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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Synergistic Computational and Microstructural Design of Next- Generation High-Temperature Austenitic Stainless Steels

The purpose of this project was to: 1) study deformation twinning, its evolution, thermal stability, and the contribution on mechanical response of the new advanced stainless steels, especially at elevated temperatures; 2) study alumina-scale formation on the surface, as an alternative for conventional chromium oxide, that shows better oxidation resistance, through alloy design; and 3) design new generation of high temperature stainless steels that form alumina scale and have thermally stable nano-twins. The work involved few baseline alloys for investigating the twin formation under tensile loading, thermal stability of these twins, and the role of deformation twins on the mechanical response of the alloys. These baseline alloys included Hadfield Steel (Fe-13Mn-1C), 316, 316L and 316N stainless steels. Another baseline alloy was studied for alumina-scale formation investigations. Hadfield steel showed twinning but undesired second phases formed at higher temperatures. 316N stainless steel did not show signs of deformation twinning. Conventional 316 stainless steel demonstrated extensive deformation twinning at room temperature. Investigations on this alloy, both in single crystalline and polycrystalline forms, showed that deformation twins evolve in a hierarchical manner, consisting of micron–sized bundles of nano-twins. The width of nano-twins stays almost constant as the extent of strain increases, but the width and number of the bundles increase with increasing strain. A systematic thermomechanical cycling study showed that the twins were stable at temperatures as high as 900°C, after the dislocations are annealed out. Using such cycles, volume fraction of the thermally stable deformation twins were increased up to 40% in 316 stainless steel. Using computational thermodynamics and kinetics calculations, we designed two generations of advanced austenitic stainless steels. In the first generation, Alloy 1, which had been proposed as an alumina-forming austenitic stainless steel, is fully austenitic, but possesses carbides that were not dissolvable and could not be controlled. This alloy also did not show deformation twinning. Alloy 2 was designed based on alloy 1, but was not fully austenitic and had significant traces of uncontrollable precipitates as well. Alloy 3, also designed based on alloy 1, was mainly austenitic with evolution of a second phase along the grain boundaries, but also had precipitates that were not controllable. Based on the knowledge gained from the first generation of the designed steels, two more steels, called PGAA1 and PGAA2, were proposed using genetic algorithms that, based on the modelling, were supposed to exhibit alumina-scale formation. PGAA1, however, did not demonstrate a fully austenitic structure. PGAA2 could achieve a mostly austenitic structure through thermo-mechanical processing, and was then used for oxidation tests. The oxidation tests of PGAA2, with and without nitrogen impurities, along with alloy 1, suggested that PGAA2 can form alumina-scale similar to alloy 1, but N impurity will prevent formation of such a scale, probably through formation of aluminum nitrides. For the above mentioned genetic algorithm framework of alloy design, separate models were developed for specific design criteria. For prediction of alumina formation in stainless steels, a model was constructed based off of two criteria – effective valence and third element effect. These criteria capture the thermodynamics and kinetics of alumina formation in steels. To test the efficacy and robustness of this model, they were tested against alloys in the literature which had been experimentally verified to exhibit alumina formation and the predictions were in excellent agreement with the experiments. Another meta-model for prediction of twinning in unknown steel compositions was developed by an informatics based machine learning/data mining approach. Stacking Fault Energy data was captured from the literature for a large number of steel compositions and then this data was used to build a classifier to predict deformation mechanisms. Here a training set-test set based analysis was performed to test performance. The above genetic algorithm based optimization framework for alloy design was exhibited to be a successful methodology for accelerated materials discovery in the context of alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as well as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

From genomes to metabolomes: Understanding mechanisms of symbiosis and cell-cell signaling using the archaeal system Ignicoccus-Nanoarchaeum

The main objective of this project was to use symbiotic Nanoarchaeaota, a group of thermophilic Archaea that are obligate symbionts/parasites on other Archaea, to develop an integrated multi-omic approach to study inter-species interactions as well as to understand fundamental mechanism that enable such relationships. As part of this grant we have achieved a number of important milestone on both technical and scientific levels. On the technical side, we developed immunofluorescence labeling and tracking methods to follow Nanoarchaeota in cultures and in environmental samples, we applied such methods in conjunction with flow cytometry to quantify and isolate uncultured representatives from the environment and characterized them by single cell genomics. On the proteomics side, we developed a more efficient and sensitive method to recover and semi-quantitatively measure membrane proteins, while achieving high total cellular proteome coverage (70-80% of the predicted proteome). Metabolomic analyses used complementary NMR and LC/GC mass spectrometry and led to the identification of novel lipids in these organisms as well as quantification of some of the major metabolites. Importantly, using several informatics approaches we were also able to integrate the transcriptomic, proteomic and metabolomic datasets, revealing aspects of the interspecies interaction that were not evident in the single omic analyses (manuscript in review). On the science side we determined that N. equitans and I. hospitalis are metabolically coupled and that N. equitans is strictly dependent on its host both for metabolic precursors and energetic needs. The actual mechanism by which small molecules move across the cell membrane remains unknown. The Ignicoccus host responds to the metabolic and energetic burned by upregulating of key primary metabolism steps and ATP synthesis. The two species have co-evolved, aspect that we determined by comparative genomics with other species of Ignicoccus (manuscript in preparation) and by characterizing other similar Nanoarchaeota systems. Using a single cell genomics approach we characterized the first terrestrial geothermal Nanoarchaeota system, from Yellowstone National Park. That nanoarchaeon uses a different host, a species of Sulfolobales, and comparative genomics with N. equitans-Ignicoccus allowed us to come up with an evolutionary model for the evolution of this group of organisms across marine and terrestrial ecosystems. Based on metabolic inferences we were also able to isolate in culture the first such terrestrial nanoarchaeal system, also from Yellowstone, which involves a species of Acidilobus. The novel nanoarchaeal system was characterized using proteomics and it helped us better understand the metabolic capabilities of these organisms as well as how co-evolution shapes the genomes of interacting species. It was also one of the very few cases in which prior genomic data was used to successfully design an approach to culture an organism, which remains the gold standard in microbiology research. As a better understanding of interspecies interaction requires multiple model systems, we have pursued identification and genomic characterization or isolation of additional nanoarchaeal systems from geographically and geochemically distinct environments. Two additional nanoarchaeal systems are presently being characterized from hot springs in Yellowstone and Iceland and will be the subject to future publications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Accelerating High-Level Waste Glass Corrosion Research with Big Data

Savannah River National Laboratory (SRNL) is accelerating the research of high-level nuclear waste (HLW) glass corrosion using advanced big data techniques available through the Accelerated Leaching and Testing of GLASS (ALTGLASS) database. Developed, maintained and distributed by SRNL, ALTGLASS gives HLW researchers access to chemical durability data for hundreds of glass compositions, allowing scientists to use an “informatics approach” to analyze existing and new experimental data contained in the database to advance glass corrosion science.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Compendium of HFIR and REDC Infrasound and Low-Frequency Acoustic Emissions

This document is a compilation of infrasound and low-frequency acoustic signatures that characterize select nuclear reactor operations and hot-cell operations observed at Oak Ridge National Laboratory (ORNL) in fiscal year 2018. Also included here are the results of digital signal analyses describing these signatures. This effort provides insight about the feasibility of using acoustic signatures for local and remote-sensing and identification of nuclear fuel-cycle operations. The signatures included here, in this first year of on-site observations at ORNL, will become part of the broader basis set of signatures collected by researchers involved in the Multi-Informatics for Nuclear Operations Scenarios (MINOS) venture. The effort by MINOS researchers is to gain a deeper knowledge base of nuclear facility operations which may be used to improve nuclear facility monitoring capabilities. This compendium comprises signatures developed from sound signals that were measured at ORNL's High Flux Isotope Reactor and the Radiochemical Engineering Development Center during the week of June 18, 2018. Most facility operations were performed specifically for this campaign, although some measurements were obtained in the course of normal operations. The knowledge gained from developing the signatures included herein will provide a better understanding that will be useful for upcoming measurement campaigns; as additional data becomes available it will be included in newer versions of this compendium.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Compendium of HFIR and REDC Infrasound and Low-Frequency Acoustic Emissions

This document is a compilation of infrasound and low-frequency acoustic signatures that characterize select nuclear reactor operations and hot-cell operations observed at Oak Ridge National Laboratory (ORNL) in fiscal year 2018. Also included here are the results of digital signal analyses describing these signatures. This effort provides insight about the feasibility of using acoustic signatures for local and remote-sensing and identification of nuclear fuel-cycle operations. The signatures included here, in this first year of on-site observations at ORNL, will become part of the broader basis set of signatures collected by researchers involved in the Multi-Informatics for Nuclear Operations Scenarios (MINOS) venture. The effort by MINOS researchers is to gain a deeper knowledge base of nuclear facility operations which may be used to improve nuclear facility monitoring capabilities. This compendium comprises signatures developed from sound signals that were measured at ORNL's High Flux Isotope Reactor and the Radiochemical Engineering Development Center during the week of June 18, 2018. Most facility operations were performed specifically for this campaign, although some measurements were obtained in the course of normal operations. The knowledge gained from developing the signatures included herein will provide a better understanding that will be useful for upcoming measurement campaigns; as additional data becomes available it will be included in newer versions of this compendium.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Pathways to Carbon Liberation: A Systems Approach to Understanding Carbon Transformations and Losses from Thawing Permafrost

Our objective in this project was to discover how microbial communities mediate the fate of carbon in thawing permafrost under climate change. We proposed a systems approach integrating (a) molecular microbial and viral ecology, (b) molecular organic chemistry and stable and radiocarbon isotopes, and (c) state-of-the-art modeling, along a chronosequence of permafrost thaw in subarctic Sweden. The fate of carbon (C) in thawing permafrost is an outstanding challenge of modern biogeochemistry and climate change. Permafrost C pools are large (~1700 PgC), and C dynamics of thawing permafrost complex: old C decomposes as it is liberated from thawing permafrost as CO 2 or CH 4 , with a significant fraction cycling through lake sediments, even as new C accumulates due to thaw-initiated ecological succession. Our work is allowing better prediction of the net effect of these processes. Microbes mediate C dynamics in thawing permafrost, but a mechanistic understanding of how to scale microbial population dynamics, genomic potential, and expression to ecosystem-scale processes has been missing. A key question was: What is the interplay of microbial communities and organic matter chemical structure in the decomposition/preservation of organic C across a thaw gradient? And intriguingly, what (if any) is the role of phage (viruses that infect prokaryotic cells) in mediating these processes? Viruses appear to play a large role in driving oceanic function, but these phenomena are virtually unstudied in terrestrial systems. This endeavor linking microbial and viral dynamics, organic geochemistry and trace gas production will improve models of C cycling in permafrost systems, and clarify the fate of C under future climates. Technical Approach – We conducted a study at Stordalen Mire, Sweden, along a permafrost thaw chronosequence encompassing permafrost palsas and their initial collapse stage, thawing bog sites (dominated by Sphagnum moss), fully-thawed and inundated fen sites (dominated by Eriophorum spp or Carex spp.), and lakes. Our project used cutting edge technologies in both biogeochemistry and molecular microbial ecology to advance systems biology research on microbial carbon cycling through: (a) systems-level mapping of chemical states and ages of organic matter (via FT-ICR-MS and 14 C analysis) along thaw gradients to associated microbial communities, biochemical potentials, and activities (via meta-genomics, -transcriptomics, –proteomics, and viral genomics), and to CO 2 and CH 4 fluxes; (b) experimental incubations using Quantum Dot Probing, to test key hypotheses, arising from (a), about particular microbially-driven biochemical degradation pathways; (c) bioinformatics designed to simultaneously enhance the DOE Knowledgebase, and (d) integrated ecosystem C-cycle modeling testable by soil organic chemical and microbial data. Results from this project spanned 4 categories: (i) integrative across disciplines, and disciplinarily-focused in (ii) microbiology, (iii) biogeochemistry, and (iv) modeling. In integrated understanding of the system, we answered the questions posed above, mapping resident microbes to carbon transformations to reveal a carbon-processing shift with permafrost thaw, and discovering abundant, diverse and habitat-specific viruses that appear to be impacting carbon cycling, while also relating microbial communities to modeled processes. In microbiology-specific papers, we dug into viromes, microbial community networks, and informatic methods, while also introducing a new permafrost-associated phylum and its ecogenomic context. Relevance to DOE FOA - With a focus on discovering how microbial communities mediate the fate of old and new carbon in permafrost systems with climate change, our work directly responds to the call for “–omics driven basic research on the contribution of... microbial communities to C cycling processes in terrestrial ecosystems.” We bridge all three of the focal areas of the call, including (i) “Systems biology studies”, and development of –omics approaches (ii) “to investigate microbial community functional processes”, and (iii) “for imaging and analysis of microbially-mediated carbon cycling.”

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Advancing Science Through Computation at NDSU (Final Report)

The Center for Computationally Assisted Science and Technology (CCAST), a research unit within the Office for the Vice President for Research and Creative Activity, is in its build-out phase at North Dakota State University (NDSU). CCAST is developing high-performance computing (HPC) capabilities, designed to support existing HPC-oriented NDSU programs as well as to support its own research program which will initially concentrate, but not be limited to, application of theory and computation toward development of high-value functional materials and systems for renewable energy and energy efficiency. The following initial priorities for the Center were established: (1) reconstruct the NDSU HPC hardware and software infrastructure, (2) define CCAST research focus, (3) hire research and HPC support staff, (4) fit-out CCAST computer rooms to ensure continuation and growth of the Center’s HPC activities and, (5) support the NDSU existing thrust in chemo- and bio-informatics (as applied to the high-throughput combinatorial chemistry, biology, and genomics). These priorities are subject to change as the Center grows and evolves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Jiangsu Nanjing – Lawrence Berkeley Life Sciences Center for Translational Medicine (CRADA AWD00000392)

Project 1: Our previous work demonstrated that the CES biomarker can be an approximate measure for the levels of chromosomal instability (CIN) in cancer, and that the CES (for Centromere and kinetochore gene Expression Score) biomarker effectively predicts which cancer patients benefit from adjuvant chemotherapies in lung and other cancer types. Such a utility can potentially reduce overtreatment that is common in lung cancer treatment. We determined performance of the multiplex Quantigene RNA assay platform for measuring mRNA levels of CES genes using clinical tumor samples. These experiments determined sensitivity, specificity and detection limit of 12 CES genes and 5 control genes using clinical tumor samples with paired normal tissue samples. We determined how sample concentration and storage conditions affect measurement sensitivity and specificity, and whether sample storage over time affects result reproducibility. Importantly, we confirmed that early stage lung cancer CES values are significantly higher than those of paired normal tissues in all cases. This result greatly extended our bioinformatic data mining by confirming that we can measure CES values directly from patient tumor tissue, and that tumors have higher CES values than normal tissues. Unfortunately, our CRADA partner in China did not finish measuring CES expression levels for a larger group of ~100 tumor samples, due to the long period time required for human subject review in China. This delay prevented us from generating enough additional data to generate a significant statistical model. We finished analyzing all the data acquired during the CRADA, and are trying to work collaboratively with the Shanghai University Lung Cancer Hospital to complete the analysis of more tumor samples. Although no publications have resulted from this research, the findings are crucial for ongoing efforts to obtain funding to develop the technology to address problems in deciding on appropriate treatment regimens for individual patients, in clinical settings. Project 2: The overarching milestones of CRADA Project 2 were to investigate the role of FAM83 family members in cancer progression. Two publications resulted from this work which are highlighted below. The first major goal of the CRADA was to broadly assess the role of FAM83 family oncogenes in human cancer using a multi-omic bioinformatic approach (Snijders et al 2017 Mol. Oncol 11). The second major goal was to specifically assess the role of FAM83D in a cancer progression and metastasis (Yin et al 2019 Cell Oncol; in revision). Role of FAM83 family oncogenes in human cancers Recent studies from our laboratory as well as other groups have demonstrated that FAM83 family members have oncogenic properties and have significantly elevated expression levels in multiple human tumor types, including breast cancers (BC). We performed an integrative multi-omics bio-informatic analysis of the role of FAM83 family oncogenes in human cancers. Our findings were published in the journal Molecular Oncology (Snijders et al; Mol Oncol. 2017 Feb;11(2):167-179). Highlights from this manuscript are: • Transcriptional levels of FAM83 family members are frequently and dramatically elevated across tumor types in human • DNA copy number increase appears to be a potential mechanism for increased expression of FAM83 genes • FAM83 family gene signature is associated with poor survival across human tumor types • FAM83 gene alterations are significantly correlated with TP53, PIK3CA and Ecadherin mutations. In conclusion, this analysis of FAM83 family members opened up a new horizon for further understanding the significance of FAM83 genes in cancer and clinical applications in diagnosis, prognosis and therapy. Role of FAM83D in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy. Lung cancer is one of the most common malignant tumors in the world and non-small cell lung cancer (NSCLC) accounts for 80~85% of all diagnosed lung cancer cases, one of the deadliest cancers worldwide. FAM83D has been proposed as an oncoprotein in several types of human cancer, yet its role and molecular mechanism in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy are poorly understood. We evaluated the expression of FAM83D in NSCLC cells and cancerous tissues and its prognostic significance, then investigated the effect of FAM83D on tumorigenic phenotypes such as cell proliferation, EMT and invasive capability, and finally assessed impact of FAM83D on NSCLC chemo-drug resistance. The results of this study are currently in revision at the journal Cellular Oncology. Highlights from this manuscript are: • FAM83D expression is elevated in NSCLC cells and tissues and associated with poor prognosis • FAM83D promotes NSCLC cell proliferation • FAM83D induces EMT in NSCLC cells • FAM83D promotes NSCLC cell migration and invasion • FAM83D promotes EMT and invasive capability of NSCLC cells by regulating AKT/mTOR pathway • FAM83D silencing increases cisplatin sensitivity of NSCLC cells In summary, this second study demonstrated for the first time that FAM83D promoted EMT and metastasis of NSCLC cells through AKT/mTOR signal pathway as well as increased the sensitivity of NSCLC to cisplatin. This study may provide potential targets for advanced NSCLC treatment and personalized therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ICME for Creep of Ni-Base Superalloys in Advanced Ultra-Supercritical Steam Turbines

The goal of this project contains three major aspects: (1) critical assessment of existing experimental data, (2) critical assessment of existing modeling capabilities and (3) development of new modeling capabilities that are critical but currently missing in predicting long-term creep behavior of Ni-base superalloys used in advanced ultra- supercritical (A-USC) steam turbines for coal-fired power plants. The project uses advanced materials informatics to datamine the experimental data, uncertainty quantification tools to rigorously assess existing creep-life models, and integration of phase-field and FFT-based crystal plasticity to develop a multi-scale, physics-based, microstructure-sensitive creep model.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

MINOS Infrasound Analysis Synopsis

This report was written as a guide to working with infrasound data collected as part of the Multi-Informatics for Nuclear Operations Scenarios (MINOS)project, an NA-22 funded venture. The main purpose of overall MINOS project is the combination of multiple, disparate data modalities to characterize the operations at a nuclear facility, specifically instrumenting and studying the High-Flux Isotope Reactor (HFIR) and Radiochemical Engineering Development Center (REDC) locate at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are constructed, processed, and dissolved at REDC. REDC also hosts other glove-box and hot-cell type activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Guide to the Distributed Energy Resource Risk Management Framework

The emergence of distributed energy resources (DERs) has transformed the electric power sector and will likely have even more profound impacts on the future evolution of the United States energy sector as it modernizes and becomes more reliant upon complex informatics programming and systems to ensure that our power grid remains safe from malicious interference. To mitigate risks associated with the increased and diversified use of DERs, the Distributed Energy Resource Cybersecurity Framework (DER-CF) was developed in 2019. The National Renewable Energy Laboratory extended the scope of the DER-CF to include the RMF. To address the challenges faced by federal energy managers and energy system stakeholders in applying the RMF to DER systems, the Distributed Energy Resource Risk Manager (DER-RM) is a six-step process to proactively manage cybersecurity risk in a methodical manner. The DER-RM is independent of the DER-CF's existing assessment, allowing users to focus specifically on the RMF steps. The tools are targeted to different processes - DER-CF enables organizations to perform self-assessments to improve their cybersecurity posture, while DER-RM assists organizations in achieving compliance with specific requirements. This document provides an overview of the DER-RM. The RMF process outlined in this report serves as a guide to diagnose information and operational system threats, gather required materials to comply with industry standards, and document plans for achieving Authority to Operate. Using the DER-RM, federal agencies and other organizations can easily and intuitively follow the RMF process, manage the risks to their grid-edge infrastructure through the integration of their on-site DERs, and comply with appropriate requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Integrated Design of Ultradurable, Low CO 2 Alternative Binder Systems via Machine Learning

This ARPA-E project developed a machine learning tool to use in formulation design of cementitious binders for concrete having 50% less embodied CO 2 and possessing twice the durability compared to concrete based on ordinary portland cement (OPC) binders. The technical focus was on limestone/calcined clay cement (LC3), the leading replacement for OPC. Here, hierarchical machine learning (HML) was used to model the flowability, set time, strength, and durability of LC3 concrete. This methodology identifies latent variables derived from domain knowledge and empirical models that develop an accurate model for a response surface from small datasets. For the flowability metric, particle packing was a dominant factor, while strength and durability were both strongly determined by the fraction of metakaolin and the water:solids ratio. Under constraints of water:binder ratio, material performance metrics, embodied CO 2 , and cost per tonne of OPC, multi-objective optimization was used to design binders parameterized by the mineral composition replacing OPC, particle size distributions, and water:solids ratio. The trained algorithm was able to predict multiple mixes met these performance criteria, and experimental testing validated the predictions. The model demonstrated here is relevant for North America, where pure kaolin deposits are found broadly. The approach is being taken forward into commercial application by Ansatz AI, a materials informatics company founded by PI Washburn and co-PI Poczos. Through collaborations with the cement and concrete industry, and funding from SBIR programs, a commercial software will be developed in future research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Root Zone Soil Moisture and Herpetofaunal Biodiversity in the Southern Great Plains

(1) Goal: The scientific objective to the proposed research is to develop a proto-type downscaled (<1 km) version of the root-zone soil moisture product called SoilMERGE or SMERGE. This objective is linked as root zone soil moisture is a critical climate variable that has a more direct influence on plant growth than precipitation. Therefore, the developed downscaled version of SMERGE can be used as input into biodiversity informatic techniques (i.e., maximum entropy modeling) Development of downscaled SMERGE will be facilitated by Department of Energy (DOE) resources specifically data from the DOE Atmospheric Radiation Measurement (ARM) facility and modeling platforms such as the Energy Exascale Earth System Model (E3SM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗