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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual’s susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals’ inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 “healthy” type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of “white” ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals’ inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A round Earth for climate models

Sunlight drives the Earth’s weather, climate, chemistry, and biosphere. Recent efforts to improve solar heating codes in climate models focused on more accurate treatment of the absorption spectrum or fractional clouds. A mostly forgotten assumption in climate models is that of a flat Earth atmosphere. Spherical atmospheres intercept 2.5 W·m −2 more sunlight and heat the climate by an additional 1.5 W⋅m −2 globally. Such a systematic shift, being comparable to the radiative forcing change from preindustrial to present, is likely to produce a discernible climate shift that would alter a model’s skill in simulating current climate. Regional heating errors, particularly at high latitudes, are several times larger. Unlike flat atmospheres, constituents in a spherical atmosphere, such as clouds and aerosols, alter the total amount of energy received by the Earth. To calculate the net cooling of aerosols in a spherical framework, one must count the increases in both incident and reflected sunlight, thus reducing the aerosol effect by 10 to 14% relative to using just the increase in reflected. Simple fixes to the current flat Earth climate models can correct much of this oversight, although some inconsistencies will remain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Mixed valent metals

Here, we review the theory of mixed-valent metals and make comparison with experiments. A single-impurity description of the mixed-valent state is discussed alongside the description of the nearly-integer valent or Kondo limit. The degeneracy N of the f-shell plays an important role in the description of the low-temperature Fermi-liquid state. In particular, for large N , there is a rapid cross-over between the mixed-valent and the Kondo limit when the number of f electrons is changed. We discuss the limitations on the application of the single-impurity description to concentrated compounds such as those caused by the saturation of the Kondo effect and those due to the presence of magnetic interactions between the impurities. This discussion is followed by a description of a periodic lattice of mixed-valent ions, including the role of the degeneracy N . The article concludes with a comparison of theory and experiment. Topics covered include the single-impurity Anderson model, Luttinger's theorem, the Friedel sum rule, the Schrieffer–Wolff transformation, the single-impurity Kondo model, Kondo screening, the Wilson ratio, local Fermi-liquids, Fermi-liquid sum rules, the Nozieres exhaustion principle, Doniach's diagram, the Anderson lattice model, the Slave-Boson method, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Distributed State Estimation Over Time-Varying Graphs: Exploiting the Age-of-Information

Here, we study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over non-overlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. We also develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a "freshness-index" that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

42 ENGINEERING↗
DOE OSTI2022

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Development of mooring-anchor program in public domain for coupling with floater program for FOWTs (Floating Offshore Wind Turbines)

This report presents the development of offshore anchor data sets which are intended to be used to develop a database that allows preliminary selection and sizing of anchors for the conceptual design of floating offshore wind turbines (FOWTs). The study is part of a project entitled “Development of Mooring-Anchor Program in Public Domain for Coupling with Floater Program for FOWTs (Floating Offshore Wind Turbines)”, under the direction of Dr. Moo-Hyun Kim at the Texas A&M University and with the sponsorship from the US Department of Energy (Contract No. DE-EE0005479, CFDA # 81.087 for DE-FOA-0000415, Topic Area 1.3: Subsurface Mooring and Anchoring Dynamics Models).

17 WIND ENERGY↗
DOE OSTI2016

Predictive Modeling in Actinide Chemistry and Catalysis

These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

2023 SAGE-Camp Report

The 7 th Summer Physics Camp for Young Women was successfully held in person in 2023 from June 5 th to 16 th at the New Mexico School for the Arts in Santa Fe, NM at Hilo Intermediate School in Hawaii. This year’s camp was dedicated to the topic of Energy Security and was made possible thanks to the strong collaboration of Los Alamos, Sandia and Hawaii teams and the logistical and financial support of Los Alamos and Sandia National Laboratories, New Mexico Consortium, SAGE- Moore Foundation, LANL Foundation, ACS, IEE, APS four corners, N3B, Hawaii Museum of Science and Technology, New Mexico School for the Arts (NMSA) and Tech Source. The camp mobilized more than 120 volunteers who made the camp a success. The camp is free of charge to the students and included free lunch and snacks for the busy brains to have plenty of energy, also included all materials needed for the hands-on activities (like drone building, crystal structure, solar panels, wind turbine fabrication, soldering, coding etc) and also a stipend for students who attended for the full two weeks and for two educators and two student mentors in NM. The camp offered 32 high school students from New Mexico and 8 from Hawaii a unique opportunity to explore science topics and meet a broad range of role model professionals across STEM fields including astrophysics, cybersecurity, Energy fields, space science, engineering, biophysics, environmental science, robotics, computer science, nuclear engineering, radiological science, physics and chemistry. With nearly 120 volunteers who came mainly from Los Alamos National Laboratory (66%) and Sandia national laboratories (18%), two funded educators from NM, Dr. Weldon Beauchamp and Dr. Ellee Cook, and two educators from Hilo, Dr. Pascale Creek Pinner and LeAnn Ragasa, the camp was educationally sound and extremely varied. The collaboration with school educators is critical for the goal of this camp to not only impact students' lives but also improve STEM education in NM and Hawaii. The ultimate goal of the camp is to increase higher education aspirations of students, empower them to consider careers in STEM and learn more about the opportunities available to them in our local colleges and DOE National Laboratories. In addition, the camp also hired 2 past students as student mentors, Megan Odom and Elisea Jackson, who currently attend NMSA and were students at the camp in 2022 when it was virtual. The in-person camp which aims at empowering under-represented minorities in STEM in our community received more than 52 applications this year from all over NM and 8 applications from Hawaii. Our selection criteria are based on diversity, equity and inclusion, and students for whom the camp can be a life-changing opportunity are given a chance to attend the camp. During COVID, the camp was held virtually and gave the opportunity to students from remote areas in NM and Hawaii to attend from their homes. This year, fantastic families supported students everyday even when home was in remote areas in NM like Lea county, Sandoval county, Bernalillo or Mora county. The organizers hope next year they can offer a residential option for students from remote areas.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Corrosion of Magnesium in Multimaterial System

The TMS Magnesium Committee has been actively involved in presenting cutting-edge research and development and the latest trends related to magnesium and its alloys to industry and academia. Topics including magnesium alloy development, applications, mechanism of deformation and corrosion, thermomechanical processing, modelling, etc. have been captured year after year through the Magnesium Technology symposium and conference proceedings at TMS and through special topics in JOM. Every year, based on the unanimous endorsement from the industry and academia, a topic is selected to address the latest developments within this subject in JOM. In continuation with last year’s coverage of Advances and Achievements in In-Situ Analysis of Corrosions and Structure–Property Relationship in Mg Alloys,[1] this year’s topic focuses on the Corrosion of Magnesium in Multimaterial Systems. Magnesium, the lightest of all the structural materials, has garnered much interest in the transportation, electronics packaging, defense equipments and industries alike and are more commonly being incorporated in multimaterial design concepts.[2-4] However, the application of the same is limited due to its highly corrosive nature, and understanding and mitigating the corrosion of magnesium has been a major research challenge.

Joshi, Vineet V.↗
DOE OSTI2019

Modeling and Optimization of Trip Level Energy Consumption and Charging Management for Connected Automated Electric Vehicles

This will be a presentation for a tutorial session in 2019 ITEC based on reviewing the publicly existing work in the literature. Trip aware energy consumption and charging management for electric vehicles is a key component to ensure sustainability of electrified transportation. The upcoming vehicle communication and autonomous driving technologies will provide opportunities to achieve more intelligent management strategies. This tutorial will introduce the state-to-art technologies that are being designed for optimizing the trip level energy cost and recharging operations of connected automated electric vehicles (CAEVs). The information needs, modeling, control and optimization techniques for optimal trip level energy management will be introduced. Opportunities and benefits from vehicle communication and automation will be discussed. In general, three main sub-topics will be presented in detail: first, advanced EV energy consumption modeling and prediction technologies; second, energy efficient driving technologies, e.g. eco-driving and eco-routing, etc.; third, optimally automatic charging decision making for CAEVs. Following these details, the research challenges, opportunities for co-optimization between the energy consumption and energy recharging of CAEVs will be illustrated in this tutorial.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

WE-A-12A-01: Medical Physics 1.0 to 2.0, Session 2: Radiography, Mammography and Fluoroscopy

Medical Physics 2.0 is a bold vision for an existential transition of clinical imaging physics in face of the new realities of value-based and evidencebased medicine, comparative effectiveness, and meaningful use. It speaks to how clinical imaging physics can expand beyond traditional insular models of inspection and acceptance testing, oriented toward compliance, towards team-based models of operational engagement, prospective definition and assurance of effective use, and retrospective evaluation of clinical performance. Organized into four sessions of the AAPM, this particular session focuses on three specific modalities as outlined below. Radiography 2.0: The development of electronic capture in recent years has changed the landscape and spurred reinvestment by healthcare providers. The radiography presentation will explore how the diagnostic medical physicist must adapt to these changes to support radiographic imaging, and how she/he can add value in radiography practice over the next 5-10 years. Topics of discussion include new metrology of evaluation, new models of clinical engagement, and effective integration of new technologies. Mammography 2.0: Mammography has been an interesting testing ground on the effectiveness of close involvement of medical physicists with equipment in the past twenty years. The outcomes have clearly shown major improvements in image quality and significant reduction in the average glandular dose. However, the medical physicist's role in mammography has been largely focused to annual surveys and with limited input on operational issues with image artifacts, optimal mammographic acquisition mode and problems with image quality. This mammography presentation will address why and how medical physicists must be prepared to address the new models of practice that include new metrics of performance and the integration of new technologies (DBT, syncretized mammograms, contrast mammography, breast CT) into clinical practice. Fluoroscopy 2.0: Physics support of fluoroscopy should be operationally as opposed to compliance focused. Testing protocols must address new hardware, acquisition methods, and image processing. Future available tools are discussed. Proper configuration of acquisition parameters (focal spot size, voltage and added filter, tube current, pulse width, pulse rate, scatter removal) as a function of patient size from the neonate to bariatric patient is key to providing diagnostic image quality at properly managed radiation doses. Learning Objectives: Appreciate the limitations of the currently available tools and techniques in clinical medical physics in radiography, mammography, and fluoroscopy, and ways to improve upon current deficiencies. Appreciate the changing environment of imaging practice and the need for the medical physicist to be an expert consultant and educator in a capacity that extends beyond the annual survey of equipment. Understand the status of the rapidly changing environment in breast imaging from planar imaging to tomosynthesis and possibly to breast CT. Identify appropriate configuration of acquisition parameters as a function of patient size to manage radiation dose and ensure diagnostic image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Comprehensive Loss Model and Comparison of AC and DC Boost Converters

DC microgrids have become a prevalent topic in research in part due to the expected superior efficiency of DC/DC converters compared to their AC/DC counterparts. Although numerous side-by-side analyses have quantified the efficiency benefits of DC power distribution, these studies all modeled converter loss based on product data that varied in component quality and operating voltage. To establish a fair efficiency comparison, this work derives a formulaic loss model of a DC/DC and an AC/DC PFC boost converter. These converters are modeled with identical components and an equivalent input and output voltage. Simulated designs with real components show AC/DC boost converters between 100 W to 500 W having up to 2.5 times more loss than DC/DC boost converters. Although boost converters represent a fraction of electronics in buildings, these loss models can eventually work toward establishing a comprehensive model-based full-building analysis.

42 ENGINEERING↗
DOE OSTI2015

Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Characterization of xenobiotic metabolizing enzymes of a reconstructed human epidermal model from adult hair follicles

In this study, a comprehensive characterization of xenobiotic metabolizing enzymes (XMEs) based on gene expression and enzyme functionality was made in a reconstructed skin epidermal model derived from the outer root sheath (ORS) of hair follicles (ORS-RHE). The ORS-RHE model XME gene profile was consistent with native human skin. Cytochromes P450 (CYPs) consistently reported to be detected in native human skin were also present at the gene level in the ORS-RHE model. The highest Phase I XME gene expression levels were observed for alcohol/aldehyde dehydrogenases and (carboxyl) esterases. The model was responsive to the CYP inducers, 3-methylcholanthrene (3-MC) and β-naphthoflavone (βNF) after topical and systemic applications, evident at the gene and enzyme activity level. Phase II XME levels were generally higher than those of Phase I XMEs, the highest levels were GSTs and transferases, including NAT1. The presence of functional CYPs, UGTs and SULTs was confirmed by incubating the models with 7-ethoxycoumarin, testosterone, benzo(a)pyrene and 3-MC, all of which were rapidly metabolized within 24 h after topical application. The extent of metabolism was dependent on saturable and non-saturable metabolism by the XMEs and on the residence time within the model. In conclusion, the ORS-RHE model expresses a number of Phase I and II XMEs, some of which may be induced by AhR ligands. Functional XME activities were also demonstrated using systemic or topical application routes, supporting their use in cutaneous metabolism studies. Such a reproducible model will be of interest when evaluating the cutaneous metabolism and potential toxicity of innovative dermo-cosmetic ingredients. - Highlights: • Phase I and II xenobiotic metabolizing enzymes are expressed in ORS-RHE model derived from adult hair follicles. • Xenobiotic metabolizing enzymes are functional in ORS-RHE following either systemic or topical application. • Xenobiotic metabolizing enzymes are inducible in ORS-RHE. • ORS-RHE is a suitable model for studying skin metabolism. • ORS-RHE is a convenient and reliable tool for the evaluation of xenobiotics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Modeling of transient conduction in building envelope assemblies: A review

As buildings age, retrofits are becoming an increasingly important topic for the ever-growing and aging existing building stock. To compare designs or evaluate in-service building envelopes, thermal modeling is utilized to evaluate the thermal performance of envelope assemblies; however, it can be difficult to model the thermal performance of as-built assemblies due to degradation or missing documentation. To address this issue, inverse modeling can be applied to infer the properties of as-built envelope assemblies. This paper presents a review of the literature and published methods to model and infer the transient conductive performance of building envelopes. This review serves as a survey of existing transient conduction algorithms to evaluate performance, computational speed, and relevance for inverse modeling applications. In addition to the literature review, this work also evaluates the computational performance of the most prevalent transient conduction algorithms against the ASHRAE 1052RP toolkit to assess inverse modeling potential. This methodology serves as a foundation for future research to characterize the transient thermal performance of as-built building envelope assemblies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗