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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Activating Opportunity Zones for Rapid Solar+Storage Deployment in Low Income Communities (Final Report)

Millions of Texans choose their own power provider, giving them the option to have low-cost and even renewable energy delivered through their retail electric plan. However, Texans with less disposable income often pay more for electricity and have limited access to green energy and emergency backup power even though the costs of solar and wind power are at record lows and continue to decline. The Powered for Good initiative aimed to help deliver clean, affordable, 100% renewable electricity to low-income households in Texas’s Competitive Retail Areas, where households can choose their electricity provider. Objectives: The primary goal of this project was to develop and validate one or more affordable solar+storage products (i.e., priced less than of $0.14/kWh) that Retail Electricity Providers (REP) can offer to LI households. The team achieved three objectives: 1. Investigate how to best reduce electricity costs and increase availability of emergency power for LI customers; 2. Build momentum for, and facilitate an approach to, a Texas-based pilot deployment of such solar+storage products, with a goal of greatly expanding this approach to a large segment of the LI population in Texas; 3. Provide the structural framework, finance model, and roadmap to potentially increase investment of solar+storage projects in LI communities across U.S. states when modified to meet their state-specific laws and regulations. The team evaluated the market of viable solutions for low-income Texans through interviews and focus groups with professionals and residents with lived experience. The team then piloted a low-cost retail electric product. Following the pilot, the team developed educational materials, including fact sheets, a Go Green Save Green interactive guide, and an Electricity Bill Analysis Tool. The team is now working to increase power resilience and reduce energy insecurity with micro solar and storage in partnership with local entities in the Harris County area. Key Findings: Informed by Powered for Good research, including the experiences on Texas residents and electricity system experts, the Powered for Good team developed a pilot that was implemented by Energy Well Texas, a new company formed in late 2020. The pilot featured a combination of a 100% clean residential energy offering delivered through the electrical grid plus a selection of batteries, lights and a solar panel that provided participants with varying levels of backup power. For the 8 customers who submitted previous bills, the Energy Well Texas pilot reduced energy bills by about 30%. In Houston, residents earning less than 30% of Area median Income (AMI) spend an average 13% of their income on energy or about $1,555 per year. Repeating the pilot results for these residents could yield $466 in savings per customer or about 4% of their income. While this will not end energy poverty, it is a big step toward that goal. The Powered for Good and Energy Well Texas teams are currently planning their post-pilot phase of service offerings.

14 SOLAR ENERGY↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Leveraging of Funds in Performance Contracting Projects

Performance contracts are widely used in the public sector (and to a lesser extent in the private sector) to implement energy efficiency, renewable energy, and energy resilience projects. While such projects can be accomplished at no up-front cost to the customer, capital contributions from the customer, when available, provide several advantages. The objective of this report was to determine the amount of additional investment that is achieved by leveraging customer funding contributions to the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Advanced Material Studies for High Intensity Proton Production Targets and Windows

The titanium alloy Ti6Al4V is widely used in accelerator facilities as beam windows, which are exposed to high cycle compressive stress waves from intense pulsed proton beams. Such materials interacting with the beam are subject to various forms of radiation damage, which can adversely affect their endurance limit. However, no fatigue data is currently available for high energy proton irradiated titanium alloy. Due to limitations in proton irradiation facilities, only miniature flat samples can be used for irradiation. To address this issue, we have developed a custom-made bend fatigue tester at Fermilab specifically for testing proton irradiated titanium alloy. In this presentation, we report on the fatigue test results obtained from this custom-fatigue tester using a non-ASTM standard specimen design. We plan to validate these sparse fatigue data with ASTM standard samples using standard fatigue samples. Furthermore, we have modified another commercial bend fatigue tester to accommodate miniature samples, and discuss some inherent deficiencies of the commercial fatigue tester to test miniature samples. To overcome this issue, a new fixture design is presented, which enables satisfactory fatigue testing on miniature samples over long periods. Finally, we present an upgrade to the custom-fatigue tester, featuring this new fixture design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Resilience Development for Electric Energy Delivery Sytems (ResDEEDS): A Tool for Power System Resilience Planning (Rev. 1)

The INL Resilience Framework is built to be customized to a particular system’s characteristics, resilience goals, and hazards that it is likely to face. The challenge with a framework that is built to be customized is that it requires more effort from the user to understand the framework and apply it correctly to a particular system. The goal of the web application described here is to automate the framework application as much as possible. Inputs from users are standardized into a common format, but the modeling platform used is a modular framework built to adapt to any system. Baseline effects from common hazards are programmed with stochastic variables to allow for quick exploration of hazard impacts, but can be customized down to component-level impact if a user desires a high level of detail and specificity.

17 WIND ENERGY↗
DOE OSTI2023

3-D Printed Electro-Magnetic Objects for THz Communications

Advanced materials and manufacturing techniques will pave the path towards high quality electro-magnetic circuits that operate up through THz and into optics regime. Digital additive manufacturing (AM) techniques offer game-changing potential for large scale manufacturing of Radio-frequency (RF) circuits and system in custom 2D/3D geometries. The technical collaboration will explore the design flexibility and product customization capabilities of additive manufacturing techniques to process magnetic materials in custom 3D geometries with tailored dielectric properties and frequency response characteristics. Transformation of an RF circuit design idea into a 3D printed system using additive manufacturing techniques will bring together the elements of advanced materials, additive integration, and low-cost manufacturing for future advanced wireless communication systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Survey of Direct Heating Equipment Market Actors in California

Direct Heating Equipment (DHE) is a category of ductless space heating appliances that provide warm air directly to the space where they are installed. We investigate, from the perspective of the DHE supply chain in California, what types of DHE are marketed in the state, how they are sold and serviced, and whether any potential barriers to adoption of high-efficiency DHE exist. We find that the market for DHE in the state is almost exclusively a replacement market, with some additional repair and maintenance services. We also find that, even though manufacturers sell high-efficiency DHE models in California, most contractors report installing baseline models most of the time. Contractors say their customers do not see any value in high-efficiency units, and they (contractors) do not believe that high-efficiency DHE is worth the additional cost to their customers. In addition, the majority of contractors purchase DHE from distributors and, therefore, distributors are a key piece of the supply chain that can constrain DHE purchase choice. Distributors do not see a demand for high-efficiency DHE and, hence, do not keep stock of these units. They argue that, since the market is driven mostly by replacements and these replacements occur typically in an unplanned way, homeowners are unwilling to pay for any additional costs associated with a high-efficiency model. Rather, homeowners usually prefer to replace the failed unit with a new unit that can fit in the same location with no- or minimal additional replacement costs. Considering that most DHE purchases are made by contractors, on behalf of their customers, informing contractors (and homeowners) of the potential cost-effectiveness of efficient DHE could help mitigate some of the barriers to the adoption of high-efficiency DHE in the state. In addition, monetary incentives and more stringent building codes that apply to DHE replacements can contribute to increasing adoption of high-efficiency DHE in California. This will not only reduce energy costs, but also increase safety and comfort to DHE users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Building efficiency, electrification, and distributed solar PV bill savings under time-based retail rate designs

Building energy technologies and distributed generation, including energy efficiency (EE), distributed solar PV (DPV), and building electrification, are critical to meeting decarbonization goals. Rate design may play an important role in determining the customer economics of adopting these technologies, but it is unclear whether – and to what extent – current rate design trends support or impede progress toward these goals. In this study, we answer these questions by quantifying the range of residential customer bill impacts of EE, DPV, and building electrification investments under current and emerging time-based retail electricity rate designs (i.e., time-of-use, event-based pricing, coincident demand charges, and real time pricing). We also compare these customer bill savings to power system and societal benefits and assess how well the investments are compensated relative to the societal value they provide.

14 SOLAR ENERGY↗
DOE OSTI2024

Microgrid Controller Survey Report: 2024 Update

In the past decade, the number of microgrids deployed by electric utilities, end-use customers, and third parties has been increasing significantly both in the US and worldwide. The increase is driven by various reasons, such as carbon emission reduction, energy efficiency improvement, reliability and resilience enhancement, energy infrastructure modernization, and more. Microgrid controllers, as the most important components in these deployed microgrids, optimally coordinate the operation and control of the microgrid assets (e.g., distributed energy resources [DERs], energy storage systems, demand response, and even electric vehicles) and ensure that the microgrid is being operated in an efficient, clean, and resilient manner. To that end, the market for microgrid controllers has been growing rapidly in recent years. Various microgrid controllers have been released and continuously updated by vendors to satisfy the different needs of microgrid customers. However, the functionalities demonstrated by microgrid controllers released by different vendors show a huge difference because of a lack of widely known and accepted industrial standards, and various microgrid customers have specific requirements for different applications and scenarios. To obtain a clear view of the current state of the commercial microgrid controllers’ functionalities and identify potential research gaps, a survey of the functionalities of commercial microgrid controllers and the Advanced Distribution Management System (ADMS) developed by vendors and national laboratories was conducted in 2023. The results of the survey are presented in this report with the current status of commercial microgrid controllers analyzed, potential research gaps identified, and future research trends revealed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Window Film

This documentation focuses on a single end-use savings shape measure - window film. The window film studied in this analysis, called solar control film, is a passive retrofit solution for windows that does not involve window replacement. This type of film is composed of transparent, tinted, or metalized laminated polyester layers and can be attached to an existing window surface (either on the exterior or interior side of the window). The properties of the window film are designed to shift the thermal and optical performances of the overall glazing system in order to serve various needs the customer would have (e.g., heat, glare). While the practical goal of purchasing and installing a window film varies widely in the real market, this study only focuses on the goal of energy savings, and highlighting the corresponding emissions. Other important aspects that customers typically consider include visual comfort, privacy, aesthetics, ultraviolet protection, etc. Thus, in practice, customers often choose a window film product not only to save energy (or cost) but also to mitigate issues around glare, excessive light, daytime privacy, or inconsistent appearance of the building. Window film products that were modeled in this analysis significantly reduced the solar heat gain coefficient of the overall glazing system, resulting in better energy savings for buildings in hot climate regions. However, significantly reducing the solar heat gain coefficient that can block unfavorable heat during the summer can actually harm blocking favorable heat during the winter. By applying window films on a stock of buildings covering various load and weather conditions, this analysis highlights when (e.g., time of day) and where (e.g., geospacial location) we can save energy with window films.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Cold Spray Additive Manufacturing For New Pipeline Fabrication In Live, Natural Gas Distribution Mains

ULC Technologies (ULC) and the Penn State University Applied Research Lab (PSU ARL) developed a process and approach for rehabilitating live, natural gas distribution pipelines using Cold Spray Additive Manufacturing (CSAM). Robotic inline fabrication of a new pipe will be performed inside a natural gas main without disrupting customers. The material studied was blended with stainless steel which offers corrosion resistance, high strength and compatibility with hydrogen to enable a robust pipeline system that is future proofed. Stainless steel coatings sprayed and tested in the lab showed excellent strength, ductility, and porosity values. ULC initially sought to use methane as the process gas for compatibility with the host pipe, but after evaluating the field and lab challenges, nitrogen was deemed to be more suitable. Early robotic concepts have been developed that indicate technical feasibility. While the technology development originally targeted natural gas distribution mains, market research showed a broader need. Spot repairs performed in natural gas transmission mains may be a more economical approach for reinforcing distribution and transmission mains compared with full pipe-in-pipe rehabilitation. Additive manufacturing offers custom programming, as well as high accuracy and precision. This means custom contours can be manufactured in situ, offering flexibility in the repair solution, such as fabricating internal full circumferential sleeves. The value of the repair can be high at difficult-to-reach locations and in pipes that need reinforcement before the injection of methane-hydrogen blends.

03 NATURAL GAS↗
DOE OSTI2025

Smart Contracts for Power Grid Applications Using the Advanced DLT Cyber Grid Guard Testbed

In this study is presented two power system applications with distributed ledger technology (DLT) and smart contracts (SC) that were assessed in a Cyber-Grid-Guard System (CGGS) advanced testbed, with protective relays, power meters, communication devices, DLT devices, synchronized time source, clock displays and real time simulator. This CGGS testbed was set in the Advanced Protection Lab, 252 lab space of the Grid Research Integration and Deployment Center (GRID- C), at Oak Ridge National Laboratory. In power grids, customer-owned distributed energy resources (DERs) are more frequent than in the past, and the numbers of points of interconnection (POI) with customer-owned DERs have increased. Disruptive operation from DERs presents a risk to grid operations, and protective relays located at the POI are used to isolate out-of-tolerance or poorly behaving of DERs. Ensuring the integrity of data from the relays at the POI, and DLT could enhance the security of the power grids. The first application is a SC to define and control the allowable total power factor (TPF) of the DER (wind farm) output, and the terms of the SC are implemented using DLT with a CGGS for a customer-owned DER. The TPF SC was implemented by the CGGS using DLT. The experimental model was performed with a real-time simulator using a CGGS and relay in-the-loop. The data collected from the CGGS were used to execute the TPF SC. The TPF limits were between +0.9 and +1.0, and the breakers’ operation in the POI was controlled by the relay using the SC. The events were collected from the real-time simulator, CGGS, and SEL 700GT relay to validate a successful application of the TPF SC using DLT. The second application is a SC to measure and control the allowable voltage service limits (VSL) by the CGGS using DLT. The tests were performed by using a real-time simulator, CGGS and relay in-the-loop. The data was collected from the CGGS that executed the SC. The main constraints were defined based on ANSI C84.1 service voltage limits, and the operation of the breakers in the POI. The events were collected from the CGGS, and SEL 700GT relay to assess a successful operation of the VSL SC using DLT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The AI-Base NDA Catalog

The project objective was to create a catalog of NDA capabilities so that as future challenges emerge, potential solutions can be quickly identified. The initial direction was to create a custom database where we would manually add various NDA technologies. During the year, LANL leadership heavily embraced AI tools and established LANL’s Enterprise ChatGPT license. To align with this vision, instead of a database we created a custom GPT to achieve the same capability. We provided a series of references on NDA technologies, the most comprehensive of which is the 2024 “PANDA manual” which is a 700-page textbook. A custom GPT is a semi-isolated version of OpenAI’s GPT model that can be tailored to specific tasks with context documents and instructions. Anyone with a LANL ChatGPT Enterprise account can access the NDA Catalog. Since the GPT exists on the green network, only non-sensitive questions may be asked. The Catalog can be used by simply asking questions in conversational English. We have found the Catalog to be quite accurate, even for heavily technical, complex queries. The Catalog allows the user to describe a measurement problem and it will respond with potential technology solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Automatic Generation of Algorithms for High-Speed Reliable Lossy Data Compression (Final Report)

Fast reliable data compression is urgently needed for many leading-edge scientific instruments and for exascale high-performance computing applications because they produce vast amounts of data at extremely high rates. The goal of this project has been to develop a framework named LC that is able to automatically generate high-speed lossless and reliable lossy compression and decompression algorithms that can be customized for different kinds of data. The resulting LC framework is freely available on GitHub. To achieve high-speed operation, LC outputs optimized and parallelized CPU and GPU implementations of the generated algorithms. To ensure the quality of lossily compressed data, LC guarantees the user-provided error bound. To be able to customize the compression algorithm to various use cases, LC can synthesize millions of different algorithms and automatically search for the one that works best for the given data. We have already employed LC to create state-of-the-art lossless and lossy compressors for scientific data as well as leading lossless compressors for images. We hope that LC and the customized, fast, reliable, and CPU/GPU-compatible compression algorithms that it can generate will greatly benefit the many scientific applications that need not only high trustworthiness but also high performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NUM-DAT File Format Specification: Used in M-9 Gun Experiment Data Archiving

The M-9 Shock and Detonation Physics group executes experiments on gun and explosive platforms with large numbers of oscilloscopes used for data acquisition. The data acquisition from these oscilloscopes was automated many years ago using a custom piece of software called RunDig . The default save format from this software is a custom structure referred to as "NUM-DAT" format. This file format includes a text ".DAT" file which is a header file used to interpret the binary ".NUM" file which contains the oscilloscope data. The data save format was originally developed by John Vorthman and has been in use by M-9 personnel for over 20 years. This data format has been used for archiving data from experiments performed by M-9 personnel at TA-40, TA-39, and the TA-55 Impact Test Facility. Numerous custom analysis and visualization programs have also been developed, and continue to be used, that utilize this data format. This document describes the NUM-DAT format and provides code examples for reading the format and converting it to other formats.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Real-time Object Bounding in LiDAR Data With Computer Vision

The Multimodal Measurement System is a roadside radiation measurement testbed used to detect radiation sources in passing vehicles. It works by combining sensor signals from various modalities to produce a thorough scan of the source. A LiDAR sensor is used to measure the dimensions of the vehicle and provide a velocity estimate. However, the current LiDAR setup uses propriety software for which the source code is unavailable and cannot be updated to improve performance. Therefore, it is imperative to the accuracy of the analysis to create a custom vehicle detection that can return the dimensions and velocity of passing vehicles in real time. This new custom detection is written in C++ using the PointCloud Library, which keeps it lightweight. It also utilizes Docker and the Robot Operating System, which allows the versatility of running both on a small computer or the Lawrence Livermore National Laboratory cluster while utilizing different models of LiDAR sensors. The custom detection outperforms the current detection model, which increases the accuracy of radiation source detection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

From Modular ADMS to Plug-and-Play Ops: Distribution Grid Operations with Platform-Level Orchestration to Enable Ambitious App Hosting

The core function of the distribution grid is to provide electricity to consumers affordably, reliably, and securely. In pursuing these core objectives, distribution utilities are accountable to customers, regulators, and in some cases, shareholders. Other third parties such as aggregators and microgrids can also have a stake in the smooth operation of the grid. Each of these stakeholders has economic, business, and/or governance objectives that inform their expectations of the distribution grid. This multi-objective, multi-stakeholder environment creates tension that must be reconciled to successfully design and operate the distribution grid. Innovative companies are competing to bring high-tech solutions to electric utilities and their customers that address each of these objectives. Many developers of advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS) have adopted a modular architecture that allows grid operators to select functions and features according to their individual system needs. A modular platform also allows the solution provider to develop and integrate specific new product modules; however, the need to pursue multiple objectives with a fixed set of controllable devices makes integration expensive whether it is done at the product development stage or the deployment stage. This cost creates a significant barrier to adoption and can lengthen the product to market time of new solutions. To fundamentally address the complexity of system integration for distribution grid operations, the U.S. Department of Energy Office of Electricity has funded the GridAPPS-D project at PNNL, which streamlines integration by contributing to standards development, defining system architecture, applying advanced mathematics, and developing open-source software to demonstrate the concept of an open data-integration platform for distribution operations. The open data-integration platform concept enables system operators and solution providers to deploy ambitious, best-of-breed applications (or apps) without continually reengineering for integration. Ambitious apps developed by different solution providers will inevitably attempt to achieve different control objectives with the same set of controllable devices. If the open platform itself can resolve these conflicts in a way that achieves the best available outcomes for all apps, doesn’t restrict the ambitious design of apps, and ensures safe and secure operations, apps will be able to plug-and-play with the platform at the same time as other ambitious apps. In this paper, we describe a framework called App Deconfliction that empowers a platform to assign setpoints to controllable devices based on the values preferred by different apps (and even external stakeholder entities like customers or aggregators). The App Deconfliction framework is compatible with several methods for determining setpoint values. We present two methods based on game theory that provide a subtle built-in incentive structure for developers to adapt their apps to the fact that they will be operating in a moderated multi-app environment and to favor device setpoints that have the most effect on their objectives over those that have the least effect. Our simulation-based demonstrations have shown that game-theory-based deconfliction can lead to a 7% improvement in control space utilization compared to design-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗