Global to Coastal Multiscale Modeling via Land-river-ocean Coupling in the Energy Exascale Earth System Model (E3SM)
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Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes
The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.
14 SOLAR ENERGY↗
Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization
Traditional data-driven machine learning (ML) techniques were combined with the physics-based SOFC-MP model toward improving SOFC system-level performance prediction. Four different physics-informed ML methods were proposed to demonstrate better prediction accuracy compared with traditional ML methods. First, it was preferred that the existing Kriging-based surrogate model not be entirely abandoned, so errors between Kriging regression prediction and true solution are learned using ML. The goal was to find the pattern of the error distribution as a function of model input so discrepancies between the Kriging prediction and true solution could be reduced by adding the additional ML-trained error term to the existing prediction. Second, deep neural networks (DNN) coupled with the mass balance model (MBM) have been developed to significantly decrease the reduced order model (ROM) prediction error with fewer training data compared to the traditional Kriging-based ROM and traditional DNN regression approaches. Third, DNN regression coupled with a neural networks (NN) classifier and MBM was developed. It provides superior performance on classifying the physically operational conditions for natural gas fuel cell (NGFC) than traditional classification approaches. Finally, two kinds of DNN transferring were studied and tested. One was transferring DNN for low-fidelity model data to a high-fidelity model. The second one was transferring DNN from state-of-the-art NGFC to advanced NGFC. These physic-informed ML frameworks can serve as a solid foundation for estimating U.S. Department of Energy-targeted fuel cell performance and exhibit great potential to be employed to other engineering applications.
30 DIRECT ENERGY CONVERSION↗
Description of quasifission reactions in the dinuclear system model
The formation and evolution of dinuclear systems in quasifission reactions are investigated. The process of formation of reaction products is analyzed based on the concept of a dinuclear system. Isotopic trends of cross sections of production of superheavy nuclei in quasifission reactions are discussed. The yields of new neutron-rich isotopes of nuclei with Z = 64–80 in quasifission reactions are predicted. The mechanism of production of complex fragments in complete fusion and quasifission reactions is analyzed.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Nuclear Hybrid Energy System Model Stability Testing
A Nuclear Hybrid Energy System (NHES) uses a nuclear reactor as the basic power generation unit, and the power generated is used by multiple customers as combinations of thermal power or electrical power. The definition and architecture of a particular NHES can be adapted based on the needs and opportunities of different localities and markets. For example, locations in need of potable water may be best served by coupling a desalination plant to the NHES. Similarly, a location near oil refineries may have a need for emission-free hydrogen production. Using the flexible, multi-domain capabilities of Modelica, Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory are investigating the dynamics (e.g., thermal hydraulics and electrical generation/consumption) and cost of a hybrid system. This paper examines the NHES work underway, emphasizing the control system developed for individual subsystems and the overall supervisory control system.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems
Background: Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades. Results: In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms. Conclusions: This is the first demonstration of a global regulatory network modeling conserved host response between in vitro and in vivo models.
59 BASIC BIOLOGICAL SCIENCES↗
Towards a Measurement of the Half-Life of {sup 60}Fe for Stellar and Early Solar System Models
Radioisotopes, produced in stars and ejected into the Interstellar Medium, are important for constraining stellar and early Solar System (ESS) models. In particular, the half-life of the radioisotope, Fe-60, can have an impact on calculations for the timing for ESS events, the distance to nearby Supernovae, and the brightness of individual, non-steady-state Fe gamma ray sources in the Galaxy. A half-life measurement has been undertaken at the University of Notre Dame and measurements of the Fe-60/Fe-56 concentration of our samples using Accelerator Mass Spectrometry has begun. This result will be coupled with an activity measurement of the isomeric decay in Co-60, which is the decay product of Fe. Preliminary half-life estimates of (2.53 +/- 0.24) x 10(6) years seem to confirm the recent measurement by Rugel et al. (2009). (C) 2015 Elsevier B.V. All rights reserved.
Accelerator Mass Spectrometry↗
Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System
The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.
chromophores↗
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data
Not Available
14 SOLAR ENERGY↗
Developing Fully Coupled Dynamical Reactor Core Isolation System Models in RELAP-7 for Extended Station Black-Out Analysis
The reactor core isolation cooling (RCIC) system in a boiling water reactor (BWR) provides makeup water to the reactor vessel for core cooling when the main steam lines are isolated and the normal supply of water to the reactor vessel is lost. It was one of the very few safety systems still available during the Fukushima Daiichi accidents after the tsunamis hit the plants and the system successfully delayed the core meltdown for a few days for unit 2 & 3. Therefore, detailed models for RCIC system components are indispensable to understand extended station black-out accidents (SBO) for BWRs. As part of the effort to develop the new generation reactor system safety analysis code RELAP-7, major components to simulate the RCIC system have been developed. This paper describes the models for those components such as turbine, pump, and wet well. Selected individual component test simulations and a simplified SBO simulation up to but before core damage is presented. The successful implementation of the simplified RCIC and wet well models paves the way to further improve the models for safety analysis by including more detailed physical processes in the near future.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
ANALYSIS OF TERRESTRIAL PLANET FORMATION BY THE GRAND TACK MODEL: SYSTEM ARCHITECTURE AND TACK LOCATION
The Grand Tack model of terrestrial planet formation has emerged in recent years as the premier scenario used to account for several observed features of the inner solar system. It relies on the early migration of the giant planets to gravitationally sculpt and mix the planetesimal disk down to ∼1 au, after which the terrestrial planets accrete from material remaining in a narrow circumsolar annulus. Here, we investigate how the model fares under a range of initial conditions and migration course-change (“tack”) locations. We run a large number of N-body simulations with tack locations of 1.5 and 2 au and test initial conditions using equal-mass planetary embryos and a semi-analytical approach to oligarchic growth. We make use of a recent model of the protosolar disk that takes into account viscous heating, includes the full effect of type 1 migration, and employs a realistic mass–radius relation for the growing terrestrial planets. Our results show that the canonical tack location of Jupiter at 1.5 au is inconsistent with the most massive planet residing at 1 au at greater than 95% confidence. This favors a tack farther out at 2 au for the disk model and parameters employed. Of the different initial conditions, we find that the oligarchic case is capable of statistically reproducing the orbital architecture and mass distribution of the terrestrial planets, while the equal-mass embryo case is not.
79 ASTRONOMY AND ASTROPHYSICS↗
Building Large-Scale U.S. Synthetic Electric Distribution System Models
Rapid increases in distributed energy resources on distribution systems are prompting research efforts to improve and evaluate electric power distribution algorithms; however, there is a shortage of realistic, large-scale, U.S.-style test systems for the evaluation of such advanced algorithms. Some available tools to build large-scale test systems are of European style, and their application to studies focused on the United States might not be desirable given very different characteristics between the European and U.S. distribution designs. Motivated by this need, this paper develops detailed algorithms to build large-scale U.S. distribution systems and incorporates them in a new Reference Network Model, RNM-US. The approach starts with information from street maps and a catalog with electric equipment that includes power lines, transformers, voltage regulators, capacitors, and switching devices. The paper presents the algorithms through an illustrative case study of the different steps that comprise the process of building a synthetic distribution grid. Finally, the paper presents a medium- and a large-scale data set covering 10 million electrical nodes and 120,000 km of power lines, demonstrating the applicability of the proposed method to build very large-scale synthetic distribution systems.
27 ARPA - Advanced Research Projects Agency-Energy↗
Improving Power System Modeling. A Tool to Link Capacity Expansion and Production Cost Models
Capacity expansion models (CEM) provide a high-level long-term view at the prospects of the evolving power system. In simulating the possibilities of long-term capacity expansion, it is important to maintain the viability of power system operation in the short-term (daily, hourly and sub-hourly) scales. Production-cost models (PCM) simulate routine power system operation on these shorter time scales using detailed load, transmission and generation fleet data by minimizing production costs and following reliability requirements. When based on CEM 'predictions' about generating unit retirements and buildup, PCM provide more detailed simulation for the short-term system operation and, consequently, may confirm the validity of capacity expansion predictions. Further, production cost model simulations of a system that is based on capacity expansion model solution are 'evolutionary' sound: the generator mix is the result of logical sequence of unit retirement and buildup resulting from policy and incentives. The above has motivated us to bridge CEM with PCM by building a capacity expansion - to - production cost model Linking Tool (CEPCoLT). The Linking Tool is built to onset capacity expansion model prescriptions onto production cost model inputs. NREL's ReEDS and Energy Examplar's PLEXOS are the capacity expansion and the production cost models, respectively. Via the Linking Tool, PLEXOS provides details of operation for the regionally-defined ReEDS scenarios.
24 POWER TRANSMISSION AND DISTRIBUTION↗
An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting
Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.
59 BASIC BIOLOGICAL SCIENCES↗
Bimetallic Nanoparticles as a Model System for an Industrial NiMo Catalyst
An in-depth understanding of the reaction mechanism is required for the further development of Mo-based catalysts for biobased feedstocks. However, fundamental studies of industrial catalysts are challenging, and simplified systems are often used without direct comparison to their industrial counterparts. Here, we report on size-selected bimetallic NiMo nanoparticles as a candidate for a model catalyst that is directly compared to the industrial system to evaluate their industrial relevance. Both the nanoparticles and industrial supported NiMo catalysts were characterized using surface- and bulk-sensitive techniques. We found that the active Ni and Mo metals in the industrial catalyst are well dispersed and well mixed on the support, and that the interaction between Ni and Mo promotes the reduction of the Mo oxide. We successfully produced 25 nm NiMo alloyed nanoparticles with a narrow size distribution. Characterization of the nanoparticles showed that they have a metallic core with a native oxide shell with a high potential for use as a model system for fundamental studies of hydrotreating catalysts for biobased feedstocks.
36 MATERIALS SCIENCE↗
Development of the reactor core isolation cooling system model for the extended station black-out accident
In order to accurately predict the behavior of the Reactor Core Isolation Cooling System (RCIC) in the context of the FLEX strategies under the extended station black-out accident (SBO), we present a mechanistic model of the RCIC in this work. The control volume formulation of angular momentum conservation is employed to model the Terry turbine in the RCIC system. The two-phase nozzle flow model developed in the previous work is utilized to simulate the convergent-divergent nozzles in the Terry turbine. A nozzle flow table is generated to cover the operation range of the RCIC during beyond design basis accidents (BDBAs). The mechanistic RCIC turbine-pump model and the nozzle flow table are incorporated in a TRACE BWR model. Simulations of short-term and long-term SBO transients are performed to demonstrate dynamic RCIC responses. A system analysis code with the proposed mechanistic model of the RCIC system will have a better prediction of system safety response during SBO accidents. With the addition of such more realistic models, the system code will be capable of providing expanded understanding of safety margin, which can help help to form the technical basis for the successful implementation of the FLEX and SAMG measures under Extended Loss of AC Power (ELAP) conditions for BWR or PWR reactor designs.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Bacterial Communities of Diverse Drosophila Species: Ecological Context of a Host–Microbe Model System
Drosophila melanogaster is emerging as an important model of non-pathogenic host–microbe interactions. The genetic and experimental tractability of Drosophila has led to significant gains in our understanding of animal–microbial symbiosis. However, the full implications of these results cannot be appreciated without the knowledge of the microbial communities associated with natural Drosophila populations. In particular, it is not clear whether laboratory cultures can serve as an accurate model of host–microbe interactions that occur in the wild, or those that have occurred over evolutionary time. To fill this gap, we characterized natural bacterial communities associated with 14 species of Drosophila and related genera collected from distant geographic locations. To represent the ecological diversity of Drosophilids, examined species included fruit-, flower-, mushroom-, and cactus-feeders. In parallel, wild host populations were compared to laboratory strains, and controlled experiments were performed to assess the importance of host species and diet in shaping bacterial microbiome composition. We find that Drosophilid flies have taxonomically restricted bacterial communities, with 85% of the natural bacterial microbiome composed of only four bacterial families. The dominant bacterial taxa are widespread and found in many different host species despite the taxonomic, ecological, and geographic diversity of their hosts. Both natural surveys and laboratory experiments indicate that host diet plays a major role in shaping the Drosophila bacterial microbiome. Despite this, the internal bacterial microbiome represents only a highly reduced subset of the external bacterial communities, suggesting that the host exercises some level of control over the bacteria that inhabit its digestive tract. Finally, we show that laboratory strains provide only a limited model of natural host–microbe interactions. Bacterial taxa used in experimental studies are rare or absent in wild Drosophila populations, while the most abundant associates of natural Drosophila populations are rare in the lab.
59 BASIC BIOLOGICAL SCIENCES↗