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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipelines ability to ingest several modalities of information. We present (1) a curated dataset of 93 questions designed to evaluate a pipeline's ability to ingest textual data, tables, images, multimodal data, and cross-document multimodal data; (2) a phrase-level recall metric for correctness; (3) a nearest-neighbor embedding classifier in an attempt to classify pipeline hallucinations; (4) a comparative evaluation of 2 pipelines built with open-source retrieval mechanisms and 4 closed-source foundational models; and (5) a third-party human evaluation of the alignment of our correctness and hallucination metrics. We find that closed-source pipelines significantly outperform open-source pipelines in both the correctness and halucination metrics, with a wider performance gap in questions relying on multimodal and cross-document information. We also find after a human evaluation of our correctness and hallucination metric compared with our questions and pipeline responses, average agreement was 4.62 for correctness 4.53 for hallucination detection on a 1-5 Likert scale with 5 being strongly agree with our determination.

Hildebrand, Samuel [ORNL] (ORCID:0009000465963104)↗
DOE OSTI2022

Technoeconomic Analysis of Infrastructure Buildout Scenarios

The success of CCUS deployment in the southeast will depend on the optimized pipeline network from CO 2 point sources to geologic sinks for CO 2 storage. An optimal pipeline framework will reduce both environmental impacts and pipeline building cost. However, finding an optimal pipeline/transport infrastructure design is a non-trivial task and requires simultaneous usage of large volumes of data, computational resources, and a state-of-the-art simulator. Such a transport infrastructure model must consider point sources for CO 2 capture and associated volumes, sinks for CO 2 storage, and transportation from source to sink via pipeline networks. These considerations must be addressed simultaneously and systemically in an optimization process, in which a defined objective function (i.e., capital, variable CO 2 capture, transport, and storage cost function) is required to be minimized with the consideration of practical constraints (e.g., CO 2 flow through pipelines is less than the maximum capacity). Although optimization has been widely used in subsurface resources production and CO 2 storage, its application in large scale CCUS infrastructure design is rarely reported in the literature. SimCCS, developed by Los Alamos National Laboratory, is an open-source CCUS pipeline infrastructure design toolset that facilitates the optimization of pipeline infrastructure networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

EV-ELM (Electric Vehicle Policies with the Energy Language Model) [SWR-25-156]

Electric Vehicle Policies with the Energy Language Model (EV-ELM) leverages previous work using Large Language Models (LLMs) to find, download, and parse policy information related to energy infrastructure. In this application, we use LLMs to find policy documents related to the permitting and installation of electric vehicle charging infrastructure. This software contains the code to find, download, and parse these documents, while a related data record in the Open Energy Data Initiative (OEDI) will include the resulting output dataset that can be used for downstream analysis. The EV-ELM repository contains code for the EV-ELM project, which focuses on retrieving and processing EV permitting processes using large language models. The project is composed of two pipelines: (1) a web scraping pipeline for discovering and downloading EV permitting documents, and (2) a document parsing and extraction pipeline that processes the downloaded files to produce structured data. The web scraping pipeline is designed to extract relevant information from various websites, while the document parsing pipeline processes and analyzes the extracted documents to derive meaningful insights. Both pipelines depend on the NLR elm repository, which provides essential tools and functionalities for handling and processing the data. The web scraping pipeline is a modified version of the ordinance_gpt example within the elm repository. It has been adapted to fit the specific requirements of the EV-ELM project, ensuring that it effectively captures and processes the necessary information related to EV permitting.

Olson, Reid [National Laboratory of the Rockies (N↗
DOE OSTI2018

Assessment of CO 2 Storage Resources in Depleted Oil and Gas Fields in the Ship Shoal Area, Gulf of Mexico

The Gulf of Mexico is one of the most important regions in the United States for energy resources and infrastructure. Gulf of Mexico federal offshore oil and gas production accounts for 17% of total U.S. crude oil production and 5% of total U.S. gas production (EIA Gulf of Mexico Fact Sheet). This region presents an excellent combination of high need and significant opportunity for large scale geologic storage of CO 2 . The Ship Shoal Area is located about 20 miles offshore Louisiana within the Gulf Coast federal waters. Miocene and Pliocene sediments in the Ship Shoal Area are proven to provide excellent and secure traps for oil and gas. The Ship Shoal Area contains a large number of depleted oil and gas fields either currently abandoned, or planned for abandonment by 2025, which may provide very significant potential CO 2 storage capacity. GeoMechanics Technologies conducted a comprehensive research project to better characterize Neogene sediments in the Ship Shoal Area for high volume CO 2 storage. The data generated from literature, well data, well log, and formation evaluation were input into Rockwork 16 geologic software to create a detailed geologic model for the Ship Shoal study area. The geologic grids were then fed into TOUGH2, the gas migration model software and FLAC 3D, the geomechanical model software. The fluid flow modeling for both the Miocene and Pliocene simulation from both Block 107 and Block 84 indicated that Pliocene and Miocene are a good reservoir for the CO 2 sequestration. Thirty years of CO 2 injection (at a rate of 1 million metric tons CO 2 per year) and 30 years of observation simulations were run with the fluid flow modeling. The 3D geomechanical modeling for Ship Shoal Block 107 and Block 84 were developed to evaluate induced surface deformation and potential fault reactivation after 30 years CO 2 injection at base of Pliocene and at upper Miocene injection locations. Evaluation results indicate low to no risks for fault slips or fault reactivation. Twelve well bores and 76 well bores for Ship Shoal Blocks 84 and 107 respectively were reviewed for their cement history. Most wells (57 out of 76 and 7 out of 12 wells in Ship Shoal Block 107 and 84 respectively) have good integrity. Nineteen and 5 wells (in Ship Shoal Block 107 and 84 respectively) with no top plug, incomplete cement or Plug and Abandonment information are given yellow cautionary indicators. These cautionary wells may provide leakage paths of CO 2 through the well bores to the USDWs. Using our Quantitative Risk & Decision Analysis Tool (QRDAT) for caprock integrity evaluation, we compared Ship Shoal’s risk to that of In Salah, Sleipner, Kevin Dome, Loudon, Illinois Industrial CCS and Wilmington Graben. We found the risk at the Ship Shoal Blocks 84 and 107 fields are similar to the known CO 2 active sequestration sites, but lower than the Wilmington Graben turbidities offshore California studied site. The risk of natural seismicity in the Gulf of Mexico is relatively low. Geomechanics Technologies has documented the top 25 CO 2 emission sources within the close proximity of the Ship Shoal Block 84 and 107 fields. All the offshore and onshore pipelines have been digitized and can be viewed in an interactive website (http://www.geomechanicstech.com/shipshoal.html). We also performed a feasibility study on the potential for converting existing oil or gas pipelines for CO 2 transport. There are abandoned, idled and retired onshore and offshore pipelines. However, there is no standard specification for maximum pipeline pressures needed for CO 2 transport. It is a function of design, materials, and testing provided; thus it will be the responsibility of the pipeline operator to correctly determine, maintain and operate within the limits of the pipelines. There are a few transit corridors extending from onshore Louisiana (Cailou Bay) to offshore trunk-lines. The cost for constructing a pipeline has increased about 46% from 2015 to an average cost of $5,064,046 per mile in 2016. We use the NETL CO 2 storage resource mass estimate to calculate the potential resources for Ship Shoal Block 84 and 107 fields using the depleted oil and gas reservoir volume and sand volume generated from our geologic modeling. The estimated storage resource results are greater when using the sand volumes derived through geologic modeling than the oil and gas reservoir data. The difference is due to the depleted oil and gas reservoir information not accounting for the water-flooded sand located below the oil/gas-water contact or unproductive sand units. Resource calculation using the sand volume obtained through geologic modeling overestimate the storage capacity as the model accounts for all sand within the formation, not just the interconnected sand.

02 PETROLEUM↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2015

From Pixels to Planets

The Kepler Mission was launched in 2009 as NASAs first mission capable of finding Earth-size planets in the habitable zone of Sun-like stars. Its telescope consists of a 1.5-m primary mirror and a 0.95-m aperture. The 42 charge-coupled devices in its focal plane are read out every half hour, compressed, and then downlinked monthly. After four years, the second of four reaction wheels failed, ending the original mission. Back on earth, the Science Operations Center developed the Science Pipeline to analyze about 200,000 target stars in Keplers field of view, looking for evidence of periodic dimming suggesting that one or more planets had crossed the face of its host star. The Pipeline comprises several steps, from pixel-level calibration, through noise and artifact removal, to detection of transit-like signals and the construction of a suite of diagnostic tests to guard against false positives. The Kepler Science Pipeline consists of a pipeline infrastructure written in the Java programming language, which marshals data input to and output from MATLAB applications that are executed as external processes. The pipeline modules, which underwent continuous development and refinement even after data started arriving, employ several analytic techniques, many developed for the Kepler Project. Because of the large number of targets, the large amount of data per target and the complexity of the pipeline algorithms, the processing demands are daunting. Some pipeline modules require days to weeks to process all of their targets, even when run on NASA's 128-node Pleiades supercomputer. The software developers are still seeking ways to increase the throughput. To date, the Kepler project has discovered more than 4000 planetary candidates, of which more than 1000 have been independently confirmed or validated to be exoplanets. Funding for this mission is provided by NASAs Science Mission Directorate.

supercomputers↗
DOE OSTI2022

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC

We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.

Instruments & Instrumentation↗
DOE OSTI2019

The Iterative Processing Framework: A New Paradigm for Automatic Event Building

Abstract In a traditional data-processing pipeline, waveforms are acquired, a detector makes the signal detections (i.e., arrival times, slownesses, and azimuths) and passes them to an associator. The associator then links the detections to the fitting-event hypotheses to generate an event bulletin. Most of the time, this traditional pipeline requires substantial human-analyst involvement to improve the quality of the resulting event bulletin. For the year 2017, for instance, International Data Center (IDC) analysts rejected about 40% of the events in the automatic bulletin and manually built 30% of the legitimate events. Here, we introduce an iterative processing framework (IPF) that includes a new data-processing module that incorporates automatic analyst behaviors (auto analyst [AA]) into the event-building pipeline. In the proposed framework, through an iterative process, the AA takes over many of the tasks traditionally performed by human analysts. These tasks can be grouped into two major processes: (1) evaluating small events with a low number of location-defining arrival phases to improve their formation; and (2) scanning for and exploiting unassociated arrivals to form potential events missed by previous association runs. To test the proposed framework, we processed a two-week period (15–28 May 2010) of the signal-detections dataset from the IDC. Comparison with an expert analyst-reviewed bulletin for the same time period suggests that IPF performs better than the traditional pipelines (IDC and baseline pipelines). The majority of the additional events built by the AA are low-magnitude events that were missed by these traditional pipelines. The AA also adds additional signal detections to existing events, which saves analyst time, even if the event locations are not significantly affected.

58 GEOSCIENCES↗
DOE OSTI2023

The catalog-to-cosmology framework for weak lensing and galaxy clustering for LSST

We present TXPipe, a modular, automated and reproducible pipeline for ingesting catalog data and performing all the calculations required to obtain quality-assured two-point measurements of lensing and clustering, and their covariances, with the metadata necessary for parameter estimation. The pipeline is developed within the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC), and designed for cosmology analyses using LSST data. In this paper, we present the pipeline for the so-called 3x2pt analysis -- a combination of three two-point functions that measure the auto- and cross-correlation between galaxy density and shapes. We perform the analysis both in real and harmonic space using TXPipe and other LSST-DESC tools. We validate the pipeline using Gaussian simulations and show that it accurately measures data vectors and recovers the input cosmology to the accuracy level required for the first year of LSST data under this simplified scenario. We also apply the pipeline to a realistic mock galaxy sample extracted from the CosmoDC2 simulation suite (Korytov et al. 2019). TXPipe establishes a baseline framework that can be built upon as the LSST survey proceeds. Furthermore, the pipeline is designed to be easily extended to science probes beyond the 3x2pt analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale

Most of today’s visualization libraries and applications are based off of what is known today as the visualization pipeline. In the visualization pipeline model, algorithms are encapsulated as “filtering” components with inputs and outputs. These components can be combined by connecting the outputs of one filter to the inputs of another filter. The visualization pipeline model is popular because it provides a convenient abstraction that allows users to combine algorithms in powerful ways. Unfortunately, the visualization pipeline cannot run effectively on exascale computers. Experts agree that the exascale machine will comprise processors that contain many cores. Furthermore, physical limitations will prevent data movement in and out of the chip (that is, between main memory and the processing cores) from keeping pace with improvements in overall compute performance. To use these processors to their fullest capability, it is essential to carefully consider memory access. This is where the visualization pipeline fails. Each filtering component in the visualization library is expected to take a data set in its entirety, perform some computation across all of the elements, and output the complete results. The process of iterating over all elements must be repeated in each filter, which is one of the worst possible ways to traverse memory when trying to maximize the number of executions per memory access. This project investigates a new type of visualization framework that exhibits a pervasive parallelism necessary to run on exascale machines. Our framework achieves this by defining algorithms in terms of functors, which are localized, stateless operations. Functors can be composited in much the same way as filters in the visualization pipeline. But, functors’ design allows them to be concurrently running on massive amounts of lightweight threads. Only with such fine-grained parallelism can we hope to fill the billions of threads we expect will be necessary for efficient computation on an exascale computer. This project concludes with a functional prototype containing pervasively parallel algorithms that perform demonstratively well on many-core processors. These algorithms are fundamental for performing data analysis and visualization at extreme scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

PICS: SIMULATIONS OF STRONG GRAVITATIONAL LENSING IN GALAXY CLUSTERS

Gravitational lensing has become one of the most powerful tools available for investigating the “dark side” of the universe. Cosmological strong gravitational lensing, in particular, probes the properties of the dense cores of dark matter halos over decades in mass and offers the opportunity to study the distant universe at flux levels and spatial resolutions otherwise unavailable. Studies of strongly lensed variable sources offer even further scientific opportunities. One of the challenges in realizing the potential of strong lensing is to understand the statistical context of both the individual systems that receive extensive follow-up study, as well as that of the larger samples of strong lenses that are now emerging from survey efforts. Motivated by these challenges, we have developed an image simulation pipeline, Pipeline for Images of Cosmological Strong lensing (PICS), to generate realistic strong gravitational lensing signals from group- and cluster-scale lenses. PICS uses a low-noise and unbiased density estimator based on (resampled) Delaunay Tessellations to calculate the density field; lensed images are produced by ray-tracing images of actual galaxies from deep Hubble Space Telescope observations. Other galaxies, similarly sampled, are added to fill in the light cone. The pipeline further adds cluster member galaxies and foreground stars into the lensed images. The entire image ensemble is then observed using a realistic point-spread function that includes appropriate detector artifacts for bright stars. Noise is further added, including such non-Gaussian elements as noise window-paning from mosaiced observations, residual bad pixels, and cosmic rays. The aim is to produce simulated images that appear identical—to the eye (expert or otherwise)—to real observations in various imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the $L$-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9' beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake figure of merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is continuously transmitting a narrowband signal with an equivalent isotropic radiated power (EIRP) > 10 13 W. For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 10 16 W) is at most 3 × 10 -4 . Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2017

Status of the TESS Science Processing Operations Center

The Transiting Exoplanet Survey Satellite (TESS) science pipeline is being developed by the Science Processing Operations Center (SPOC) at NASA Ames Research Center based on the highly successful Kepler Mission science pipeline. Like the Kepler pipeline, the TESS science pipeline will provide calibrated pixels, simple and systematic error-corrected aperture photometry, and centroid locations for all 200,000+ target stars, observed over the 2-year mission, along with associated uncertainties. The pixel and light curve products are modeled on the Kepler archive products and will be archived to the Mikulski Archive for Space Telescopes (MAST). In addition to the nominal science data, the 30-minute Full Frame Images (FFIs) simultaneously collected by TESS will also be calibrated by the SPOC and archived at MAST. The TESS pipeline will search through all light curves for evidence of transits that occur when a planet crosses the disk of its host star. The Data Validation pipeline will generate a suite of diagnostic metrics for each transit-like signature discovered, and extract planetary parameters by fitting a limb-darkened transit model to each potential planetary signature. The results of the transit search will be modeled on the Kepler transit search products (tabulated numerical results, time series products, and pdf reports) all of which will be archived to MAST.

high performance computing↗
DOE OSTI2012

Capturing and Using Knowledge about the Use of Visualization Toolkits

When constructing visualization pipelines using toolkits such as Visualization Toolkit (VTK) and Generic Mapping Tools (GMT), developers must understand (1) what toolkit operators will transform their data from its raw state to some required view state and (2) what viewers are available to present the generated view. Traditionally, developers learn about how to construct visualization pipelines by reading documentation and inspecting code examples, which can be costly in terms of the time and effort expended. Once an initial pipeline is constructed, developers may still have to undergo a trial and error process before a satisfactory visualization is generated. This paper presents the Visualization Knowledge Project (VisKo) that is built on a knowledge base of visualization toolkit operators and how they can be piped together to form visualization pipelines. Developers may now rely on VisKo to guide them when constructing visualization pipelines and in some cases, when VisKo has complete knowledge about some set of operators (i.e., sequencing and parameter settings), automatically generate a fully functional visualization pipeline.

Chemical Imaging↗
DOE OSTI2014

SUBTASK 2.19 – OPERATIONAL FLEXIBILITY OF CO2 TRANSPORT AND STORAGE

Carbon dioxide (CO2) is produced in large quantities during electricity generation and by industrial processes. These CO2 streams vary in terms of both composition and mass flow rate, sometimes substantially. The impact of a varying CO2 stream on pipeline and storage operation is not fully understood in terms of either operability or infrastructure robustness. This study was performed to summarize basic background from the literature on the topic of operational flexibility of CO2 transport and storage, but the primary focus was on compiling real-world lessons learned about flexible operation of CO2 pipelines and storage from both large-scale field demonstrations and commercial operating experience. Modeling and pilot-scale results of research in this area were included to illustrate some of the questions that exist relative to operation of carbon capture and storage (CCS) projects with variable CO2 streams. It is hoped that this report’s real-world findings provide readers with useful information on the topic of transport and storage of variable CO2 streams. The real-world results were obtained from two sources. The first source consisted of five full-scale, commercial transport–storage projects: Sleipner, Snøhvit, In Salah, Weyburn, and Illinois Basin–Decatur. These scenarios were reviewed to determine the information that is available about CO2 stream variability/intermittency on these demonstration-scale projects. The five projects all experienced mass flow variability or an interruption in flow. In each case, pipeline and/or injection engineers were able to accommodate any issues that arose. Significant variability in composition has not been an issue at these five sites. The second source of real- world results was telephone interviews conducted with experts in CO2 pipeline transport, injection, and storage during which commercial anecdotal information was acquired to augment that found during the literature search of the five full-scale projects. The experts represented a range of disciplines and hailed from North America and Europe. Major findings of the study are that compression and transport of CO2 for enhanced oil recovery (EOR) purposes in the United States has shown that impurities are not likely to cause transport problems if CO2 stream composition standards are maintained and pressures are kept at 10.3 MPa or higher. Cyclic, or otherwise intermittent, CO2 supplies historically have not impacted in-field distribution pipeline networks, wellbore integrity, or reservoir conditions. The U.S. EOR industry has demonstrated that it is possible to adapt to variability and intermittency in CO2 supply through flexible operation of the pipeline and geologic storage facility. This CO2 transport and injection experience represents knowledge that can be applied in future CCS projects. A number of gaps in knowledge were identified that may benefit from future research and development, further enhancing the possibility for widespread application of CCS. This project was funded through the Energy & Environmental Research Center–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FC26-08NT43291. Nonfederal funding was provided by the IEA Greenhouse Gas R&D Programme.

Jensen, Melanie↗
DOE OSTI2021

Magnetic patch system

A magnetic patch system includes a sealing member configured for attachment to a pipeline proximate to a breach in a wall of the pipeline. A magnet is positioned at least partially within a housing arrangement. The magnet may be oriented to generate a force toward the wall of the pipeline to attach the housing arrangement to the wall of the pipeline and capture the sealing member between the housing arrangement and the wall of the pipeline. The system may be installed inside a pipeline using a remotely controlled robotic system.

Symington, Alex↗
DOE OSTI2024

Corrosion Behavior of Zinc Cold Spray Coatings in a Simulated Natural Gas Environment Containing H2O, CO2, and H2S

The natural gas transmission pipeline network in the U.S. consists of approximately 300,000 miles, is made mainly of carbon steel, and was installed in the 1950s and 1960s. Understanding the pipeline steel behavior in natural gas will inform new pipeline designs and repurposing of existing pipelines. Sulfide stress cracking (SSC) is a problem for steel pipelines transporting natural gas or CO2 containing partial pressures of H2S higher than 0.3 kPa (0.05 psi). Therefore, the objective of this work is to mitigate internal corrosion in steel pipelines transporting natural gas containing H2S using cold spray coatings.

Belarbi, Zineb↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗