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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rooftop unit comparison calculator: a framework for comparing performance of rooftop units with building energy simulation

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. In conclusion, it also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Prediction of Thermal Conditions of DED With FEA Metal Additive Simulation

This paper presents the integration of wire-arc additive manufacturing (WAAM) using Gas Metal Arc Welding (GMAW) into a machine tool to create a retrofit hybrid computer numeric control (CNC) machine tool. GMAW, along with other direct energy deposition systems, has the capacity to deposit material faster than the excess thermal energy can dissipate. This results in the need to allow the part to cool between consecutive layers, which is the most time-consuming part of the additive process. Finite element analysis (FEA) was used in conjunction with monitored build plate surface temperatures during deposition samples to improve adequate dwell time prediction and to develop a cooling system. A deposition was completed where no dwell time was used and the build plate along with the machine table temperatures were monitored. A second deposition was completed where only one bead was deposited and the traverse speed was increased. The GMAW welder was mounted on a 3-axis CNC machine where two square deposition samples were completed. A FEA model was designed and verified using the monitored samples. The model will be used to determine improved depositions speeds and whether forced cooling would allow for an increased deposition rate without structural failure. It was determined the FEA software can be used to accurately model and predict the thermal response of WAAM AM components.

Heinrich, Lauren↗
DOE OSTI2025

MARIE: Software Methods & Documentation

This document describes the theory and function of the Model for the Assessment of Reprocessing and Recycle with Innovative Execution (MARIE) software developed by Oak Ridge National Laboratory (ORNL). Specifically presented are the supported unit operations, underlying theory for processes such as dissolution and generic solvent extraction, execution, and supported output capabilities

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Modeling and Simulation of Fuel Burnup in Pebble Bed Reactors

Modeling and simulation of fuel burnup plays important roles in reactor design, operation, safety, and security as well as nuclear material control and accounting (MC&A) [1]. This task is uniquely challenging for pebble bed reactors (PBR) because the pebbles are continuously added and recycled into the reactor core, and their paths through the core are random. To address this problem, we present two simulation models in this paper. Brookhaven National Laboratory (BNL) developed a simple lattice model of a PBR in Serpent software to generate used pebble isotopic concentrations. The benefit of using Serpent software in this specific application is that it helps streamline the data generation process without having to use too many independent software codes in combination to achieve a simple task. For example, transport, burnup and zero power decay can be implemented in a single pass. Three-dimensional core models were developed using Serpent to simulate the burnup process of 5 subject pebbles starting from fresh till they reach nearly target burnup, with each pebble placed in one of the five artificially designated radial channels to capture the changing neutron spectra along the core radius. Equilibrium isotopic concentrations were assumed in all other pebbles in the core. To provide a verification for the Serpent isotope transmutation and decay results, Oak Ridge National Laboratory (ORNL) performed simple SCALE/ORIGEN calculations using the average neutron spectra calculated by Serpent for each of the 5 pebbles. The 252-group neutron spectra from Serpent were then used by ORIGEN to produce the one-group library for depletion and decay calculations. The isotopic concentrations of a few nuclides of interest and neutron and gamma source terms produced from the ORIGEN calculations were compared with the ones from the Serpent calculations. The model simulated in this work was based on the Pebble Bed Modular Reactor (PBMR)-400 design because many data needed for the simulation such as core power profiles, fuel and reflector temperatures, and equilibrium core composition are publicly available. In this paper, we will compare the results between these two approaches and benchmark the results against a set of well-established simulation results for PBMR-400.

Dim, Odera↗
DOE OSTI2023

Modeling and Simulation of Fuel Burnup in Pebble Bed Reactors

Modeling and simulation of fuel burnup plays important roles in reactor design, operation, safety, and security as well as nuclear material control and accounting (MC&A) [1]. This task is uniquely challenging for pebble bed reactors (PBR) because the pebbles are continuously added and recycled into the reactor core, and their paths through the core are random. To address this problem, we present two simulation models in this paper. Brookhaven National Laboratory (BNL) developed a simple lattice model of a PBR in Serpent software to generate used pebble isotopic concentrations. The benefit of using Serpent software in this specific application is that it helps streamline the data generation process without having to use too many independent software codes in combination to achieve a simple task. For example, transport, burnup and zero power decay can be implemented in a single pass. Three-dimensional core models were developed using Serpent to simulate the burnup process of five subject pebbles starting from fresh till they reach nearly target burnup, with each pebble placed in one of the five artificially designated radial channels to capture the changing neutron spectra along the core radius. Equilibrium isotopic concentrations were assumed in all other pebbles in the core. To provide a verification for the Serpent isotope transmutation and decay results, Oak Ridge National Laboratory (ORNL) performed simple SCALE/ORIGEN calculations using the average neutron spectra calculated by Serpent for each of the five pebbles. The 252-group neutron spectra from Serpent were then used by ORIGEN to produce the one-group library for depletion and decay calculations. The isotopic concentrations of a few nuclides of interest and neutron and gamma source terms produced from the ORIGEN calculations were compared with the ones from the Serpent calculations. The model simulated in this work was based on the Pebble Bed Modular Reactor (PBMR)-400 design because many data needed for the simulation such as core power profiles, fuel and reflector temperatures, and equilibrium core composition are publicly available. In this paper, we will compare the results between these two approaches and benchmark the results against a set of well-established simulation results for PBMR-400.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Python Group Additivity (pGrAdd) software for estimating species thermochemical properties

ncreasingly complex chemistry models require thermochemical data for many species often estimated from costly first-principles DFT computations. Here we introduce the Python Group Additivity software (pGrAdd) that implements comprehensive group additivity in a simple, modular, lightweight Python package that is extensible and easy to implement. It includes 6 group additivity databases for gas species and Pt(111) adsorbates allowing users to immediately compute thermochemical properties for a wide range of molecules and build new databases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

gcm_eval (Global Climate Model Evaluation) [SWR-24-37]

The interplay between energy, climate, and weather is becoming more complex as our changing climate continues to affect the weather we experience which in turn drives changes in the ever increasing share of renewable energy generation and energy demand. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and often subjective process. There is no single perfect climate model or dataset for all applications. In this repository, we include software and the corresponding assessments of various global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP6), evaluating their skills with respect to the historical climate and comparing of their future projections of climate change. We focus on variables that directly affect the energy system including the representation of extreme values that can drive grid resilience events. The objective of this repository is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and datasets in subsequent work.

Buster, Grant↗
DOE OSTI2024

DECIDER

This software offers methods and functions for building failure detectors for deep image classification models with the aid of vision-language models and LLMs. It includes functionalities for training baseline image classifiers, debiasing classifiers using vision-language models and LLMs, evaluating failure between models along with baselines. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2023

SABATH: Surrogate AI Benchmarking Applications' Testing Harness

SABATH provides benchmarking infrastructure for evaluating scientific ML/AI models. It contains support for scientific machine learning surrogates from external repositories. The software dependences are explicitly exposed in the surrogate model definition, which allows the use of advanced optimization, communication, and hardware features.

Luszczek, Piotr [Univ. of Tennessee, Knoxville, TN↗
DOE OSTI2023

Thermal Modeling Aluminum-Clad Spent Fuel Dry Storage Configurations [Slides]

A presentation covering the work done by the C430 Department at Idaho National Lab on thermal concerns for extended dry storage of aluminum clad spent nuclear fuel (ASNF). Computational fluid dynamics software Star CCM+ was used to model hypothetical accident conditions applied to a dry storage configuration containing ASNF. The hypothetical accident conditions being examined were a fire in which the dry storage configuration is fully engulfed in flames and a situation in which the vent ports of the overpack are completely blocked.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗
DOE OSTI2026

Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next

Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AIaugmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains.

ab initio calculations↗
DOE OSTI2019

Extremum Seeking Library (ESL) v1.0

The software developed is a library for the Modelica language. The library includes different extremum seeking control algorithms and examples of possible usecases. It is also intended to export the models as so called Functional Mock-up Units, which are models supported by any kind of software which supports the Functional Mock-up Interface standard.

Mueller, Joscha↗
DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improving Dose Modeling With Dynamic Modeling Tools [Slides]

Utilities aiming for higher fuel enrichment for power uprates or extended operation times before refueling must conduct a new dose analysis. Current conservative dose estimation standards may cause utilities to exceed regulatory limits for proposed increased fuel enrichment. A more accurate modeling of doses from reactor accidents can lower these conservative assumptions. Prescott et al. (2022) demonstrated that the Event Modeling Risk Assessment using Linked Diagrams (EMRALD) software tool, developed at Idaho National Laboratory (INL), can be coupled with the Modular Accident Analysis Program (MAAP5) for dynamic accident analysis in reactor plants. EMRALD forms models of potential accident scenarios, while MAAP5 simulates the accident progression and dose consequences. By integrating these software tools with utility-specific data, a more precise estimation of dose consequences from plant accidents can be achieved. Preliminary findings indicate that EMRALD provides accurate mean core damage frequencies for generalized accident scenarios. Future work includes expanding the model to account for plant-specific data and mitigation factors.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Reassessing energy deposition for the ITER 5 MA vertical displacement event with an improved DINA model

The beryllium (Be) main chamber wall interaction during a 5 MA/1.8 T upward, unmitigated VDE scenario, first analysed in [J. Coburn et al., Phys. Scr. T171 (2020) 014076] for ITER, has been re-evaluated using the latest energy deposition analysis software. Updates to the DINA disruption model are summarized, including an improved numerical convergence for the 0D power balance, limitations on the safety factor within the plasma core, and the choice to maintain a constant plasma +halo poloidal cross-section. Such updates result in a broad halo region and higher radiated power fractions compared to previous models. The new scenario lasts for ~75 ms and deposits ~29 MJ of energy, with the radial distribution of parallel heat flux q‖(r)resembling an exponential falloff with an effective λ_q=75-198 mm. A maximum halo width w_h of 0.52 m at the outboard midplane is observed. SMITER field line tracing and energy deposition simulations calculate a q_{⊥,max} of ~83 MW/m^2 on the upper first wall panels (FWP). Heat transfer calculations with the MEMOS-U code show that the FWP surface temperature reaches ~1000 K, well below the Be melt threshold. Variations of this 5 MA scenario with Be im-purity densities from 0 to 3∙10^19 m^-3 also remain below the melt threshold despite differences in energy deposition and duration. These results are in contrast to the early study which predicted melt damage to the first wall [J. Coburn et al., Phys. Scr. T171 (2020) 014076], and emphasize the importance of accurate models for the halo width w_h and the heat flux distribution q‖(r)within that halo width. The 2020 halo model in DINA has been compared with halo current experiments on COMPASS, JET, and Alcator C-Mod, and the preliminary results build confidence in the broad halo width predictions. Results for the 5 MA VDE are compared with those for a 15 MA equivalent, generated using the new DINA model. At the higher current, significant melting of the upper FWP is to be expected.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

An Open-Source Python Library for Modeling LETID and LID in Silicon Solar Cells and Wafers

This poster demonstrates an (in development) open-source software library, written in python, to aid in modeling and understanding light- and elevated temperature-induced degradation (LETID) and boron-oxygen light-induced degradation (B-O LID) in silicon solar cells and wafers. We discuss the underlying equations and necessary input parameters for constructing realistic models. We demonstrate several use cases for the library, including modeling LETID progression in scenarios like indoor, accelerated tests and outdoor field deployment. We also demonstrate using the library to model B-O LID, and we demonstrate using the library to model degradation in passivated wafers, rather than solar cells.

LETID↗
DOE OSTI2022

Aging of galaxies along the morphological sequence, marked by bulge growth and disk quenching

Aims: We revisit the color bimodality of galaxies using the extensive EFIGI morphological classification of nearby galaxies. Methods: The galaxy profiles from the Sloan Digital Sky Survey (SDSS) gri images were decomposed as a bulge and a disk by controlled profile modeling with the Euclid SourceXtractor++ software. The spectral energy distributions from our resulting gri SDSS photometry complemented with Galaxy Evolution Explorer (GALEX) NUV photometry were fitted with the ZPEG software and PEGASE.2 templates in order to estimate the stellar masses and specific star formation rates (sSFR) of whole galaxies as well as their bulge and disk components. Results: The absolute NUV-r color versus stellar mass diagram shows a continuous relationship between the present sSFR of galaxies and their stellar mass, which spans all morphological types of the Hubble sequence monotonously. Irregular galaxies to intermediate-type Sab spirals make up the “Blue Cloud” across 4 orders of magnitude in stellar mass but a narrow range of sSFR. This mass build-up of spiral galaxies requires major mergers, in agreement with their frequently perturbed isophotes. At high mass, the Blue Cloud leads to the “Green Plain”, dominated by S0a and Sa early-type spirals. It was formerly called the “Green Valley”, due to its low density, but we rename it because of its wide stretch and nearly flat density over ~2 mag in NUV-r color (hence sSFR), despite a limited range of stellar mass (1 order of magnitude). The Green Plain links up the “Red Sequence”, containing all lenticular and elliptical galaxies with a 2 order of magnitude mass interval, and systematically higher masses for the ellipticals. We confirm that the Green Plain cannot be studied using u - r optical colors because it is overlayed by the Red Sequence, hence NUV data are necessary. Galaxies across the Green Plain undergo a marked growth by a factor 2 to 3 in their bulge-to-total mass ratio and a systematic profile change from pseudo to classical bulges, as well as a significant reddening due to star formation fading in their disks. The Green Plain is also characterized by a maximum stellar mass of 10 11.7 M ⊙ beyond which only elliptical galaxies exist, hence supporting the scenario of ellipticals partly forming by major mergers of massive disk galaxies. Conclusions: The EFIGI attributes indicate that dynamical processes (spiral arms and isophote distortions) contribute to the scatter of the Main Sequence of star-forming galaxies (Blue Cloud), via the enhancement of star formation (flocculence, HII regions). The significant bulge growth across the Green Plain confirms that it is a transition region, and excludes a predominantly quick transit due to rapid quenching. The high frequency of bars for all spirals as well as the stronger spiral arms and flocculence in the knee of the Green Plain suggest that internal dynamics, likely triggered by flybys or (mainly minor) mergers, may be the key to the bulge growth of massive disk galaxies, which is a marker of the aging of galaxies from star forming to quiescence. The Hubble sequence can then be considered as an inverse sequence of galaxy physical evolution.

79 ASTRONOMY AND ASTROPHYSICS↗