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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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Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework
The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.
Nutrient and carbon concentrations in dated soil cores at US-OWC Ameriflux wetland site (OWC NERR)
Soil cores were sampled at the Old Woman Creek (OWC) National Estuarine Research Reserve at the south shore of Lake Erie, near Huron, Ohio, USA. OWC is a temperate mineral soil marsh. We dated the soil cores using lead isotope analysis and measured the concentrations of carbon and nutrients (nitrogen, phosphorus) throughout the core depths. We analyzed 36 cores, sampled along three transects at areas of the wetland with different hydrological regimes. Each transect included three coring sites at different water depth categories (shallow, intermediate deep) with four core samples per coring site. The data can be used to determine the carbon sequestration rates and nutrient accumulation rates, at multiple locations throughout OWC wetland.Dataset, in csv format, with data variables in columns, and different cores and core depths slices in rows, includes results from 36 sediment cores (0-30 cm depth) taken at 9 locations (4 replicates at each location) along 3 gradients of water depth (shallow, intermediate, deep), each at a different hydrologic location (outflow, backflow, middle). At each depth slice within a core, we provide depth, date (using 210Pb), bulk density, and the concentrations of carbon, nitrogen, phosphorus, d13C, and d15N.
Automating Component-Level Stress Measurements for Inverter Reliability Estimation
In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.
Identification of hidden N4-like viruses and their interactions with hosts
The N4-like viruses, which were recently assigned to the novel viral family Schitoviridae in 2021, belong to a podoviral-like viral lineage and possess conserved genomic characteristics and a unique replication mechanism. Despite their significance, our understanding of N4-like viruses is primarily based on viral isolates. To address this knowledge gap, this study has established a comprehensive N4-like viral data sets comprising 342 high-quality N4-like viruses/proviruses (144 viral isolates, 158 uncultured viruses, and 40 integrated N4-like proviruses). These viruses were classified into 97 subfamilies (89 of which are newly identified), 148 genera (100 of which are newly identified), and 253 species (177 of which are newly identified). The study reveals that N4-like viruses inhibit the polar region, oligotrophic open oceans, and the human gut, where they infect various bacterial lineages, such as Alpha/Beta/Gamma/Epsilon-proteobacteria in the Proteobacteria phylum. Although N4-like viral endogenization appears to be prevalent in Proteobacteria, it has also been observed in Firmicutes. Additionally, the phylogenetic analysis has identified evolutionary divergence within the hallmark genes of N4-like viruses, indicating a complex origin of the different conserved parts of viral genomes. Moreover, 1,101 putative auxiliary metabolic genes (AMGs) were identified in the N4-like viral pan-proteome, which mainly participate in nucleotide and cofactor/vitamin metabolisms. Of these AMGs, 27 were found to be associated with virulence, suggesting their potential involvement in the spread of bacterial pathogenicity. The findings of this study are significant, as N4-like viruses represent a unique viral lineage with a distinct replication mechanism and a conserved core genome. This work has resulted in a comprehensive global map of the entire N4-like viral lineage, including information on their distribution in different biomes, evolutionary divergence, genomic diversity, and the potential for viral-mediated host metabolic reprogramming. As such, this work significantly contributes to our understanding of the ecological function and viral-host interactions of bacteriophages.
Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research
Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.
Folate deficiency drives mitotic missegregation of the human FRAXA locus
The instability of chromosome fragile sites is implicated as a causative factor in several human diseases, including cancer [for common fragile sites (CFSs)] and neurological disorders [for rare fragile sites (RFSs)]. Previous studies have indicated that problems arising during DNA replication are the underlying source of this instability. Although the role of replication stress in promoting instability at CFSs is well documented, much less is known about how the fragility of RFSs arises. Many RFSs, as exemplified by expansion of a CGG trinucleotide repeat sequence in the fragile X syndrome-associated FRAXA locus, exhibit fragility in response to folate deficiency or other forms of “folate stress.” We hypothesized that such folate stress, through disturbing the replication program within the pathologically expanded repeats within FRAXA, would lead to mitotic abnormalities that exacerbate locus instability. Here, we show that folate stress leads to a dramatic increase in missegregation of FRAXA coupled with the formation of single-stranded DNA bridges in anaphase and micronuclei that contain the FRAXA locus. Moreover, chromosome X aneuploidy is seen when these cells are exposed to folate deficiency for an extended period. We propose that problematic FRAXA replication during interphase leads to a failure to disjoin the sister chromatids during anaphase. This generates further instability not only at FRAXA itself but also of chromosome X. These data have wider implications for the effects of folate deficiency on chromosome instability in human cells.
Splash v1.0
A database, web API and portal will be developed that stores data from synchotron experiments. The data stored will be: - metadata about the experiment (who ran it, when it was conducted, what proposal and ESAF were covered) - references to raw data (e.g. images taken at the beamline) - references to derived data Beamline users and Beamline staff will have private access to view experiment data and assign web-friendly visualizations to it. They will also be use the portal to download some of the data taken at the beamline. They we will be able to create and approve experiment pages that can be viewed publicly. The software created for this project will include replication and fail over technologies to maintain a high level of uptime. Software developed in this project will be: - database schema - web middleware to create a web API - web-based user interface code
NGEE Arctic Plant Traits: Soil Cores, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016
Soil cores were collected from twelve vegetation biomass plots at the Kougarok hillslope in late July of 2016. The sampled plots were located across six ecotypes present at this site (n=2 replicates per ecotype). Soil cores were separated into depth intervals in the field and frozen for transport to Oak Ridge National Laboratory. Laboratory sample processing of these soils generated data on soil texture, pH, gravimetric water content, bulk density, bulk soil %C and %N. Water extracts were performed on subsamples for determination of extractable total nitrogen, inorganic nitrogen, organic nitrogen, phosphorus, and dissolved organic carbon. This dataset contains 1 csv, and 1 pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Transcriptome divergence during leaf development in two contrasting switchgrass (Panicum virgatum L.) cultivars
The genetics and responses to biotic stressors of tetraploid switchgrass (Panicum virgatum L.) lowland cultivar ‘Kanlow’ and upland cultivar Summer are distinct and can be exploited for trait improvement. In general, there is a paucity of data on the basal differences in transcription across tissue developmental times for switchgrass cultivars. Here, the changes in basal and temporal expression of genes related to leaf functions were evaluated for greenhouse grown ‘Kanlow’, and ‘Summer’ plants. Three biological replicates of the 4 th leaf pooled from 15 plants per replicate were harvested at regular intervals beginning from leaf emergence through senescence. Increases and decreases in leaf chlorophyll and N content were similar for both cultivars. Likewise, multidimensional scaling (MDS) analysis indicated both cultivar-independent and cultivar-specific gene expression. Cultivar-independent genes and gene-networks included those associated with leaf function, such as growth/senescence, carbon/nitrogen assimilation, photosynthesis, chlorophyll biosynthesis, and chlorophyll degradation. However, many genes encoding nucleotide-binding leucine rich repeat (NB-LRRs) proteins and wall-bound kinases associated with detecting and responding to environmental signals were differentially expressed. Several of these belonged to unique cultivar-specific gene co-expression networks. Analysis of genomic resequencing data provided several examples of NB-LRRs genes that were not expressed and/or apparently absent in the genomes of Summer plants. It is plausible that cultivar (ecotype)-specific genes and gene-networks could be one of the drivers for the documented differences in responses to leaf-borne pathogens between these two cultivars. Incorporating broad resistance to plant pathogens in elite switchgrass germplasm could improve sustainability of biomass production under low-input conditions.
Domain decomposition in the GPU-accelerated Shift Monte Carlo code
The GPU solver within the Shift continuous-energy Monte Carlo neutron transport code has been extended to provide domain decomposition in addition to domain replication to enable the solution of problems with memory requirements exceeding the capacity of a single GPU. The strategy follows the Multiple Set, Overlapping Domain (MSOD) approach that is used in Shift’s CPU solver and integrates into the event-based algorithm used for Shift’s GPU solver. Furthermore, the ability to assign processors to spatial domains non-uniformly has been maintained. In this work, two different approaches for communicating particle data between domains are considered, and multiple criteria for load balancing problems have been investigated. Numerical results are presented for both fresh and depleted small modular nuclear reactor (SMR) cores. A parallel efficiency of approximately 80% was achieved with up to 16 spatial domains measured relative to full domain replication. A scaling study on the Summit supercomputer demonstrates a weak scaling parallel efficiency of over 90% on over 24000 GPUs.
Discovering a Transferable Charge Assignment Model Using Machine Learning
Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics. Machine learning has emerged as a powerful tool for modeling chemistry at unprecedented computational speeds given accurate reference data. However, certain tasks, such as charge assignment, do not have a unique solution. We use a machine learning algorithm to discover a new charge assignment model by learning to replicate molecular dipole moments across a large, diverse set of nonequilibrium conformations of molecules containing C, H, N, and O atoms. The new model, called Affordable Charge Assignment (ACA), is computationally inexpensive and predicts dipoles of out-of-sample molecules accurately. Furthermore, dipole-inferred ACA charges are transferable to dipole and even quadrupole moments of much larger molecules than those used for training. We apply ACA to dynamical trajectories of biomolecules and produce their infrared spectra. Additionally, we find that ACA assigns similar charges to Charge Model 5 but with greatly reduced computational cost.
Wave Energy Converter Power Take-Off Modeling and Validation From Experimental Bench Tests
This article describes the implementation of a new numerical model of the power take-off system installed in the Monterey Bay Aquarium Research Institute wave energy converter, a device developed to provide power to various oceanic research missions. The simultaneous presence of hydraulic, pneumatic, and electrical subsystems in the power take-off system represents a significant challenge in forging an accurate model able to replicate the main dynamic characteristics of the system. The validation of the new numerical model is addressed by comparing simulations with the measurements obtained during a series of bench tests. Data from the bench tests show good agreement with the numerical model. The validated model provides deeper insights into the complex nonlinear dynamics of the power take-off system and will support further performance improvements in the future.
Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level
To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).
Utility Managed Distributed Energy Resources Intelligent Community - UDERMS iCommunity (Final Technical Report)
The Utility Managed Distributed Energy Resources Intelligent Community (UDERMS iCommunity) project, led by PacifiCorp and supported by the U.S. Department of Energy’s Building Technologies Office (Award No. DEEE0009782), aimed to demonstrate an integrated, utility-managed “behind-the-meter” distributed energy resource (DER) program connecting multifamily, commercial, institutional, and industrial buildings to deliver grid services and improve energy efficiency.
Data from A Designed Heme-[4Fe-4S] Metalloenzyme Catalyzes Sulfite Reduction Like the Native Enzyme
Enzymatic reduction of oxyanions such as sulfite (SO32−) requires the delivery of multiple electrons and protons, a feat accomplished by cofactors tailored for catalysis and electron transport. Replicating this strategy in protein scaffolds may expand the range of enzymes that can be designed de novo. Mirts et al. selected a scaffold protein containing a natural heme cofactor and then engineered a cavity suitable for binding a second cofactor—an iron-sulfur cluster (see the Perspective by Lancaster). The resulting designed enzyme was optimized through rational mutation into a catalyst with spectral characteristics and activity similar to that of natural sulfite reductases.
Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia
Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.
SPRUCE Peat Mercury, Methylmercury and Sulfur Concentrations from Experimental Plot Cores, 2012
This data set reports the results of analyses of peat core samples for total mercury and methylmercury, as well as carbon, nitrogen and sulfur. Samples were collected as part of a larger sampling campaign from the SPRUCE experimental study plots located in the S1-Bog. From 2012-08-13 to 2012-10-15, a team of SPRUCE investigators and collaborators collected core samples of peat in the SPRUCE experimental plots. The goal was to characterize the biological, physical, and chemical characteristics of peat, and how those characteristics changed throughout the depth profile of the bog, prior to the initialization of the SPRUCE experimental warming and CO2 treatments. Cores were collected from 16 experimental plots; peat cores were collected from the hummock and hollow surfaces to depths of 300 cm in 10 cm, 25, and 50 cm increments (Pierce et al, 2022). Three replicate cores were collected from both hummock and hollow locations in each plot. The coring locations within each plot were mapped. This dataset contains one file in comma separated (*.csv) format. User Note: The coring descriptions and some reported data are the same as in the related data set, Iverson et al. (2014). Samples for mercury and carbon, nitrogen and sulfur analyses by the University of Minnesota were collected as part of this sampling campaign.