Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model of Models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of Modeled to Measured Spectra using MCNP and GADRAS to Benchmark and Contrast Modeling Limitations

The desire to improve nuclear material detection through portal monitors and other low resolution detectors has led to interest in benchmarking the performance of radiation transport codes. These codes can be used to generate a variety and quantity of spectra that may be cost prohibitive to measure directly. Particular characteristics of typical detection scenarios were isolated to compare the performance of radiation transport codes to laboratory experiments. These benchmark experiments were performed to validate simulations in a number of key areas. These experiments included high areal density configurations in both one- and three-dimensional configurations. In addition, off-angle measurements were conducted to investigate the impact of detector response assumptions on complex geometries, for example where an unknown source is not physically collocated with an apparent hot spot. Furthermore, by examining both simple shielding and backscatter configurations, isolation of the scatter emanating from the object and environmental background scatter contributions to the spectra could be elucidated. A series of experiments were performed in which the order of shielding layers and the thickness were evaluated to examine the ability of the simulations to address these variables. Experiments with increasing thickness of polyethylene around a neutron source also allows for examinations of the ability of the transport simulations to properly model n,γ reactions. Finally, depleted uranium (DU) measurements have more complicated spectra than the cobalt-60 and consequently allow for investigations into the ability of transport simulations to model self-shielding. In each case, the results of the experiments were compared to MCNP simulations to judge the performance of the code, as was done previously with GADRAS 18.7.9 simulations [7]. All of the benchmark measurements were taken with a liquid nitrogen cooled 140% high purity germanium (HPGe) detector with a bismuth collimator that had the front tin filter removed. The measurement location and detector configuration were subsequently used for all experimental configurations. The remainder of this report details the experimental measurements, Section 2 and the MCNP model, Section 3. Comparisons between the experimental results and MCNP-generated spectra are performed, Section 4. Section 5 compares the results obtained with MCNP with the previously reported results obtained with GADRAS [2]. Finally, Section 6 presents the conclusions.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

SNL will be able to provide well-calibrated models to weapons systems analysts by integrating an engine for constitutive model calibration with the GRANTA materials database

Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FY19 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Preliminary Testing of a Selected Process Model

Sandia National Laboratories continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project. This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY19 task included continued operation of the cluster; maintenance of the TSPA-LA models (with Gold Sim 9.60.300); preliminary assessment of the status of the Infiltration Model (a process model that feeds the TSPA-LA). In addition, precautionary actions were needed to extend the life of the cluster hardware. To do that, three new nodes were added to the cluster. In the event any of the original nodes fail they will be replaced with the new nodes, thereby maintaining the core capability. The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mesoscale modeling to inform Bison models of accident tolerant fuel concepts

U 3 Si 2 and doped UO 2 have been under investigation in recent years as potential accident-tolerant fuel concepts. In this report, lower-length scale studies of these fuel concepts carried out during Fiscal Year 2020 are detailed. U 3 Si 2 is a potential accident-tolerant fuel that shows promise due to its high thermal conductivity and higher uranium density relative to UO 2 . However, its swelling and fission gas release behavior in light water reactor (LWR) conditions is relatively unknown. To provide mechanistic insight and determine parameters for engineering-scale fuel performance modeling of pellet-form U 3 Si 2 , phase-field simulations of the growth, interconnection, and venting of intergranular fission gas bubbles were performed. The fractional coverage of the grain boundary and the fraction of bubble area that is vented were calculated as a function of time. From the simulation data, the fractional grain boundary coverage at saturation, an important parameter needed in engineering-scale modeling of swelling and fission gas release, was determined. Multiple simulations were run to determine the uncertainty in the calculated value. The effect of model assumptions and input parameters that are not well known was evaluated. Simulation results are compared to related theoretical and computational work. Based on the simulation results, a value of 0.60 for the fractional grain boundary coverage at saturation is recommended for U 3 Si 2 fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Geochemical Modeling Using OLI Systems Mixed Solvent Electrolyte Model in Support of the Waste Isolation Pilot Plant

Aqueous electrolyte thermodynamic models were developed for synthetic Salado (GWB) and Castile (ERDA-6) brines to inform the experimental design and to facilitate laboratory data interpretation in geochemical studies supporting disposal in the Waste Isolation Pilot Plant (WIPP). The thermodynamic models, which include the relevant vapor-liquid-solid equilibria for the oxidation-reduction conditions of interest, enable computation of system parameters such as activity-based pH, E h [vs. standard hydrogen electrode (SHE)], ionic strength, total dissolved solids, and solution density; the chemical speciation of all phases; saturation tendencies for thermodynamically feasible solids; and changes in brine chemistry because of the addition of H 2 (g), argon, MgO, Fe(0), and other waste form components. The impacts of oxidation-reduction potential (ORP) and pH on aqueous speciation and dominant solid phases were also assessed with the aid of real-solution Pourbaix diagrams for key elements of interest. In addition to the OLI modeling results, limited measurement data for pH and ORP standards are provided, analyzed, and discussed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Implementation of a turbine farm model into the Energy Exascale Earth System Model for investigation and quantification of global climate impacts

Although there has been widespread deployment of wind farms in the United States, and plans to continue deployment into the future, the complete effects of wind farms on Earth systems are not well understood. The work performed here has incorporated wind farm models into the Energy Exascale Earth System Model (E3SM) capable of simulating the effects of extracting momentum from the atmospheric flow field using power generating wind farms. This new capability will allow scientists to quantify the impacts of wind farm induced changes on Earth systems by exploiting E3SM’s ability to couple atmospheric, oceanic, and biogeochemical (BGC) models on a global scale and monitor precipitation levels, extreme weather events, soil moisture content and jet stream location over decades-long time periods. This tool will be used to inform decision making on wind farm citing and will contribute to the Lab’s ability to assess energy technology impacts on the environment and evaluate the trade-offs between energy infrastructure investments and their impacts on natural systems.

17 WIND ENERGY↗
DOE OSTI2026

Modeling Material Interfaces with the Six-Equation Model in FLEXO

FLEXO is a multiphysics code developed at Sandia National Laboratories for predictive simulation of pulsed power target physics with extended magnetohydrodynamics modeling. Given this application space, FLEXO must be able to perform high-fidelity simulations of pulsed-power systems that include multiple materials. This work presents the augmentation of FLEXO with a six-equation model for multimaterial flow. We design and analyze discretizations of the six-equation model, emphasizing desirable properties for robust multimaterial simulation. We also present solution limiting, bounds preservation, and pressure equilibration/relaxation techniques that enhance the robustness of the overall multimaterial scheme. We present a suite of test problems to document these new capabilities of the FLEXO code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Core Mass Estimates in Strong Lensing Galaxy Clusters: A Comparison between Masses Obtained from Detailed Lens Models, Single-halo Lens Models, and Einstein Radii

The core mass of galaxy clusters is both an important anchor of the radial mass distribution profile and probe of structure formation. With thousands of strong lensing galaxy clusters being discovered by current and upcoming surveys, timely, efficient, and accurate core mass estimates are needed. Here, we assess the results of two efficient methods to estimate the core mass of strong lensing clusters: the mass enclosed by the Einstein radius (M(<θ E ) where θ E is approximated from arc positions; Remolina González et al. 2020), and single-halo lens model (M SHM ; Remolina González et al. 2021), against measurements from publicly available detailed lens models (M DLM ) of the same clusters. We use data from the Sloan Giant Arc Survey, the Reionization Lensing Cluster Survey, the Hubble Frontier Fields, and the Cluster Lensing and Supernova Survey with Hubble. We find a scatter of 18.2% (8.2%) with a bias of -7.1% (1.0%) between M corr (<θ arcs ) (M SHM ) and M DLM . Last, we compare the statistical uncertainties measured in this work to those from simulations. This work demonstrates the successful application of these methods to observational data. As the effort to efficiently model the mass distribution of strong lensing galaxy clusters continues, we need fast, reliable methods to advance the field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model

Compound riverine and coastal flooding is usually driven by complex interactions among meteorological, hydrological, and ocean extremes. However, existing efforts to model this phenomenon often do not integrate hydrological processes across atmosphere–land–river–ocean systems, leading to substantial uncertainties that have not been fully examined. To bridge this gap, we leverage the new capabilities of the Energy Exascale Earth System Model (E3SM) that enable a multi-component framework that integrates coastally refined atmospheric, terrestrial, and oceanic components. We evaluate compound uncertainties arising from two-way land–river–ocean coupling in E3SM and track the cascading meteorological and hydrological uncertainties through ensemble simulations over the Delaware River basin and estuary during Hurricane Irene (2011). Our findings highlight the importance of two-way river–ocean coupling to compound flood modeling and demonstrate E3SM's capability in capturing compound flood extent near the coast, with a hit rate over 0.75. Our study shows the growing uncertainties that transition from atmospheric forcings to flood distribution and severity. Furthermore, an analysis based on artificial neural networks is used to assess the roles of hydrological drivers, such as infiltration and soil moisture, in the generation of compound flooding. The response of compound floods to tropical cyclones (TCs) is found to be susceptible to these often overlooked drivers. For instance, the flooded area could increase more than 2-fold (∼2.4) if Hurricane Irene were preceded by an extreme antecedent soil moisture condition (AMC). The results not only support the use of a multi-component framework for interactive flooding processes, but also underscore the necessity of broader definitions of compound flooding that encompass the simultaneous occurrence of intense precipitation, storm surge, and high AMC during TCs.

Feng, Dongyu [Pacific Northwest National Laborator↗
DOE OSTI2024

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Modeling the use of mobile modular gas samplers in near-field detection

The Wireless Independent Noble Gas Sampler (WINGS) is a mobile, modular gas sampling system designed for use in low infrastructure environments. In the case of a suspected underground nuclear explosion (UNE), WINGS units could be deployed to detect noble gases emanating from the suspected explosion site or identify gases originating from another source entering the local area from offsite. In conclusion, this work uses the atmospheric transport modeling tool inline WRF-HYSPLIT to test the effectiveness of WINGS units around a suspected UNE site as a function of sampler density and distance from the emission point of gases from a UNE.

Air sampling↗
DOE OSTI2020

Cryo-based Structural Characterization and Growth Model of Salt Film on Metal

Metal salt films play a critical role in high rate dissolution processes such as within an active pit. However, structural and compositional characterization of salt films is challenging owing to the dynamic nature of the dissolving interface. Here, the salt film formed on SS304 one-dimensional artificial pit surface was directly characterized using flash freezing and cryo-based focused ion beam/scanning electron microscopy. The salt film exhibits a porous structure. Ni is co-located with Fe in the salt film, indicating the formation of co-precipitated salt containing Fe2+ and Ni2+. Additionally, a salt film growth model is presented based on ion transport.

Salt film, Pitting corrosion, Cryogenic, Cryo-FIB/↗
DOE OSTI2024

Styrene Thermal Decomposition and Its Reaction with Acetylene under Shock Tube Pyrolysis Conditions: an Experimental and Kinetic Modeling Study

Styrene is an important compound for polymer production and a key intermediate in gas-phase kinetics of polycyclic aromatic hydrocarbons (PAHs). For the first time, the pyrolysis of styrene with and without the presence of acetylene is investigated in a single-pulse shock tube coupled to gas chromatography and mass spectrometry. For each reaction system, quantitative speciation profiles are probed within the temperature range of 1100-1730 K, nominal pressure of 20 bar, and reaction duration of similar to 4 ms. A kinetic model is built to simulate the results. The model explains how styrene is consumed under high-pressure pyrolytic conditions, how the secondary chemistry of intermediate products affect subsequent PAH formation, and how acetylene addition alters the reaction pathways. Throughout the temperature range, styrene breakdown is dominated by the bimolecular interaction between styrene and hydrogen atom, which produces benzene+vinyl or phenyl and ethylene through the stabilization of 2-phenylethyl and its subsequent dissociation. As a result, large amounts of phenyl accumulate, which react with styrene to form C14H12 species while simultaneously releasing H atoms through addition/elimination reactions. The reactivity of fuel consumption is preserved by the regeneration of H atoms as chain carriers. Several C14H10 compounds are formed as a result of the following breakdown of the C14H12 isomers, particularly stilbene, 1,1-diphenyl ethylene, and 9-methyl-9H-fluorene. The presence of acetylene as a co-reactant with styrene allows the Hydrogen-Abstraction-Acetylene-Addition (HACA) pathway to proceed from phenyl radical to enhance the production of phenylacetylene at very low temperatures and acenaphthylene. This hinders the formation of C14H12 isomers, exclusive products from pure styrene dissociation by competing with the styrene+phenyl routes.

Kinetic Modeling↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

NMR Database of Lignin and Cell Wall Model Compounds

This database was designed to provide a coherent, single source of NMR data of lignin and other plant cell wall model compounds. The database exists as an Adobe pdf cross-platform file for viewing and printing. This is the latest public version of the Database, version 2024/08 updated from the 2009 version.

cell wall↗