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At least 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Study of hydraulic fracturing processes in shale formations with complex geological settings

Hydraulic fracturing has been applied to extract gas from shale-gas reservoirs. Complicated geological settings, such as spatial variability of the rock mass properties, local heterogeneities, complex in situ stress field, and pre-existing bedding planes and faults, could make hydraulic fracturing a challenging task. In order to effectively and economically recover gas from such reservoirs, it is crucial to explore how hydraulic fracturing performs in such complex geological settings. For this purpose, numerical modelling plays an important role because such conditions cannot be reproduced by laboratory experiments. This study focuses on the analysis of the influence of confining formations and pre-existing bedding planes and faults on the hydraulically-induced propagation of a vertical fracture, which will be called injection fracture, in a shale-gas reservoir. An elastic-brittle model based on material property degradation was implemented in a 2D finite-difference scheme and used for rock elements subjected to tension and shear failure. A base case is considered, in which the ratio SR between the magnitudes of the horizontal and vertical stresses, the permeability k c of the confining formations, the elastic modulus E p and initial permeability k p of the bedding plane and the initial fault permeability k F are fixed at reasonable values. In addition, the influence of multiple bedding planes, is investigated. Changes in pore pressure and permeability due to high pressure injection lasting 2 h were analysed. Results show that in our case during the injection period the fracture reaches the confining formations and if the permeability of those layers is significantly larger than that of the shale, the pore pressure at the extended fracture tip decreases and fracture propagation becomes slower. After shut-in, the pore pressure decreases more and the fracture does not propagate any more. For bedding planes oriented perpendicular to the maximum principal stress direction and with the same elastic properties as the shale formation, results were found not to be influenced by their presence. In such a scenario, the impact of multiple bedding planes on fracture propagation is negligible. On the other hand, a bedding plane softer than the surrounding shale formation leads to a fracture propagation asymmetrical vertically with respect to the centre of the injection fracture with a more limited upward fracture propagation. A pre-existing fault leads to a decrease in fracture propagation because of fault reactivation with shear failure. This results in a smaller increase in injection fracture permeability and a slight higher injection pressure than that observed without the fault. Results of a sensitivity analysis show that fracture propagation is influenced by the stress ratio SR, the permeability k c of the confining formations and the initial permeability k p of the bedding plane more than the other major parameters.

58 GEOSCIENCES↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni

High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs

Soft errors have become one of the major concerns for the error resilience of the HPC applications as those errors may cause HPC applications to generate serious outcomes such as silent data corruptions (SDCs). Protecting the applications from soft errors is an essential while challenging task. Among different approaches, obtaining a profound understanding of the resilience proneness of an application is very important to devise efficient error detection and recovery strategies. Given the scale of the HPC applications both in the code size and execution time, there are often cases that the error propagation analysis on such applications would produce a massive volume of unstructured data, which requires a significant amount of efforts, to process and to obtain indicating actions towards error protection. In this paper, we present a control-flow based visual analysis framework to help the users conduct error propagation analysis and identify the critical sections of a program that may have a higher likelihood of leading to erroneous outcomes when affected by the control flow related errors. We also design and implement the scalable visualization framework - ResilienceVis that efficiently and effectively visualizes the affected program states under errors and the propagation traces for an application in a user-friendly manner, and eventually, we combine the analysis and visualization to exhibit the error-proneness of the different sections of applications.

Jiang, Hailong↗
DOE OSTI2011

Survey of Tools for Risk Assessment of Cascading Outages

Cascading failure can cause large blackouts, and a variety of methods are emerging to study this challenging topic. In parts 1 and 2 of this paper, the IEEE task force on cascading failure seeks to consolidate and review the progress of the field towards methods and tools of assessing the risk of cascading failure. Part 2 summarizes and discusses the state of the art in the available cascading failure modeling tools. The discussion integrates industry and research perspectives from a variety of institutions. Strengths, weaknesses, and gaps in current approaches are indicated.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Characterization of scintillator-based detectors for few-ten-keV high-spatial-resolution x-ray imaging

Purpose: High-spatial-resolution x-ray imaging in the few-ten-keV range is becoming increasingly important in several applications, such as small-animal imaging and phase-contrast imaging. The detector properties critically influence the quality of such imaging. Here the authors present a quantitative comparison of scintillator-based detectors for this energy range and at high spatial frequencies. Methods: The authors determine the modulation transfer function, noise power spectrum (NPS), and detective quantum efficiency for Gadox, needle CsI, and structured CsI scintillators of different thicknesses and at different photon energies. An extended analysis of the NPS allows for direct measurements of the scintillator effective absorption efficiency and effective light yield as well as providing an alternative method to assess the underlying factors behind the detector properties. Results: There is a substantial difference in performance between the scintillators depending on the imaging task but in general, the CsI based scintillators perform better than the Gadox scintillators. At low energies (16 keV), a thin needle CsI scintillator has the best performance at all frequencies. At higher energies (28–38 keV), the thicker needle CsI scintillators and the structured CsI scintillator all have very good performance. The needle CsI scintillators have higher absorption efficiencies but the structured CsI scintillator has higher resolution. Conclusions: The choice of scintillator is greatly dependent on the imaging task. The presented comparison and methodology will assist the imaging scientist in optimizing their high-resolution few-ten-keV imaging system for best performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

In-Line Crack and Stress Detection in Silicon Solar Cells Using Resonance Ultrasonic Vibrations

The Phase II project has the following specific tasks: (i) specify optimal configurations of the in-line system's component hardware and software; (ii) develop and justify a system prototype that meets major specifications for an in-line crack detection unit, such as high throughput rate, high level of stability, reproducibility of data acquisition and analysis, and high sensitivity with respect to crack length and crack location; (iii) design a system platform that allows easy integration within and adaptation to various solar cell belt-type production lines; (iv) develop a testing protocol providing quality certification of the production-grade system. Commercial Application of the proposed activity consists of bringing to the solar market a new high-tech product based on an innovative solution and patented methodology to contribute to cost reduction of silicon solar module production. The solar industry, with crystalline silicon as a dominant segment, shows outstanding performance, with approximately 25% yearly growth during the last years. Despite a slowdown with only 5.6 GW installations in 2009, solar module production for the 2010 and 2011 years was recovered. According to European Photonics Industry Consortium new solar PV installations grow by 56% compared to 2010 reached 64.7 GW in 2011. Revenues in the PV industry reached a record high of $93 billion in 2011, a 13.4 percent gain over 2010 and 150 percent over 2009. This growth was forecasted to continue in 2013 with double digits growth. The solar industry is economically driven to make solar panels of the highest conversion efficiency and reliability at the lowest production cost. The Resonance Ultrasonic Vibration system addresses critical needs of the silicon-based solar industry by providing a quality control method and tool, which will improve productivity, increase reliability of products and reduce manufacturing cost of solar panels.

14 SOLAR ENERGY↗
DOE OSTI2013

A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance (Final Report)

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) was tasked by the Defense Threat Reduction Agency (DTRA)-Joint Science and Technology Office (JSTO) to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. The long-term, broad objective of this project was to provide information and analysis that can be leveraged from existing and developing national and international disease surveillance systems and methodologies, to create a global disease monitoring system, ultimately providing decision makers with timely information to prepare for and mitigate the spread of disease. Specifically, this project evaluated data streams that are currently being used in surveillance systems and data streams that had the potential for being used in surveillance to enable early disease detection. LANL’s focus was on infectious disease surveillance and the term “biosurveillance” will be used to refer to this scope hereafter. Data stream evaluation was conducted using two different methods: Multi criteria decision analysis (MCDA) using a tool called Logical Decisions ® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a Surveillance Window concept developed at LANL that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. A cross method analysis was performed between the surveillance window based evaluation and the MCDA-based evaluation to identify data stream categories that showed high utility for both methods. In addition, algorithms that can integrate useful data streams to facilitate early disease detection were also examined. This project provided an understanding of data streams relevant to early warning, early detection and monitoring of disease outbreaks. The report describes the results of LANL’s evaluation of 16 data stream categories using the two methods, and recommendations for next steps. The project evolved significantly over its course, and significant improvements in our approaches were made compared to the initial proposed processes. A key outcome of this effort was that LANL was able to bring together several diverse entities involved in disease surveillance and lay the foundation for new collaborations that straddled military and civilian health surveillance. The robust evaluation framework developed by LANL has generated significant interest and there is interest in adopting this framework for various applications both within disease surveillance as well as other public health initiatives. Finally, LANL developed the Biosurveillance Resource Directory (BRD), a relational database that underwent pilot testing by members of the human, plant and animal disease surveillance community. The BRD is intended to be a global resource to facilitate rapid information access.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

New Stream-reach Development: A Comprehensive Assessment of Hydropower Energy Potential in the United States

The rapid development of multiple national geospatial datasets related to topography, hydrology, and environmental characteristics in the past decade have provided new opportunities for the refinement of hydropower resource potential from undeveloped stream-reaches. Through 2011 to 2013, the Oak Ridge National Laboratory (ORNL) was tasked by the Department of Energy (DOE) Water Power Program to evaluate the new stream-reach development (NSD) resource potential for more than 3 million US streams. A methodology was designed that contains three main components: (1) identification of stream-reaches with high energy density, (2) topographical analysis of stream-reaches to estimate inundated surface area and reservoir storage, and (3) environmental attribution to spatially join information related to the natural ecological systems, social and cultural settings, policies, management, and legal constraints to stream-reaches of energy potential. An initial report on methodology (Hadjerioua et al., 2013) was later reviewed and revised based on the comments gathered from two peer review workshops.After implementing the assessment across the entire United States, major findings were summarized in this final report. The estimated NSD capacity and generation, including both higher-energy-density (>1 MW per reach) and lower-energy-density (<1 MW per reach) stream-reaches is 84.7 GW, around the same size as the existing US conventional hydropower nameplate capacity (79.5 GW; NHAAP, 2013). In terms of energy, the total undeveloped NSD generation is estimated to be 460 TWh/year, around 169% of average 2002–2011 net annual generation from existing conventional hydropower plants (272 TWh/year; EIA, 2013). Given the run-of-river assumption, NSD stream-reaches have higher capacity factors (53–71%), especially compared with conventional larger-storage peaking-operation projects that usually have capacity factors of around 30%. The highest potential is identified in the Pacific Northwest Region (32%), followed by Missouri Region (15%) and California Region (9%). In terms of states, the highest potential is found in Oregon, Washington, and Idaho, the three states in the Pacific Northwest, followed by California, Alaska, Montana, and Colorado. In addition to the resource potential, abundant environmental attributes were also organized and attributed to the identified stream-reaches to support further hydropower market analysis. The prevalence of environmental variables and proportion of capacity from stream-reaches intersecting environmental variables varied according to hydrologic region. Detailed NSD findings are organized by hydrologic regions and presented in each chapter of this report.

Kao, Shih-Chieh↗
DOE OSTI2017

Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in theMidwest United States

This report presents final technical results for the project Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in the Midwest United States (DE-FE0023330). The project was a three-year effort consisting of seven technical tasks focused on defining geomechanical factors for CO 2 storage applications in deep saline rock formations in Ohio and the Midwest United States, because geomechancial issues have been identified as a significant risk factor for large-scale CO 2 storage applications. A basin-scale stress-strain analysis was completed to describe the geomechanical setting for rock formations of Ordovician-Cambrian age in Ohio and adjacent areas of the Midwest United States in relation to geologic CO 2 storage applications. The tectonic setting, stress orientation-magnitude, and geomechanical and petrophysical parameters for CO 2 storage zones and caprocks in the region were cataloged. Ten geophysical image logs were analyzed for natural fractures, borehole breakouts, and drilling-induced fractures. The logs indicated mostly less than 10 fractures per 100 vertical feet in the borehole, with mostly N65E principal stress orientation through the section. Geophysical image logs and other logs were obtained for three wells located near the sites where specific models were developed for geomechanical simulations: Arches site in Boone County, Kentucky; Northern Appalachian Basin site in Chautauqua County, New York; and E-Central Appalachian Basin site in Tuscarawas County, Ohio. For these three wells, 9,700 feet of image logs were processed and interpreted to provide a systematic review of the distribution within each well of natural fractures, wellbore breakouts, faults, and drilling induced fractures. There were many borehole breakouts and drilling-induced tensile fractures but few natural fractures. Concentrated fractures were present at the Rome-basal sandstone and basal sandstone-Precambrian contacts at the Arches and East-Central Appalachian Basin sites. Geophysical logs were utilized to develop local-scale geologic models by determining geomechanical and petrophysical parameters within the geologic formations. These data were ported to coupled fluid-flow and reservoir geomechanics multi-phase CO 2 injection simulations. The models were developed to emphasize the geomechanical layers within the CO 2 storage zones and caprocks. A series of simulations were completed for each site to evaluate whether commercial-scale CO 2 could be safely injected into each site, given site-specific geologic and geomechanical controls. This involved analyzing the simulation results for the integrity of the caprock, intermediate, and reservoir zones, as well quantifying the areal uplift at the surface. Simulation results were also examined to ensure that the stress-stress perturbations were isolated within the subsurface, and that there was only limited upward migration of the CO 2 . Simulations showed capacity to inject more than 10 million metric tons of CO 2 in a single well at the Arches and East Central Appalachian Basin sites without excessive geomechanical risks. Low-permeability rock layers at the Northern Appalachian Basin study area well resulted in very low CO 2 injection capacity. Fracture models developed for the sites suggests that the sites have sparse fracture network in the deeper Cambrian rocks. However, there were indicators in image logs of a moderate fracture matrix in the Rose Run Sandstone at the Northern Appalachian Basin site. Dual permeability fracture matrix simulations suggest the much higher injection rates may be feasible in the fractured interval. Guidance was developed for geomechanical site characterization in the areas of geophysical logging, rock core testing, well testing, and site monitoring. The guidance demonstrates that there is a suitable array of options for addressing geomechanical issues at CO 2 storage sites. Finally, a review of Marcellus and Utica-Point Pleasant shale gas wells and CO 2 storage intervals indicates that these items are vertically separated, except for the Oriskany sandstone and Marcellus wells in southwest Pennsylvania and northern West Virginia. Together, project results present a more realistic portrayal of geomechanical risk factors related to CO 2 storage for existing and future coal-fired power plants in Ohio.

58 GEOSCIENCES↗
DOE OSTI2020

E-PiPEline: Quick to Market Emulated-PPE using Commonly Available Materials

The Center for Disease Control has recommended the public to wear cloth face coverings in public settings that reduce potential exposure to COVID-19 where other social distancing measures are difficult to maintain (e.g., grocery stores and pharmacies) especially in areas of significant community based transmission. These face coverings and other Emulated-Personal Protective Equipment (EPPE) can be made by using Commonly Available Materials (CAMs). As part of the Sandia COVID-19 LDRD effort (funded under the Materials Science Investment Area), the Sandia E-PiPEline task evaluated E-PPE design options for face coverings and face shields considering their effectiveness, durability, build difficulty, build cost, and comfort. Observations from this investigation are presented here to provide guidelines for home construction of E-PPE. This executive summary includes a brief roadmap of the analysis methodology, two one-page handouts geared to be distributed to the public at large (one for E-PPE face coverings and one for E-PPE face shields), and additional observations regarding the potential solutions for E-PPE face coverings and face shields included to further support the one-page handouts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

JIRIAF: JLAB Integrated Research Infrastructure Acros Facilities

The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks which exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time cross-correlation analysis to determine which applications cause performance degradation. We then ameliorate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. We relocate the CLAS12 data processing workflow to a remote data center for a case study, preventing file migration and temporal data persistency.

Lawrence, David↗
DOE OSTI2024

Capability Enhancements for System-level thermal Hydraulic Modeling of Lead Fast Reactors

This project has focused on the use, assessment, and development of the SAS4A/SASSYS-1 (SAS) safety analysis software. Although SAS was originally intended as a safety analysis tool for Liquid Metal cooled Fast Reactors (LMFRs), which includes both Sodium Fast Reactors (SFRs) and Lead Fast Reactors (LFRs), the vast majority of its recent development and customization has been tailored to SFRs. In general, enhancements that are made to the software for SFRs are applicable to LFRs, however, the fuel composition and corrosive nature of lead requires careful consideration when performing safety analysis of an LFR. In this project an emphasis was placed on closing gaps that are associated with modeling LFRs using SAS. The principal objective of the project was to enhance the ability of SAS as a licensing tool for LFRs. This objective was to be accomplished through three tasks: 1) Enhance the ability of SAS to couple with external software; 2) Improve the underlying physics models in SAS, with priority given to models that are highly relevant to LFRs; 3) Extend the Verification and Validation (V&V) basis of the software

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Super resolution convolutional neural network for feature extraction in spectroscopic data

Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, which thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale

The field of bioinformatics and computational biology is currently experiencing a data revolution. The exciting prospect of making fundamental biological discoveries is fueling the rapid development and deployment of numerous cost-effective, high-throughput next-generation sequencing technologies. The result is that the DNA and protein sequence repositories are being bombarded with new sequence information. Databases are continuing to report a Moore’s law-like growth trajectory in their database sizes, roughly doubling every 18 months. In what seems to be a paradigm-shift, individual projects are now capable of generating billions of raw sequence data that need to be analyzed in the presence of already annotated sequence information. While it is clear that data-driven methods, such as sequencing homology detection, are becoming the mainstay in the field of computational life sciences, the algorithmic advancements essential for implementing complex data analytics at scale have mostly lagged behind. Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. In this dissertation, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz. work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for a collection of 2.56M sequences show parallel efficiencies of ~75-100% on up to 8K cores, representing a time-to-solution of 33 seconds. We extend this work with a detailed analysis of single-node sequence alignment performance using the latest CPU vector instruction set extensions. Preliminary results reveal that current sequence alignment algorithms are unable to fully utilize widening vector registers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Nuclear Fuel Cycle System Simulation Tool Based on High-Fidelity Component Modeling

The DOE is currently directing extensive research into developing fuel cycle technologies that will enable the safe, secure, economic, and sustainable expansion of nuclear energy. The task is formidable considering the numerous fuel cycle options, the large dynamic systems that each represent, and the necessity to accurately predict their behavior. The path to successfully develop and implement an advanced fuel cycle is highly dependent on the modeling capabilities and simulation tools available for performing useful relevant analysis to assist stakeholders in decision making. Therefore a high-fidelity fuel cycle simulation tool that performs system analysis, including uncertainty quantification and optimization was developed. The resulting simulator also includes the capability to calculate environmental impact measures for individual components and the system. An integrated system method and analysis approach that provides consistent and comprehensive evaluations of advanced fuel cycles was developed. A general approach was utilized allowing for the system to be modified in order to provide analysis for other systems with similar attributes. By utilizing this approach, the framework for simulating many different fuel cycle options is provided. Two example fuel cycle configurations were developed to take advantage of used fuel recycling and transmutation capabilities in waste management scenarios leading to minimized waste inventories.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Codes and Standards Update: February 2019

The goal of the DOE OE Energy Storage System Safety Roadmap is to foster confidence in the safety and reliability of energy storage systems. There are three interrelated objectives to support the realization of that goal: research, codes and standards(C/S)and communication/coordination. The objective focused on C/S is "To apply research and development to support efforts that refocused on ensuring that codes and standards are available to enable the safe implementation of energy storage systems in a comprehensive, non-discriminatory and science-based manner." The following activities support that objective and realization of the goal: (a.) Review and assess C/S which affect the design, installation, and operation of energy storage systems(ESS). (b.) Identify gaps in knowledge that require research and analysis that can serve as a basis for criteria in those C/S. (c.) Identify areas in C/S that are potentially in need of revision or enhancement and can benefit from activities conducted under research and development. (d.) Develop input for new or revisions to existing C/S through individual stakeholders, facilitated task forces, or through laboratory staff supporting these efforts. The purpose of this Codes and Standards Update is to support the above activities by providing information on efforts being conducted by U.S. standards developing organizations(SDOs)and other entities that are focused on ESS safety. The information is organized relative to the scope of each document in relation to ESS from the "macro to the micro"(e.g., from overarching covering considerable scope ,to installation specific, to ESS and then ESS components). Note that more macro documents are also likely to adopt by reference more micro documents.

25 ENERGY STORAGE↗