are usually accompanied by disturbance and resuspension, which can induce metal remobilization and secondary pollution. Evaluating the risk of metal release under various redox conditions is fundamental for predicting contaminant mobilization and guiding remediation measures. An abandoned oxidation pond, Yanjia Lake, China, was selected as a typical industrially polluted site. Re-suspension experiments were carried out by mixing polluted sediments with lake water under oxic or anoxic conditions, then investigating the effect of oxidation conditions on the release of multiple metals. Metal concentrations and aqueous chemistry in the overlying water were monitored. Synchrotron-based methods, including X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS), were used to characterize oxidation states and coordination conditions of metals in sediments. The release of metals, including Cr, Co, Ni, Cu, Zn, Se, Mo, Sn, Cd, and Pb, was enhanced under oxic vs. anoxic conditions. The XANES analysis revealed that elevated Cr and Zn concentrations under oxic conditions likely resulted from the oxidation of Cr(III) and oxidizing dissolution of ZnS, respectively. K-edge Cu XANES, S XANES, and Cu EXAFS analyses reconstructed the Cu–S association, indicating that S-related oxidation promoted Cu release and Cu–O partly replaced Cu–S in the sediment after a 7-day oxic treatment. The release of most metals was promoted under oxic conditions, resulting from the oxidation of sulfides and metals as indicated by aqueous and synchrotron-based evidence. The risk of secondary pollution is greatly enhanced under oxic conditions, which suggests that measures should be taken to minimize the redox disturbance during sediment remediation. This information can guide the management of sediments in Yanjia Lake and other contaminated sites with similar properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. High resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding regional climate variability and change, and are critical for supporting societal responses to changing climate.

Gutowski, William↗
DOE OSTI2022

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗