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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterization and application of a high-resolution microfluidic device in atmospheric ice nucleation research and integrated science teaching

Ambient ice-nucleating particles (INPs) represent an important subset of aerosol particles that facilitate heterogeneous formation of ice crystals in the atmosphere, which can impact Earth system and energy. In this work, we developed an affordable high-resolution platform to replicate atmospheric immersion freezing through the unique capabilities of microfluidics to measure INP concentration of known ice nucleation active materials, as well as high-latitude soil samples. The new instrument produced comparable and extended freezing efficiency data for a multitude of samples over the temperature range of -5 to -35 °C as compared to an existing freezing assay within known uncertainties. Moreover, the new assay was integrated into science teaching at a primarily undergraduate and Hispanic-serving institute, contributing to fostering a deeper understanding of ice nucleation processes among college students, instilling their enthusiasm for science, and addressing the critical need for a well-informed citizenry. Student research assistants were involved in both research and pedagogical development processes. Three students from our team visited the Pacific Northwest National Laboratory to help characterize physicochemical properties of our test samples and to test modeling of atmospheric ice nucleation using a box model as well as a global climate model (E3SM) for the regional-scale conditions over the North Slope of Alaska (NSA) and Southern Great Plains (SGP). We also assessed student learning experience via hands-on training, research processes, and development of educational materials. On pre- and post-participation surveys and in exit interviews, students reported personal growth as advancements in breadth of experience, research-related skill sets, and regarding their educational and career goals. They also affirmed that the methods enacted incorporated the NASA Minority University Research and Education Program (MUREP) STEM Engagement Best Practices.

58 GEOSCIENCES↗
DOE OSTI2023

A High-Pressure Shock Tube Study of Hydrogen and Ammonia Addition to Natural Gas for Reduced Carbon Emissions in Power Generation Gas Turbines

Ignition delay times from undiluted mixtures of natural gas (NG)/H2/Air and NG/NH3/Air were measured using a high-pressure shock tube at the University of Central Florida. The combustion temperatures were experimentally tested between 1000-1500 K near a constant pressure of 25 bar. Mixtures were kept undiluted to replicate the same chemistry pathways seen in gas turbine combustion chambers. Recorded combustion pressures exceeded 200 bar due to the large energy release, hence why these were performed at the high-pressure shock tube facility. The data is compared to the predictions of the NUIGMech 1.1 mechanism for chemical kinetic model validation and refinement. An exceptional agreement was shown for stoichiometric conditions in all cases but strayed at lean and rich equivalence ratios, especially in the lower temperature regime of H2 addition and all temperature ranges of the baseline NG mixture. Hydrogen addition also decreased ignition delay times by nearly 90%, while NH3 fuel addition made no noticeable difference in ignition delay time. NG/NH3 exhibited similar chemistry to pure NG under the same conditions, which is shown in a sensitivity analysis, demonstrating hydrogen chemistry to be dominant in NG/H2 mixtures and hydrocarbon chemistry to be dominant in NG/NH3 mixtures. The reaction CH3 + O2 = CH3O + O is identified and suggested as a possible modification target to improve model performance. Increasing the robustness of chemical kinetic models via experimental validation will directly aid in designing next-generation combustion chambers for use in gas turbines, which in turn will greatly lower global emissions and reduce greenhouse effects.

Pierro, Michael↗
DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Development of experimental and computational frameworks to predict subcooled flow boiling in the LANL Isotope Production Facility

Cooling is crucial to maintain the integrity of target systems in isotope production facilities. At Los Alamos National Laboratory (LANL)’s Isotope Production Facility (IPF), multiple encapsulated targets are stacked and irradiated in tandem with a 100 MeV, ~250μA proton beam. To facilitate effective heat removal, these stacked targets are separated and cooled via a series of water channels. At these beam currents, this high-energy proton beam heats the target system, likely initiating subcooled flow boiling in the cooling channels. However, in-beam monitoring of the IPF target system is not possible due to the extreme radiation environment, and the necessarily significant shielding. To better understand high-power target performance, we developed ex-situ experimental and computational frameworks to predict the behavior of subcooled flow boiling at IPF. Subcooled flow boiling experiments on Inconel 625 samples under IPF conditions (2 bar pressure, 10 GPM flow rate (i.e., 2249 kg/m 2 /s), 85 K subcooling) revealed that IPF's average operating power is at the early stage of boiling with a heat transfer coefficient of 48,000 W/m 2 /s. The proposed modeling framework enables us to predict a complete boiling curve, i.e., single-phase heat transfer, onset of nucleate boiling, two-phase heat transfer, and critical heat flux (CHF), with specification of input boiling parameters up to intermediate heat flux levels. The estimated CHF under IPF conditions is 5.2 MW/m 2 . Experimental data under reduced conditions (2 bar pressure, 1.5 GPM flow rate (i.e., 337 kg/m 2 /s), 45 K subcooling) served as validation cases for the computational modeling. This computational model can be further extended to more complicated systems replicating the real IPF configuration, for instance, to study void distribution as a function of the incident proton beam profile and coolant velocity profile of multiple cooling channels. Finally, the proposed experimental and computational frameworks provide a means to better understand cooling systems in the isotope production facilities at different accelerators, where in-beam monitoring of the cooling process is not available.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Short Chain Fatty Acids and Bacterial Taxa Associated with Reduced Salmonella enterica serovar I 4,[5],12:i:- Shedding in Swine Fed a Diet Supplemented with Resistant Potato Starch

Salmonella enterica serovar I 4,[5],12:i:- is a foodborne pathogen of concern because many isolates are multidrug-resistant (resistant to ≥3 antimicrobial classes) and metal tolerant. In this study, three in-feed additives were individually tested for their ability to reduce Salmonella I 4,[5],12:i:- shedding in swine: resistant potato starch (RPS), high amylose corn starch, and a fatty acid blend, compared with a standard control diet over 21 days. Only RPS-fed pigs exhibited a reduction in Salmonella fecal shedding, different bacterial community compositions, and different cecal short chain fatty acid (SCFA) profiles relative to control animals. Within the RPS treatment group, pigs shedding the least Salmonella tended to have greater cecal concentrations of butyrate, valerate, caproate, and succinate. Additionally, among RPS-fed pigs, several bacterial taxa (Prevotella_7, Olsenella, and Bifidobacterium, and others) exhibited negative relationships between their abundances of and the amount of Salmonella in the feces of their hosts. Many of these same taxa also had significant positive associations with cecal concentrations of butyrate, valerate, caproate, even though they are not known to produce these SCFAs. Together, these data suggest the RPS-associated reduction in Salmonella shedding may be dependent on the establishment of bacterial cross feeding interactions that result in the production of certain SCFAs. However, directly feeding a fatty acid mix did not replicate the effect. RPS supplementation could be an effective means to reduce multidrug-resistant (MDR) S. enterica serovar I 4,[5],12:i:- in swine, provided appropriate bacterial communities are present in the gut.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Review of Grey Box/Black Box Data Contamination Metrics on Open and Commercial Models

Dataset contamination is a problem where benchmarks and tasks used to evaluate the capabilities of Large Language Models (LLMs) have been incorporated into the training dataset of the models. This gives a false sense of performance that can overestimate how these models will function on truly unseen data. This problem becomes worse with commercial LLMs with larger and non-accessible training data, so techniques have been developed to try to measure the degree to which a model is contaminated with a benchmark’s data. To understand the effectiveness of these techniques, particularly when evaluating contamination on coding tasks, we review trends and categorize techniques by the degree of access to the model that is required. The research literature on this topic has reported mixed effectiveness of these techniques, so we select a set of black box (text access only) and grey box (access to model loss/probabilities required) techniques and apply them to both commercial and non-commercial models. We implement these metrics as part of a framework to test the contamination of Python code in LLMs to see to what extent we can replicate the effectiveness (or ineffectiveness) of these contamination detection techniques. Though we find mixed results in the capabilities of these metrics to identify contamination, we do observe evidence that they can identify contamination (broadly) in fine-tuned models when both a baseline and fine-tuned model is present. Additionally, similarity metrics were able to identify between contaminated and uncontaminated data even in situations where the data is distributionally similar (e.g., drawn from the same set of code projects).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Measurement of top-quark pair production in association with charm quarks in proton–proton collisions at √s = 13 TeV with the ATLAS detector

Inclusive cross-sections or top-quark pair production in association with charm quarks are measured with proton-proton collision data at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 140 fb -1 , collected with the ATLAS experiment at LHC between 2015 and 2018. The measurements are performed by requiring one or two charged leptons (electrons and muons), two b-tagged jets, and at least one additional jet in the final state. A custom flavor-tagging algorithm is employed for the simultaneous identification of b-jets and c-jets. In a fiducial phase space that replicates the acceptance of the ATLAS detector, the cross-sections for $t\bar{t}$ + ≥ 2c and $t\bar{t}$ + 1c production are measured to be $1.28^{+0.27}_{-0.24}$ pb and $6.4^{+1.0}_{-0.9}$ pb, respectively. The measurements are primarily limited by uncertainties in the modeling of inclusive $t\bar{t}$ and $t\bar{t}$ + $b\bar{b}$ production, in the calibration of the flavor-tagging algorithm, and by data statistics. Cross-section predictions from various $t\bar{t}$ simulations are largely consistent with the measured cross-section values, though all underpredict the observed values by 0.5 to 2.0 standard deviations. In a phase-space volume without requirements on the $t\bar{t}$ decay products and the jet multiplicity, the cross-section ratios of $t\bar{t}$ + ≥ 2c and $t\bar{t}$ + 1c to total $t\bar{t}$ + jets production are determined to be (1.23 ± 0.25)% and (8.8 ± 1.3)%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗
DOE OSTI2024

Survey of Use Cases and Scenarios on the Open Energy Data Initiative Solar Systems Integration (OEDI SI) Platform

The Open Energy Data Initiative Solar Systems Integration (OEDI SI) Data and Modeling Platform offers a comprehensive set of use cases tailored for power systems analysis. Each use case is centered around a specific power system analysis problem, supported by composite input data and reference algorithms. These composite input datasets are meticulously assembled using OEDI SI's data preprocessing tools, which integrate raw data from various sources. The primary objectives of the OEDI SI Platform include facilitating access to composite input data through widely accepted input/output formats and verified results. This accessibility enables power system network researchers and developers to validate their algorithms and showcase their applications' capabilities to the broader community. Moreover, the platform strives to promote reproducible, robust, replicable, and generalizable solar systems integration research.

14 SOLAR ENERGY↗
DOE OSTI2021

MULTIPHYSICS-MODELING OF FIRE-INDUCED URANIUM AEROSOL FORMATION – A-POSTERIORI BENCHMARKING OF EXPERIMENTS

The formulation and solution of a model that properly described the temperature profiles of uranium in fire conditions was prepared, verified and validated, to support a-posteriori benchmarking of historical experiments. Data from the multi-physics model combined with data regression from the experiments provides a useful tool for exploring sources of potential bias in historical experiments. The approaches used are augmented by visual observations and photographic evidence allowing for correlations between various phenomena, independent of whether or not that specific phenomena are part of the validated model. In this manner, the validated model is a tool for a-posteriori benchmarking, and not a model designed to replicate the experimental output exactly for any specific experiment. For experiments like the Clark (2015) experiments, this integrated model (including chemistry) demonstrated that the largest bias was associated in the metallurgy of the coupons. The model developed does serve as a basis for defending which of the historical alloys tested are the most appropriate for any other alloy being considered (e.g., one that has not yet been tested). The model identifies key reactions that impact uranium chemistry and can significantly bias experimental results. Through use of the model and energy balances, data from historical experiments by Elder and Tinkle (1980) were regressed and benchmarked. This study identifies that all uranium alloys are not equal and that there are distinct differences between the dominant alloys representing distinct metallurgical phases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Say where you sample: Increasing site selection transparency in urban ecology

Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The landscape of Chlamydomonas histone H3 lysine 4 methylation reveals both constant features and dynamic changes during the diurnal cycle

Chromatin modifications are epigenetic regulatory features with major roles in various cellular events, yet they remain understudied in algae. Here, we interrogated the genome-wide distribution pattern of mono- and trimethylated histone H3 lysine 4 (H3K4) using chromatin-immunoprecipitation followed by deep-sequencing (ChIP-seq) during key phases of the Chlamydomonas cell cycle: early G 1 phase, Zeitgeber Time 1 (ZT1), when cells initiate biomass accumulation, S/M phase (ZT13) when cells are replicating DNA and undergoing mitosis, and late G 0 phase (ZT23) when they are quiescent. Tri-methylated H3K4 was predominantly enriched at transcription start sites of the majority of protein coding genes (85%). The likelihood of a gene being marked by H3K4me3 correlated with it being transcribed at some point during the life cycle but not necessarily by continuous active transcription, as exemplified by early zygotic genes, which may remain transcriptionally dormant for thousands of generations between sexual cycles. The exceptions to this rule were around 120 loci, some of which encode non-poly-adenylated transcripts, such as small nuclear RNAs and replication-dependent histones that had H3K4me3 peaks only when they were being transcribed. Mono-methylated H3K4 was the default state for the vast majority of histones that were bound outside of transcription start sites and terminator regions of genes. A small fraction of the genome that was depleted of any H3 lysine 4 methylation was enriched for DNA cytosine methylation and the genes within these DNA methylation islands were poorly expressed. Besides marking protein coding genes, H3K4me3 ChIP-seq data served also as a annotation tool for validation of hundreds of long non-coding RNA genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for Filling the Cellulosic Bio-economy Gap by Utilizing a Wedge Approach Combined with Stakeholder Collaboration

The price gap between the market and breakeven prices of cellulosic biomass for farmers represents a significant barrier to the development of a low-carbon cellulosic bioeconomy. Using a bottom-up, agent-based modeling tool that replicates the behaviors and interactions of key stakeholders, this study analyzes the emergence of a cellulosic bioeconomy at the local scale through a wedge approach that examines an integrated portfolio of multiple policy options, including subsidies for small-scale bioproducts and environmental credits. The role of collaboration among multiple stakeholders, such as biomass producers (farmers), bio-refinery industry, government, and society, is assessed for filling the price gap. Using the Sangamon River Basin as a case study site, we evaluate the effectiveness of the wedge approach by comparing simulation results from multiple scenarios, each incorporating different combinations of bioeconomy wedges, with and without stakeholder collaboration. Results underscore that active collaboration among stakeholders acts as a catalyst enlarging the effectiveness of bioeconomy wedges. Including the carbon credits and environmental value in the policy portfolio is found to bridge the price gap through collective contributions from diverse stakeholders, where the cellulosic biofuel and bioproduct industry plays a pivotal role. Although this study is conducted at the local watershed scale, the methodology and findings offer valuable insights for market development in other watersheds and the potential scaling of local markets to regional and national levels.

Economics↗
DOE OSTI2025

Data from: Soil carbon change in intensive agriculture after 25 years of conservation management

Changes in soil organic carbon (SOC) and nitrogen (SON) are strongly affected by land management, but few long-term comparative studies have surveyed changes throughout the whole soil profile. We quantified 25-year SOC and SON changes to 1 m in 10 replicate ecosystems at an Upper Midwest, USA site. We compared four annual cropping systems in maize ( Zea mays )-soybean ( Glycine max)-winter wheat ( Triticum aestivum ) rotations, each managed differently (Conventional, No-till, Reduced input, and Biologically based); in three managed perennial systems (hybrid Poplar ( Populus × euramericana ), Alfalfa ( Medicago sativa ), and Conifer ( Pinus spp.); and in three successional systems (Early, Mid- and Late succession undergoing a gradual change in species composition and structure over time).

agricultural sciences↗
DOE OSTI2022

16s Amplicon Analysis of Soil Data for Interactive effects of depth and differential irrigation on soil microbiome composition and functioning

Genomic DNA was isolated from soil and rhizosphere samples using the Zymo Quick-DNA fecal/soil microbe miniprep kit (catalog no. D6010) according to the manufacturer’s instructions (Zymo Research; Irvine, CA) with the modification of eluting in 100 uL elution buffer. Sample concentrations were quantified using the Qubit dsDNA HS assay kit (Thermo Fisher). For rhizosphere samples only, DNA was subsequently purified using Zymo’s ZR-96 DNA Clean & Concentrator kit (catalog no. D4024) to account for low DNA concentrations of these samples. . In each replicate block, there were five drip irrigation treatments (T1 = 100% normal irrigation, T2 = 56.25%, T3 = 37.5%, T4 = 18.75% and T5=no irrigation. On July 20, the strength of the drought treatments was increased: T1 remained at 100%, whereas T2 changed from 75% to 56.25%, T2 changed from 50% to 37.5%, and T4 changed from 25% to 18.75%. Sequencing was performed as described previously (Naylor, Fansler, et al. 2020). Sequences were amplified on the MiSeq platform (Illumina, San Diego, CA) using 16S primers (515F and 806R) specific to the V4 region. Raw sequence data was processed with the pipeline Hundo for amplicon quality control and annotation. Downstream statistical analyses on 16S datasets were performed using the program R and the packages ‘phyloseq’ and ‘vegan’.

Soil microbiome, metatranscriptomics↗
DOE OSTI2023

A new efficient grain growth model using a random Gaussian-sampled mode filter

This paper presents the use of a Gaussian neighborhood mode filter for predicting grain growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This flexible grain growth model can quickly utilize modern, computationally optimized data science strategies on graphics processing units to simulate grain growth up to 100 times faster than the state-of-the-art, publicly available Monte Carlo Potts model. We show that, given the correct neighborhood, the mode filter can replicate normal grain growth in two or three dimensions. In addition, the paper briefly demonstrates the ability to model limited anisotropic in grain boundary energy and mobility. Anisotropic grain boundary energy is modeled by defining a weighted mode filter operation. Anisotropic grain boundary mobility is modeled by scaling and orienting the Gaussian neighborhood in a particular direction.

Anisotropy↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations Version 2.0

This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗