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NASA NTRS2012

HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

Narasimhan, Sriram↗

DOE OSTI2021

The Baghdad Atlas: A relational database of inelastic neutron-scattering (n,n ' γ) data

A relational database has been developed based on the original (n,n'γ) work carried out by A. M. Demidov et al., at the Nuclear Research Institute in Baghdad, Iraq (Demidov et al., 1978) for 105 independent measurements comprising 76 elemental samples of natural composition and 29 isotopically-enriched samples. The information from this Atlas includes: γ-ray energies and relative intensities; nuclide and level data corresponding to the residual nucleus and meta data associated with the target sample that allows for the extraction of the flux-weighted (n,n'γ) cross sections for a given transition relative to a defined value. The optimized angular-distribution-corrected fast-neutron flux-weighted partial γ-ray cross section for the production of the 846.8-keV 21+→0gs+γ-ray transition in 56Fe, determined to be $\langle$σγ$\rangle$=143(29) mb, is used for this purpose. However, different values for the adopted cross section can be readily implemented to accommodate user preference based on revised determinations of this quantity. The Atlas (n,n'γ) data has been compiled into a series of CSV-style ASCII data sets and a suite of Python scripts have been developed to build and install the database locally. The database can then be accessed directly through the SQLite engine, or using alternative methods such as the Jupyter Notebook Python-browser interface. Several examples exploiting different interaction methodologies are distributed with the complete software package.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Exploring Grid-Interactive Efficient Building Strategies for Laboratories Through Energy Modeling

Laboratories are often overlooked in demand flexibility research due to constraints on their operations as mission critical facilities, despite the major role they play in an organization's emissions. Laboratories consume 3-4 times more energy than a typical office building and are commonly the largest energy users on any campus. Consequently, most laboratories in the United States are significant contributors to their organization's carbon footprint if their energy needs are met through the combustion of fossil fuels. As part of the initiative to decarbonize laboratories, this report documents an analysis on specifically grid-interactive efficient building (GEB) opportunities for reducing energy costs and emissions associated with laboratory operations. The goal of this initiative was to provide a case study and guidance on how to use OpenStudio and REopt as modeling tools for GEB technologies and strategies in laboratory environments across different climate zones in the United States. The analysis found that efficiency-based GEB strategies had the most significant impact on laboratory operations, while load-shedding and load-shifting GEB strategies produced smaller results. The culmination of these approaches applied across all five climate zones generated on average: 1) 28% energy cost savings and 30% greenhouse gas (GHG) emissions reductions, and 2) 4% enhanced energy cost savings under a time-of-use (TOU) pricing schedule compared to traditional pricing schemes. Grid-interactive efficiency building measures were found to produce the greatest energy savings in both electricity and natural gas, particularly in regions with high electrical loads, such as warm climates for cooling. Laboratories that had high levels of natural gas consumption, meanwhile, experienced the greatest emission reductions. The report concludes with an analysis on the opportunities for flexible loads in lab spaces and how small-scale measures in addition to opaque pricing structures for peak demand could become barriers to demand flexibility planning. The report also explores how electrifying laboratory buildings with heat pumps could reduce energy costs and GHG emissions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Informing NLP Learning Tasks by Tracking User Features: An ASRS Use Case using Kaona

There has been growing interest in utilizing natural language processing (NLP) algorithms in Aviation Safety. This interest has extended to leveraging the decades of records publicly available on the Aviation Safety Reporting System (ASRS). While related literature has given more emphasis in lessons learned from the narratives, our prior work has focused on using NLP to support narrative search in the ASRS. Specifically, we evaluated if the use of alternative search mechanisms to keyword search, such as the retrieval of related narratives even without matching keywords could improve narrative discovery. A difficulty in experimenting alternative search mechanisms in any information retrieval task is the lack of ground truth. To address this limitation, we propose Kaona, a lightweight interface which enables the prototyping of alternative search retrieval tasks, by tracking user experience both explicitly (user-specified feedback), or implicitly (user navigation through interface affordances). Differently from distracting requests for feedback during user navigation, Kaona collects explicit feedback from users by mapping them to affordances which support the user workflow, while obtaining ground truth information for learning tasks.

human-computer-interaction↗

DOE OSTI2020

Genomics-enabled analysis of specialized metabolism in bioenergy crops: Current progress and challenges

Plants produce a staggering diversity of specialized small molecule metabolites that play vital roles in mediating environmental interactions and stress adaptation. This chemical diversity derives from dynamic biosynthetic pathway networks that are often species-specific and operate under tight spatiotemporal and environmental control. A growing divide between demand and environmental challenges in food and bioenergy crop production have intensified research on these complex metabolite networks and their contribution to crop fitness. High-throughput omics technologies provide access to ever-increasing data resources for investigating plant metabolism. However, the efficiency of using such system-wide data to decode the gene and enzyme functions controlling specialized metabolism has remained limited; due largely to the recalcitrance of many plants to genetic approaches and the lack of ‘user-friendly’ biochemical tools for studying the diverse enzyme classes involved in specialized metabolism. With emphasis on terpenoid metabolism in the bioenergy crop switchgrass as an example, this review aims to illustrate current advances and challenges in the application of DNA synthesis and synthetic biology tools for accelerating the functional discovery of genes, enzymes and pathways in plant specialized metabolism. These technologies have accelerated knowledge development on the biosynthesis and physiological roles of diverse metabolite networks across many ecologically and economically important plant species and can provide resources for application to precision breeding and natural product metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2008

Small-Body Extensions for the Satellite Orbit Analysis Program (SOAP)

An extension to the SOAP software allows users to work with tri-axial ellipsoid-based representations of planetary bodies, primarily for working with small, natural satellites, asteroids, and comets. SOAP is a widely used tool for the visualization and analysis of space missions. The small body extension provides the same visualization and analysis constructs for use with small bodies. These constructs allow the user to characterize satellite path and instrument cover information for small bodies in both 3D display and numerical output formats. Tri-axial ellipsoids are geometric shapes the diameters of which are different in each of three principal x, y, and z dimensions. This construct provides a better approximation than using spheres or oblate spheroids (ellipsoids comprising two common equatorial diameters as a distinct polar diameter). However, the tri-axial ellipsoid is considerably more difficult to work with from a modeling perspective. In addition, the SOAP small-body extensions allow the user to actually employ a plate model for highly irregular surfaces. Both tri-axial ellipsoids and plate models can be assigned to coordinate frames, thus allowing for the modeling of arbitrary changes to body orientation. A variety of features have been extended to support tri-axial ellipsoids, including the computation and display of the spacecraft sub-orbital point, ground trace, instrument footprints, and swathes. Displays of 3D instrument volumes can be shown interacting with the ellipsoids. Longitude/latitude grids, contour plots, and texture maps can be displayed on the ellipsoids using a variety of projections. The distance along an arbitrary line of sight can be computed between the spacecraft and the ellipsoid, and the coordinates of that intersection can be plotted as a function of time. The small-body extension supports the same visual and analytical constructs that are supported for spheres and oblate spheroids in SOAP making the implementation of the more complex algorithms largely transparent to the user.

Carnright, Robert↗

NASA NTRS1997

Computer Based Training: Field Deployable Trainer and Shared Virtual Reality

Astronaut training has traditionally been conducted at specific sites with specialized facilities. Because of its size and nature the training equipment is generally not portable. Efforts are now under way to develop training tools that can be taken to remote locations, including into orbit. Two of these efforts are the Field Deployable Trainer and Shared Virtual Reality projects. Field Deployable Trainer NASA has used the recent shuttle mission by astronaut Shannon Lucid to the Russian space station, Mir, as an opportunity to develop and test a prototype of an on-orbit computer training system. A laptop computer with a customized user interface, a set of specially prepared CD's, and video tapes were taken to the Mir by Ms. Lucid. Based upon the feedback following the launch of the Lucid flight, our team prepared materials for the next Mir visitor. Astronaut John Blaha will fly on NASA/MIR Long Duration Mission 3, set to launch in mid September. He will take with him a customized hard disk drive and a package of compact disks containing training videos, references and maps. The FDT team continues to explore and develop new and innovative ways to conduct offsite astronaut training using personal computers. Shared Virtual Reality Training NASA's Space Flight Training Division has been investigating the use of virtual reality environments for astronaut training. Recent efforts have focused on activities requiring interaction by two or more people, called shared VR. Dr. Bowen Loftin, from the University of Houston, directs a virtual reality laboratory that conducts much of the NASA sponsored research. I worked on a project involving the development of a virtual environment that can be used to train astronauts and others to operate a science unit called a Biological Technology Facility (BTF). Facilities like this will be used to house and control microgravity experiments on the space station. It is hoped that astronauts and instructors will ultimately be able to share common virtual environments and, using telephone links, conduct interactive training from separate locations.

Mullen, Terence J.↗

NASA NTRS2019

Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Rainfall↗

NASA NTRS2020

Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

An integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/MultiSensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in globalscale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Pierre-Emmanuel Kirstette↗

DOE OSTI2021

Building Neutron Scattering Infrastructure in Louisiana for Advanced Materials (Final Report)

The main objective of the Louisiana Consortium for Neutron Scattering (LaCNS) is to build a major neutron scattering infrastructure capable of treating both soft and hard materials. The goal is to create a sustainable effort having the quality, breadth, and depth necessary to produce competitive proposals including collaborative and center type proposals. Our scientific aim is to understand the role of coupling in emergent complex materials and its impact on the structure/property relationship and to explore how to tune the key couplings to guide the design of materials with the desired properties. This naturally includes building a base of users of the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) in Louisiana; to train highly talented graduate and post-doctoral students in synthesis, neutron scattering, and simulation and modeling techniques, thereby helping to produce the next generation of scientist who use neutron scattering techniques as a crucial part of their research. The goal of our hard matter program is to understand the interplay between spin, charge, orbital, and lattice degrees of freedom in carefully selected complex materials. We have made considerable progress on number of complex systems including the oxide Sr3(Ru1-xMnx)2O7 where short-range magnetic ordering with anisotropic spin texture is initiated at the metal-insulator transition that clearly indicates a strong spin-charge coupling. Another critical area is in quantum materials. For example, in the topological semimetal BaMnSb2, we found a 3D canted antiferromagnetic Weyl semimetal with a 2D electronic structure and a nontrivial Berry phase. In addition, in the hexagonal chiral Mn1/3NbS2 system, we found soliton/soliton and soliton/antisoliton domain walls where the application of small fields or small currents can be used to control nanoscopic magnetic domains where the control of domain walls is crucially important for information storage. In addition, chemical transformation investigations were performed on the VISION instrument at Spallation Neutron Source (SNS). The soft matter effort was focused on understanding the role of non-covalent interactions on the structure and dynamics of fluid-based soft matter. One key focus was on sequence-defined (SD) amphiphilic peptoid polymers that allow encoding of molecular interactions and thereby systematic investigations of how charge directs the solution self-assembly of amphiphilic polymers in water. Another important area was on the dynamics of lipids self-assembled into membranes for exploring the permeability and mechanical using both neutron spin echo (NSE) spectroscopy and quasielastic neutron scattering (QENS) to distinguished between viscoelasticity and permeability at the molecular scale. Overall, the LaCNS project was quite successful, generating 145 publications and 245 presentations. Our graduate and post-doctoral students were well trained resulting in positions in national laboratories (Oak Ridge National Laboratory (ORNL), Argonne National Laboratory and Los Alamos National Laboratory), major research universities, and industry. We also developed a uniaxial pressure cell along with ORNL for SNS. A critical goal of this project was to establish a foundation for competing nationally in federally funded research programs. To this end, we were quite successful in generating over twenty-two federally and non-federally funded grants including awards from NSF, DOE, and DOD, and two early career awards. Equally important, we were able to secure a key major piece of instrumentation via a large ARO grant for a state-of-the-art electron microscope.

36 MATERIALS SCIENCE↗

NASA NTRS2005

Giovanni: A System for Rapid Access, Visualization and Analysis of Earth Science Data Online

Collecting data and understanding data structures traditionally are the first steps that a user must take, before the core investigation can begin. This is a time-consuming and challenging task, especially when science objectives require users to deal with large multi-sensor data that are usually in different formats and internal structures. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has created the GES DISC Interactive Online Visualization and ANalysis Infrastructure, Giovanni, to enable Web-based visualization and analysis of satellite remotely sensed meteorological, oceanographic, and hydrologic data sets, without users having to download data. The current operational Giovanni interfaces provide the capability to process a number of important satellite measurements, such as (1) ozone and other trace gases from TOMS, OMI, HALOE, and MLS; (2) air temperature, water vapor, and geopotential height from AIRS; (3) aerosols from MODIS TerrdAqua, and GOCART model; (4) precipitation from TRMM and ground measurements; (5) chlorophyll and other ocean color products from SeaWiFS and MODIS Aqua; and (6) sea surface temperature from MODIS Aqua. Depending on the input data structure, the system provides simple statistical analysis and creates time-averaged area plot, area-averaged time series, animations, Hovmoller latitude vs. time and longitude vs. time plots, as well as vertical profiles. The inter-comparison interfaces allow a user to compare observations from different instruments, to conduct anomaly analysis, and to study basic relationships between physical parameters. Giovanni handles data with different temporal and spatial resolutions and, thus, enables both regional and global long-term climate research and short-term special events investigation, as well as data validations and assessments. Because of its simplicity of usage, Giovanni is powerful and versatile, able to assist a wide range of users, from the discipline scientists conducting preliminary research in various fields, to students in the classroom learning about weather, climate, and other natural phenomena. Giovanni can be accessed from: http://disc. esfc.nasa.gov/techlab/giovanni/index.shtml

Shen, S.↗

NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA): Formation of an International Working Group

There is a natural partitioning of scientific interest amongst three specialties of aerosol re- search: modeling, in situ measurements, and remote sensing. The broader aerosol community benefits when these groups interact, and this strengthens overall scientific impact on climate and air quality research and predictions. The new MIRA working group establishes a forum for identifying collaborations and improving discussions amongst specialties and across regional boundaries. One area of keen interest is uniting satellite and ground-based lidar groups with other aerosol disciplines. Elastic backscatter lidars depend upon a priori knowledge of aerosol properties to convert measured lidar profiles into aerosol extinction profiles. Acquiring additional insight on aerosol properties and transport is highly valuable to these lidar groups to improve the data quality and aid in their scientific interpretation. Another area of interest is to facilitate the incorporation of measurements into global aerosol models. Modelers need aerosol optical look-up tables that enable quick conversions of hydrated (and dry) polydisperse size distributions into aerosol optical properties (extinction, scattering, etc.), but many of the existing tables are based upon outdated measurements. Thus, MIRA is building the Tables of Aerosol Optics (TAO), which is a community collection of aerosol optical calculations. This expands on the historical efforts of Shettle and Fenn, d’Almeida, GADS, OPAC, etc., except that TAO seeks continual input from the community. Thus, as aerosol measurement and computational techniques advance, so does the TAO collective. Since TAO is a community collective, it is expected that users will optionally upload computations for aerosol type as well as computations for all of the traditional aerosol species (like amm sulfate, amm nitrate, organics, etc.). The near-term purpose of the MIRA working group is to: * Characterize regional aerosol lidar ratios to support improvements of aerosol extinction profiles and understanding of aerosol typing. * Create TAO -- a community cooperative of aerosol optical tables. * Facilitate international communications between aerosol measurement and modeling groups. * Encourage the use of regional knowledge to develop and improve remote sensing techniques for current and future backscatter lidars located in space. * Enable and foster communication between the scientists who run global aerosol models and scientists who analyze space-based lidar data. We will discuss how a collaborative aerosol working group will be organized on this topic. Send email to calipso_v5alr-join@lists.nasa.gov with the word `subscribe' in the subject line to join MIRA.

Gregory Schuster↗

NASA NTRSDate not supplied

Models, In situ, and Remote Sensing of Aerosols (MIRA)

There is a natural partitioning of scientific interest amongst three specialties of aerosol re-search: modeling, in situ measurements, and remote sensing. The broader aerosol community benefits when these groups interact, and this strengthens overall scientific impact on climate and air quality research and predictions. The new MIRA working group establishes a forum for identifying collaborations and improving discussions amongst specialties and across regional boundaries. One area of keen interest is uniting satellite and ground-based lidar groups with other aerosol disciplines. Elastic backscatter lidars depend upon a priori knowledge of aerosol properties to convert measured lidar profiles into aerosol extinction profiles. Acquiring additional insight on aerosol properties and transport is highly valuable to these lidar groups to improve the data quality and aid in their scientific interpretation. Another area of interest is to facilitate the incorporation of measurements into global aerosol models. Modelers need aerosol optical look-up tables that enable quick conversions of hydrated (and dry) polydisperse size distributions into aerosol optical properties (extinction, scattering, etc.), but many of the existing tables are based upon outdated measurements. Thus, MIRA is building the Tables of Aerosol Optics (TAO), which is a community collection of aerosol optical calculations. This expands on the historical efforts of Shettle and Fenn, d’Almeida, GADS, OPAC, etc., except that TAO seeks continual input from the community. Thus, as aerosol measurement and computational techniques advance, so does the TAO collective. Since TAO is a community collective, it is expected that users will optionally upload computations for aerosol type as well as computations for all of the traditional aerosol species(like amm sulfate, amm nitrate, organics, etc.). The near-term purpose of the MIRA working group is to: *Characterize regional aerosol lidar ratios to support improvements of aerosol extinction profiles and understanding of aerosol typing. *Create TAO --a community cooperative of aerosol optical tables. *Facilitate international communications between aerosol measurement and modeling groups. *Encourage the use of regional knowledge to develop and improve remote sensing techniques for current and future backscatter lidars located in space. *Enable and foster communication between the scientists who run global aerosol models and scientists who analyze space-based lidar data. We will discuss how a collaborative aerosol working group will be organized on this topic. Those who are interested in MIRA can sign up for the MIRA email listserver at https://forms.gle/qdbCnngzNJc5YJi57.

Greg Schuster↗

DOE OSTI2023

Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure

OCPP protocol manages the interaction between the EVSE and its respective back-end communication network. It plays a pivotal role in both error reporting and troubleshooting, carried out primarily through the CSMS. OCPP defines both standard error codes and a flexible framework for creating and communicating custom error codes. The OCPI protocol orchestrates the communication between different backhaul communication networks, incorporating the exchange of error codes. These error codes are instrumental in pinpointing and rectifying issues that can surface prior to, during, or after charging operations, fortifying the reliability and resilience of the EV charging infrastructure. The flexibility offered by the OCPP and OCPI frameworks through the introduction of custom error codes also creates its own set of challenges. While the integration of custom error codes allows for enhanced granularity, it also introduces inconsistencies and fragmentation within the overarching diagnostic reporting system. To address the challenges with custom error codes this report proposes a set of Minimum Required Error Codes (MRECs) for streamlined error reporting, interpretability, and diagnostics. Recommendations in this report are based on independent analysis of custom error codes from multiple stakeholders within the EV charging ecosystem. For better error resolution, this report also assigns one or more entities responsible for the resolution of every mentioned error code. Finally, a functional classification for each mentioned error code is also identified to describe the nature of the error. In summary, the purpose of this document is to simplify the troubleshooting process and increase charging reliability for all EV users. This report serves as a recommendation for industry stakeholders, encouraging a unified methodology to define, transmit, and interpret common error codes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

NSA Site Science: Use of ARM Observations from Northern Alaska to Evaluate and Improve Prediction Capabilities

The Arctic is warming at a rate nearly double that of the rest of the planet, leading to profound changes in atmospheric, oceanic, and ice processes. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility has played a significant role in Arctic research, operating observatories in Alaska's North Slope for over 25 years. These observatories provide a rich, and wide-reaching dataset that offers insight into atmospheric processes in northern Alaska. This report details the results of a nine-year research project (2015–2024) supported by the DOE Atmospheric Systems Research (ASR) program, that leverages data from ARM’s deployment of observing facilities at Utqiaġvik (known as the North Slope of Alaska, or NSA, site) and Oliktok Point, Alaska. The project was conducted in two phases: - Phase 1 (2015–2019): Focused on understanding key atmospheric processes at Oliktok Point, including cloud formation, high-latitude precipitation, aerosol-cloud interactions, and cloud properties. - Phase 2 (2019–2024): Extended the research to the broader North Slope region, using data from both Oliktok Point and Utqiaġvik. Topics explored included surface energy budgets, atmospheric stability, ice nucleation processes, and microphysics in Arctic clouds. The project resulted in numerous research products, including 50 peer-reviewed publications and dissertations, 169 presentations, and 10 data products. These products cover a variety of topics, including: - Cloud Macro- and Microphysical Properties: Arctic clouds play a crucial role in energy transfer, and accurate representation in models is critical. The study explored cloud transitions, ice crystal shapes, and dual-wavelength radar data to understand ice crystal habits and size distributions. - Aerosol Properties and Processes: The team examined aerosol sources in the Arctic, including industrial emissions and natural sources. Observations showed significant spatial gradients in aerosol concentrations due to human activities and wildfire smoke. The influence of aerosols on cloud formation and the surface energy budget was also assessed. - Aerosol-Cloud Interactions: Research revealed that aerosols might suppress cloud ice production, affecting cloud radiative forcing and precipitation. The impact of local industrial emissions on cloud properties was also investigated. - Contextualizing the North Slope of Alaska in the context of the broader Arctic: To understand broader trends, the project evaluated large-scale circulation patterns and the influence of weather systems on the Arctic. Studies indicated that large-scale processes play a significant role in temperature patterns and the timing of snowmelt. - Advancing ARM Observational and Modeling Capabilities: The project developed new radar data products and advanced measurement techniques, including clutter mitigation and drizzle detection. Uncrewed aerial systems (UAS) and tethered balloon systems (TBS) were deployed to gather detailed atmospheric data. Additionally, the project supported 10 early career scientists, providing training and mentorship to undergraduate interns, graduate students, postdoctoral researchers, and early career researchers. These efforts contributed to the advancement of ARM research capabilities and fostered a new generation of scientists skilled in Arctic atmospheric research. Ultimately, this ASR-supported project has provided valuable insights into Arctic atmospheric processes and their broader climate implications. Recommendations for future work include continuing support for long-term observing at Arctic locations to foster additional research, further exploration of aerosol-cloud interactions and the potential impacts of enhanced industrialization of the Arctic, and expanded use of uncrewed systems to gather data in this remote and harsh environment. Additionally, the data products developed by this work, and the data products developed through the ARM infrastructure, leave a treasure-trove of additional information that should be explored for many years to come to gain additional insight into physical processes in the Arctic atmosphere that drive the rapid changes occurring in at high latitudes and their global impact.

58 GEOSCIENCES↗

DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2026

PRIMO – The Oil & Gas Well Plugging Optimizer

This work presents PRIMO’s main capabilities and introduces the PRIMO web application, an intuitive user interface that leverages our sophisticated mathematical optimization model to rigorously optimize P&A priorities and plugging campaign efficiency. The web app simplifies user interaction, provides a powerful data management framework, and supports a broad user base (e.g., state agencies, well owners/operators, and plugging companies) to use PRIMO for decision-making. Specifically, we provide a demonstration of how to input the information on candidate wells, plugging campaign budget, user-defined priority and efficiency criteria to PRIMO. A real-world case study that consists of 1411 oil and gas wells and impact and efficiency priorities (e.g., well age, well proximity to schools/hospitals, well accessibility, distance between wells in projects) is presented to showcase PRIMO’s core capabilities: (i) ranking a candidate well population based on priorities, (ii) recommending high-impact and high-efficiency P&A projects, and (iii) assigning impact and efficiency scores to projects allowing for rigorous quantitative comparison among them.

02 PETROLEUM↗

DOE OSTI2022

Deployment Readiness Framework Subtask 1.1 (Literature Review)

Island and remote coastal communities face some of the most challenging environments for building, operating, and maintaining energy infrastructure, as well as the highest costs for electricity, fuels, and other essential energy sources. As sea-levels rise and storms become more intense and frequent, these communities and the energy infrastructure that supports coastal lives and livelihoods are also at increasing risk from natural hazards. To address these challenges, many coastal communities are envisioning energy solutions that will support the triple bottom line goals of the blue economy: economic growth, environmental sustainability, and social equity. Yet, island and remote coastal communities often face limited resources and capacity to tackle complex energy and coastal resilience issues. To support community-driven energy transitions in island and remote communities, and to better understand relationships between energy, community, and ecosystem resilience, the Department of Energy’s Water Power Technologies Office (WPTO) has initiated the development of a Deployment Readiness Framework (DRF). The objective of the work is to co-produce and test practical tools and approaches that assess the readiness of coastal communities for marine energy demonstration, deployment, and operation. The DRF aims to build on and support the Energy Transitions Initiative Partnership Program (ETIPP) and other community-oriented energy transition programs. This project is jointly led by Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL). The development of the DRF includes three main phases: 1) a learning phase involving stakeholder engagement and literature review to synthesize metrics of community readiness to advance through an energy transition and to understand the state of the research and practice of participatory science-policy processes in various sectors, 2) a design phase to define readiness approaches and tools that will be developed as part of the DRF, and 3) an implementation phase to create the applications and interfaces for WPTO and the national laboratories to interact with the DRF. All three phases include close collaboration with communities and end-users of the framework, first to identify gaps in the science and tools needed to achieve community-driven energy transition goals and second, to test and improve the framework iteratively. Through technical assistance programs like ETIPP and utilizing the completed DRF to understand the influencing factors which motivate or deter energy transitions, WPTO hopes to engage a number of near-term marine energy demonstration opportunities. Here we report on the results from the literature review (Subtask 1.1) to inform the stakeholder engagement (Subtask 1.2) and design phase (Task 2) of the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗