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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data for The Effects of Sequential Hydrothermal-Mechanical Refining Pretreatment on Cellulose Structure Changes and Sugar Recoveries

The recalcitrance of lignocellulosic biomass necessitates an efficient pretreatment protocol for operating a successful cellulosic biorefinery. It is critical to improve cellulose accessibility for hydrolysis and fermentation by altering the plant cell wall’s physical structure and chemical composition. Sequential hydrothermal-mechanical refining pretreatment (HMR) allows efficient recovery of cellulosic sugars without utilizing any hazardous chemicals. HMR has been successfully applied to Liberty switchgrass, a bioenergy cultivar released by the USDA, and now it is being applied to oilcane, a recently developed transgenic sugarcane variety engineered to accumulate lipids in its vegetative tissues. Sugar yields of oilcane bagasse (OCB) and switchgrass (SG) treated with HMR are 96.4% and 75.4%, respectively. This study sought to correlate cellulosic sugar yields with structural changes within the cell wall caused by HMR on two distinct bioenergy crops. Simon’s staining technique for the specific surface area analysis showed that HMR increased the specific surface area of pretreated biomass residues by 80-112%. In addition, ATR-FTIR was performed to determine the effects of HMR on physical structures based on the total crystallinity index (TCI) and hydrogen bonding intensity (HBI). Irrespective of biomass type, HMR decreased the initial crystalline cellulose contents of untreated biomass residues by 3.5% and reduced TCI and HBI by 7-13%. The study found that sugar yields were negatively correlated to reducing values of hydrogen bonding intensity, crystalline cellulose content, and total crystallinity index.

Biomass Analytics↗
DOE OSTI2020

High Speed, Localized Multi-Point Strain Measurements on a Containment Vessel at 1.7 MHz Using Swept-Wavelength Laser-Interrogated Fiber Bragg Gratings

Dynamic elastic strain in ~1.8 and 1.0 m diameter containment vessels containing a high explosive detonation was measured using an array of fiber Bragg gratings. The all-optical method, called real-time localized strain measurement, recorded the strain for 10 ms after detonation with additional measurements being sequentially made at a rate of 1.7 MHz. A swept wavelength laser source provided the repetition rate necessary for such high-speed measurements while also providing enough signal strength and bandwidth to simultaneously measure 8 or more unique points on the vessel’s surface. The data presented here arethen compared with additional diagnostics consisting of a fast spectral interferometer and an optical backscatter reflectometer to show a comparison between the local and global changes in the vessel strain, both dynamically and statically to further characterize the performance of the localized strain measurement. The results are also compared with electrical resistive strain gauges and finite element analysis simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗
DOE OSTI2021

Electron-ion coincidence measurements of molecular dynamics with intense X-ray pulses

Molecules can sequentially absorb multiple photons when irradiated by an intense X-ray pulse from a free-electron laser. If the time delay between two photoabsorption events can be determined, this enables pump-probe experiments with a single X-ray pulse, where the absorption of the first photon induces electronic and nuclear dynamics that are probed by the absorption of the second photon. Here we show a realization of such a single-pulse X-ray pump-probe scheme on N 2 molecules, using the X-ray induced dissociation process as an internal clock that is read out via coincident detection of photoelectrons and fragment ions. By coincidence analysis of the kinetic energies of the ionic fragments and photoelectrons, the transition from a bound molecular dication to two isolated atomic ions is observed through the energy shift of the inner-shell electrons. Via ab-initio simulations, we are able to map characteristic features in the kinetic energy release and photoelectron spectrum to specific delay times between photoabsorptions. In contrast to previous studies where nuclear motions were typically revealed by measuring ion kinetics, our work shows that inner-shell photoelectron energies can also be sensitive probes of nuclear dynamics, which adds one more dimension to the study of light-matter interactions with X-ray pulses.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Structural and Electronic Complexities of a Sulfur‐Bridged Di‐Iron Complex Composed of Mono‐ and Di‐Nitrosyl Units

The delocalized, thermodynamically stable cation, [(N 2 S 2 )Fe(NO)•Fe(NO) 2 ] + , an adduct of mono-nitrosyl and dinitrosyl iron units, is analyzed to address the unusual stability of the sulfur-bridged diiron complex in its three overall redox levels, +, 0, and −. X-ray diffraction and myriad spectroscopic techniques probe products of sequential electron uptake in the corresponding neutral and anionic species. Conundrums include unified blueshifts of the overall 3-band, ν(NO), pattern with added electrons. One-electron reduction changes the anti-ferromagnetically coupled, S = 0, cationic diiron species to the neutral analog, S = ½, with unpaired spin mainly localized on the MNIU, which decreases its ∠Fe–N–O angle by 10 degrees in response to the extra electron density. Subsequent reduction to the anionic species, S = 1, involves a major geometric change at the MNIU, which moves the Fe in {Fe(NO)} 8 out of the N 2 S 2 plane. Site-specific 15 N labeling of nitrosyl in the MNIU confirms the IR analysis and shows rapid NO exchange between the MNIU/DNIU (mono-nitrosyl iron unit/dinitrosyl iron unit) pairs during its synthesis at RT. Mössbauer spectroscopy, S K-edge XAS, and molecular orbital calculations confirm the ability of NO and the versatility of sulfur bridges to buffer and distribute electrons, a key to their major importance in metalloenzymes.

DNIC↗
DOE OSTI2023

Cr silicate as a prototype for engineering magnetic phases in air-stable two-dimensional transition-metal silicates

Abstract Identifying environmentally inert, ferromagnetic two-dimensional (2D) materials with high Curie temperatures ( T c ) down to the single layer limit has been an obstacle to fundamental studies of 2D magnetism and application of 2D heterostructures to spin-polarized devices. To address this challenge, the growth, structure and magnetic properties of a 2D Cr-silicate single layer on Pt(111) was investigated experimentally and theoretically. The layer was grown by sequentially depositing SiO and Cr followed by annealing in O 2 . Scanning tunneling microscopy (STM), low-energy electron diffraction (LEED), and low energy electron microscopy all indicated a well-ordered layer that uniformly covered the surface, with STM and LEED indicating that the silicate relaxed to its favored lattice constant. Further experimental characterizations demonstrated that the Cr was nominally 3+ but with a lower electron density than typical trivalent Cr compounds. Comparison with theory identified a Cr 2 Si 2 O 9 structure that resembles a single layer of a dehydrogenated dioctahedral silicate. Magnetic circular dichroism in x-ray absorption spectroscopy revealed a ferromagnetically ordered state up to at least 80 K. Theoretical analysis revealed that the Cr in a dehydrogenated Cr-silicate/Pt(111) is more oxidized than Cr in freestanding Cr 2 Si 2 O 9 H 4 layers. This greater oxidation was found to enhance ferromagnetic coupling and suggests that the magnetism may be tuned by doping. The 2D Cr-silicate is the first member of a broad series of possible layered first-row transition metal silicates with magnetic order; thus, this paper introduces a new platform for investigating 2D ferromagnetism and the development of magnetoelectronic and spintronic devices by stacking 2D atomic layers.

2D materials↗
DOE OSTI2023

Perspectives on biorefineries in microbial production of fuels and chemicals

Microbes drive our complex biosphere by regulating the global ecosystem through cycling elements and energy. Humankind has barely begun leveraging this biotransformation capacity to impact global economies and ecologies. Advances in genetic engineering, molecular analysis, metabolic flux modeling, microbial consortia/biome mapping and engineering, cell-free bioproduction, artificial intelligence/machine learning and the ever expanding -omics frontiers have set the stage for paradigm changes to how humankind produces, uses, transforms, and recycles carbon and energy through microbes. Harnessing this enormous potential could drive a global bioeconomy and manage carbon at a planetary level but requires understanding and application at a grand scale across a broad range of science and engineering disciplines. The penultimate manifestation of these advances is the “bio-refinery”, which is often referenced, but is a long way from being fully developed as a global carbon management platform. Broadening the feed stocks, processing operations, and product portfolio to a sequential cascade optimizing the conversion as a whole instead of limited outputs could greatly advance deployment and stability of a bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2026

Emergent quasi-one-dimensional antiferromagnetism in the distorted kagome magnet CePtPb

CePtPb hosts a distorted kagome lattice of Ce 3+ ions, providing a clean platform to investigate how reduced local symmetry and strong spin-orbit coupling reshape frustrated magnetism. Magnetization, specific heat, and magnetocaloric effect measurements, combined with a symmetry analysis of the single-ion anisotropy, demonstrate that the local 𝑚⁢2⁢𝑚 site symmetry selects a nearly Ising-like Kramers doublet with easy axes lying within the 𝑎⁢𝑏 plane. This results in three distinct in-plane Ising directions and an overall easy-plane anisotropy. The low-energy magnetic response is well captured by a three-sublattice Ising model, which quantitatively reproduces the saturation magnetization for arbitrary in-plane field orientations, including $[110]$ and $[1\bar{⁢1}⁢0]$, as well as the ratio of the field-induced critical fields. For 𝐵∥$[110]$, the phase diagram exhibits two quantum critical points at 𝐵 c⁢1 = 0.25T and 𝐵 c⁢2 = 0.55T, arising from the sequential polarization of the three Ising sublattices. In conclusion, these results reveal that the system develops quasi-one-dimensional spin chains along the 𝑐 axis, emerging from the nominally three-dimensional crystal structure composed of stacked kagome layers, and illustrate how reduced local symmetry can drive effective dimensional reduction in rare-earth Ising magnets.

Li, Fangli [Southern University of Science and Tec↗
DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2024

Direct Evidence for a Sequential Electron Transfer–Proton Transfer Mechanism in the PCET Reduction of a Metal Hydroxide Catalyst

Here, the proton-coupled electron transfer (PCET) mechanism for the reaction M ox -OH + e − + H + → M red -OH 2 was de-termined through kinetic resolution of the independent electron transfer (ET) and proton transfer (PT) steps. The reaction of interest was triggered by visible light excitation of [Ru II (tpy)(bpy′)H 2 O] 2+ , Ru II -OH 2 , where tpy is 2,2′:6′,2″-terpyridine and bpy′ is 4,4′-diaminopropylsilatrane-2,2′-bipyridine, anchored to In 2 O 3 :Sn (ITO) thin films in aqueous solutions. Interfacial kinetics for the PCET reduction reaction were quantified by nanosecond transient absorption spectroscopy as a function of solution pH and applied potential. Data acquired from pH = 5-10 revealed a stepwise electron-transfer proton-transfer (ET-PT) mechanism, while kinetic measurements made below the pK a (Ru III -OH/OH 2 ) = 1.3 were used to study the analogous interfacial reaction where electron transfer was the only mechanistic step. Analysis of this data with a recently reported multi-channel kinetic model was used to construct a PCET zone diagram and supported the assignment of an ET-PT mechanism from pH = 5-10. Ultimately, this study represents a unique example amongst M ox -OH/M red -OH 2 reactivity where the protona-tion and oxidation state of the intermediate was kinetically and spectrally resolved to firmly establish the PCET mechanism.

Charge transfer↗
DOE OSTI2022

Ethanol to diesel: a sustainable alternative for the heavy-duty transportation sector

The combustion of middle distillates (diesel and jet fuel) is responsible for the emission of more than 2 GTon of CO 2 per year worldwide. While sustainable alternatives exist for gasoline and jet fuel, we still lack sustainable alternatives for diesel. This fact is especially relevant if we consider that electrification of sectors where diesel is used is challenging. One sustainable approach for diesel production is the catalytic upgrading of ethanol. While most work in this field has focused on the dehydration/oligomerization of ethanol, this approach is limited to producing fuels that have a high degree of branching and low cetane number. Another approach is the sequential use of ethanol Guerbet coupling, leading to higher alcohols, followed by etherification, leading to large ethers which, importantly, result in a product with high cetane number. In this work, we explore the catalytic upgrading of ethanol into diesel following an approach based on the initial Guerbet coupling of ethanol followed by etherification. The results presented are a collaborative and synergic effort among process and systems engineers, experimentalist in the area of catalysis, and fuel property modelers. Here, we demonstrate experimentally the feasibility of upgrading ethanol into a diesel fuel with properties that surpass its fossil counterpart. The diesel produced has a predicted cetane number of ~70 and outstanding cold flow properties, while maintaining other properties (viscosity, density, and flash point) within expected ranges. A technoeconomic analysis performed based on a detailed biorefinery model shows that the MFSP is ~5.89$ per Gal in 2021 dollars when lignocellulosic ethanol is used, with the most relevant economic driver the cost of the ethanol feedstock. The upgrading process can be performed with a net energy gain (EROI = 1.49 > 1). An LCA analysis of greenhouse gas (GHG) emissions reveals that the use of lignocellulosic ethanol may lead to more than 50% reduction in GHG emissions compared to fossil diesel. Depending on the CO 2 emissions associated with the production of ethanol we show that in some instances the production of carbon neutral diesel fuel is possible.

09 BIOMASS FUELS↗
DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗
DOE OSTI2024

Effects of 5-ion 6-beam sequential irradiation in the presence and absence of hindlimb or control hindlimb unloading on behavioral performances and plasma metabolic pathways of Fischer 344 rats

Introduction Effects and interactions between different spaceflight stressors are expected to be experienced by crew on missions when exposed to microgravity and galactic cosmic rays (GCRs). One of the limitations of previous studies on simulated weightlessness using hindlimb unloading (HU) is that a control HU condition was not included. Methods We characterized the behavioral performance of male Fischer rats 2 months after sham or total body irradiation with a simplified 5-ion 6-mixed-beam exposure representative of GCRs in the absence or presence of HU. Six months later, the plasma, hippocampus, and cortex were processed to determine whether the behavioral effects were associated with long-term alterations in the metabolic pathways. Results In the open field without and with objects, interactions were observed for radiation × HU. In the plasma of animals that were not under the HU or control HU condition, the riboflavin metabolic pathway was affected most for sham irradiation vs. 0.75 Gy exposure. Analysis of the effects of control HU on plasma in the sham-irradiated animals showed that the alanine, aspartate, glutamate, riboflavin, and glutamine metabolisms as well as arginine biosynthesis were affected. The effects of control HU on the hippocampus in the sham-irradiated animals showed that the phenylalanine, tyrosine, and tryptophan pathway was affected the most. Analysis of effects of 0.75 Gy irradiation on the cortex of control HU animals showed that the glutamine and glutamate metabolic pathway was affected similar to the hippocampus, while the riboflavin pathway was affected in animals that were not under the control HU condition. The effects of control HU on the cortex in sham-irradiated animals showed that the riboflavin metabolic pathway was affected. Animals receiving 0.75 Gy of irradiation showed impaired glutamine and glutamate metabolic pathway, whereas animals receiving 1.5 Gy of irradiation showed impaired riboflavin metabolic pathways. A total of 21 plasma metabolites were correlated with the behavioral measures, indicating that plasma and brain biomarkers associated with behavioral performance are dependent on the environmental conditions experienced. Discussion Phenylalanine, tyrosine, and tryptophan metabolism as well as phenylalanine and tryptophan as plasma metabolites are biomarkers that can be considered for spaceflight as they were revealed in both Fischer and WAG/Rij rats exposed to simGCRsim and/or HU.

Physiology↗
DOE OSTI2021

An Economics-by-Design Approach Applied to a Heat Pipe Microreactor Concept

Microreactors present a potential paradigm shift in the nuclear industry. Emphasis thus far has been on large-scale multi-billion-dollar projects that cater solely to grid electricity market. These projects can be challenging to finance and execute. On the other hand, microreactors are intended to target a wide variety of smaller niche markets and are expected to be factory-fabricated and more readily deployable. While diseconomies of scale for microreactors may tend to raise their costs per energy output (MWh) relative to large nuclear plants, offsetting gains can be expected from standardization, simplification, passive safety, lower radionuclide inventories, factory fabrication, fast installation, and low financing costs. To adequately assess these contributions, designers should have a different perspective on cost drivers than for large nuclear plants and can utilize novel approaches for systematic cost reduction. To account for these important aspects of microreactors, this report proposes an economics-by-design approach that places economic considerations at the center of the design process. The methodology builds on existing frameworks such as design-to-cost and value engineering, expanding them to new markets (beyond the grid), new attributes (beyond costs alone), and introducing the approach at earlier points in the design cycle. Design parameters and technical specifications are systematically evaluated until costs meet market entry points, while also providing the high-priority performance attributes of the particular use case. Determining first-order estimates for different components early in the process enables designers to focus R&D efforts on the biggest overall cost contributors and components with the most cost uncertainty. The analysis is always guided by market needs and threshold prices. In addition to microreactors, the approach is expected to be useful for other classes of nuclear reactors as well. The analysis was applied to a concept found in the open literature (the Design A heat-pipe reactor). A comprehensive bottom-up estimate was generated by leveraging a new microreactor-specific code of accounts and a range of cost equations. The initial estimate for levelized cost of electricity (LCOE) unsurprisingly exceeded market ranges since the use case had prioritized technological readiness over economic considerations in design choices. An alternate concept was then proposed, with various assumptions/targets made to reduce the largest cost contributors. Changes in the neutron spectrum, the power output, and building structures were found to make even the first-of-a-kind of this modified concept competitive with diesel generation in some remote communities. Learning rate (LR) assumptions indicated cost reductions achieved from sequential unit deployments could expand the range of competitiveness to include additional markets as deployments proceed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Multiband superconductivity in V 3 Si determined from studying the response to controlled disorder

The London penetration depth, λ(T), was measured in a single crystal V 3 Si. The superfluid density obtained from this measurement shows a distinct signature of two almost decoupled superconducting gaps. This alone is insufficient to distinguish between s ± and s ++ pairing states, but it can be achieved by studying the effect of a controlled non-magnetic disorder on the superconducting transition temperature, T c . For this purpose, the same V 3 Si crystal was sequentially irradiated by 2.5 MeV electrons three times, repeating the measurement between the irradiation runs. A total dose of 10 C/cm 2 (6.24 × 10 19 electrons/cm 2 ) was accumulated, for which T c has changed from 16.4 K in a pristine state to 14.7 K (9.3 %). This substantial suppression is impossible for a single isotropic gap, but also it is not large enough for a sign-changing s± pairing state. Our electronic band-structure calculations show how five bands crossing the Fermi energy can be naturally grouped to support two effective gaps, not dissimilar from the iron pnictides physics. We analyze the results using two-gap models for both, λ(T) and T c , which describe the data very well. Finally, the experimental results and theoretical analysis provide strong support for an s ++ superconductivity with two unequal gaps, Δ 1 (0) ≈ 2.53 meV and Δ 2 (0) ≈ 1.42 meV, and a very weak inter-band coupling in V 3 Si superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

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

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

04 OIL SHALES AND TAR SANDS↗