Mapping Cation Disorder in Irradiated Gd2Ti2O7 Pyrochlore by 4D-STEM
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4D-STEM, microscopy, pyrochlore↗
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Igniting Weak Interactions in Neutron Star Postmerger Accretion Disks
The merger of two neutron stars or a neutron star and a black hole typically results in the formation of a postmerger accretion disk. Outflows from disks may dominate the overall ejecta from mergers and be a major source of r-process nuclei in our universe. We explore the parameter space of such disks and their outflows and r-process yields by performing 3D general-relativistic magnetohydrodynamic simulations with weak interactions and approximate neutrino transport. We discuss the mapping between the initial binary parameters and the parameter space of the resulting disks, chiefly characterized by their initial accretion rate. We demonstrate the existence of an ignition threshold for weak interactions at around ~10 -3 M⊙ s -1 for typical parameters by means of analytic calculations and numerical simulations. We find a degenerate, self-regulated, neutrino-cooled regime above the threshold and an advection-dominated regime below the threshold. Excess heating in the absence of neutrino cooling below the threshold leads to ≳60% of the initial disk mass being ejected in outflows, with typical velocities of ~(0.1–0.2)c, compared to ≲40% at ~(0.1–0.15)c above the threshold. While disks below the threshold show suppressed production of light r-process elements, disks above the threshold can produce the entire range of r-process elements, in good agreement with the observed solar system abundances. Disks below the ignition threshold may produce an overabundance of actinides seen in actinide-boost stars. As gravitational-wave detectors start to sample the neutron star merger parameter space, different disk realizations may be observable via their associated kilonova emission.
79 ASTRONOMY AND ASTROPHYSICS↗
Visual Systems Mapping to Define and Compare Woody Biomass LCAs for Sustainable Systems
The challenge addressed in this research centres on the need to choose between several biomass sources and energy production processes, while supporting rural economies and resilience of forest systems. A key barrier to effective decision-making for strategies using biomass is the lack of standardized and transparent life cycle assessment (LCA) baselines. These baselines are critical for assessing the impacts of biomass strategies but often vary due to regional factors and chosen simplifying assumptions of the LCAs. However, omitting key variables can mean the LCA omits key feedback and balancing loops relevant to fully assessing impacts of the change or test scenario. To address these complexities, this project employs a systems engineering approach: visual systems mapping. This technique is used to define the boundaries and dynamic behaviours of LCA baselines, enhancing transparency. By examining five literature sources and their documented baseline scenarios, the systems mapping case-studies demonstrates an approach to documenting and archiving these baselines. Recommendations are that visual systems mapping should be used to document key assumptions, such as baselines, of LCAs. Further, where possible open data repositories should hold key information about LCA baselines and reproducible workflows (e.g., using open-source tools) should be used to improve transparency and comparability in LCAs. Given the consensus within the broader scientific community on the importance of replicable data practices, this research reinforces the need for standardized frameworks and systems engineering tools in LCAs. This research demonstrates a pathway to more transparent, standardized, and comparable LCAs, that may bolster decisions for biomass systems.
Davis, Maggie [ORNL] (ORCID:0000000181319328)↗
A hybrid data–model approach to map soil thickness in mountain hillslopes
Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.
58 GEOSCIENCES↗
Twin related domains in 3D microstructures of conventionally processed and grain boundary engineered materials
The concept of twin-limited microstructures has been explored in the literature as a crystallographically constrained grain boundary network connected via only coincident site lattice (CSL) boundaries. The advent of orientation imaging has made classification of twin-related domains (TRD) or any other orientation cluster experimentally accessible in 2D using EBSD. With the emergence of 3D orientation mapping, a comparison of TRDs in measured 3D microstructures is performed in this paper and compared against their 2D counterparts. The TRD analysis is performed on a conventionally processed (CP) and a grain boundary engineered (EM) high purity copper sample that have been subjected to successive anneal procedures to promote grain growth. Finally, the EM sample shows extremely large TRDs which begin to approach that of a twin-limited microstructure, while the TRDs in the CP sample remain relatively small and remote.
36 MATERIALS SCIENCE↗
Distributed Macroscopic Traffic Simulation with Open Traffic Models
This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.
macro-scopic traffic simulation↗
Resolving the geometrically necessary dislocation content in severely deformed aluminum by transmission Kikuchi diffraction
Highlights: • Grain fragmentation and steady-state stages occur during HPT processing. • A gradual transformation of LAGBs to HAGBs is characterized in both regimes. • During plastic deformation, dislocations gather collectively as geometrical walls. • The presence of GNDs is more pronounced in the vicinity of already existing HAGBs. • The density of GNDs is greater in the fragmentation stage. - Abstract: In this paper, severe plastic deformation (SPD) is applied to commercially pure aluminum. Monotonic high pressure torsion (HPT) processing is employed at room temperature, and the microstructure of samples deformed up to an equivalent strain of 50 is investigated by electron backscatter diffraction (EBSD). The distribution pattern and the density of geometrically necessary dislocations (GNDs) are evaluated by examination of transmission Kikuchi diffraction (TKD) maps. Three different methodologies are utilized for assessment of the GND density. It was observed that two distinct stages of grain fragmentation and steady-state occur during processing. During the first stage, a severe grain refinement was observed as the average grain size decreased from ~85 μm to ~1 μm at an equivalent strain of 10. Quantification of the density of dislocations in both deformation regimes showed that, independent of the choice of model, the GND density is greater in the fragmentation stage than in the steady-state stage. This observation was linked with the prevalence of the continuous dynamic recrystallization (CDRX) phenomenon in each stage. Furthermore, a significant presence of GNDs in the steady-state stage was characterized. Formation of microstructure, grain refinement and saturation of grain size are discussed in the light of statistics of GNDs.
36 MATERIALS SCIENCE↗
The geometry of signal space: a case study of direct mapping between seismic signals and event distribution
SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.
58 GEOSCIENCES↗
Electrochemical Reactions – Fundamentals Mapped from the Atomic to the Macroscopic Scale
Electrochemistry more important than ever Electrocatalysis great prospects Enhanced selective vs thermocatalysis More benign conditions New chemical processes needed for, e.g., sustainability Demand for tailor-made catalysts Atomic scale modeling useful guide Surge to develop methods that can capture realistic systems
Stenlid, Joakim Halldin↗
Predicting CO 2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network
Numerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large-scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input-output relations of physics-based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC-GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC-GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC-GAN is that it includes a self-training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC-GAN model is designed to learn cross-domain mappings between high-dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC-GAN for predicting the migration of carbon dioxide (CO 2 ) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC-GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO 2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC-GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC-GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics-based models used in both groundwater and subsurface energy exploration applications.
54 ENVIRONMENTAL SCIENCES↗
Residual Stresses and Plastic Deformation in Self-Pierce Riveting of Dissimilar Aluminum-to-Magnesium Alloys
In this study, the complex relationship between deformation history and residual stresses in a magnesium-to-aluminum self-pierce riveted (SPR) joint is elucidated using numerical and experimental approaches. Non-linear finite element (FE) simulations incorporating strain rate and temperature effects were performed to model the deformation in the SPR process. In order to accurately capture the deformation, a stress triaxiality-based damage material model was employed to capture the sheet piercing from the rivet. Strong visual comparison between the physical cross-section of the SPR joint and the simulation was achieved. To aid in understanding of the role of deformation in the riveting process and to validate the modeling approach, several experimental measurements were conducted. To quantify the plastic deformation from the piercing of the rivet, micro hardness mapping was performed on a cross-section of the SPR joint. The FE model showed very strong correlation to the experimental hardness mapping results suggesting the nonlinear model captured the plastic deformation with high accuracy. To measure the elastic residual stresses in the SPR joint, neutron and x-ray diffraction mapping techniques were conducted and in general, the FE model correlated well to the trends and magnitudes of the elastic stresses. While some error occurred in between the model and the neutron and x-ray diffraction results, the numerical approach developed in this study shows potential as a tool for understanding SPR behavior as well as optimizing the process parameters.
36 MATERIALS SCIENCE↗
Analysis of Selected Legacy 85 Kr Samples
Legacy samples composed of 85 Kr encapsulated in solid zeolite 5A material and five small metal tubes containing a mixture of the zeolite combined with a glass matrix resulting from hot isostatic pressing have been preserved. The samples were a result of krypton R&D encapsulation efforts in the late 1970s performed at the Idaho Chemical Processing Plant. These samples were shipped to Oak Ridge National Laboratory (ORNL) in mid-FY 2014. Upon receipt the outer shipping package was opened, and the inner package, removed and placed in a radiological hood. The individual capsules were double bagged as they were removed from the inner shipping pig and placed into individual glass sample bottles for further analysis. The five capsules were then x-ray imaged. Capsules 1 and 4 appear intact and to contain an amorphous mass within the capsules. Capsule 2 clearly shows the saw marks on the capsule and a quantity of loose pellet or bead-like material remaining in the capsule. Capsule 3 shows similar bead-like material within the intact capsule. Capsule 5 had been opened at an undetermined time in the past. The end of this capsule appears to have been cut off, and there are additional saw marks on the side of the capsule. X-ray tomography allowed the capsules to be viewed along the three axes. Of most interest was determining whether there was any residual material in the closed end of Capsule 5. The images confirmed the presence of residual material within this capsule. The material appears to be compacted but still retains some of the bead-like morphology. Based on the nondestructive analysis (NDA) results, a proposed path forward was formulated to advance this effort toward the original goals of understanding the effects of extended storage on the waste form and package. Based on the initial NDA and the fact that there are at least two breached samples, it was proposed that exploratory tests be conducted with the breached specimens before opening the three intact capsules. Portions of these would be analyzed to determine the fraction of krypton/xenon remaining in the matrix and the amount of rubidium remaining in the matrix. The inner surface of the breached capsules would be examined for corrosion. The materials contained in Capsules 2 and 5 have been examined. There appears to be a relatively uniform distribution of Kr and Rb throughout the pellets examined. The chemical composition of the pellets appears to be consistent with 5A molecular sieves. The material contained within Capsule 5 showed ~1 at. % lead. The origin of the Pb is currently indeterminate. X-ray diffraction analysis shows a significant shift from the 5A structure, most likely due to the Kr encapsulation / sintering process that occurred when the samples were made. The capsule walls were also examined and showed extensive corrosion throughout. Elemental mapping of the capsule material appeared consistent with carbon steel, while the weld material appeared consistent with a stainless steel. The interior surface of the capsule appeared to have a layer of material containing Al, Si, and Ca similar to the 5A molecular sieve. Analysis for Rb within the corrosion sites was inconclusive.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Automated Model for Improved Mapping of Country-Scale High-Resolution Coastal Bathymetry
Accurate and up-to-date maps of coastal bathymetry are critical for a variety of sectors from vessel navigation to port construction and are increasingly vital to inform coastal infrastructure management amid accelerating sea-level rise and more frequent and severe storm-surge events. The primary challenges to accurate and efficient bathymetric mapping from optical remote sensing are: accurately modeling light attenuation in both the atmosphere and water column; and inefficient data processing pipelines for highresolution satellite imagery. To address the effects of light attenuation within the water-column we have developed a novel physics-based bathymetry model that accounts for variations in water-column inherent optical properties on a pixel-by-pixel basis in optically-shallow aquatic environments, which represents a major advancement. For applications to satellite imagery, we have developed a software package that automatically applies the robust 6S atmospheric correction algorithm, estimates the bottom depth from the physics-based bathymetry model, which corrects for attenuation by suspended particulate matter and colored dissolved organic matter in the water column on a pixel-by-pixel basis, and leverages highperformance computing for rapid application to region-scale (e.g., CONUS) 2-meter resolution imagery datasets. Our approach is tailored to sensors that collect data at two-meter resolution with high return times for frequent updates. We mapped 591 WorldView satellite images for Florida, North Carolina, and Alaska and validated the maps against LiDAR-derived bathymetry data. Results show median RMSE of 1.75, 1.59, and 2.67 meters, respectively. By combining the strengths of Oak Ridge National Laboratory in high-performance computing and remote-sensing science with the water-column modeling expertise of Scripps Institution of Oceanography we advance the state of bathymetric mapping capability.
54 ENVIRONMENTAL SCIENCES↗
A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation
Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.
Gaussian process regression↗
Raptor
Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.
Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
Mapping Hsp104 interactions using cross‐linking mass spectrometry
Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.
60 APPLIED LIFE SCIENCES↗
Revisiting solidification microstructure selection maps in the frame of additive manufacturing
Understanding microstructural development in additive manufacturing under highly non-equilibrium cooling conditions and the consequent effects on mechanical properties of the final component is critical for accelerating industrial adoption of these manufacturing techniques. In this study, simple but effective theoretical solidification models are recalled to evaluate their ability to predict of microstructural features in additive manufacturing applications. As a case study, the resulting solidification microstructure selection maps are created to predict the stable growth modality and the columnar to equiaxed transition (CET) of an Al-10Si-0.5Mg alloy processed via Selective Laser Melting. The potential of this method in microstructural predictions for additively manufactured products, as well as outstanding challenges and limitations, are discussed.
36 MATERIALS SCIENCE↗
The RNAi machinery controls distinct responses to environmental signals in the basal fungus Mucor circinelloides
Here, RNA interference (RNAi) is a conserved mechanism of genome defence that can also have a role in the regulation of endogenous functions through endogenous small RNAs (esRNAs). In fungi, knowledge of the functions regulated by esRNAs has been hampered by lack of clear phenotypes in most mutants affected in the RNAi machinery. Mutants of Mucor circinelloides affected in RNAi genes show defects in physiological and developmental processes, thus making Mucor an outstanding fungal model for studying endogenous functions regulated by RNAi. Some classes of Mucor esRNAs map to exons (ex-siRNAs) and regulate expression of the genes from which they derive. To have a broad picture of genes regulated by the silencing machinery during vegetative growth, we have sequenced and compared the mRNA profiles of mutants in the main RNAi genes by using RNA-seq. In addition, we have achieved a more complete phenotypic characterization of silencing mutants Deletion of any main RNAi gene provoked a deep impact in mRNA accumulation at exponential and stationary growth. Genes showing increased mRNA levels, as expected for direct ex-siRNAs targets, but also genes with decreased expression were detected, suggesting that, most probably, the initial ex-siRNA targets regulate the expression of other genes, which can be up- or down-regulated. Expression of 50% of the genes was dependent on more than one RNAi gene in agreement with the existence of several classes of ex-siRNAs produced by different combinations of RNAi proteins. These combinations of proteins have also been involved in the regulation of different cellular processes. Besides genes regulated by the canonical RNAi pathway, this analysis identified processes, such as growth at low pH and sexual interaction that are regulated by a dicer -independent non-canonical RNAi pathway. In conclusion, this work shows that the RNAi pathways play a relevant role in the regulation of a significant number of endogenous genes in M. circinelloides during exponential and stationary growth phases and opens up an important avenue for in-depth study of genes involved in the regulation of physiological and developmental processes in this fungal model.
59 BASIC BIOLOGICAL SCIENCES↗