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At least 1,027 records · Page 57

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DOE OSTI2019

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids (Phase II, Final Report)

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Deep Learning Model Segmentations on Computed Tomography 3D Reconstructions of Coffee Beans to Determine Void Ratio (U-Net) and Roast Level (LinkNet)

This project will evaluate the functionality of Object Research Dragonfly software’s user produced deep learning (DL) models on Computed Tomography (CT) scanned coffee beans from green (unroasted) through a dark roast. DL models will be expected to identify voids within the coffee beans, and identify the level of roast of the bean from the CT reconstruction. The scope of this project is intended to meet the Capstone Project requirements of University of California San Diego (UCSD) Structural Engineering master’s degree and offer useful insight on Dragonfly’s DL capability for LANL’s Non-Destructive Evaluation (NDE) CT team. The results of this project will be presented to the E-6 NDE group within LANL. Data acquisition was completed with a North Star Imaging (NSI) X-25 CT Cabinet.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Geodesic Models of Quasi-periodic-oscillations as Probes of Quadratic Gravity

Future very-large-area X-ray instruments (for which the effective area is larger than >3 m{sup 2}) will be able to measure the frequencies of quasi-periodic oscillations (QPOs) observed in the X-ray flux from accreting compact objects with sub-percent precision. If correctly modeled, QPOs can provide a novel way to test the strong-field regime of gravity. By using the relativistic precession model and a modified version of the epicyclic resonance model, we develop a method to test general relativity against a generic class of theories with quadratic curvature corrections. With the instrumentation being studied for future missions such as eXTP, LOFT, or STROBE-X, a measurement of at least two QPO triplets from a stellar mass black hole can set stringent constraints on the coupling parameters of quadratic gravity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Recent Improvements in Pronghorn for Advanced Reactor Modeling

Pronghorn is a thermal-hydraulics computational tool developed using the Idaho National Laboratory's Multiphysics Object-Oriented Simulation Environment (MOOSE). It is designed to support Computational Fluid Dynamics (CFD) modeling, ranging from subchannel and porous media analysis to Reynolds Averaged Navier-Stokes (RANS) turbulence modeling. As an integral part of the MOOSE-based suite of tools, Pronghorn seamlessly couples with other MOOSE-based applications to simulate a variety of physical phenomena. This article highlights recent significant enhancements to Pronghorn's CFD modeling capabilities and demonstrates their application to advanced nuclear reactor designs. The recent improvements in Pronghorn primarily focus on modifications to its turbulence modeling capabilities, near-wall corrections and numerical schemes. In terms of turbulence modeling, the two-equation $k-\epsilon$ and $k-\omega$ SST models have been implemented and validated with both equilibrium and non-equilibrium wall treatments. Additionally, corrections for wall roughness, and curvature, and wall-channeling in pebble beds have been introduced in the near-wall modeling. These developments enable more accurate simulations of advanced nuclear reactors. Two case studies are presented in this work: a pool-type Molten Chloride Reactor and a salt-cooled Pebble-Bed High Temperature Reactor. In both cases, the previous models in Pronghorn are compared with the new implementations, demonstrating the improved accuracy achieved with the updated models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Computational general relativistic force-free electrodynamics: I. Multi-coordinate implementation and testing

General relativistic force-free electrodynamics is one possible plasma-limit employed to analyze energetic outflows in which strong magnetic fields are dominant over all inertial phenomena. The amazing images of black hole (BH) shadows from the Galactic Center and the M87 galaxy provide a first direct glimpse into the physics of accretion flows in the most extreme environments of the universe. The efficient extraction of energy in the form of collimated outflows or jets from a rotating BH is directly linked to the topology of the surrounding magnetic field. We aim at providing a tool to numerically model the dynamics of such fields in magnetospheres around compact objects, such as BHs and neutron stars. To do so, we probe their role in the formation of high energy phenomena such as magnetar flares and the highly variable teraelectronvolt emission of some active galactic nuclei. In this work, we present numerical strategies capable of modeling fully dynamical force-free magnetospheres of compact astrophysical objects. Here, we provide implementation details and extensive testing of our implementation of general relativistic force-free electrodynamics in Cartesian and spherical coordinates using the infrastructure of the E INSTEIN T OOLKIT . The employed hyperbolic/parabolic cleaning of numerical errors with full general relativistic compatibility allows for fast advection of numerical errors in dynamical spacetimes. Such fast advection of divergence errors significantly improves the stability of the general relativistic force-free electrodynamics modeling of BH magnetospheres.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Global stellarator coil optimization with quadratic constraints and objectives

Most present stellarator designs are produced by costly two-stage optimization: the first for an optimized equilibrium, and the second for a coil design reproducing its magnetic configuration. Few proxies for coil complexity and forces exist at the equilibrium stage. Rapid initial state finding for both stages is a topic of active research. Most present convex coil optimization codes use the least square winding surface method by Merkel (NESCOIL), with recent improvements in conditioning, regularization, sparsity, and physics objectives. While elegant, the method is limited to modeling the norms of linear functions in coil current. We present QUADCOIL, a global coil optimization method that targets combinations of linear and quadratic functions of the current. It can directly constrain and/or minimize a wide range of physics objectives unavailable in NESCOIL and REGCOIL, including the Lorentz force, magnetic energy, curvature, field-current alignment, and the maximum density of a dipole array. QUADCOIL requires no initial guess and runs nearly $10$ 2 x faster than filament optimization. Integrating it in the equilibrium optimization stage can potentially exclude equilibria with difficult-to-design coils, without significantly increasing the computation time per iteration. QUADCOIL finds the exact, global minimum in a large parameter space when possible, and otherwise finds a well-performing approximate global minimum. It supports most regularization techniques developed for NESCOIL and REGCOIL. We demonstrate QUADCOIL’s effectiveness in coil topology control, minimizing non-convex penalties, and predicting filament coil complexity with three numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Statistical Analysis of CO 2 Exposed Wells to Predict Long Term Leakage through the Development of an Integrated Neural-Genetic Algorithm

The objective of this project is to develop a computerized statistical model with the Integrated Neural-Genetic Algorithm (INGA) for predicting the probability of long-term leak of wells in CO 2 sequestration operations. This object has been accomplished by conducting research in three phases: 1) data mining of CO 2 -explosed wells, 2) INGA computer model development, and 3) evaluation of the predictive performance of the computer model with data from field tests. Data mining was conducted for 510 wells in two CO 2 sequestration projects in the Texas Gulf Coast region. They are the Hasting West field and Oyster Bayou field in the Southern Texas. Missing wellbore integrity data were estimated using an analytical and Finite Element Method (FEM) model. The INGA was first tested for performances of convergence and computing efficiency with the obtained data set of high dimension. It was concluded that the INGA can handle the gathered data set with good accuracy and reasonable computing time after a reduction of dimension with a grouping mechanism. A computerized statistical model with the INGA was then developed based on data pre-processing and grouping. Comprehensive training and testing of the model were carried out to ensure that the model is accurate and efficient enough for predicting the probability of long-term leak of wells in CO 2 sequestration operations. The Cranfield in the southern Mississippi was select as the test site. Observation wells CFU31F2 and CFU31F3 were used for pressure-testing, formation-logging, and cement-sampling. Tools run in the wells include Isolation Scanner, Slim Cement Mapping Tool (SCMT), Cased Hole Formation Dynamics Tester (CHDT), and Mechanical Sidewall Coring Tool (MSCT). Analyses of the obtained data indicate no leak of CO 2 cross the cap zone while it is evident that the well cement sheath was invaded by the CO 2 from the storage zone. This observation is consistent with the result predicted by the INGA model which indicates the well has a CO 2 leak-safe probability of 72%. This comparison implies that the developed INGA model is valid for future use in predicting well leak probability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Sensitivity ZAM Modeling Study of Tank 10H VDS Waste Sample (U)

The objective of this work is to study the sensitivity of cesium loading expected on the Tank Closure Cesium Removal (TCCR) columns. Savannah River National Laboratory (SRNL) utilized ZAM, a computer program developed by the research group of Professor Rayford G. Anthony of Texas A&M University, for prediction of cesium loading on the Crystalline Silicotitanate (CST) resin during the processing of Tank 10H Variable Depth Sample (VDS) waste solution.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Console Water-to-Air Geothermal Heat Pump

Executive Summary Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past several years, the objective of this work is to produce national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover most of the high-impact, market-ready (or nearly market-ready) measures. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. This measure models the conversion of an existing heating, ventilating, and air-conditioning (HVAC) system to a series of “console” water-to-air heat pumps served by a ground heat exchanger. Console water-to-air geothermal heat pumps (GHP) are all-in-one packages that have no or minimal ductwork and serve individual spaces. Properly designed ground heat exchanger-coupled systems can offer benefits in energy efficiency relative to “conventional” HVAC systems, as well as facilitating beneficial electrification. Console GHPs can be coupled to a ground loop on the source side and can directly replace electric baseboard heaters or air-source packaged terminal heat pumps. Console GHPs can also bring in outdoor air for ventilation. This measure will be referred to throughout the document as the “Console GHP” upgrade.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

On Residual Stresses and Reference Temperatures in Thermomechanical Simulations of Photovoltaic Modules Using the Finite Element Method

Thermomechanical simulation of photovoltaic (PV) modules using the finite element method (FEM) is a useful tool to evaluate module design features in terms of structural integrity, reliability, and durability. One of the main challenges in the numerical modeling of a PV module is the incorporation of residual stresses induced by the manufacturing process. Modeling assumptions and abstractions are necessary to limit the model complexity and reduce the computational time. However, oversimplifications and incorrect assumptions can lead to erroneous numerical results. Unfortunately, much simulation work still neglects process-induced stresses. This can lead to incorrect predictions of the stress-strain history and erroneous conclusions during the design process. In this work, we review current modeling practices for incorporating process-induced stresses, and contrast numerical models that consider residual stresses with those that neglect them. We find that the simulation objective and available material properties dictate which process steps need to be modeled, and explore in depth the modeling of residual stresses induced by the lamination process. We demonstrate that a simplified cooldown procedure at the beginning of the simulation can increase the model accuracy and discuss appropriate choices for starting and reference temperatures in the finite element model.

14 SOLAR ENERGY↗

DOE OSTI2023

Community Geothermal: Borefield Design, Thermal Conductivity, and Subsurface Modeling Data - Chicago, IL

This dataset encompasses the development of a geothermal energy system for the West Woodlawn neighborhood in Chicago, Illinois. This project is part of a broader initiative to design and deploy geothermal heating and cooling systems at a community scale. The dataset includes thermal conductivity test results, calculations for borehole sizing based on the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) method, as well as simulated thermal loads based on actual energy usage from individual buildings. Also provided here are files used for numerical modeling via COMSOL Multiphysics to simulate borefield design and subsurface thermal behavior. Two manuscripts are attached, which outline the broad objectives of the project and a description of the numerical modeling methodology and results.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Model Validation and Uncertainty Quantification on the KRUSTY Microreactor Design Using GRIFFIN Neutron Transport Code [Poster]

Argonne National Laboratory (ANL) and INL have developed a GRIFFIN steady state neutronics model for the multiphysics simulations of the Kilopower Reactor Using Sterling TechnologY (KRUSTY) microreactor in the Multiphysics Object Oriented Simulation Environment (MOOSE). The reliability of such deterministic neutronics models can be validated by comparing with computations from Monte Carlo codes (e.g. MCNP, SERPENT, OpenMC, Shift, etc). Furthermore, potential modeling/design improvements can be identified by incorporating uncertainty quantification (UQ), which can be performed by MOOSE’s Stochastic Tools Module (STM). KRUSTY is a prototype for a 5-kW thermal nuclear-powered space reactor. Its primary components consist of nuclear fuel, heat pipes, a control rod, a reflector, and the shielding. The fuel consists of 3 stacked U-7.65Mo cylinders with a hole in the center for the control rod. 8 liquid sodium heat pipes transfer fission energy from the solid fuel block to the Sterling power conversion system where the energy is extracted, and the cooled sodium flows back to the core via capillary action . The movable Boron Carbide control rod regulates the neutron population during startup or when a reactor temperature boost is needed . The beryllium oxide reflector is in 3 places in the reactor; it surrounds the core axially, it lies beneath the core on a platen, and it is present in the shim. The axial and lower reflectors rest on an adjustable stainless-steel platen that moves upward to cover the fuel and help the reactor reach criticality. Lastly, radial stainless steel surrounds the core offering protection from radiation exposure .

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

LAROMance Grade 91 Model Integration in NEML2

New reactor designs are targeting higher operating temperatures for increased thermal efficiency when compared to the current fleet of light water reactors. Designing structural components for these high temperature environments with reliable long-term operations requires material models that can accurately capture the deformation mechanisms active in these environments. The LAROMance surrogate material models are based on a database of mechanistic crystal plasticity simulations for high-temperature conditions. Inputs to the LAROMance models reflect the microstructural pedigree of the material, like dislocation densities and precipitate contents. Based on the evolution of these microstructural features, the LAROMance model provides the engineering scale constitutive model response. The LAROMance model was recently parameterized for Grade 91, a high temperature alloy. In the present work, the Grade 91 LAROMance model is implemented in the New Material Model Library, version 2 (NEML2). NEML2 provides a modular way to build material models from smaller blocks and was developed to vectorize the material update to efficiently run on modern computational architectures with graphics processing unit accelerators. NEML2 constitutive models can be used in simulations based on the multiphysics object-oriented simulation environment (MOOSE). This report provides details on the implementation of the Grade 91 LAROMance model in NEML2 and its verification of engineering scale finite element simulations in MOOSE.

42 - ENGINEERING↗

DOE OSTI2022

Platform for probing radiation transport properties of hydrogen at conditions found in the deep interiors of red dwarfs

We describe an experimental concept at the National Ignition Facility for specifically tailored spherical implosions to compress hydrogen to extreme densities (up to ∼800× solid density, electron number density ne∼4×1025 cm−3) at moderate temperatures (T∼200 eV), i.e., to conditions, which are relevant to the interiors of red dwarf stars. The dense plasma will be probed by laser-generated x-ray radiation of different photon energy to determine the plasma opacity due to collisional (free–free) absorption and Thomson scattering. The obtained results will benchmark radiation transport models, which in the case for free–free absorption show strong deviations at conditions relevant to red dwarfs. This very first experimental test of free–free opacity models at these extreme states will help to constrain where inside those celestial objects energy transport is dominated by radiation or convection. Moreover, our study will inform models for other important processes in dense plasmas, which are based on electron–ion collisions, e.g., stopping of swift ions or electron–ion temperature relaxation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Enhancing the cooling performance of thermocouples: a power-constrained topology optimization procedure

Abstract Heat pumping through thermoelectric devices has many advantages over traditional cooling. However, their current efficiency is a limiting factor in their implementation. In this paper, we approach the non-convex topology optimization of thermoelectrical elements for cooling applications through the method of moving asymptotes (MMA) to improve their cooling capabilities per watt usage. The optimization problem is defined for a given power budget, aiming for the minimum temperature with a known heat pumping need. The introduction of power as a constraint justifies the introduction of the voltage gradient across the thermocouple as a design variable to maintain the thermoelectrical device in its optimum power-to-heat extraction ratio. To better understand the convergence of this non-convex problem, we present a two-variable analytical thermoelectric optimization model. This example provides information on how to select the penalty parameters used to scale the three material coefficients involved in the problem to obtain lower objective values and better convergence using MMA. The analytical model shows the non-convexity of the problem and provides the recommendation to use penalization coefficients of the form $$p_k=p_{\sigma }>p_{\alpha }=1$$ p k = p σ > p α = 1 for the thermal conductivity, electrical conductivity, and Seebeck coefficients. We tested these penalization coefficients through optimizations of a model based on the 1MC10-031 commercial thermoelectric-cooler (TEC) using the finite element method (FEM). These penalization coefficients provided local minima without the need for volume constraints. With this procedure, we found designs that provided temperatures close to 10 degrees lower using 60% less semiconductor material volume compared to the initial design.

Gutiérrez, G. Reales↗