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A fast analytical model for predicting battery performance under mixed kinetic control
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Discriminating Underground Nuclear Explosions Leading To Late-Time Radionuclide Gas Seeps
Utilizing historical data from the U.S. nuclear test program and freely available barometric pressure data, we performed an analytical barometric-pumping efficiency analysis to determine factors resulting in late-time radionuclide gas seeps from underground nuclear explosions. In this work, we considered 16 underground nuclear explosions with similar geology and test setup, of which five resulted in the measurement of late-time radionuclide gas concentrations at the ground surface. Additionally, the factors we considered include barometric frequency and amplitude, depth of burial, air-filled porosity, intact-rock permeability, fracture aperture, and fracture spacing. The analysis indicates that the best discriminators of late-time radionuclide gas seeps for these explosions are barometric frequency and amplitude and air-filled porosity. While geologic information on fracture aperture and spacing is not available for these explosions, the sensitivity of barometric-pumping efficiency to fracture aperture indicates that it would likely also be a good discriminator.
MISPR : an open-source package for high-throughput multiscale molecular simulations
Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.
On the origins of the continuum radiation of an underwater nanosecond pulsed discharge: an absolute-intensity optical emission spectroscopy study
The plasma properties of an underwater nanosecond pulsed discharge remain not fully understood despite being extensively studied for several decades. In this work, we focus on the continuum radiation generated in such discharges. The discharge is characterized by power measurements as well as by absolute emission spectroscopy. When observed, Stark broadenings of H α , H β and O (777 nm) are employed for electron number densities measurements. The discharge was generated by a 10 ns main voltage pulse followed by multiple secondary pulses, which last up to 4 μs after the primary pulse. It is shown that a peak power of 3.5 MW and energy of 35 mJ is coupled during the main voltage pulse. A quantitative estimation of the different possible continuum radiation sources is performed through analytical calculations. This includes emission (blackbody, free–bound and free–free bremsstrahlung radiations) and absorption (electron–ion and electron–neutral free–free inverse bremsstrahlung) mechanisms. Our results suggest that electron–neutral free–free bremsstrahlung is the principal mechanism responsible for the strong continuum radiation observed experimentally during the primary pulse. We also show that self-absorption through electron–neutral (and electron–ion) inverse bremsstrahlung plays an important role in the main discharge pulse. Further, our results indicate the non-negligible additional contribution of the H 2 continuum during the first reflected pulse which is likely ignited in bubbles generated by the first discharge pulse.
Multiphonon excitations from dark matter scattering in crystals
For direct detection of sub-MeV dark matter, a promising strategy is to search for individual phonon excitations in a crystal. We perform an analytic calculation of the rate for light dark matter ( keV < m DM < MeV ) to produce two acoustic phonons through scattering in cubic crystals such as GaAs, Ge, Si, and diamond. The multiphonon rate is always smaller than the rate to produce a single optical phonon, whenever the latter is kinematically accessible. In Si and diamond, there is a dark matter mass range for which multiphonon production can be the most promising process, depending on the experimental threshold.
Tensor network formulation of two-dimensional gravity
We show how to formulate a lattice gauge theory whose naive continuum limit corresponds to two-dimensional (Euclidean) quantum gravity including a positive cosmological constant. More precisely the resultant continuum theory corresponds to gravity in a first-order formalism in which the local frame and spin connection are treated as independent fields. Recasting this lattice theory as a tensor network allows us to study the theory at strong coupling without encountering a sign problem. In two dimensions this tensor network is exactly soluble and we show that the system has a series of critical points that occur for pure imaginary coupling and are associated with first order phase transitions. We then augment the action with a Yang-Mills term which allows us to control the lattice spacing and show how to apply the tensor renormalization group to compute the free energy and look for critical behavior. Finally we perform an analytic continuation in the gravity coupling in this extended model and show that its critical behavior in a certain scaling limit depends only on the topology of the underlying lattice. We also show how the lattice gauge theory can be naturally generalized to generate the Polyakov or Liouville action for two dimensional quantum gravity.
A Step-Down Test Procedure for Wavelet Shrinkage Using Bootstrapping
Wavelet thresholding (or shrinkage) attempts to remove the noises existing in the signals while preserving inherent pattern characteristics in the reconstruction of true signals. For data-denoising purpose, we present a new wavelet thresholding procedure which employs the step-down testing idea of identifying active contrasts in unreplicated fractional factorial experiments. The proposed method employs bootstrapping methods to a step-down test for thresholding wavelet coefficients. By introducing the concept of a false discovery error rate in testing wavelet coefficients, we shrink the wavelet coefficients with p -values higher than the error rate. The error rate controls the expected proportion of wrongly accepted coefficients among chosen wavelet coefficients. Bootstrap samples are used to approximate the p -value for computational efficiency. We also present some guidelines for selecting the values of hyper-parameters which affect the performance in the step-down thresholding procedure. Based on some common testing signals and an air-conditioner sounds example, the comparison of our proposed procedure with other thresholding methods in the literature is performed. The analytical results show that the proposed procedure has a potential in data-denoising and data-reduction in a variety of signal reconstruction applications.
A Knowledge Network-Based Approach to Facilitate Annotation of Clinical Pathway Component Clusters
Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs’s major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.
Elastic Resource Management for Deep Learning Applications in a Container Cluster
The increasing demand for learning from massive datasets is restructuring our economy. Effective learning, however, involves nontrivial computing resources. Most businesses utilize commercial infrastructure providers (e.g., AWS) to host their computing clusters in the cloud, where various jobs compete for available resources. While cloud resource management is a fruitful research field that has made many advances in production, such as Kubernetes and YARN, few efforts have been invested to further optimize the system performance, especially for deep learning (DL) training jobs in a container cluster. This work introduces FlowCon, a system that is able to monitor the individual evaluation functions of DL jobs at runtime, and thus to make placement decisions on resource allocations elastically. Here, we present a detailed design and implementation of FlowCon and conduct intensive experiments over various DL models. The results demonstrate that FlowCon significantly improves DL job completion time and resource utilization efficiency, compared to default systems. According to the results, FlowCon is able to improve the completion time by up to 68.8% and meanwhile, reduce the makespan by 18.0%, in the presence of various DL job workloads.
High Temperature, High AN2 Last Stage Blade for 65% Efficiency Phase I (Final Report)
GE Power (GEP), in partnership with GE Global Research (GEGR)(GEP and GEGR are divisions of General Electric Company) developed hot and large Last Stage Blade (LSB) technologies necessary for state-of-the art gas turbines to successfully realize ~3100°F turbine inlet temperatures (TIT), an essential element to reaching the DOE goal of 65% combined cycle efficiency (CCeff). The objective of this project was to develop conceptual LSB designs and advance the technological understanding of synchronous vibrations (SV), nonsynchronous vibrations (NSV), and damping that limit the current state-of-the-art LSB designs and applications. In the Phase I project GE Power leveraged existing design and analysis knowledge and techniques for LSB design and performed extensive analytical evaluations to identify and characterize conceptual design concepts.
High Temperature, High AN2 Last Stage Blade for 65% Efficiency (Phase I Final Report)
GE Power (GEP), in partnership with GE Global Research (GEGR)(GEP and GEGR are divisions of General Electric Company) have hot and large Last Stage Blade (LSB) technologies necessary for state-of-the art gas turbines to successfully realize ~3100°F turbine inlet temperatures (TIT), an essential element to reaching the DOE goal of 65% combined cycle efficiency (CCeff). The objective of this project was to develop conceptual LSB designs and advance the technological understanding of synchronous vibrations (SV), nonsynchronous vibrations (NSV), and damping that limit the current state-of-the-art LSB designs and applications. In the Phase I project GE Power leveraged existing design and analysis knowledge and techniques for LSB design and performed extensive analytical evaluations to identify and characterize conceptual design concepts. The most promising designs are the basis for development and testing in a potential Phase II project.
Multi-Axis Resonant Plate Shock Testing Evaluation and Test Specification Development
Resonant plate testing is a shock test method that is frequently used to simulate pyroshock events in the laboratory. Recently, it was discovered that if the unit under test is installed at an off-center location, a tri-axial accelerometer would record a shock response in three directions and the resulting shock response spectra implied that the test may have qualified the component in three directions simultaneously. The purpose of this research project was to evaluate this idea of multi-axis shock testing to determine if it was truly a multi-axis shock environment and if such a test could be used as an equivalent component qualification test. A study was conducted using generic, additively manufactured components tested on a resonant plate, along with an investigation of plate motion to evaluate the component response to off- center plate excitation. The data obtained here along with the analytical simulations performed indicate that off-center resonant plate tests are actually not three-axis shock tests, but rather single axis shocks at an arbitrary angle dictated by the location of the unit under test on the plate. This conclusion is supported by the fact that only one vectored shock input is provided to the component in a resonant plate test. Thus, the output response is a coupled response of the transverse plate vibration and the rotational motion of the component on the plate. Additionally, a multi-axis shock test defined by three single axis test environments always results in a significant component over-test in one direction.
Chemistry and Metallurgy Research Building Historical Perchlorate Data and Discussion
The Chemistry and Metallurgy Research Building (CMR), located at TA-03-0029, was built in 1952 and was occupied by Los Alamos National Laboratory (LANL) employees in 1953. The facility was built to perform actinide analytical chemistry and material characterization in support of LANL and Department of Energy (DOE) mission of stockpile stewardship and research.
Analytical Exploration of Iron Disulfide Performance in Molten Salt Batteries
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Concrete Structure Health Monitoring Using Vibro-acoustic Testing and Machine Learning
Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.
Methods and systems for evaluating data transportability in distribution grids
This document describes systems and techniques for evaluating and improving distribution-grid data transportability. These systems and techniques allow engineers to quantify the data transportability of a communication system within or connected to a distribution grid, which represents an ability to transport in real-time telemetry from source locations (e.g., sensors in the distribution grid) to control mechanisms. Distribution engineers can use the sensor readings to perform grid analytics, control operating parameters, and operate protection systems. Distribution engineers can also use the transportability of the communication system to evaluate the observability of the distribution grid, which represents an ability to combine actual measurements and various types of computations (e.g., analytics, estimators, forecasters) from a system model. Distribution engineers can then generate a sensor allocation plan that indicates the number and location of sensors to maximize observability for a fixed sensor cost and/or minimize sensor cost for predetermined observability.