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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Process Modeling of CO 2 Absorption with Monoethanolamine Aqueous Solutions Using Rotating Packed Beds

A first-principle process simulation model is presented for the chemical absorption of carbon dioxide (CO 2 ) with monoethanolamine (MEA) aqueous solutions using rotating packed beds (RPB). Built on a proven rate-based packed bed absorber model, the RPB model rigorously simulates the phase and chemical equilibria at the vapor-liquid interface, the heat and mass transfer across the gas and liquid films, the fast reactions between MEA and CO 2 in the liquid film, and the RPB hydraulics. Here, estimation of the mass transfer rate across the liquid film is central to accurate simulation of the CO 2 absorption process with MEA aqueous solutions. We show that the literature lab-scale RPB data for CO 2 removal efficiency can be satisfactorily correlated by introducing a correction factor for the effective packing surface area predicted by the Onda correlation. Given the validated RPB model, we further show that, among the gas-phase mass transfer coefficient, the liquid-phase mass transfer coefficient, and the reaction rate constant for the reaction between amine and CO 2 , the reaction rate constant is the controlling step with the highest potential to enhance the CO 2 absorption performance in RPB.

42 ENGINEERING↗
DOE OSTI2021

Life Cycle Assessment and Techno-Economic Assessment of Lithium Recovery from Geothermal Brine

Lithium-ion batteries (LIB) play an essential role in the electrification of the transportation sector, and battery demand for lithium compounds will see a significant increase in the coming decades. This has raised concerns on the supply of lithium, and as a result, technologies are being developed to process unconventional lithium sources. One promising technology is to extract lithium from geothermal brine using lithium-aluminum-layered double hydroxide chloride (LDH) sorbent and forward osmosis. A combined life cycle assessment (LCA) and techno-economic assessment (TEA) is conducted to evaluate the environmental and economic performance of this technology. It is assumed that the lithium extraction unit is an add-on to a 50 MW geothermal power plant located in California. The analysis is based on lab-scale experimental data and stoichiometry while considering the economy of scale for an industrial system. LCA results suggest that, compared with conventional LiOH and Li 2 CO 3 production pathways, the new technology achieves 1–95% reduction in environmental impacts. Even higher reduction can be achieved for LiOH produced via electrolysis. This add-on unit for lithium extraction could achieve a payback period of less than 1 year and reach net present values of $454M and $315M and internal rates of return of 792 and 1130% for LiOH and Li 2 CO 3 production pathways, respectively. The favorable environmental and economic performance suggests that LDH sorption coupled with forward osmosis has great potential to enable the domestic production of battery lithium compounds and that further development should be carried out.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Measurement of the nucleon spin structure functions for 0.01 < 𝑄 2 < 1 GeV 2 using CLAS

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH 3 and ND 3 targets, for 𝑄 2 values as small as 0.012 and 0.02 GeV 2 , respectively, using the CEBAF Large Acceptance Spectrometer. This is the archival paper of the EG4 experiment that summarizes the previously reported results of the polarized structure functions 𝑔 1 , 𝐴 1 ⁢𝐹 1 , and their moments $\bar{Γ}$ 1 , $\bar{𝛾}$ 0 , and $\bar{𝐼}$ TT , for both the proton and the deuteron. In addition, we report on new results on the neutron 𝑔 1 extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\bar{Γ}$ 1 , $\bar{𝛾}$ 0 , and $\bar{𝐼}$ TT formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for 𝑔 1 and the Bjorken integral $\bar{Γ}^{𝑝−𝑛}_1$, and it was compared to available theoretical predictions. All of our results, to the best of our knowledge, provide for the first time extensive tests of spin observable predictions from chiral effective field theory (𝜒⁢EFT) in a 𝑄 2 range commensurate with the pion mass. Finally, they motivate further improvement in 𝜒⁢EFT calculations from other approaches such as the lattice gauge method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

First Global Extraction of Generalized Parton Distributions from Experiment and Lattice Data with Next-to-Leading-Order Accuracy

We report the first global extraction of generalized parton distributions, GUMP 1.0, by combining deeply virtual Compton scattering and 𝜌-meson production data from Jefferson Lab and the Hadron-Electron Ring Accelerator with global fits of parton distribution functions, charge form factors, and lattice quantum chromodynamics simulations. Using a conformal moment space parametrization, we achieve a unified description across low- and high-𝑥 regions at next to leading order accuracy in perturbative corrections. The results provide state-of-the-art generalized parton distributions consistent with almost all known facts, enabling three-dimensional nucleon imaging in impact parameter space and, at the same time, establishing a benchmark for future theoretical and experimental studies of the nucleon structure.

Form factors↗
DOE OSTI2025

X-ray and Neutron Radiography for Quantitative Material Reconstructions

Radiography is a powerful tool to determine the interior structure of objects. X-ray radiography is widely used and provides high-resolution images, though X-rays have limited transmission through materials of high atomic number (Z) and density. In contrast, neutrons can penetrate many materials that are heavily attenuating to X-rays, such as metals, providing contrast in the inner layers of highly attenuating items. Past work has shown the value in using both X-ray and neutron radiography for estimating material thicknesses, though that work was limited to simulated data. Here, we demonstrate quantitative material reconstructions using experimental X-ray and neutron radiography data from lab-based systems, accurately modeling radiography system responses to within a few percent to enable quantitative measures of material thickness. We demonstrate the utility of neutron radiography and X-ray radiography for these quantitative reconstructions and introduce methods for using their complementarity to improve image quality and optimize experimental design.

Gilbert, Andrew J.↗
DOE OSTI2023

BLAST-Lite (Battery Lifetime Analysis and Simulation Tool - Lite) [SWR-22-69] Related to: BLAST aka: BLAST-Py

Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are identified from publicly available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see NREL's Battery Lifespan webpage.

Smith, Kandler↗
DOE OSTI2023

Integrating biomechanics in evolutionary studies, with examples from the amphidromous goby model system

The functional capacities of animals are a primary factor determining survival in nature. In this context, understanding the biomechanical performance of animals can provide insight into diverse aspects of their biology, ranging from ecological distributions across habitat gradients to the evolutionary diversification of lineages. To survive and reproduce in the face of environmental pressures, animals must perform a wide range of tasks, some of which entail tradeoffs between competing demands. Moreover, the demands encountered by animals can change through ontogeny as they grow, sexually mature or migrate across environmental gradients. To understand how mechanisms that underlie functional performance contribute to survival and diversification across challenging and variable habitats, we have pursued diverse studies of the comparative biomechanics of amphidromous goby fishes across functional requirements ranging from prey capture and fast-start swimming to adhesion and waterfall climbing. The pan-tropical distribution of these fishes has provided opportunities for repeated testing of evolutionary hypotheses. By synthesizing data from the lab and field, across approaches spanning high-speed kinematics, selection trials, suction pressure recordings, mechanical property testing, muscle fiber-type measurements and physical modeling of bioinspired designs, we have clarified how multiple axes of variation in biomechanical performance associate with the ecological and evolutionary diversity of these fishes. Here our studies of how these fishes meet both common and extreme functional demands add new, complementary perspectives to frameworks developed from other systems, and illustrate how integrating knowledge of the mechanical underpinnings of diverse aspects of performance can give critical insights into ecological and evolutionary questions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Demonstration of Scaled-Production of Rare Earth Oxides and Critical Materials from U. S. Coal-Based Sources (Final Report)

The project objective was to demonstrate scaled production of high purity rare earth oxides (REO), nominally exceeding 90% grade, from coal refuse sources using innovative technologies that reduce cost and improve environmental outcomes relative to traditional rare earth processing technologies. The project utilized a critical material pilot plant constructed and tested as part of a previous U.S. Department of Energy project. Target performance criteria was a 50% reduction in production cost based on previous optimum values, 150% increase in recovery and greater than 2% concentrates of rare earth oxides, cobalt and manganese. Concentrate production goals were to produce a rare earth mix oxide product at a rate of 200 grams per day having a minimum purity of 50% as well as products of cobalt and manganese having a minimum purity of 2%. A previous pilot plant investigation identified acid cost as the major contributor to an operating cost that made the recovery of rare earth and other critical metals from bituminous coal sources economically challenging. As such, acid cost reduction was a major target using bio-oxidation reactors to produce sulfuric acid from naturally occurring coal pyrite. Based on laboratory data, a bio-oxidation circuit was designed for the pilot plant to produce 7.5 l/min of acid using two 11-m3 (3000 gallon) reactors equipped with 40 hp aerators for air dispersion. The pilot-scale tests revealed that acid concentration equivalent to as high as 1.0 M sulfuric acid could be continuously produced. However, the bio-acid contained exceptionally high iron concentrations, which complicated downstream processing of the pregnant leach solution (PLS). A TEA of the bio-oxidation circuit showed that production cost was approximately $0.13 per kg acid equivalent if produced using a four-day retention time in the reactors. This value represents a 48% reduction from that of purchased bulk sulfuric acid. Calcination (or roasting) studies were conducted on coarse refuse from West Kentucky No. 13 and Fire Clay coal seam sources. The test results revealed the potential to increase recovery by nearly 100% using temperatures between 500°C to 700°C with light REE recovery value being the most improved. Acid baking of the calcined products using sulfuric acid at 250°C increased heavy rare earth recovery from around 40% to 80% while decreasing the acid requirements by over 50%. The existing pilot plant was upgraded for the pilot scale demonstration of REE and CM recovery. The primary feedstocks were West Kentucky No.13 and heap leach pregnant leach solution (PLS) while a secondary feedstock was a lignite waste material from a construction sand operation. The pilot scale operation started with PLS generation through leaching followed by iron and aluminum removal, nominally at 3.3 and 4.5 pH, respectively. Leaching lixiviants used for the test were industrial grade sulfuric acid or bio-acid generated at the pilot scale facility. For most of the tests, the solid feed rate was 200 lb/hr whereas lixiviant was added at 2 gpm to provide an optimal residence time of 45 minutes. Following the contaminant removal step, several different process schematics were tested with the goal of maximizing REE recovery and purity. In the first test, direct processing of aluminum precipitation raffinate for REE recovery using oxalic acid at pH 1.5 was investigated. Overall REE recovery was approximately 45%. Unfortunately, elevated calcium content in the PLS significantly impacted the RE-Oxide product grade. Similarly, high calcium content decreased both the product purity and grades of CM products. As such, a new flowsheet was tested with the same initial process schematic but different precipitation stages for REEs and CMs at pH 6.0 and 9.0, respectively. It was noted that the overlapping precipitation behavior of Co, Ni and Zn with REEs limited the applicability of this process schematic. While this change increased the RE-Oxide grade from 36% in the first test to 87%, the loss of critical metals to the REE cake and bypass of the REEs to the CM cake significantly impacted the recovery of both REEs and CMs. Therefore, the modified process flowsheet combined oxalic acid precipitation stage raffinate and redissolved CM cake filtrate to maximize both the recovery and purity of the products. Consequently, a RE-Oxide product with 85% purity and CM cakes with over 19% Co, 38% Ni, 14% Zn and 9% Mn content were generated with significantly higher recoveries. While the modified process flowsheet improved recoveries and grades, elemental losses observed in separate precipitation and redissolution losses inspired the adaptation of a single precipitation stage at pH 9.0 for both REEs and CMs. This change was anticipated to maximize the REE recovery while minimizing the costs associated with separated redissolution and processing stages. As expected, REE recovery in this new circuit arrangement was over 56% with a product grade of over 87% RE-Oxide content. Similarly, Co, Ni, Mn, and Zn recoveries of 54%, 40%, 67%, and 66%, respectively, were achieved. Unfortunately, the elevated calcium content present in the solution due to its precipitation at pH 9.0 caused a decrease in the CM cake quality. Therefore, the final process flowsheet involved the addition of calcium oxalate precipitation following the oxalic acid precipitation stage, which effectively eliminated calcium contamination of the CM products. Finally, the pilot scale experiments conducted using bio-acid achieved comparable REE leaching recoveries to the conventional sulfuric acid leaching. Elevated iron concentration in the solution caused the co-precipitation of REEs with the iron and aluminum cake, resulting in the REE losses. Furthermore, elevated iron content bypassing the iron and aluminum precipitation stages contaminated the metal sulfide and manganese cake, respectively. A techno-economic analysis was performed based on a commercial facility capable of treating 500 tph of coal-based material. The production cost for West Kentucky No. 13 coarse refuse material ranged from approximately $500-$700/kg of total rare earth oxide whereas the lignite source had significantly lower production cost of $100-$300/kg. The significant difference in cost was due to the easier leaching characteristics of the lignite material and the higher feed concentrations. All process scenarios resulted in a negative net present value (NPV). For the lignite feedstock, laboratory REE leach recovery values were about 30% higher than the pilot plant data. Using the lab leach results, a positive net present value was achieved and the production cost decreased from $100-$300 $/kg to less than $150/kg of total REO.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Nucleon structure studies: DVCS on polarised protons with the CLAS12 experiment, and development of Micromegas detectors for EIC

The quark and gluon structure of nucleons is crucial for understanding the origin of their mass and spin. This information is encoded in structure function such as Generalised Parton Distributions (GPDs), which provide a three-dimensional picture of the nucleon in terms of its constituents. Deeply Virtual Compton Scattering (DVCS) offers the most direct access to GPDs, but their extraction requires high-precision measurements of multiple observables over a wide kinematic range. In 2022 and 2023 the CLAS12 experiment at Jefferson Lab (JLab) collected data from polarised electron scattering on a longitudinally polarised proton target, enabling the first measurement of polarised DVCS asymmetries at JLab 12GeV kinematics. This thesis will present preliminary measurement of the beam, target and double spin DVCS asymmetries from the CLAS12 polarised proton target data. Nucleon structure studies will also constitute a major part of the physics program at the future Electron Ion Collider (EIC). The development of the first detector at the EIC is ongoing and it requires light Micro-Pattern Gaseous Detectors (MPGDs) for its tracking system. This thesis further reports initial tests of Micromegas MPGDs with a two-dimensional readout developed for EIC.

Polcher, Samy [Univ. Paris-Saclay, Gif-sur-Yvette ↗
DOE OSTI2025

Q1-2024 Solar Cost Benchmarks

Each year, the U.S. Department of Energy’s (DOE) Solar Energy Technologies Office (SETO) and its national laboratory partners develop cost benchmarks for U.S. solar photovoltaic (PV) systems. These benchmarks track progress toward reducing solar costs and guide R&D priorities. Unlike typical studies that report only $/W, SETO uses intrinsic units (e.g., $/m² for mounting structures) to better capture how technology improvements such as module efficiency would impact system costs. This allows flexible modeling where inputs can vary significantly to assess cost sensitivity. Costs are reported in two ways: Minimum Sustainable Price (MSP): Long term, financially viable price under stable market conditions. Modeled Market Price (MMP): Actual market price, influenced by short term distortions such as tariffs or subsidies. Three national labs collect cost data from industry stakeholders, ensuring no duplication in outreach to stakeholders. Data reflects real transactions (primarily from Q1) and is weighted based on the number of sources per cost element. The PV System Cost Model (PVSCM) divides total installed system cost into eight categories: 1. Module (PV) 2. Inverter 3. Energy Storage System (ESS) 4. Structural BOS (SBOS) 5. Electrical BOS (EBOS) 6. Fieldwork 7. Office work 8. Other (developer/EPC costs) The first five are hardware costs, while the last three are soft costs. Each category includes fixed and variable cost components, where “size” depends on context (e.g., manufacturing capacity for modules vs. system capacity for installation costs). Variable costs are expressed using appropriate intrinsic units. The model reflects the owner’s upfront overnight capital cost, excluding tax credits. Tariffs and subsidies are treated as temporary market distortions affecting MMP but not MSP. PVSCM is implemented in Excel, where cost elements are aggregated into total system cost. Additional sheets handle unit conversions and operation & maintenance (O&M), with O&M costs levelized over the system’s lifetime.

14 SOLAR ENERGY↗
DOE OSTI2022

Critical review and analysis of hydrogen safety data collection tools

The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. QRA requires reliability data, and currently hydrogen QRA is limited by the lack of hydrogen specific reliability data, thereby hindering the development of necessary safety codes and standards [1]. Four tools have been identified that collect hydrogen system safety data: H2Tools Lessons Learned, Hydrogen Incidents and Accidents Database (HIAD), National Renewable Energy Lab's (NREL) Composite Data Products (CDPs), and the Center for Hydrogen Safety (CHS) Equipment and Component Failure Rate Data Submission Form. This work critically reviews and analyzes these tools for their quality and usability in QRA. It is determined that these tools lay a good foundation, however, the data collected by these tools needs improvement for use in QRA. Areas in which these tools can be improved are highlighted, and can be used to develop a path towards adequate reliability data collection for hydrogen systems.

08 HYDROGEN↗
DOE OSTI2023

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

2.3.3.404 - National Lab and University Collaboration for MHK Instrumentation and Data Processing Tools

Field and laboratory validation, testing, demonstration, and operation are critical steps for increasing the technology readiness level of marine energy (ME) converters because they provide high-quality testing and performance data that are critical information used to feed all aspects of technology development. This project, in partnership with industry, enables the marine and hydrokinetic energy (MHK) community to reliably and efficiently collect, process, manage, and share quality data by facilitating access to and development of instrumentation, guidelines and data processing/QA tools. Under this project, open-source data processing code (MHKiT) and tools (ME Data Pipeline, MRE Code Hub, PRIMRE Code Catalog), instrumentation (loads measurements), data acquisition systems (miniDAQ), and measurement guidance tools (Telesto, high EMI guidance) were developed to facilitate the collection and processing of quality laboratory and field data. Overall, this project is intended to improve the quality of the data collected during laboratory and field demonstration projects by standardizing the collection and processing techniques, as well as by improving access to instrumentation, code, and measurement guidance. Quality data will, in turn, lead to improved knowledge capture following ME device testing.

data processing↗
DOE OSTI2020

Offsite Data Processing for the GlueX Experiment

The Thomas Jefferson National Accelerator Facility (JLab) 12GeV accelerator upgrade completed in 2015 is now producing data at volumes unprecedented for the lab. The resources required to process this data now exceed the capacity of the onsite farm necessitating the use of offsite computing resources for the first time in the history of JLab. GlueX is now utilizing NERSC and PSC for raw data production. Details of the workflow are presented.

Lawrence, David↗
DOE OSTI2025

An Advanced Machine Learning and Artificial Intelligence System for Demonstrating Radiation Regulatory Compliance in DOE Accelerator Facilities

In this Phase II proposal, Applied Research LLC (ARLLC), Thomas Jefferson National Accelerator Facility (Jefferson Lab), and Old Dominion University (ODU) propose the combination of domain knowledge (beam characteristics, fixed structural shielding, earthen burden (the soil and foliage added to the dome of the experimental halls as additional shielding), etc.), machine learning (ML) and/or artificial intelligence (AI) to correlate a variety of multi-modal onsite signals and the radiation fields seen in accessible areas of the accelerator site and the site boundary. The ML/AI will consider the complex influence of environmental parameters affecting the radon contribution of the measurements, focusing on actual data obtained from Jefferson Lab. In Phase I, the coded beam and location data were fed into a deep learning model to predict doses at several designated locations in Jefferson Lab’s facility. Moreover, a dense radiation map was generated using only a sparse collection of the samples in a facility. In Phase II, we will develop a software prototype containing a radiation prediction algorithm, dense radiation map algorithms, and background noise prediction algorithms, with actual data used to evaluate the prototype. This work will provide a framework for evaluation of radiation measurement results around the site based on learned responses. In addition, the proposed approach allows more granular mapping of radiation levels. Better understanding and communication of these levels is related to the overall approach in keeping doses to personnel ALARA.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Queued Up: Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2020

Proposed large-scale electric generation and storage projects must apply for interconnection to the bulk power system via interconnection queues. While many projects that apply for interconnection are not subsequently built, data from these queues nonetheless provide a general indicator for mid-term trends in developer interest. Berkeley Lab compiled and analyzed data from all seven ISOs/RTOs in concert with 35 non-ISO utilities, representing an estimated 85% of all U.S. electricity load. We include all "active" projects in these generation interconnection queues through the end of 2020, as well as data on "completed" and "withdrawn" projects for five of the ISOs (CAISO, ISO-NE, MISO, NYISO, PJM). We find that the total capacity active in the queues is growing year-over-year, with over 750 GW of generation and an estimated 200 GW of storage capacity as of the end of 2020. Solar (462 GW) accounts for a large – and growing – share of generator capacity in the queues. Substantial wind (209 GW) capacity is also in development, 29% of which is for offshore projects (61 GW). In total, about 680 GW of zero-carbon capacity is currently seeking transmission access, as is 74 GW of natural gas capacity. Hybrids now comprise a large – and increasing – share of proposed projects, particularly in CAISO and the non-ISO West. 159 GW of solar hybrids (primarily solar+battery) and 13 GW of wind hybrids are currently active in the queues. However, much of this proposed capacity will not ultimately be built. Among a subset of queues for which data are available, only 24% of the projects seeking connection from 2000 to 2015 have subsequently been built. Completion percentages appear to be declining, and are even lower for wind and solar than other resources. Additionally, wait times are on the rise: in four ISOs, the typical duration from connection request to commercial operation increased from ~1.9 years for projects built in 2000-2009 to ~3.5 years for those built in 2010-2020. There are growing calls for queue reform to reduce cost, lead times, and speculation.

24 POWER TRANSMISSION AND DISTRIBUTION↗