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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Version of GCAM-USA used for National Climate Assessment 5, Chapter 5: GCAM-USA-v5.3-IM3-NCA5

Overview GCAM-USA-v5.3-IM3-NCA5 is developed using GCAM-USA-v5.3 as its core, with the impact of the thermodynamic global warming scenario "RCP8.5_hotter" on runoff, heating and cooling degree-hours, and agricultural yield and the SSP5 socioeconomic scenario. These scenarios were produced as part of the Integrated Multisector, Multiscale Modeling (IM3) project and used by National Climate Assessment 5. Detailed Information This software package includes the output dataset, which is located in "output/database_rcp85hotter_ssp5". For reproducibility, the model is setup and ready to run for the selected IM3 scenario "rcp85hotter_ssp5". To execute the model, double click "exe/run-gcam.bat".

Climate Change↗
DOE OSTI2020

An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

This paper presents an enhanced vertical ground heat exchanger (GHE) model for whole-building energy simulation (WBES). WBES programs generally have computational constraints that affect the development and implementation of component simulation sub-models. WBES programs require models that execute quickly and efficiently due to how the programs are utilized by design engineers. WBES programs also require models to be formulated so their performance can be determined from boundary conditions set by upstream components and environmental conditions. The GHE model developed during this work utilizes an existing response factor model and extends its capabilities to accurately and robustly simulate at timesteps that are shorter than the GHE transit time. This was accomplished by developing a simplified dynamic borehole model and then exercising that model to generate exiting fluid temperature response factors. This approach blends numerical and analytical modeling methods. The existing response factor models are then extended to incorporate the exiting fluid temperature response factor to provide a better estimate of the GHE exiting fluid temperature at short simulation timesteps.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A cross-platform execution engine for the quantum intermediate representation

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

LLVM↗
DOE OSTI2024

pnnl/emp-gnn

Efficient Graph Neural Network for Predicting Molecular Properties software can compare the prediction quality with ab initio DFT results reported by the high-performance state-of-theart NWChem quantum chemistry package [1] through Mean Absolute Error (MAE) obtained by the fitting between DFT and model predictions i.e, MPNN [2]. We also demonstrated the performance benefits by grouping large molecules by atom sizes, and executing GNN models on different types of resources. Since the training times depend on the number of atoms, we demonstrate the impact of distributing the workloads on two GPUs with varying capabilities (e.g., NVIDIA A100 vs. GeForce RTX 2080 Ti) to optimize the efficiency. We are at the precipice of broad adoption of GNNs for molecular property prediction tasks; hence, our work is timely by comparing model prediction against classical approaches with the intent of providing initial screening for specific classes of molecules.

Lee, Hyungro↗
DOE OSTI2016

Study on Modeling of Nuclear Fuel End Drop during a Hypothetical Accident

It is well known that shell elements have been used to model the cladding of single nuclear fuel pins. These have been used, for example, to analyze the response of a fuel assembly by assuming that all fuel pins in a fuel assembly deform in the same pattern. It is increasingly significant to model the entire fuel assembly in order to capture more realistically the behavior of each individual nuclear fuel during a hypothetical accident in a manner that can account for interactions among single pins. However, modeling each individual fuel pin in an entire fuel assembly using shell elements will result in models with large amount of elements and degrees of freedom that take correspondingly larger amount of computational times to execute. Therefore, modeling the nuclear fuel assembly with beam elements may become an option. This paper presents the results of a set of analysis with the LS-DYNA finite element software that investigated the differences and similarities of modeling nuclear fuel assemblies using either shell elements or beam elements. This study provides additional insights on the appropriateness of using beam element models of the fuel pins in the simulation of a hypothetical accident. In this study, two single pin models, a single pin model using shell elements and a single beam model using beam elements, are constructed to analyze end drop scenarios with the LS-DYNA code. Each model is then analyzed for a 19 ft end drop scenario (linear behavior) and 30 ft end drop scenario (non-linear behavior). Comparisons are made between the results obtained with the single pin shell model and with the single pin beam model to study the differences and similarities resulting from the two modeling approaches. In this study, a B and W 15x15 PWR fuel is analyzed for both 30 ft and 19 ft end hypothetical accident drops using both single pin finite element model with shell elements and beam elements for the fuel cladding. It is found that for the 19 ft end drop, the difference in the Max/Min total axial strains is about 12% between the two models. For the 30 ft end drop, the maximum total axial strains calculated with both models is similar, and a difference of about 12% is observed for the minimum total axial strains calculated by the two models. Advantages of the single pin shell model are that it permits consideration of internal pressures and may capture local buckling. Therefore, use of beam elements to model the fuel cladding needs to take into account the possible significance of local buckling and internal pressurization for the problem being analyzed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Testing, Characterization, and Modeling of the Resonant Plate Test Environment

The resonant plate shock test is a dynamic test of a mid-field pyroshock environment where a projectile is struck against a plate. The structure undergoing the simulated field shock is mounted to the plate. The plate resonates when struck and provides a two sided shock that is representative of the shock observed in the field. This test environment shock simulates a shock in a single coordinate direction for components looking to provide evidence that they will survive a similar or less shock when deployed in their operating environment. However, testing in one axis at a time provides many challenges. The true environment is a multi-axis environment. The test environment exhibits strong off-axis motion when only motion in one axis is desired. Multiple fixtures are needed for a single test series. It would be advantageous if a single test could be developed that tests the multi-axis environment simultaneously. In order to design such a test, a model must be developed and validated. The model can be iterated in design and configuration until the specified multi-axis environment is met. The test can then execute the model driven test design. This report discusses the resonant plate model needed to design future tests and the steps and methods used to obtain the model. This report also details aspects of the resonant plate test discovered during the process of model development that aids in our understanding of the test.

42 ENGINEERING↗
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2015

Local respiratory motion correction for PET/CT imaging: Application to lung cancer

Purpose: Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications. Methods: Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions. Results: Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV. Conclusions: The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Predicting Emission Source Terms in a Reduced-Order Fire Spread Model—Part 1: Particulate Emissions

A simple, easy-to-evaluate, surrogate model was developed for predicting the particle emission source term in wildfire simulations. In creating this model, we conceptualized wildfire as a series of flamelets, and using this concept of flamelets, we developed a one-dimensional model to represent the structure of these flamelets which then could be used to simulate the evolution of a single flamelet. A previously developed soot model was executed within this flamelet simulation which could produce a particle size distribution. Executing this flamelet simulation 1200 times with varying conditions created a data set of emitted particle size distributions to which simple rational equations could be tuned to predict a particle emission factor, mean particle size, and standard deviation of particle sizes. These surrogate models (the rational equation) were implemented into a reduced-order fire spread model, QUIC-Fire. Using QUIC-Fire, an ensemble of simulations were executed for grassland fires, southeast U.S. conifer forests, and western mountain conifer forests. Resulting emission factors from this ensemble were compared against field data for these fire classes with promising results. Also shown is a predicted averaged resulting particle size distribution with the bulk of particles produced to be on the order of 1 μm in size.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Validation for 233U-Fueled Systems in KENO V.a in SCALE 6.2

Validation of the KENO V.a Monte Carlo code has been performed in recent years using benchmark experiments contained in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The models used in these recent validation efforts have been drawn from the Verified, Archived Library of Inputs and Data (VALID). Models must be generated and reviewed by qualified individuals to be added to the VALID library, which leads to high confidence in the models and reduces the probability that apparent validation issues are a result of modeling errors. The contents of the VALID library are somewhat limited because of the effort involved in generating and reviewing models, so no 233U-fueled experiments are included in VALID. A large number of models of 233U experiments have been modeled in the past in support of the criticality safety of 233U in storage at Oak Ridge National Laboratory (ORNL). In June 2016, two midshipmen from the United States Naval Academy were assigned to ORNL, and they reviewed and updated these models for execution in SCALE 6.2. The validation of 233U is of interest because some discrepancies in SCALE results were identified by a beta tester at the Nuclear Energy Agency modeling these systems. These discrepancies were related to a data thinning procedure in the continuous-energy nuclear data process, and had been resolved prior to the release of SCALE 6.2

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Validation for {sup 233}U-Fueled Systems in KENO V.a in SCALE 6.2

Validation of the KENO V.a Monte Carlo code has been performed in recent years using benchmark experiments contained in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The models used in these recent validation efforts have been drawn from the Verified, Archived Library of Inputs and Data (VALID). Models must be generated and reviewed by qualified individuals to be added to the VALID library, which leads to high confidence in the models and reduces the probability that apparent validation issues are a result of modeling errors. The contents of the VALID library are somewhat limited because of the effort involved in generating and reviewing models, so no {sup 233}U-fueled experiments are included in VALID. A large number of models of {sup 233}U experiments have been modeled in the past in support of the criticality safety of {sup 233}U in storage at Oak Ridge National Laboratory (ORNL). In June, 2016, two midshipmen from the United States Naval Academy were assigned to ORNL, and they reviewed and updated these models for execution in SCALE 6.2. The validation of {sup 233}U is of interest because some discrepancies in SCALE results were identified by a beta tester at the Nuclear Energy Agency modeling these systems. These discrepancies were related to a data thinning procedure in the continuous-energy nuclear data process, and had been resolved prior to the release of SCALE 6.2. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Computer-implemented security evaluation methods, security evaluation systems, and articles of manufacture

Computer-implemented security evaluation methods, security evaluation systems, and articles of manufacture are described. According to one aspect, a computer-implemented security evaluation method includes accessing information regarding a physical architecture and a cyber architecture of a facility, building a model of the facility comprising a plurality of physical areas of the physical architecture, a plurality of cyber areas of the cyber architecture, and a plurality of pathways between the physical areas and the cyber areas, identifying a target within the facility, executing the model a plurality of times to simulate a plurality of attacks against the target by an adversary traversing at least one of the areas in the physical domain and at least one of the areas in the cyber domain, and using results of the executing, providing information regarding a security risk of the facility with respect to the target.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Sensitivity and Uncertainty Workflow of Full System SIERRA Models Supporting High Consequence Applications

Credibility of end-to-end CompSim (Computational Simulation) models and their agile execution requires an expressive framework to describe, communicate and execute complex computational tool chains representing the model. All stakeholders from system engineering and customers through model developers and V&V partners need views and functionalities of the workflow representing the model in a manner that is natural to their discipline. In the milestone and in this report we define workflow as a network of computation simulation activities executed autonomously on a distributed set of computational platforms. The FY19 ASC L2 Milestone (6802) for the Integrated Workflow (IWF) project was designed to integrate and improve existing capabilities or develop new functionalities to provide a wide range of stakeholders a coherent and intuitive platform capable of defining and executing CompSim modeling from analysis workflow definition to complex ensemble calculations. The main goal of the milestone was to advance the integrated workflow capabilities to support the weapon system analysts with a production deployment in FY20. Ensemble calculations supporting program decisions include sensitivity analysis, optimization and uncertainty quantification. The goal of the L2 milestone aligned with the ultimate goal of the IWF project is to foster cultural and technical shift toward and integrated CompSim capability based on automated workflows. Specific deliverables were defined in five broad categories: 1) Infrastructure, including development of distributed-computing workflow capability, 2) integration of Dakota (Sandia's sensitivity, optimization and UQ engine) with SAW (Sandia Analysis Workbench), 3) ARG (Automatic Report Generator introspecting analysis artifacts and generating human-readable extensible and archivable reports), 4) Libraries and Repositories aiding capability reuse, and 5) Exemplars to support training, capturing best practices and stress testing of the platform. A set of exemplars was defined to represent typical weapon system qualification CompSim projects. Analyzing the required capabilities and using the findings to plan implementation of required capabilities ensured optimal allocation of development resources focused on production deployment after the L2 is completed. It was recognized early that the end-to-end modeling applications pose a considerable number of diverse risks, and a formal risk tracking process was implemented. The project leveraged products, capabilities and development tasks of IWF partners. SAW, Dakota, Cubit, Sierra, Slycat, and NGA (NexGen Analytics, a small business) contributed to the integrated platform developed during this milestone effort. New products delivered include: a) NGW (Next Generation Workflow) for robust workflow definition and execution, b) Dakota wizards, editor and results visualization, and c) the automatic report generator ARG. User engagement was initiated early in the development process eliciting concrete requirements and actionable feedback to assure that the integrated CompSim capability will have high user acceptance and impact. The current integrated capabilities have been demonstrated and are continually being tested by a set of exemplars ranging from training scenarios to computationally demanding uncertainty analyses. The integrated workflow platform has been deployed on both SRN (Sandia Restricted Network) and SCN (Sandia Classified Network). Computational platforms where the system has been demonstrated span from Windows (Creo the CAD platform chosen by Sandia) to Trinity HPC (Sierra and CTH solvers). Follow up work will focus on deployment at SNL and other sites in the nuclear enterprise (LLNL, KCNSC), training and consulting support to democratize the analysis agility, process health and knowledge management benefits the NGW platform provides.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Dynamic Modeling of Adjustable-Speed Pumped Storage Hydropower Plant: Preprint

Hydropower is the largest producer of renewable energy in the U.S. More than 60% of the total renewable generation comes from hydropower. There is also approximately 22 GW of pumped storage hydropower (PSH). Conventional PSH uses a synchronous generator, and thus the rotational speed is constant at synchronous speed. This work details a hydrodynamic model and generator/power converter dynamic model. The optimization of the hydrodynamic model is executed by the hydro-turbine controller, and the electrical output real/reactive power is controlled by the power converter. All essential controllers to perform grid-interface functions and provide ancillary services are included in the model.

13 HYDRO ENERGY↗
DOE OSTI2013

Outcomes of the 2013 GTO Workshop on Geothermal Code Comparison

Pacific Northwest National Laboratory (PNNL) is supporting the Department of Energy (DOE) Geothermal Technologies Office (GTO) in organizing and executing a model comparison activity. This project is directed at testing, diagnosing differences, and demonstrating modeling capabilities of a worldwide collection of numerical simulators for evaluating geothermal technologies. A key element of the project was the planning and implementation of a one-day project kickoff workshop, held February 14, 2013 in Palo Alto, CA. The primary goals of the workshop were to 1) introduce the project and its objectives to potential participating team members, and 2) develop an initial set of test problem descriptions for use in the execution stage. This report summarizes the outcomes of the workshop.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components

Elevated temperatures limit the peak performance of systems because of frequent interventions by thermal throttling. Non-uniform thermal states across system nodes also cause performance variation within seemingly equivalent nodes leading to significant degradation of overall performance. In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods. Second, we develop alternative methods using neural network and linear regression-based methods to perform a comprehensive comparative study of prediction methods. We show that our optimized models achieve improved performance with better prediction accuracy and lower overhead as compared with the Gaussian process model proposed previously. Specifically we present a reduced version of the Gaussian process model, a neural network-based model, and a linear regression-based model. Using the optimization methods, we are able to reduce the average prediction errors in the Gaussian process from 4: 2 degrees C to 2: 9 degrees C. We also show that the newly developed models using neural network and Lasso linear regression have average prediction errors of 2: 9 degrees C and 3: 8 degrees C respectively. The prediction overheads are 0.22, 0.097, and 0.026 ms per prediction for reduced Gaussian process, neural network, and Lasso linear regression models, respectively, compared with 0.57 ms per prediction for the previous Gaussian process model. We have implemented our proposed thermal prediction models on a two-node system configuration to help identify the optimal task placement. The task placement identified by the models reduces the average system temperature by up to 11: 9 degrees C without any performance degradation. Furthermore, these models respectively achieve 75, 82.5, and 74.17 percent success rates in correctly pointing to those task placements with better thermal response, compared with 72.5 percent success for the original model in achieving the same objective. Lastly, we extended our analysis to a 16-node system and we were able to train models and execute them in real time to guide task migration and achieve on average 17 percent reduction in the overall system cooling power.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Achieving Energy Efficiency In Small Businesses In Low-Income Areas

Lime Energy, with funding from the Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE), recently concluded a project demonstrating a new business model for executing energy efficiency for small businesses, in which we aspired to focus on low-income urban areas. As initially conceived, the Project would advance beyond the current best practices used to foster energy efficiency action in small and midsized commercial buildings (under 25,000 square feet), which represent 95% of all commercial customers in the United States. As the project proceeded, Lime Energy discovered that the business model needed to be further refined in areas where Lime already had a presence in the community and the benefit of a relationship with the known utility. The need for this intermediate step – of deploying the business model in an established region – has since been validated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Deep residual networks for crystallography trained on synthetic data

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

36 MATERIALS SCIENCE↗