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DOE OSTI2023

ECAR-7210 Rev 0 Steady-state Thermo-mechanical Analysis of the MARVEL Fuel Bundle

This document reports the steady-state thermal-hydraulic and static mechanical analyses of the MARVEL micro-reactor core. The objective of this work is to investigate whether fuel-to-fuel contact could occur due to the thermal deformation of the fuel cladding during the normal operation and to assess the resulting impact on the peak cladding temperatures (PCT) if contact occurs. The scope of the work consists of three computational tasks. First, a conjugate heat transfer analysis using computational fluid dynamics (CFD) model was performed to evaluate the PCTs during normal operating conditions. Second, a finite element analysis (FEA) using the cladding temperature field obtained from the CFD analysis was performed to evaluate the thermal deformation of cladding and determine the occurrence of fuel-to-fuel contact. Finally, a CFD analysis of FEA-informed fuel-to-fuel gap (represented by a conservative 0.05 mm uniform gap) was performed to evaluate whether the PCT exceeds the safety criteria.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2020

Operando NRIXS and XAFS Investigation of Segregation Phenomena in Fe-Cu and Fe-Ag Nanoparticle Catalysts during CO 2 Electroreduction

Operando nuclear resonant inelastic X-ray scattering (NRIXS) and X-ray absorption fine-structure spectroscopy (XAFS) measurements were used to gain insight into the structure and surface composition of FeCu and FeAg nanoparticles (NPs) during the electrochemical CO 2 reduction (CO 2 RR) and to extract correlations with their catalytic activity and selectivity. The formation of a core–shell structure during CO 2 RR for FeAg NPs was inferred from the analysis of the operando NRIXS data (phonon density of states, PDOS) and XAFS measurements. Electrochemical analysis of the FeAg NPs revealed a faradaic selectivity of 36 % for CO in 0.1 M KHCO 3 at -1.1 V vs. RHE, similar to that of pure Ag NPs. In contrast, a predominant selectivity towards H 2 evolution is obtained in the case of the FeCu NPs, analogous to the results obtained for pure Fe NPs, although small Cu NPs have also been shown to favor H 2 production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Nanostructure Scaling in Semi-Dilute Triblock Copolymer Gels

Here, there is a considerable body of work that describes the scaling of diblock copolymer micelle dimensions in dilute and semi-dilute solution based upon block degrees of polymerization and copolymer concentration. However, there is a lack of analogous information for semi-dilute ABA triblock copolymer gels, which consist of ABA triblock copolymer dissolved in midblock-selective (B-selective) solvent. The present study uses small angle X-ray scattering to extract micelle dimensions for numerous triblock copolymer gels that vary in copolymer identity (and hence block lengths) and copolymer concentration, as well as gels that contain various ratios of two unique triblock copolymers. Analysis of micelle structural data subsequently translates to universal scaling expressions for the micelle core radius – r A ≈ N A 0.53 N B -0.14 $\phi$ ABA 0.16 where N A and N B are the endblock and midblock degrees of polymerization, respectively, and $\phi$ ABA is the volume fraction of triblock copolymer in the gel – and for the intermicelle spacing – l AA ≈ N A 0.09 N B 0.29 $\phi$ ABA -0.35 . Each scaling expression describes the full collection of experimental data very well. Additionally, these scaling expressions are partially in line with expectations from semi-dilute diblock copolymer solution theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Manufacturing of magnesium/aluminum bimetallic ring components by friction stir assisted simultaneous forging and solid-state joining

Here, a bimetallic ring component is forged by a friction stir assisted forging (FS-forging). A cylindrical bimetallic blank, magnesium AZ31 cylinder (Mg core) tightly fitted inside an aluminum 6061-T6 tube (Al skin), is used. In the FS-forging, the frictional heat and stirring of rotating tool forge the blank into a desired shape, while simultaneously generate a solid-state joint between the Mg core and the Al skin, without additional external heating, as confirmed by the microstructural analysis and mechanical tests. The microstructural analysis also shows that the characteristics of the solid-state joint can be different depending on the process parameters.

42 ENGINEERING↗

DOE OSTI2021

High Flux Isotope Reactor (HFIR)

Oak Ridge National Laboratory’s High Flux Isotope Reactor (HFIR) is the highest-flux reactor-based neutron source in the United States. HFIR’s intense neutron flux and state-of-the-art facilities result in world-class capabilities, including neutron scattering, radioisotope production, materials irradiation, and neutron activation analysis. HFIR’s multimission capabilities are attributed to its versatile high-power-density core design consisting of a series of concentric regions, including a flux trap target region, an inner fuel element, an outer fuel element, a control element region, and a beryllium reflector. The pressurized, light-water-cooled research reactor operates at 85 MW and is fueled by 9.4 kg 235U.

Chandler, David↗

DOE OSTI2021

Internal Microstructure Dictates Interactions of Polymer-grafted Nanoparticles in Solution

Understanding the effects of polymer brush architecture on particle interactions in solution is requisite to enable the development of functional materials based on self-assembled polymer-grafted nanoparticles (GNPs). Static and dynamic light scattering of polystyrene-grafted silica particle solutions in toluene reveals that the pair interaction potential, inferred from the second virial coefficient, A 2 , is strongly affected by the grafting density, σ, and degree of polymerization, N, of tethered chains. In the limit of intermediate σ (~0.3 to 0.6 nm -2 ) and high N, A 2 is positive and increases with N. This confirms the good solvent conditions and can be qualitatively rationalized on the basis of a pair interaction potential derived for grafted (brush) particles. In contrast, for high σ > 0.6 nm -2 and low N, A 2 displays an unexpected reversal to negative values, thus indicating poor solvent conditions. These findings are rationalized by means of a simple analysis based on a coarse-grained brush potential, which balances the attractive core-core interactions and the excluded volume interactions imparted by the polymer grafts. The results suggest that the steric crowding of polymer ligands in dense GNP systems may fundamentally alter the interactions between brush particles in solution and highlight the crucial role of architecture (internal microstructure) on the behavior of hybrid materials. The effect of grafting density also illustrates the opportunity to tailor the physical properties of hybrid materials by altering geometry (or architecture) rather than a variation of the chemical composition.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

Abstract The genome-scale model of metabolism and gene expression (ME-model) forPseudomonas putidaKT2440,iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model,iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization inP. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus,iPpu1676-ME offers valuable insights intoP. putida’s proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Mathematical & Computational Biology↗

DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Characterization of Field-Exposed Photovoltaic Modules Featuring Signs of Contact Degradation

Here, this work investigates several photovoltaic (PV) modules that have shown signs of metal contact corrosion due to field exposure in a hot and humid climate. This includes two multicrystalline silicon aluminum back surface field systems with 10 and 14 years of exposure and one monocrystalline silicon passivated emitter and rear cell system with four years of exposure. A comprehensive, multiscale characterization process is used to evaluate these PV modules in great detail. Current–voltage (I−V), Suns-V OC measurements, electroluminescence imaging, infrared imaging, and ultraviolet fluorescence imaging were performed, and locations of interest were cored and analyzed using cross-sectional scanning electron microscopy (SEM). A rigorous, quantitative analysis procedure for the cross-sectional SEM images is proposed and implemented. Careful characterization does reveal that some of these PV modules do indeed exhibit the same classic signs of acetic-acid-based corrosion of the glass frit that is present at the silver/silicon interface, which have been observed previously in PV modules exposed to damp heat in an environmental chamber.

14 SOLAR ENERGY↗

DOE OSTI2022

Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks

Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Cost of saving natural gas through efficiency programs funded by utility customers: 2012–2017

This study estimates the cost of saving a therm of natural gas from energy efficiency programs funded by utility customers during the period 2012 to 2017. Berkeley Lab researchers compiled and analyzed efficiency program data reported by investor-owned utilities and other program administrators in a dozen states representative of the four U.S. Census regions — Arkansas, California, Connecticut, Iowa, Massachusetts, Michigan, Minnesota, New Jersey, New York, Oklahoma, Rhode Island and Utah. Depending on the year, the dataset accounts for about 50 percent to 70 percent of annual national spending on natural gas efficiency programs. The estimated cost of saving natural gas during the study period is $0.40 per therm. The analysis also includes estimates of the program administrator cost of saved energy for three core sectors for natural gas: commercial and industrial, residential, and low-income households. It aggregates these sectors to provide regional and national values. Our metrics include savings-weighted averages, unweighted medians, and interquartile ranges (25th and 75th percentiles) of the levelized program administrator cost of saving gas, in constant 2017 dollars. In addition, the study analyzes cost trends during the study period, finding that average program costs trended downward. The U.S. Department of Energy’s Building Technologies Office supported this work.

03 NATURAL GAS↗

DOE OSTI2022

On a Unified Core Characterization Methodology to Support the Systematic Assessment of Rare Earth Elements and Critical Minerals Bearing Unconventional Carbon Ores and Sedimentary Strata

A significant gap exists in our understanding and ability to predict the spatial occurrence and extent of rare earth elements (REE) and certain critical minerals (CM) in sedimentary strata. This is largely due to a lack of existing, systematic, and well-distributed REE and CM samples and analyses in United States sedimentary basins. In addition, the type of sampling and characterization performed to date has generally lacked the resolution and approach required to constrain geologic and geographic heterogeneities typical of subsurface, mineral resources. Here, we describe a robust and systematic method for collecting core scale characterization data that can be applied to studies on the contextual and spatial attributes, the geologic history, and lithostratigraphy of sedimentary basins. The methods were developed using drilled cores from coal bearing sedimentary strata in the Powder River Basin, Wyoming (PRB). The goal of this effort is to create a unified core characterization methodology to guide systematic collection of key data to achieve a foundation of spatially and geologically constrained REEs and CMs. This guidance covers a range of measurement types and methods that are each useful either individually or in combination to support characterization and delineation of REE and CM occurrences. The methods herein, whether used in part or in full, establish a framework to guide consistent acquisition of geological, geochemical, and geospatial datasets that are key to assessing and validating REE and CM occurrences from geologic sources to support future exploration, assessment, and techno-economic related models and analyses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Effect of Preparation Conditions of Fe@SiO2 Catalyst on Its Structure Using High-Pressure Activity Studies in a 3D-Printed SS Microreactor

Fischer–Tropsch synthesis (FTS) in a 3D-printed stainless steel (SS) microchannel microreactor was investigated using Fe@SiO2 catalysts. The catalysts were prepared by two different techniques: one pot (OP) and autoclave (AC). The mesoporous structure of the two catalysts, Fe@SiO2 (OP) and Fe@SiO2 (AC), ensured a large contact area between the reactants and the catalyst. They were characterized by N2 physisorption, H2 temperature-programmed reduction (H2-TPR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron microscopy (XPS), and thermogravimetric analysis–differential scanning calorimetry (TGA-DSC) techniques. The AC catalyst had a clear core–shell structure and showed a much greater surface area than that prepared by the OP method. The activities of the catalysts in terms of FTS were studied in the 200–350 °C temperature range at 20-bar pressure with a H2/CO molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher selectivity and higher CO conversion to olefins than Fe@SiO2 (OP). Stability studies of both catalysts were carried out for 30 h at 320 °C at 20 bar with a feed gas molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher stability and yielded consistent CO conversion compared to the Fe@SiO2 (OP) catalyst.

Biochemistry & Molecular Biology↗

DOE OSTI2023

Continuous Filament Network of the Local Universe

Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ridge filter used within the framework of image analysis. We considered galaxies from the HyperLeda database with redshifts 0.02 ≤ z ≤ 0.1, and in the solid angle 120° ≤ R.A. ≤ 240°, 0° ≤ decl. ≤ 60°. We divided the sample into 16 two-dimensional celestial projections with redshift bin Δ z = 0.005, and compared our continuous filament network with a similar recent catalog covering the same region of the sky. We tested our catalog on two application scenarios. First, we compared the distributions of the distances to the nearest filament of various astrophysical sources (Seyfert galaxies and other active galactic nuclei, radio galaxies, low-surface-brightness galaxies, and dwarf galaxies), and found that all source types trace the filaments well, with no systematic differences. Next, among the HyperLeda galaxies, we investigated the dependence of the g - r color distribution on the distance to the nearest filament, and confirmed that early-type galaxies are located on average further from the filaments than late-type ones.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.

Teixeira Prates, Erica↗

DOE OSTI2021

Prediction of crystal structures and motifs in the Fe–Mg–O system at Earth’s core pressures

Abstract Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe–O, Mg–O, and Fe–Mg binary systems under pressure have been investigated, there are still very few studies of the Fe–Mg–O ternary system at relevant Earth’s core and super-Earth’s mantle pressures. Here, we use the adaptive genetic algorithm (AGA) to study ternary Fe x Mg y O z phases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered three dynamically stable phases with stoichiometries FeMg 2 O 4 , Fe 2 MgO 4, and FeMg 3 O 4 with lower enthalpy than any known combination of Fe–Mg–O high-pressure compounds at 350 GPa. With the discovery of these phases, we construct the Fe–Mg–O ternary convex hull. We further clarify the composition- and pressure-dependence of structural motifs with the analysis of the AGA-found stable and metastable structures. Analysis of binary and ternary stable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at inner core pressures.

Wang, Renhai↗

DOE OSTI2023

Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units

We present a solution of sparse ACOPF analysis on GPU. In particular, we discuss the performance bottlenecks and detail our efforts to accelerate the linear solver, a core component of ACOPF that dominates the computational time. ACOPF solutions of two large-scale systems, synthetic Northeast (25,000 buses) and Eastern (70,000 buses) \cite{birchfield2017tamu-cases} on GPU show promising speed-up compared to CPU based solution using a state-of-the-art solver. To our knowledge, this is the first result demonstrating acceleration of sparse ACOPF on GPUs.

Power grid analysis, GPU↗