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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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Binary Complex Neural Network Acceleration on FPGA
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.
Coexistent Quantum Channel Characterization Using Spectrally Resolved Bayesian Quantum Process Tomography
The coexistence of quantum and classical signals over the same optical fiber with minimal degradation of the transmitted quantum information is critical for operating large-scale quantum networks over the existing communications infrastructure. In this work, we systematically characterize the quantum channel that results from simultaneously distributing approximate single-photon polarization-encoded qubits and classical light of varying intensities through fiber-optic channels of up to 15 km. Using spectrally resolved quantum process tomography with a Bayesian reconstruction method that we develop, we estimate the full quantum channel from experimental photon counting data, both with and without classical background. Furthermore, although we find the exact channel description to be a weak function of the pump polarization, we nevertheless show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel when the noise is dominated by Raman scattering. These results provide a basis for the future development of quantum repeater designs and quantum error-correcting codes for real-world channels and inform models used in the analysis and simulation of quantum networks.
Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants
Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.
Bayesian Neural Network Implemented by Dynamically Programmable Noise in Vanadium Oxide
Analysis of electrical noise in vanadium oxide electrochemical random access memory devices and its potential for application in a Bayesian neural network
Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation
Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.
Uncertainty propagation in feed-forward neural network models
We develop new uncertainty propagation methods for feed-forward neural network architectures with leaky ReLU activation functions subject to random perturbations in the input vectors. In particular, we derive analytical expressions for the probability density function (PDF) of the neural network output and its statistical moments as a function of the input uncertainty and the parameters of the network, i.e., weights and biases. A key finding is that an appropriate linearization of the leaky ReLU activation function yields accurate statistical results even for large perturbations in the input vectors. This can be attributed to the way information propagates through the network. We also propose new analytically tractable Gaussian copula surrogate models to approximate the full joint PDF of the neural network output. To validate our theoretical results, we conduct Monte Carlo simulations and a thorough error analysis on a multi-layer neural network representing a nonlinear integro-differential operator between two polynomial function spaces. Our findings demonstrate excellent agreement between the theoretical predictions and Monte Carlo simulations.
Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks
Abstract For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features, representative quantification of which requires large volumes of tens of particles. To overcome the trade-off between the imaged volume of the material and the resolution achieved, we deploy generative adversarial networks (GAN), namely SRGANs, to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view, thus enabling representative quantification of fine features.
Geometric Interpretation of the Cluster Location Problem Part I: Theory
We present a new framing of the seismic location problem using principles drawn from differential geometry. Our interpretation relies upon the common assumption that travel times observed across a network are continuous, differentiable functions of source location. In consequence, travel‐time functions constitute a differentiable map between the source region and a Riemannian manifold. The manifold is said to be the image of the source region embedded in a generally high‐dimension travel‐time vector space. A cluster of events in the source region has an image of discrete points on the manifold, that, except in the simplest cases, cannot be viewed directly. However, it is possible to project the image of a cluster into a tangent space of the manifold for direct visualization. The projection operator can be computed directly from the data without a velocity model, but produces a distorted rendering of the cluster geometry. With a model we can predict the distortions and correct them to estimate cluster geometry. We develop these points with the simplest possible example, one for which direct visualization of the manifold is possible, using the example as an introduction to the relevant concepts from differential geometry in a familiar setting. The tangent space, a local linearization of the manifold, plays a key role. We develop a metric to estimate the limits of linearization, that is, to determine when the curvature of the manifold invalidates the linear assumption. We also examine the interplay of model error, inadequate network geometry, and pick error. We then generalize our results from the simple case to the general case of 3D source regions observed by general networks. Although we do suggest a new “project and correct” method for location, we do not develop it into a practical algorithm. In conclusion, our intention rather is to highlight new analytical methods grounded in differential geometry.
SBbadger: biochemical reaction networks with definable degree distributions
Abstract Motivation An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. Availability and implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.
Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding
Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.
The roles of learning mechanisms in services: Evidence from US residential solar installations
Non-hardware costs are majority of the cost of producing solar photovoltaic (PV) electricity. Here we use matched data on patents and over 125,000 residential PV installations to estimate the effects of three learning mechanisms in reducing PV costs: learning by doing, searching, and interacting. While previous work in this area has focused predominantly on learning by doing, we find that learning by searching and interacting are also significant mechanisms to facilitate non-hardware cost reductions. Including these two mechanisms reduces the effect of learning by doing in explaining non-hardware cost reductions by 43%. Our results suggest that prior work may overemphasize the role of learning by doing and the policies that help generate learning by doing. Analysis of the supplier-network between installers and their suppliers shows that concentrated supplier networks are associated with lower non-hardware costs, although there are key differences between installer-panel and installer-inverter manufacturer networks. An important implication is that policies for reducing non-hardware costs need to take a more complete view of how different learning mechanisms engender cost reductions. They should particularly consider the important role of learning in supplier networks in cost reductions—an effect that until now has largely been missing in analyses of solar non-hardware costs.
Flow dynamics and heat transfer in simplified battery energy storage systems with heated battery modules
Large-scale energy storage systems (ESSs) composed of batteries show promise in addressing current energy challenges, but dissipation of generated heat is important. Here, this paper focuses on buoyant convective flows in simplified ESS battery racks. Natural convection is not generally the primary cooling strategy but can be important in abnormal scenarios where there is module overheat or potentially thermal runaway. We use computational fluid dynamics to investigate the flow dynamics and heat transfer mechanisms in a simplified parameterized rack design. Despite its simplicity, this configuration produces many of the relevant features expected in real ESSs without details of module geometry or hardware, allowing broad conclusions independent of manufacture-specific designs. We start by providing visualizations of the flowfield and measurements of entrainment, heat flux, and pressure. To characterize the dependence on the system parameters, we develop an integral-scale analysis of the average temperature equation to highlight the dominant source terms. We use results from this analysis to derive a steady network model composed of simple algebraic expressions to provide first-order predictions of entrainment through the rack. The network model leads to a linear scaling of the Reynolds number based on convective mass flux with respect to the Grashof number based on the heat source. We deduce empirical relationships that relate the heat exchanged between modules using a surface-averaged Nusselt number as a function of the local Reynolds and Rayleigh numbers. Lastly, we investigate how space between the modules and rack in the spanwise direction creates flow bypass, resulting in different flow pathways.
Evidence of off-shell Higgs boson production from ZZ leptonic decay channels and constraints on its total width with the ATLAS detector
This Letter reports on a search for off-shell production of the Higgs boson using 139 fb —1 of $pp$ collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS detector at the Large Hadron Collider. The signature is a pair of $\overline{Z}$ bosons, with contributions from both the production and subsequent decay of a virtual Higgs boson and the interference of that process with other processes. The two observable final states are $\overline{Z}$ $\overline{Z}$ → 4$\ell$ and $\overline{Z}$ $\overline{Z}$ → 2$\ell$2$ν$ with $\ell$ = $e$ or $μ$. In the $\overline{Z}$ $\overline{Z}$ → 4$\ell$ final state, a dense Neural Network is used to enhance analysis sensitivity with respect to matrix element-based discrimination. The backgroundonly hypothesis is rejected with an observed (expected) significance of 3.3 (2.2) standard deviations, representing experimental evidence for off-shell Higgs boson production. Assuming that no new particles enter the production of the virtual Higgs boson, its total width can be deduced from the measurement of its off-shell production cross-section. The measured total width of the Higgs boson is ${4.5}_{—2.5}^{+3.3}$ MeV, and the observed (expected) upper limit on the total width is found to be 10.5 (10.9) MeV at 95% confidence level.
Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers
Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT’s hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.
Fast mass spectrometry search and clustering of untargeted metabolomics data
The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.
Structure and mechanism of Staphylococcus aureus oleate hydratase (OhyA)
Flavin adenine dinucleotide (FAD)-dependent bacterial oleate hydratases (OhyAs) catalyze the addition of water to isolated fatty acid carbon–carbon double bonds. Staphylococcus aureus uses OhyA to counteract the host innate immune response by inactivating antimicrobial unsaturated fatty acids. Mechanistic information explaining how OhyAs catalyze regiospecific and stereospecific hydration is required to understand their biological functions and the potential for engineering new products. In this study, we deduced the catalytic mechanism of OhyA from multiple structures of S. aureus OhyA in binary and ternary complexes with combinations of ligands along with biochemical analyses of relevant mutants. The substrate-free state shows Arg81 is the gatekeeper that controls fatty acid entrance to the active site. FAD binding engages the catalytic loop to simultaneously rotate Glu82 into its active conformation and Arg81 out of the hydrophobic substrate tunnel, allowing the fatty acid to rotate into the active site. FAD binding also dehydrates the active site, leaving a single water molecule connected to Glu82. This active site water is a hydronium ion based on the analysis of its hydrogen bond network in the OhyA•PEG400•FAD complex. We conclude that OhyA accelerates acid-catalyzed alkene hydration by positioning the fatty acid double bond to attack the active site hydronium ion, followed by the addition of water to the transient carbocation intermediate. Structural transitions within S. aureus OhyA channel oleate to the active site, curl oleate around the substrate water, and stabilize the hydroxylated product to inactivate antimicrobial fatty acids.
The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes
Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.