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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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The expressivity of classical and quantum neural networks on entanglement entropy
Abstract Analytically continuing the von Neumann entropy from Rényi entropies is a challenging task in quantum field theory. While then-th Rényi entropy can be computed using the replica method in the path integral representation of quantum field theory, the analytic continuation can only be achieved for some simple systems on a case-by-case basis. In this work, we propose a general framework to tackle this problem using classical and quantum neural networks with supervised learning. We begin by studying several examples with known von Neumann entropy, where the input data is generated by representing$${\text {Tr}}\rho _A^n$$ Tr ρ A n with a generating function. We adopt KerasTuner to determine the optimal network architecture and hyperparameters with limited data. In addition, we frame a similar problem in terms of quantum machine learning models, where the expressivity of the quantum models for the entanglement entropy as a partial Fourier series is established. Our proposed methods can accurately predict the von Neumann and Rényi entropies numerically, highlighting the potential of deep learning techniques for solving problems in quantum information theory.
Lessons from black hole quasinormal modes in modified gravity
Quasinormal modes (QNMs) of perturbed black holes have recently gained much interest because of their tight relations with the gravitational wave signals emitted during the post-merger phase of a binary black hole coalescence. One of the intriguing features of these modes is that they respect the no-hair theorem, and hence, they can be used to test black hole spacetimes and the underlying gravitational theory. In this paper, we exhibit three different aspects of how black hole QNMs could be altered in theories beyond Einstein’s general relativity (GR). These aspects are (i) the direct alterations of QNM spectra as compared with those in GR, (ii) the violation of the geometric correspondence between the high-frequency QNMs and the photon geodesics around the black hole, and (iii) the breaking of the isospectrality between the axial and polar gravitational perturbations. Several examples will be provided in each individual case. The prospects and possible challenges associated with future observations will be also discussed.
PNNL-m-q/metabolomics_ensemble_score
An ensemble machine learning method to improve spectral similarity scores for GC-MS data
Grid Event Signature Library's Signature Matching Tool
The Signature Matching Tool (SMT) is a tool that assists users in labeling any unlabeled signatures, according to the hierarchical, event tags taxonomy developed for the Department of Energy's (DOE) Grid Event Signature Library (GESL).
Transcription of hepatitis B surface antigen shifts from cccDNA to integrated HBV DNA during treatment
The cornerstone of functional cure for chronic hepatitis B (CHB) is hepatitis B surface antigen (HBsAg) loss from blood. HBsAg is encoded by covalently closed circular DNA (cccDNA) and HBV DNA integrated into the host genome (iDNA). Nucleos(t)ide analogs (NUCs), the mainstay of CHB treatment, rarely lead to HBsAg loss, which we hypothesized was due to continued iDNA transcription despite decreased cccDNA transcription. To test this, we applied a multiplex droplet digital PCR that identifies the dominant source of HBsAg mRNAs to 3,436 single cells from paired liver biopsies obtained from 10 people with CHB and HIV receiving NUCs. With increased NUC duration, cells producing HBsAg mRNAs shifted their transcription from chiefly cccDNA to chiefly iDNA. This shift was due to both a reduction in the number of cccDNA-containing cells and diminished cccDNA-derived transcription per cell; furthermore, it correlated with reduced detection of proteins deriving from cccDNA but not iDNA. Despite this shift in the primary source of HBsAg, rare cells remained with detectable cccDNA-derived transcription, suggesting a source for maintaining the replication cycle. Functional cure must address both iDNA and residual cccDNA transcription. Further research is required to understand the significance of HBsAg when chiefly derived from iDNA.
Ultra-compact hybrid silicon:chalcogenide waveguide temperature sensor
We demonstrate a real-time, reusable, and reversible integrated optical sensor for temperature monitoring within harsh environments. The sensor architecture combines the phase change property of chalcogenide glasses (ChG) with the high-density integration advantages of high index silicon waveguides. To demonstrate sensor feasibility, ChG composition Ge 40 S 60 , which is characterized by a sharp phase transition from amorphous to crystalline phase around 415 °C, is deposited over a 50 µ m section of a single mode optical waveguide. The phase transition changes the behavior of Ge 40 S 60 from a low loss to high loss material, thus significantly affecting the hybrid waveguide loss around the phase transition temperature. A transmission power drop of over 40dB in the crystalline phase compared to the amorphous phase is experimentally measured. Moreover, we recover the amorphous phase through the application of an electrical pulse, thus showing the reversible nature of our compact temperature sensor. Through integrating multiple compositions of ChG with well-defined phases transition temperatures over a silicon waveguide array, it is possible to determine, in real-time, the temperature evolution within a harsh environment, such as within a nuclear reactor cladding.
Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)
This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland. The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).
Cyber–Physical System Security of Distribution Systems
The Information and Communications Technology (ICT) for control and monitoring of power systems is a layer on top of the physical power system infrastructure. The cyber system and physical power system components form a tightly coupled Cyber–Physical System (CPS). Sources of vulnerabilities arise from the computing and communication systems of the cyber–power grid. Cyber intrusions targeting the power grid are serious threats to the reliability of electricity supply that is critical to society and the economy. In a typical Information Technology environment, numerous attack scenarios have shown how unauthorized users can access and manipulate protected information from a network domain. The need for cyber security has led to industry standards that power grids must meet to ensure that the monitoring, operation, and control functions are not disrupted by cyber intrusions. Cyber security technologies such as encryption and authentication have been deployed on the CPS. Intrusion or anomaly detection and mitigation tools developed for power grids are emerging. Furthermore, this survey paper provides the basic concepts of cyber vulnerabilities of distribution systems and CPS security. The important ICT subjects for distribution systems covered in this paper include Supervisory Control And Data Acquisition, Distributed Energy Resources, including renewable energy and smart meters.
FLUXNET-CH4 MY-MLM Maludam National Park
This is the FLUXNET-CH4 version of the carbon flux data for the site MY-MLM Maludam National Park.
IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
Energy Resilience for Mission Assurance: Agile Co-simulation for Cyber Energy System Security (ACCESS), Model Advancements for Resilience Analysis
Agile Co-simulation for Cyber Energy System Security (ACCESS) is a co-simulation platform developed by Lawrence Livermore National Laboratory (LLNL). The primary high-level use-case for ACCESS is to study existing or new cyber-physical critical infrastructure systems, with a heavy emphasis on 1) systems that utilize communication networks, and 2) studies that seek to understand cyber-related system impacts. ACCESS is currently used for several energy system resilience projects at LLNL. In the Energy Resilience for Mission Assurance (ERMA) project, ACCESS is used in the Modeling for Metric Calculation task (specifically, subtask 4.3, Communications and Cyber Modeling) to model and simulate the cyber and communication system aspects of Defense Critical Electric Infrastructure (DCEI) systems, with a focus on computing specific communication system metrics that can impact system resilience and mission performance. Simulated communication system performance will be fed back to other ERMA system components so that mission performance can be evaluated holistically. This report describes several enhancements to the ACCESS platform that were implemented during the execution of the ERMA project in support of reslience analysis. This includes the addition of new models and subsystems, enhancements to existing models, and integration with external systems. The remainder of this report is structured as follows. In Section 2, a brief background description of the ACCESS platform is provided, including an outline of ACCESS components, example usecases, and a set of communication network resilience metrics that can be computed with ACCESS. Section 3 describes the ACCESS model enhancements for ERMA in detail. Finally, Section 4 briefly outlines future integration opportunities between ACCESS and project participant capabilities identified during the progression of the project.
IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
BISON Capability to Account for Dopant Sensitivity in Relevant UO 2 Material Models
The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program aims to develop predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity and high-resolution fuel performance tool at the engineering scale. Incorporation of more physics-based models in BISON for the accident tolerant fuel applications motivated this study. This document details integration of new modeling capabilities in BISON, including: a tensile strength model for uranium dioxide (UO 2 ) fuel to incorporate the microstructural effects (e.g., grain size, fabrication pore size, and porosity), and atomistic-informed creep model for UO 2 fuel that is developed by Los Alamos National Laboratory. Sensitivity analyses are conducted on these models separately as well as a two-dimensional full rod application under normal operating conditions. Lastly, these new modeling capabilities in BISON are exercised in Halden IFA-677.1 and IFA-716.1 assessment cases.
Assessment and Improvement of Fission Product Transport Predictions of Particle Fuel in BISON
The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This study was motivated by the need to incorporate more physics-based models in BISON in order to foster tri-structural isotropic (TRISO) applications. This document details the integration of new modeling capabilities in BISON, including (1) development of pyrolytic carbon (PyC) and silicon carbide (SiC) layer anisotropic thermal and mass transport capabilities, (2) verification of the mass diffusion solution in TRISO modeling, (3) calibration of fission product diffusivity using Advanced Gas Reactor (AGR) experiments, (4) improved fission product release modeling by developing compact diffusion modeling capabilities, and (5) documentation of accelerated failure analysis on the BISON website. Improvements made to the diffusion models and parameters were documented and validated against AGR-1 and -2 experiment data.
Dense Nuclear Matter Equation of State from Heavy-Ion Collisions
The nuclear equation of state (EOS) is at the center of numerous theoretical and experimental efforts in nuclear physics, motivated by its crucial role in our understanding of the properties of nuclear matter found on Earth, in neutron stars, and in neutron-star mergers. With advances in microscopic theories for nuclear interactions, the availability of experiments probing nuclear matter under conditions not reached before, and the advent of multi-messenger astronomy, the next decade will bring new opportunities for determining the nuclear matter EOS.
Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials
The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.
Assessing the link between aerosol mixing state, structure and composition and their optical properties: Ascension Island as a testbed for the South‐East Atlantic aerosol regimes
Forest and savanna fires are a major source of pollution over the globe. Such fire events emit to the atmosphere a considerable amount of “fluffy” fractal black looking particles that are often referred to as black carbon (BC) or soot aerosols. In fact, BC particles (aerosols) emitted from savanna and agricultural fires over the African continent play an important role in the region’s and the global climate. These fires occur each year between July and October, which is referred to as the biomass burning (BB) season and are considered among the globe's largest man-made emission sources of BC particles. However, during these fire events, not only BC particles are emitted, but also other types of particles and gases that can interact with each other to create new particles, generating a slew of particles often generally referred to as BB aerosols. Although the majority of the fire emissions occur inland in the sub-Saharan part of Africa, their effect reaches far beyond the continent due to the prevailing easterly winds, which transport those aerosols over the South-East Atlantic (SEA) ocean, off the west-coast of Africa. The reason why these particles are so important to the regional and global climate is that they absorb sunlight radiation, which overall warms the climate. However, the magnitude of this warming effect is highly uncertain and depends upon a multitude of aspects. Our overarching goal for this project was to better understand the changes in the aerosol properties as they transport from fire sources downstream toward the ocean and how this can eventually affect the earth's radiative budget in terms of warming or cooling. Following the above, our work here was focused on the investigation of the African BB aerosol plumes mixing state (mixing of BB aerosols and other aerosol types), composition and size distribution as they transport from the African continent towards the SEA ocean, and their link with some of the commonly measurable bulk optical properties such as mass absorption coefficient (MAC) and single scattering albedo (SSA). We utilized three field missions that were conducted over the SEA ocean between 2016 and 2018. Specifically, we used (1) ground-based measurements from the DOE ARM Mobile Facility (AMF1), which was deployed at Ascension Island (ASI) for the LASIC (Layered Atlantic Smoke Interactions with Clouds) campaign between June 2016 and October 2017, (2) airborne measurements from the UK CLARIFY (Clouds and Aerosol Radiative impacts and Forcing) campaign during Aug-Sep-2017, and (3) airborne measurements from the ORACLES (Observations of Aerosols above Clouds and their interactions) campaign during Aug-2017, and Sep-Oct-2018. The three campaigns cover a relatively large region, from the western African coast on the east towards Ascension Island in the middle of the SEA to the west. We used the data gathered by these campaigns to find how BB properties and composition change from near emission sources (the ORACLES campaign flights) downstream (the CLARIFY flights and the ground based LASIC measurements). We used particle trajectory following methodologies to connect the measurements from the different campaigns, which allowed us to follow a certain aerosol plume (airmass) from near-source towards Ascension Island. We found out that atmospheric aging processes governed by the UV light from the sun (photochemistry) and cloud processes (aqueous chemistry) govern the changes seen in the particle composition during the transport were more dominant in dictating aerosol composition than specific emission composition or source types. We assessed how aerosol optical properties change during the BB season and what are the main drivers of this change. We found that the enhanced ratio of BC to CO is well correlated with single scattering albedo (SSA) and mass absorption coefficient (MACBC), providing a simple way to estimate the aerosol optical characteristics in the south-eastern Atlantic Ocean. From the analysis of the location of BB, the primary source fuel, the water content in the fuel, combined with the mean cloud cover and precipitation in the transport areas of the BB plume, we conclude that the increase in BC/CO from June to August is likely to be caused by burning becoming more flaming (hot fires), and the decrease in BC/CO in September and October may be caused by smoldering (colder) fires. We found that aerosol hygroscopicity increases with decreasing altitude below 2km, and that enhanced BB hygroscopicity at lower altitudes is mainly due to a lower organic aerosol (OA) fraction, increased sulfate fraction, and greater hygroscopicity parameter of OA at lower altitudes.