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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗
NASA NTRS1992

Variations in lithospheric thickness on Venus

Recent analyses of Magellan data have indicated many regions exhibiting topograhic flexure. On Venus, flexure is associated predominantly with coronae and the chasmata with Aphrodite Terra. Modeling of these flexural signatures allows the elastic and mechanical thickness of the lithosphere to be estimated. In areas where the lithosphere is flexed beyond its elastic limit the saturation moment provides information on the strength of the lithosphere. Modeling of 12 flexural features on Venus has indicated lithospheric thicknesses comparable with terrestrial values. This has important implications for the venusian heat budget. Flexure of a thin elastic plate due simultaneously to a line load on a continuous plate and a bending moment applied to the end of a broken plate is considered. The mean radius and regional topographic gradient are also included in the model. Features with a large radius of curvature were selected so that a two-dimensional approximation could be used. Comparisons with an axisymmetric model were made for some features to check the validity of the two-dimensional assumption. The best-fit elastic thickness was found for each profile crossing a given flexural feature. In addition, the surface stress and bending moment at the first zero crossing of each profile were also calculated. Flexural amplitudes and elastic thicknesses obtained for 12 features vary significantly. Three examples of the model fitting procedures are discussed.

Johnson, C. L.↗
NASA NTRS2003

Adaptation of Mesoscale Weather Models to Local Forecasting

Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

Manobianco, John T.↗
DOE OSTI2021

Thermodynamically Stable, Plasmonic Transition Metal Oxide Nanoparticle Solar Selective Absorbers towards 95% Optical-to-Thermal Conversion Efficiency at 750 °C

Generation 3 (Gen) concentrating solar power (CSP) systems requires a high operation temperature ≥750°C to increase the power-cycle efficiency towards ≥50%. However, such a high operation temperature poses a notable challenge to high temperature materials. On the receiver side, a critical challenge is the lack of solar selective absorbers that demonstrate both high optical-to-thermal energy conversion efficiency η therm approaching 95% and long-term thermal stability at >750°C in air. Existing solar absorbers either have limited η therm ≤89% or deteriorate significantly within 500 h at 750°C; some of these also require costly vacuum deposition for stringent thickness control. Therefore, it is highly desirable to simultaneously achieve η therm ~95% AND high thermal stability at 750°C for future generations of CSP receivers. This project has investigated and developed low-cost, highly scalable spray-coated transition metal oxide nanoparticle (NP) pigmented solar selective coatings on various types of Inconel alloy tube sections that are thermodynamically stable at 750-800°C in air, maintaining η therm >94.3%(93.2%) under a solar concentration ratio of C=1000 after 60 simulated day-night cycles at 750ºC (800ºC) (1 cycle=12 h at 750 or 800°C and 12h ramping down to 25°C). We have also coated up to 48 inches long receiver tubes for preliminary solar testing under Norwich Technology’s parabolic trough systems, demonstrating notably improved performance in solar heating compared to benchmark Pyromark 2500 coatings. Two key innovations have been developed in this project: (1) We achieved an unprecedented high thermal efficiency >94% by optimizing the d-band optical absorption spectra of transition metal ions, engineering their valences and stoichiometry; (2) We were able to maintain or even slightly increase the efficiency when operating at 750°C in air by engineering the interdiffusion of transition metal ions between the coating and the Inconel substrate to our advantage. Featuring high-temperature solar spectral selectivity and thermodynamic stability in air, as well as low-cost, highly scalable solution-chemical synthesis and spray coating techniques, this innovation in solar selective absorber technology alone accounts for nearly 40% of the targeted reduction in the levelized cost of electricity (LCOE) from the receiver, thermal energy storage, and operation & maintenance combined by 2030, as proposed by the U.S. Department of Energy. For a 110 MWe CSP power plant, this LCOE reduction from the solar selective coating alone transfers to ~$\$ $1.9 M increase in annual profit based on a sales price of 10¢/kWh (or an annual sale of $50 M/year). The high solar absorptance (~98%) NP pigment materials developed in this project are equally applicable to volumetric receivers either as a coating or as bigger ceramic microspheres after sintering, potentially benefiting Gen3 falling particle CSP technologies. All these contributions facilitate the larger scale deployment of CSP systems with energy storage capability to address the intermittency issue of solar energy towards dispatchable solar electricity, bridging the temporal gap between peak solar electricity production and peak electricity consumption. The collaboration with Norwich Technologies and Brayton Energy in this project will also facilitate the future commercialization of this new solar selective coating technology developed in this SIPS project.

14 SOLAR ENERGY↗
DOE OSTI2021

Multimode two-dimensional vibronic spectroscopy. I. Orientational response and polarization-selectivity

Two-dimensional Electronic–Vibrational (2D EV) spectroscopy and two-dimensional Vibrational–Electronic (2D VE) spectroscopy are among the newest additions to the coherent multidimensional spectroscopy toolbox, and they are directly sensitive to vibronic couplings. In this first of two papers, the complete orientational response functions are developed for a model system consisting of two coupled anharmonic oscillators and two electronic states in order to simulate polarization-selective 2D EV and 2D VE spectra with arbitrary combinations of linearly polarized electric fields. Here, we propose analytical methods to isolate desired signals within complicated spectra and to extract the relative orientation between vibrational and vibronic dipole moments of the model system using combinations of polarization-selective 2D EV and 2D VE spectral features. Time-dependent peak amplitudes of coherence peaks are also discussed as means for isolating desired signals within the time-domain. This paper serves as a field guide for using polarization-selective 2D EV and 2D VE spectroscopies to map coupled vibronic coordinates on the molecular frame.

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

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Optimization of stochastic feature properties in laser powder bed fusion

Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. As such, in this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm 2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.

316 L stainless steel↗
NASA NTRS1990

Flight control system design factors for applying automated testing techniques

The principal design features and operational experiences of the X-29 forward-swept-wing aircraft and F-18 high alpha research vehicle (HARV) automated test systems are discussed. It is noted that operational experiences in developing and using these automated testing techniques have highlighted the need for incorporating target system features to improve testability. Improved target system testability can be accomplished with the addition of nonreal-time and real-time features. Online access to target system implementation details, unobtrusive real-time access to internal user-selectable variables, and proper software instrumentation are all desirable features of the target system. Also, test system and target system design issues must be addressed during the early stages of the target system development. Processing speeds of up to 20 million instructions/s and the development of high-bandwidth reflective memory systems have improved the ability to integrate the target system and test system for the application of automated testing techniques. It is concluded that new methods of designing testability into the target systems are required.

Sitz, Joel R.↗
DOE OSTI2026

pH‐Mediated Strong Metal‐Support Interaction Construction Through Dynamic Fermi Level Tuning

The metal–support interface is central to governing catalytic transformations. While strong metal–support interaction (SMSI) is an established strategy to tailor the morphology and electronic properties of supported metal catalysts, the role of interfacial charge redistribution in SMSI formation remains poorly understood and rarely leveraged. Here, in this study, we report a dual-stimuli approach that combines pH modulation with ultrasonication to mediate SMSI construction in aqueous solution through dynamic Fermi level tuning. By leveraging in situ pH-driven charge redistribution at the metal–support interface, we achieve controllable SMSI encapsulation of metal nanoparticles, as verified by electrochemical analysis, work function measurements, and x-ray-based techniques. The resulting catalysts exhibit tunable SMSI features and deliver enhanced activity and selectivity in hydrogenation reactions. This work establishes a facile strategy to modulate catalyst structure and electronic properties by exploiting Fermi level variation as a driving force, thereby advancing rational SMSI design and catalytic performance across diverse environments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Distinct protein architectures mediate species-specific beta-glucan binding and metabolism in the human gut microbiota

Complex glycans that evade our digestive system are major nutrients that feed the human gut microbiota (HGM). The prevalence of Bacteroidetes in the HGM of populations worldwide is engendered by the evolution of polysaccharide utilization loci (PULs), which encode concerted protein systems to utilize the myriad complex glycans in our diets. Despite their crucial roles in glycan recognition and transport, cell-surface glycan-binding proteins (SGBPs) remained understudied cogs in the PUL machinery. Here, we report the structural and biochemical characterization of a suite of SGBP-A and SGBP-B structures from three syntenic β(1,3)-glucan utilization loci (1,3GULs) from Bacteroides thetaiotaomicron (Bt), Bacteroides uniformis (Bu), and B. fluxus (Bf), which have varying specificities for distinct β-glucans. Ligand complexes provide definitive insight into β(1,3)-glucan selectivity in the HGM, including structural features enabling dual β(1,3)-glucan/mixed-linkage β(1,3)/β(1,4)-glucan-binding capability in some orthologs. The tertiary structural conservation of SusD-like SGBPs-A is juxtaposed with the diverse architectures and binding modes of the SGBPs-B. Specifically, the structures of the trimodular BtSGBP-B and BuSGBP-B revealed a tandem repeat of carbohydrate-binding module-like domains connected by long linkers. In contrast, BfSGBP-B comprises a bimodular architecture with a distinct β-barrel domain at the C terminus that bears a shallow binding canyon. The molecular insights obtained here contribute to our fundamental understanding of HGM function, which in turn may inform tailored microbial intervention therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Stability of Metal–Organic Framework-Supported Amines under Exposure to Ozone Generated from Air

Amine compounds supported on porous materials such as metal–organic frameworks (MOFs) have shown promising performance for direct air capture (DAC) due to their enhanced affinity for CO 2 . Although features such as adsorption capacity and selectivity are paramount in these composites, their long-term stability has a major impact on the operating cost of DAC systems. In this work, changes in carbon capture performance, crystallinity, porosity and chemical environment of the constituting atoms of MOF-amine composites are explored after exposure to ozone and NOx impurities generated from corona discharge applied to air. From the obtained results, the stabilities of Mg 2 (dobpdc) (dobpdc 4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) grafted with ethylenediamine (en), N-methylethylenediamine (men), and N,N-dimethylethylenediamine (dmen), as well as MIL-101(Cr) MOF impregnated with polyethylenimine (PEI), are compared. A negative effect in the overall CO 2 adsorption capacity is observed for all MOF composites after exposure, as well as a decrease in the adsorption step pressure of CO 2 for Mg 2 (dobpdc) amine-grafted composites, as shown via dynamic gravimetric adsorption experiments. Spectroscopic analyses indicate that oxidation of amine groups through the formation of nitro functional groups occurs as well as a decrease in the electron-donation interaction between the supported amines and the metal nodes of the MOFs.

adsorption↗
DOE OSTI2020

Supported Molybdenum Carbide Nanoparticles as Hot Hydrogen Reservoirs for Catalytic Applications

Transition metal carbides have been long proposed as replacements for expensive Pt-group transition metals as heterogeneous catalysts for hydrogenation reactions, featuring similar to superior activities and selectivities. Combining experimental observations and theoretical calculations, we show that the hydrogenating capabilities of molybdenum carbide can be further improved by nanostructuring, as seen on MoC y nanoclusters anchored on an inert Au(111) support, revealing a more prominent role of Mo active sites in the easier H 2 adsorption, dissociation, H adatom diffusion, and elongated chemisorbed H 2 Kubas moieties formation when compared to the bulk δ-MoC(001) surface, thus explaining the observed stronger H 2 interaction and the larger formation of CH x species, making these systems ideal to catalyse hydrogenation reactions

36 MATERIALS SCIENCE↗
DOE OSTI2022

Photometric redshift-aided classification using ensemble learning

We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel step in our pipeline is that prior to performing the classification, SHEEP first estimates photometric redshifts, which are then placed into the data set as an additional feature for classification model training; this results in significant improvements in the subsequent classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class and (ii) one versus all with correction by a meta-learner. We demonstrate the performance of SHEEP for the classification of stars, galaxies, and quasars using a data set composed of SDSS and WISE photometry of 3.5 million astronomical sources. The resulting F1 -scores are as follows: 0.992 for galaxies; 0.967 for quasars; and 0.985 for stars. In terms of the F1-scores for the three classes, SHEEP is found to outperform a recent RandomForest-based classification approach using an essentially identical data set. Our methodology also facilitates model and data set explainability via feature importances; it also allows the selection of sources whose uncertain classifications may make them interesting sources for follow-up observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Doping-driven resistive collapse of the Mott insulator in a minimal model for VO 2

Here we study the resistive collapse of the Mott insulator state in the dimer Hubbard model. This minimal model has been used to describe the physics of VO 2 , and should be relevant to other strongly correlated materials. It incorporates the physics of correlated dimers and the explicit competition between on-site Coulomb repulsion and magnetic exchange interactions. Our results unveil that between the Mott insulator at half filling and the Fermi liquid metal at high doping there is an intermediate bad metallic phase with exotic features such as a pseudogap, orbital selectivity, and a first-order metal-metal transition. The model is solved within dynamical mean field theory by means of quantum Monte Carlo, which provides the numerically exact solution of the model in the limit of large lattice dimensionality. This model can be considered as a minimal one that captures exotic phenomena associated to the physics of a doped Mott insulator, shading light on their basic physical mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Adapted laboratory evolution of Thermotoga sp. strain RQ7 under carbon starvation

Abstract Objective Adaptive laboratory evolution (ALE) is an effective approach to study the evolution behavior of bacterial cultures and to select for strains with desired metabolic features. In this study, we explored the possibility of evolving Thermotoga sp. strain RQ7 for cellulose-degrading abilities. Results Wild type RQ7 strain was subject to a series of transfers over six and half years with cellulose filter paper as the main and eventually the sole carbon source. Each transfer was accompanied with the addition of 50 μg of Caldicellulosiruptor saccharolyticus DSM 8903 genomic DNA. A total of 331 transfers were completed. No cellulose degradation was observed with the RQ7 cultures. Thirty three (33) isolates from six time points were sampled and sequenced. Nineteen (19) of the 33 isolates were unique, and the rest were duplicated clones. None of the isolates acquired C. saccharolyticus DNA, but all accumulated small-scale mutations throughout their genomes. Sequence analyses revealed 35 mutations that were preserved throughout the generations and another 15 mutations emerged near the end of the study. Many of the affected genes participate in phosphate metabolism, substrate transport, stress response, sensory transduction, and gene regulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Report on local data recovery approaches suitable for weather and climate prediction (Deliverable 1.3) (V.1.0)

Numerical weather and climate prediction rates as one of the scientific applications whose accuracy improvements greatly depend on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increasing average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys approaches for fault-tolerance in numerical algorithms and system resilience in parallel simulations from the perspective of numerical weather and climate prediction systems. A selection of existing strategies is analyzed, featuring interpolation-restart and compressed checkpointing for the numerics, in-memory checkpointing, user-level failure mitigation-based and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analyzed and some recommendations outlined for future developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electrolyzer Integrated Modular Nano-Array Monolithic Catalytic Reactors

The project objective is to design and develop a new class of electrolyzer-integrated, modular, nanostructure array (nano-array) monolithic catalytic reactors for high-flux, robust and selective methanol synthesis. The modular reactor features metal oxide nano-array grown on an additively-manufactured channeled honeycomb monolith made of ceramic or metals; this low-pressure carbon dioxide hydrogenation reactor is fueled with hydrogen from a low-temperature water electrolyzer having reduced platinum group metal (PGM) content. Through these nanostructured monolithic reactors, methanol synthesis is enabled with cost-effective hydrogenation reactions under low-temperature (<200°C) and low-pressure (<10 atm) conditions, thus significantly reducing the energy demand of the entire process.

08 HYDROGEN↗