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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

Traditional liquid electrolytes used in lithium (Li) metal batteries (LMBs) have severe safety issues, poor power density, as well as thermal and electrochemical instability that prevents their scaling to newer applications, such as electric vehicles. Using sulfide-based solid-state electrolytes (SSEs) is a viable method to address many of the issues that plague LMBs. However, the key issue preventing the expansion of sulfide-based SSEs is an interplay between poor interfacial stability with the electrodes and massive external stack pressure required to maintain electrode-electrolyte contact. The need for extensive pressure requires large system-level (i.e., battery pack used in an electric vehicle) housing that causes a disconnect between performance at a smaller scale and actual application. Secondary prevention to sulfide-based SSE adoption are difficulties in manufacturing, as toxic deterioration occurs when these materials are exposed to humidity, as well as drastic reduction in SSE performance at low temperature. This work hypothesizes that unique interfacial designs of electrodes and the modification of the sulfide-based SSE will enable reduction of necessary external cell pressure, and therefore support the design of practical LMBs. This will be done through collaboration within Idaho National Laboratory (INL), where manufacturing practices will be parameterized to produce low defect, low porosity SSEs with slight modifications. Next, artificial interfaces will be produced on electrodes to observe the conformality and mass transport properties between the modified SSE and pre-treated anode. External pressure on Li-metal cells will also be parameterized to reduce interfacial impedance while a reduction in the pressure will allot reasonable housing design for larger scale applications. Focus on novelty in this work occurs by modifications to the solid-solid interfaces between electrolyte and electrode, done through doping of the electrolyte and production of artificial interfaces on the Li-metal anode. The modifications at these interfaces, in addition to modified external pressure could identify and address the poor interfacial impedance that hinders high performance SSEs. Fundamental understanding on how to reduce the key hindrances of all-solid-state batteries (ASSBs) will support the advancement for larger scale production and performance, of which will be held in perspective for future works. Beyond battery development, the knowledge gained from process control and defect analysis is applicable to other ceramic systems, such as nuclear fuel coatings and high temperature electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

Traditional liquid electrolytes used in lithium (Li) metal batteries (LMBs) have severe safety issues, poor power density, as well as thermal and electrochemical instability that prevents their scaling to newer applications, such as electric vehicles. Using sulfide-based solid-state electrolytes (SSEs) is a viable method to address many of the issues that plague LMBs. However, the key issue preventing the expansion of sulfide-based SSEs is an interplay between poor interfacial stability with the electrodes and massive external stack pressure required to maintain electrode-electrolyte contact. The need for extensive pressure requires large system-level (i.e., battery pack used in an electric vehicle) housing that causes a disconnect between performance at a smaller scale and actual application. Secondary prevention to sulfide-based SSE adoption are difficulties in manufacturing, as toxic deterioration occurs when these materials are exposed to humidity, as well as drastic reduction in SSE performance at low temperature. This work hypothesizes that unique interfacial designs of electrodes and the modification of the sulfide-based SSE will enable reduction of necessary external cell pressure, and therefore support the design of practical LMBs. This will be done through collaboration within Idaho National Laboratory (INL), where manufacturing practices will be parameterized to produce low defect, low porosity SSEs with slight modifications. Next, artificial interfaces will be produced on electrodes to observe the conformality and mass transport properties between the modified SSE and pre-treated anode. External pressure on Li-metal cells will also be parameterized to reduce interfacial impedance while a reduction in the pressure will allot reasonable housing design for larger scale applications. Focus on novelty in this work occurs by modifications to the solid-solid interfaces between electrolyte and electrode, done through doping of the electrolyte and production of artificial interfaces on the Li-metal anode. The modifications at these interfaces, in addition to modified external pressure could identify and address the poor interfacial impedance that hinders high performance SSEs. Fundamental understanding on how to reduce the key hindrances of all-solid-state batteries (ASSBs) will support the advancement for larger scale production and performance, of which will be held in perspective for future works. Beyond battery development, the knowledge gained from process control and defect analysis is applicable to other ceramic systems, such as nuclear fuel coatings and high temperature electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2023

ABF Past Accomplishments - TEA/LCA

The Integrated Analysis task in the Agile BioFoundry (ABF) conducts techno-economic analysis (TEA) and life cycle assessment (LCA) to quantify the economics and environmental impacts of bioprocesses under development. The team from Argonne National Laboratory (ANL) and the National Renewable Energy Laboratory (NREL) develops TEA and LCA models of selected compound targets and hosts of interest to the ABF in an effort to provide an analysis-based foundation to the R&D. To date, the Integrated Analysis task has performed process modeling and analyses on multiple pairings of bioproducts and hosts, centered around major metabolic "beachheads" and "exemplar" molecules chosen for each of the metabolic pathways of interest. This presentation provides an overview of the approach used for TEA and LCA in the ABF and of the progress, outcomes, and impacts of TEA and LCA to support ABF goals, with a focus towards developing bio-based products that are both environmentally and economically viable.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2025

Impact of Storage Conditions on the Quality and Viability of Switchgrass as a Bioenergy Crop

With growing energy demands and climate concerns, advancements in bioenergy have become imperative. Biofuels promote energy independence, reduce fossil fuel reliance, and cut greenhouse gas emissions. Switchgrass (Panicum virgatumis) is an ideal bioenergy crop due to its ability to thrive on marginal lands unsuitable for food crops and its drought tolerance, rendering it a low-maintenance, high-yield option. In the bioenergy industry, energy crops are typically stored to facilitate supply chain management and ensure a continuous supply despite seasonal availability. However, the effective utilization of bioenergy crops is typically contingent upon addressing the challenges associated with biomass storage. These challenges include high moisture content that promotes biological degradation of the biomass leading to increased risk of pathogenic microbes, and chemical degradation that increases the ratio of inconvertible, inorganic materials. These issues lead to a reduction of feedstock value, diminishing the biomass-to-biofuel conversion efficacy, making switchgrass and other bioenergy crops less effective as fuel sources. This project aims to improve the storage of purpose-grown energy crops. Aerobic storage trials were conducted under varying moisture contents to stimulate real-world environmental exposure. Switchgrass samples were rewetted to 30% or 35% and stored in a bioreactor for approximately one month, or until cellular respiration ceased. Degradation indicators, such as dry matter loss, temperature changes, and cellular respiration, were measured throughout this process. Samples for compositional analysis, 16S, and ITS were taken at three different time points: before, during, and after storage, to monitor changes in biomass, microbial communities, and metabolites over time. Preliminary data indicate that dry matter loss is proportional to cellular respiration, with higher moisture content (35%) leading to higher temperatures and elevated dry matter loss. Compositional analysis and microbial community analysis are in progress. This research will provide a comprehensive understanding of storage, helping to identify stability factors in energy crops and improve best management practices for biomass producers.

09 - BIOMASS FUELS↗
DOE OSTI2025

Low embodied energy and carbon, high lifetime silicon boules via a combined chemical vapor deposition/float zone process

This work evaluates a new process route to making float zone (Fz)-quality silicon wafers using a combination of computational fluid dynamics (CFD) modeling and technoeconomic analysis. Our analysis finds that the new process competes with Czochralski (Cz)-grown wafers on a levelized cost of energy system level. The new process also decreases embodied energy and carbon of silicon photovoltaics (PV) by ~6x circumventing the energy-costly Siemens process used in polycrystalline silicon (poly-Si) production plants to generate feedstock for Fz and Cz boules. Instead of using poly-Si from the Siemens process to feed crystallization, the new process uses the high-purity, trichlorosilane (TCS) precursor gas to grow a poly-Si feed rod in-situ during a modified Fz1,2 boule growth process. The gas-to-boule float zone process enables opportunity to produce high-purity (low metals and oxygen content), uniformly doped single crystal silicon boules and wafers with high bulk lifetimes (τ bulk > 15 ms) to enable higher efficiency cells (>27 %) with fewer known degradation mechanisms than Czochralski (Cz)-grown wafers. These benefits reduce the levelized cost of electricity (LCOE) of PV-produced electricity. Here we show the results of our CFD and chemical modeling of the process to prove feasibility and economic viability.

14 SOLAR ENERGY↗
DOE OSTI2023

Analysis of Solids Obtained from 201O, 201P, 202A, and 202B Contactors in the Salt Waste Processing Facility

In December 2022, Savannah River Mission Completion personnel recovered solids samples from four contractors (EXT-001, EXT-007, EXT-031, and EXT-040) employed at the Salt Waste Processing Facility (SWPF). These solids were submitted to Savannah River National Laboratory for analysis and characterization. This Technical Report outlines the analytical findings and observations associated with these samples and explores the potential source of solid generation within the SWPF. The following conclusions are offered as a result of this work: the light color solids recovered from EXT-007 are consistent with crystallized, soluble salt components (such as sodium nitrate, sodium hydroxide, and sodium carbonate), all of which can be easily re-dissolved in process water and do not represent a threat to SWPF CSSX processing. While the light solids from the top of EXT-040 were not analyzed, it is believed that they share similar characteristics to those recovered from EXT-007; The dark color solids recovered from EXT-001, EXT-031, and the inside of EXT-040 appear to be complex, consisting of several different types of solids: High concentrations of mercury (Hg, 40-75% by mass) partially present as elemental Hg are consistent with the precipitation of Hg 0 /Hg 2+ from disproportionation of Hg + species in pH swing conditions; Moderate concentrations of titanium (Ti) (2-7%, by mass) are also present, often co-located with Hg. This is consistent with co-precipitation, entrainment, and/or amalgamation of Ti and Hg; Moderate concentrations of iron (Fe) (1-3%, by mass) are observed in SWPF solids and are likely attributable to erosion of stainless-steel components within the CSSX process; Small concentrations of tungsten (W) and cobalt (Co) (<1%, by mass) are observed in SWPF solids. The presence of these solids is consistent with the erosion of components coated with W and Co (e.g., Stellite); Small concentrations of aluminum (Al) (<1%, by mass) are observed in the form of gibbsite and potentially sodium aluminosilicate. The presence of these compounds is likely attributable to the pH swing observed between the scrub and extraction cycles within the CSSX process. The following recommendations are made as a result of this work: Testing should be performed to determine the chemical drivers, process, and mechanism of Hg precipitation in CSSX processing. Special care should be taken to include titanium in tests to evaluate the potential for co-precipitation, entrainment, and amalgamation; Hg-mitigation options that would minimize or eliminate risks of Hg precipitation and solids accumulation (e.g., Hg-absorption techniques, ion exchange/adsorbents, flowsheet changes, etc.) should be assessed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Quantum process tomography of a Mølmer-Sørensen gate via a global beam

Here, we present a framework for quantum process tomography of two-ion interactions that leverages modulations of the trapping potential and composite pulses from a global laser beam to achieve individual-ion addressing. Tomographic analysis of identity and delay processes reveals dominant error contributions from laser decoherence and slow qubit frequency drift during the tomography experiment. We use this framework on two co-trapped 40 Ca + ions to analyze both an optimized and an overpowered Mølmer–Sørensen gate and to compare the results of this analysis to a less informative Bell-state tomography measurement and to predictions based on a simplified noise model. These results show that the technique is effective for the characterization of two-ion quantum processes and for the extraction of meaningful information about the errors present in the system. The experimental convenience of this method will allow for more widespread use of process tomography for characterizing entangling gates in trapped-ion systems.

quantum computing↗
DOE OSTI2022

Biomass burning and marine aerosol processing over the southeast Atlantic Ocean: a TEM single-particle analysis

This study characterizes single-particle aerosol composition from filters collected during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) and CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) campaigns. In particular the study describes aged biomass burning aerosol (BBA), its interaction with the marine boundary layer and the influence of biomass burning (BB) air on marine aerosol. The study finds evidence of BBA influenced by marine boundary layer processing as well as sea salt influenced by BB air. Secondary chloride aerosols were observed in clean marine air as well as in BB-influenced air in the free troposphere. Higher-volatility organic aerosol appears to be associated with increased age of biomass burning plumes, and photolysis or oxidation may be a mechanism for the apparent increased volatility. Aqueous processing and interaction with the marine boundary layer air may be a mechanism for the presence of sodium on many aged potassium salts. By number, biomass burning potassium salts and modified sea salts are the most observed particles on filter samples. The most commonly observed BC coatings are inorganic salts. These results suggest that atmospheric processes such as photolysis, oxidation and cloud processing are key drivers in the elemental composition and morphology of aged BBA. Fresh BBA inorganic salt content, as it has an important role in the particles' ability to uptake water, may be a key driver in how aqueous processing and atmospheric aging proceed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Techno-economic analysis of roll-to-roll production of perovskite modules using radiation thermal processes

Perovskite solar technology can produce economically viable photovoltaic devices due to low cost of materials and scalable manufacturing. Solution phase deposition of all active layers lends itself to roll-to-roll fabrication methods which are well-established and have production rates of several hundred square meters per minute. In this techno-economic analysis, the opportunity to scale at this production throughput is considered with manufacturing rates exceeding one gigawatt per year in a single plant. Intense pulsed light is used for the post deposition process to minimize costs associated with the footprint, equipment, and operation. The analysis is limited to the operation of a roll-to-roll manufacturing facility producing a perovskite solar film that can be incorporated into photovoltaic modules. Costs associated with materials, labor, and equipment are considered along with utilities determined from the fundamental engineering calculations. Furthermore, the results show that at very large-scale operations of several gigawatts, the costs to produce the solar films range between $0.04-$0.10 per watt.

14 SOLAR ENERGY↗
DOE OSTI2022

libwfa: Wavefunction analysis tools for excited and open‐shell electronic states

Abstract An open‐source software library for wavefunction analysis, libwfa, provides a comprehensive and flexible toolbox for post‐processing excited‐state calculations, featuring a hierarchy of interconnected visual and quantitative analysis methods. These tools afford compact graphical representations of various excited‐state processes, provide detailed insight into electronic structure, and are suitable for automated processing of large data sets. The analysis is based on reduced quantities, such as state and transition density matrices (DMs), and allows one to distill simple molecular orbital pictures of physical phenomena from intricate correlated wavefunctions. The implemented descriptors provide a rigorous link between many‐body wavefunctions and intuitive physical and chemical models, for example, exciton binding, double excitations, orbital relaxation, and polyradical character. A broad range of quantum‐chemical methods is interfaced with libwfa via a uniform interface layer in the form of DMs. This contribution reviews the structure of libwfa and highlights its capabilities by several representative use cases. This article is categorized under: Software > Quantum Chemistry Theoretical and Physical Chemistry > Spectroscopy

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Nataraj, Lakshmanan↗
DOE OSTI2022

Plant biomass fermentation by the extreme thermophile Caldicellulosiruptor bescii for co-production of green hydrogen and acetone: Technoeconomic analysis

A variety of chemical and biological processes have been proposed for conversion of sustainable low-cost feedstocks into industrial products. Here, a biorefinery concept is formulated, modeled, and analyzed in which a naturally (hemi)cellulolytic and extremely thermophilic bacterium, Caldicellulosiruptor bescii, is metabolically engineered to convert the carbohydrate content of lignocellulosic biomasses (i.e., soybean hulls, transgenic poplar) into green hydrogen and acetone. Experimental validation of C. bescii fermentative performance demonstrated 82% carbohydrate solubilization of soybean hulls and 55% for transgenic poplar. A detailed technical design, including equipment specifications, provides the basis for an economic analysis that establishes metabolic engineering targets. This robust industrial process leveraging metabolically engineered C. bescii yields 206 kg acetone and 25 kg H 2 per metric ton of soybean hull, or 174 kg acetone and 21 kg H 2 per metric ton transgenic poplar. Beyond this specific case, the model demonstrates industrial feasibility and economic advantages of thermophilic fermentation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using Natural Gas Liquids to Recover Unconventional Oil and Gas Resources (Final Report)

This document presents final technical findings for the project Using Natural Gas Liquids to Recover Unconventional Oil and Gas Resources (FE0031782). The project is part of the U.S. Department of Energy Oil and Gas Program to develop and advance technologies that can significantly improve the recovery efficiencies of unconventional oil and gas resources. The overall objective of this project is to improve the ultimate recovery from unconventional oil and gas (UOG) resources in the United States by developing a method for using unrefined natural gas liquids (NGLs) as treatment fluids to improve hydrocarbon production. Horizontal extended-lateral drilling coupled with high volume hydraulic fracturing has significantly increased production from UOG resources in the U.S. However, the recovery efficiency is low compared to the estimated oil and gas in place. Recent data indicate that less than 10% of the oil in the liquid-rich UOG reservoirs is produced. Alternative completion methods using NGLs could increase production (Battelle, 2016; Wan et al., 2013; Wan, 2013; Downey et al, 2021); however, field validation tests are needed to develop an approach that is economical, efficient, and compatible in the UOG setting to advance towards commercial deployment. This project aims to develop and field test a method to improve recovery of oil resources in UOG shale plays by using Y-Grade NGLs, or a similar combination of NGLs, as treatment fluids. Refined NGLs have been used as a hydraulic treatment fluid in UOG plays for decades and are shown to be particularly effective because their miscibility with oil allows oil to flow more freely; however, the use of Y-Grade (unrefined) NGLs has not been studied. The use of Y-Grade NGLs would be advantageous over refined NGLs because Y-Grade NGLs do not require infrastructure or investment in refining and are already being produced from many UOG reservoirs. The concept was tested and monitored in the field at a commercial well site owned by project partner Hopco, Ltd. The project team, which consists of multiple oil and gas operators, Linde Gas North America LLC (Linde) and the Ohio Division of Geological Survey (ODGS), has extensive experience with oil and gas production in the Appalachian basin and the ability to work together quickly to solve technical issues and research needs. A key part of the proposed work was the use of existing wells for field testing and monitoring. A total of four wells (three vertical and one horizontal) were available for this project. One of the vertical wells was utilized as the test well for the NGL treatment test. A nearby vertical well was used for microseismic monitoring. The remaining vertical well and the horizontal well provided a baseline for typical UOG production in the oil window of the Utica/Point Pleasant (UPP). Major technical tasks of the project include characterization of the geotechnical properties of the UPP with an emphasis on the field site; design and planning for the NGLs testing; field testing and monitoring; analysis and integration of field data; and economic and resource/reserve assessment. The Shoman monitoring well and Doughty NGL treatment well were successfully plugged back during September-October 2020 in preparation for treatment and monitoring. A nitrogen diagnostic fracture injection test (DFIT) was completed on the Doughty well on July 22, 2021, consisting of 133,000 scf (91 Bbl.) of nitrogen. A nitrogen foam frac was completed in the Utica-Point Pleasant interval on August 17, 2021 with funding from outside sources. A microseismic monitoring array was installed in the Shoman well and monitored microseismic activity during the Doughty well frac job. Y-Grade NGL injection commenced on August 26, 2021. A total of 215 Bbl. was injected but the job was shut down due to a small leak on the suction hose on the pump truck. The Y-Grade treatment resumed on August 27, 2021 and an additional 726 Bbl. of Y-Grade NGL was injected at a well head pressure of 3850 psi. Total volume of injected Y-Grade over the two days of injection was 941 Bbl. The well was shut-in for 17 days following injection to allow the Y-Grade NGLs to soak on the formation. Y-Grade treatment flow back commenced on 9/13/2021 on a weekly basis. Production data, including surface pressures, oil, nitrogen, natural gas, and flow times was measured and recorded. Periodic gas samples were collected and analyzed to determine composition of flowback gas. As of July 2022, the treatment well had produced 726 Bbl of oil and 2,888 mcf gas. In August 2022, tubing and packer in the well were removed and a pump was installed to enhance oil recovery. Currently, the operator is producing the well about 2 days a week for a few hours. The performance of the NGL treatment test was evaluated based on reservoir simulations of the treatment process, processing of well testing data, analysis of micro seismic monitoring data, and production data analysis. This analysis suggested that oil production in the small test would continue through 2025. Upscaling the treatment to a horizontal Utica Point-Pleasant well would allow more oil recovery, but the process would involve more investment, services, and operational support. An economic analysis was conducted for scenarios aimed at upscaling the NGL treatment process for more typical horizontal UPP wells in the Appalachian Basin.

02 PETROLEUM↗
DOE OSTI2021

Development of phenomena identification and ranking table for Westinghouse lead fast reactor’s safety

The Westinghouse Lead-cooled Fast Reactor (LFR) is a medium-size, passively safe, economic, Gen-IV nuclear reactor. An important effort within the Westinghouse LFR program is the development of the safety analysis methodology, which comprises computer code development, model development, and experimental testing. A key initial task associated with the development of the safety analysis methodology is the identification of processes and phenomena that affect the plant's capability to meet selected safety performance indicators. This is accomplished through the development of a Phenomena Identification and Ranking Table (PIRT) for selected accident scenarios which, for this specific PIRT effort, included selected postulated design basis accidents and hypothetical beyond design basis accidents in LFRs. This paper describes the role of PIRT in the development of the Westinghouse LFR safety analysis methodology and the process used in the PIRT development. Specifically, the Westinghouse LFR PIRT assessed importance of pertinent phenomena and identified gaps in their knowledge-base by evaluating current modeling capabilities and data available for validation. The ultimate goal was to provide guidance on computer code development and validation efforts and to prioritize testing to support LFR design and licensing. The key phenomena and processes that are deemed highly important for the safety performance indicators, but for which the state of knowledge is low, are presented. The testing program and analyses development are planned to address significant phenomena in the PIRT.

Lead fast reactor↗
DOE OSTI2026

Protocol for applying a network-enabled gene discovery pipeline to non-model plant species

Identifying upstream regulators of key genes is essential for understanding gene regulatory mechanisms and translating these insights into functional targets. Here, we present a protocol for applying the network-enabled gene discovery pipeline (NEEDLE) to non-model plant species. We describe steps for environment setup, data preparation, computational analysis, expected outputs, and parameter considerations. NEEDLE integrates RNA sequencing (RNA-seq) processing, weighted gene co-expression analysis (WGCNA), Gene Network Inference with Ensemble of trees (GENIE3), and promoter conservation analysis to prioritize candidate transcriptional regulators.

Plant Sciences↗
DOE OSTI2021

AXEAP (ARGONNE X-RAY EMISSION PACKAGE)

Argonne X-ray Emission Package (AXEAP), a singular purpose software package for processing X-ray emission (XES) images collected with a 2-dimensional position sensitive pixel array detector, has been developed. AXEAP can rapidly convert XES image files into a spectral form by applying parallel computation and unsupervised machine learning to compute vast amount of image data. Special focus has been placed on designing user-friendly-interface for processing multiple edges, non-resonant and resonant x-ray emission image analysis, in order to make data processing quick and easy. AXEAP is free software and is written in MATLAB, armed with powerful libraries and toolboxes. The software runs on all common operating systems such as Linux, Window, and Mac.

SUN, CHENGJUN↗