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At least 1,315 records · Page 73

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DOE OSTI2019

Thermal Modeling of the TN-32B Cask for the High Burnup Spent Fuel Data Project

This report describes thermal modeling done for the High Burnup Dry Storage Research Project that is being performed by the U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) research and development (R&D) program. The purpose of this project to investigate the performance of high-burnup spent nuclear fuel in dry storage. Part of this project is a demonstration test being performed with a storage module in the North Anna Nuclear Power Station’s Independent Spent Fuel Storage Installation (ISFSI). The storage module selected for this demonstration is an Orano TN-32B High Burnup cask (Fig. S-1) which has been loaded with fuel assemblies from the North Anna spent fuel pool. The main goals of this test are to provide confirmatory data for model validation and potential improvement, support license renewals and new licenses for ISFSIs, and support transportation licensing for high burnup spent nuclear fuel (EPRI 2014).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Boiling Closure Model Validation/Development Effort with TAMU Experimental Data

This report describes a model validation and developmental effort in INL based on the analysis of experimental data taken from TAMU subcooled flow boiling facility. This report first describes briefly the test loop, experimental strategy, measurement techniques, data quality achieved, and test boundary conditions. Then, two main topics are discussed based on the experimental observation, bubble departure frequency and sliding bubble characteristics. The comparison of experimental data with the prediction of bubble departure frequency models shows that the predictive capability of existing models depends strongly on flow conditions. Also, the best performance region of each bubble departure frequency model could be determined from this analysis. Another critical achievement through this study is that the limitation of existing CFD boiling models dealing with sliding bubble effect was clearly identified. Specifically, the experimental observation indicates that the wall area influenced by sliding bubbles is not just a function of bubble size, but also depends strongly on sliding bubble trajectory and sliding distance. As a result, the bubble influence factor ( K ) for the small size of sliding bubbles was often found substantially higher than those reported in literature. In this regard, an effort is currently being made in INL to develop a new model to predict the sliding bubble effect on wall heat transfer based on the insight achieved from TAMU subcooled flow boiling experiment.

42 ENGINEERING↗

DOE OSTI2016

Benchmark Progress towards TREAT M8CAL Core Loading to Support Validation of TREAT Operations

Benchmark experiment data is necessary to validate modeling and simulation activities to support Transient Reactor Experiment Test (TREAT) Facility restart and operations. Key measurements of interest include criticality, control rod worth, excess reactivity, and shutdown margin for varying core loading sizes. Benchmark evaluations are being developed according the guidelines provided in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook) that can also support advanced modeling and simulation activities, new experiment design for advanced reactors and accident tolerant fuel, and potential low-enriched uranium conversion of the TREAT reactor. This summary discusses development activities supporting a benchmark evaluation of the TREAT M8CAL core loading, which will be submitted for peer-review and publication in the IRPhEP Handbook with the identifier TREAT-FUND-RESR-002. Detailed models of TREAT were developed and evaluated to prepare benchmark models of the minimum critical mass configuration. Calculations were performed using MCNP6.1 with ENDF/B-VII.1 nuclear data libraries. Discussion of results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Modeling Shock-Driven Reaction in Low-Density Non-energetic polymeric materials

Shock experiments on low density polyurethane foams reveal evidence of reaction at low impact pressures. However, these reaction thresholds are not evident over the low pressures reported for historical Hugoniot data of highly distended polyurethane at densities below 0.1 g/cc. To fill this gap, impact data for PMDI foam with a density of 0.087 g/cc were acquired for model validation. An equation of state (EOS) was developed to predict the shock response of these highly distended materials over the full range of impact conditions representing compaction of the inert material, low-pressure decomposition, and compression of the reaction products. A tabular SESAME EOS of the reaction products was generated using the JCZS database in the TIGER equilibrium code. In particular, the Arrhenius Burn EOS, a two-state model which transitions from an unreacted to a reacted state using Arrhenius kinetics, as implemented in the shock physics code CTH, was modified to include a statistical distribution of states. Hence, a single EOS is presented that predicts the onset to reaction due to shock loading in PMDI-based polyurethane foams. This methodology was also used to predict the anomalous compaction of PMDI foams over published data sets from 0.087 to 0.87 g/cc, and solid Polyurethane at a theoretical maximum density (TMD) of 1.264 g/cc. Likewise, similar modeling techniques were used to predict the performance of SX-358 foam, an RTV-based stress cushion material at a nominal density of 0.41 g/cc, and the matrix material, with properties similar to Sylgard, at 1.1 g/cc. At the start of this study, data were only available at a single impact condition below the threshold for reaction; hence, the decomposition of this material at higher pressures was revealed as a significant finding of this work. The decomposition of SX-358 at higher impact pressures to product species including solid, liquid, and gaseous molecules was estimated with thermochemical equilibrium calculations using TIGER. with thermochemical equilibrium calculations using TIGER. This modeling approach, developed for PMDI foam, was shown to predict gas gun data, acquired as part of this study, up to pressures of 14 GPa. Furthermore, additional phase transitions were predicted in the product species under shock compression. To date, this study is the first known to the authors that demonstrates and successfully predicts the decomposition of these low-density polymer-based foams using a single model applicable to a broad range of impact loading conditions.

36 MATERIALS SCIENCE↗

NASA NTRS2011

International Space Station Passive Thermal Control System Analysis, Top Ten Lessons-Learned

The International Space Station (ISS) has been on-orbit for over 10 years, and there have been numerous technical challenges along the way from design to assembly to on-orbit anomalies and repairs. The Passive Thermal Control System (PTCS) management team has been a key player in successfully dealing with these challenges. The PTCS team performs thermal analysis in support of design and verification, launch and assembly constraints, integration, sustaining engineering, failure response, and model validation. This analysis is a significant body of work and provides a unique opportunity to compile a wealth of real world engineering and analysis knowledge and the corresponding lessons-learned. The analysis lessons encompass the full life cycle of flight hardware from design to on-orbit performance and sustaining engineering. These lessons can provide significant insight for new projects and programs. Key areas to be presented include thermal model fidelity, verification methods, analysis uncertainty, and operations support.

Iovine, John↗

NASA NTRS2018

International Space Station Passive Thermal Control System Top Ten Lessons-Learned

Final document not an Abstract attached. The International Space Station (ISS) has been on-orbit for nearly 20 years, and there have been numerous technical challenges along the way from design to assembly to on-orbit anomalies and repairs. The Passive Thermal Control System (PTCS) management team has been a key player in successfully dealing with these challenges. The PTCS team performs thermal analysis in support of design and verification, launch and assembly constraints, integration, sustaining engineering, failure response, and model validation. This analysis is a significant body of work and provides a unique opportunity to compile a wealth of real world engineering and analysis knowledge and the corresponding lessons-learned. The PTCS lessons encompass the full life cycle of flight hardware from design to on-orbit performance and sustaining engineering. These lessons can provide significant insight for new projects and programs. Key areas to be presented include thermal model fidelity, verification methods, analysis uncertainty, and operations support.

Iovine, John V.↗

NASA NTRS2018

International Space Station Passive Thermal Control System Top Ten Lessons-Learned

Iovine, John V.↗

DOE OSTI2016

Mechanical performance of short models for MQXF, the Nb3Sn low-β quadrupole for the Hi-Lumi LHC

In the framework of the Hi-Lumi LHC Project, CERN and U.S. LARP are jointly developing MQXF, a 150-mm aperture high-field Nb3Sn quadrupole for the upgrade of the inner triplet of the low-beta interaction regions. The magnet is supported by a shell-based structure, providing the preload by means of bladder-key technology and differential thermal contraction of the various components. Two short models have been produced using the same cross section currently considered for the final magnet. The structures were preliminarily tested replacing the superconducting coils with blocks of aluminum. This procedure allows for model validation and calibration, and also to set performance goals for the real magnet. Strain gauges were used to monitor the behavior of the structure during assembly, cool down and also excitation in the case of the magnets. The various structures differ for the shell partitioning strategies adopted and for the presence of thick or thin laminations. This study presents the results obtained and discusses the mechanical performance of all the short models produced up to now.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Big Area Additive Manufacturing (BAAM) Materials Development and Reinforcement with Advanced Composites

Additive manufacturing (AM) by extrusion deposition is rapidly developing as an energy efficient process that greatly reduces development time for new products including vehicle structures. Development of reinforced thermoplastic compounds that are specifically tailored to the AM process and possess the properties necessary for engineering structures is currently underway. This project focused on developing a fundamental understanding of printability by performing a number of polymer characterization tests, initial printability screenings, and basic mechanical property determinations for a number of compounds. Structural evaluations of more complex printed articles were also performed in order to build a database of mechanical performance for validation of structural simulations planned for Phase II. Included in the structural tests were components that were reinforced using a number of methods including over-wrapping with dry fabric and resin infusing to form an advanced composite having the shape of the 3D printed structure. The results indicate a number of factors that affect printability of materials, and initial structural analysis shows that the data generated will be useful for model validation.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Three-Dimensional Navier-Stokes Simulations with Two-Equation Turbulence Models of Intersecting Shock-Waves/Turbulent Boundary Layer at Mach 8.3

An investigation of the numerical simulation with two-equation turbulence models of a three-dimensional hypersonic intersecting (SWTBL) shock-wave/turbulent boundary layer interaction flow is presented. The flows are solved with an efficient implicit upwind flux-difference split Reynolds-averaged Navier-Stokes code. Numerical results are compared with experimental data for a flow at Mach 8.28 and Reynolds number 5.3x10(exp 6) with crossing shock-waves and expansion fans generated by two lateral 15 fins located on top of a cold-wall plate. This experiment belongs to the hypersonic database for modeling validation. Simulations show the development of two primary counter-rotating cross-flow vortices and secondary turbulent structures under the main vortices and in each corner singularity inside the turbulent boundary layer. A significant loss of total pressure is produced by the complex interaction between the main vortices and the uplifted jet stream of the boundary layer. The overall agreement between computational and experimental data is generally good. The turbulence modeling corrections show improvements in the predictions of surface heat transfer distribution and an increase in the strength of the cross-flow vortices. Accurate predictions of the outflow flowfield is found to require accurate modeling of the laminar/turbulent boundary layers on the fin walls.

Bardina, J. E.↗

DOE OSTI2026

Downloadable Dynamometer Database (D3): Public Test Data on Advanced-Technology Vehicles

Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Benchmark Development for TREAT Minimum Critical Mass Core Loading to Support Validation of TREAT Operations

Benchmark experiment data is necessary to validate modeling and simulation activities to support Transient Reactor Experiment Test (TREAT) Facility restart and operations. Key measurements of interest include criticality, control rod worth, excess reactivity, and shutdown margin for varying core loading sizes. Benchmark evaluations are being developed according the guidelines provided in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook) that can also support advanced modeling and simulation activities, new experiment design for advanced reactors and accident tolerant fuel, and potential low-enriched uranium conversion of the TREAT reactor. This summary discusses development activities supporting a benchmark evaluation of the TREAT minimum critical mass core loading, which will be submitted for peer-review and publication in the IRPhEP Handbook with the identifier TREAT-FUND-RESR-001. Detailed models of TREAT were developed and evaluated to prepare benchmark models of the minimum critical mass configuration. Calculations were performed using MCNP6.1 with ENDF/B-VII.1 nuclear data libraries. Discussion of results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

High-resolution leaf area index maps generated from unoccupied aerial system, Teller Mile 27, Seward Peninsula, Alaska

Leaf area index (LAI), a measure of the amount of one-side leaf area per ground unit, is an important indicator of plant carbon, energy, and water cycle. In the heterogeneous Arctic landscapes, it has been challenging to accurately measure LAI across species and space needed for Earth system model validation. Here, we use multispectral unoccupied aerial systems (UASs) to scale up and map leaf area index (LAI) , in a low-Arctic tundra landscape on the Seward Peninsula, Alaska. We linked previous published LAI measurements with high-resolution, UAS-collected multispectral data collected over the region of Next Generation Ecosystem Experiments in the Arctic (NGEE Arctic)’s Teller Mile Maker 27 site in 2022 to develop random forest (RF) machine learning models to predict and map LAI. 100 RF models were developed to account for uncertainties in ground LAI plot measurements and process scaling. This dataset includes a raster (*.tif) map of the mean LAI value of the 100 RF models, a raster (*.tif) map of the standard deviation of the RF-modeled LAI data, and a user guide (*.pdf).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗

NASA NTRS2012

The Re-Analysis of Ozone Profile Data from a 41-Year Series of SBUV Instruments

In this study we present the validation of ozone profiles from a number of Solar Back Scattered Ultra Violet (SBUV) and SBUV/2 instruments that were recently reprocessed using an updated (Version 8.6) algorithm. The SBUV dataset provides the longest available record of global ozone profiles, spanning a 41-year period from 1970 to 2011 (except a 5-year gap in the 1970s) and includes ozone profile records obtained from the Nimbus-4 BUV and Nimbus-7 SBUV instruments, and a series of SBUV(/2) instruments launched on NOAA operational satellites (NOAA 09, 11, 14, 16, 17, 18, 19). Although modifications in instrument design were made in the evolution from the BUV instrument to the modern SBUV(/2) model, the basic principles of the measurement technique and retrieval algorithm remain the same. The long term SBUV data record allows us to create a consistent, calibrated dataset of ozone profiles that can be used for climate studies and trend analyses. In particular, we focus on estimating the various sources of error in the SBUV profile ozone retrievals using independent observations and analysis of the algorithm itself. For the first time we include in the metadata a quantitative estimate of the smoothing error, defined as the error due to profile variability that the SBUV observing system cannot inherently measure. The magnitude of the smoothing error varies with altitude, latitude, season and solar zenith angle. Between 10 and 1 hPa the smoothing errors for the SBUV monthly zonal mean retrievals are of the order of 1 %, but start to increase above and below this layer. The largest smoothing errors, as large as 15-20%, were detected in in the troposphere. The SBUV averaging kernels, provided with the ozone profiles in version 8.6, help to eliminate the smoothing effect when comparing the SBUV profiles with high vertical resolution measurements, and make it convenient to use the SBUV ozone profiles for data assimilation and model validation purposes. The smoothing error can also be minimized by combining layers of data, and we will discuss recommendations for this approach as well. The SBUV ozone profiles have been intensively validated against satellite profile measurements obtained from the Microwave Limb Sounders (MLS) (on board the UARS and AURA satellites), Stratospheric Aerosol and Gas Experiment (SAGE) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Also, we compare coincident and collocated SBUV ozone retrievals with observations made by ground-based instruments, such as microwave spectrometers, lidars, Umkehr instruments and balloon-borne ozonosondes. Finally, we compare the SBUV ozone profiles with output from the NASA GSFC GEOS-CCM model. In the stratosphere between 25 and 1 hPa the mean biases and standard deviations are within 5% for monthly mean ozone profiles. Above and below this layer the vertical resolution of the SBUV algorithm decreases and the effects of vertical smoothing should be taken into account. Though the SBUV algorithm has a coarser vertical resolution in the lower stratosphere and troposphere, it is capable of precisely estimating the integrated ozone column between the surface and 25 hPa. The time series of the tropospheric - lower stratospheric ozone column derived from SBUV agrees within 5% with the corresponding values observed by an ensemble of ozone sonde stations in North Hemisphere. Drift of the ozone time series obtained from each SBUV(/2) instrument relative to ground based and satellite measurements are evaluated and some features of individual SBUV(l2) instruments are discussed. In addition to evaluating individual instruments against independent observations, we also focus on the instrument to instrument consistency in the series. Overall, Version 8.6 ozone profiles obtained from two different SBUV(l2) instruments compare within a couple of percent during overlap periods and are consistently varying in time, with some exceptions. Some of the noted discrepancies might bssociated with ozone diurnal variations, since the difference in the local time of the observations for a pair of SBUV(l2) instruments could be several hours. Other issues include the potential short-term drift in measurements as the instrument orbit drifts, and measurements are obtained at high solar zenith angles (>85 ). Based on the results of the validation, a consistent, calibrated dataset of SBUV ozone profiles has been created based on internal calibration only.

Kramarova, Natalya↗

DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗

NASA NTRS1985

Application of CFD techniques toward the validation of nonlinear aerodynamic models

Applications of Computational fluid dynamics (CFD) methods to determine the regimes of applicability of nonlinear models describing the unsteady aerodynamic responses to aircraft flight motions are described. The potential advantages of computational methods over experimental methods are discussed and the concepts underlying mathematical modeling are reviewed. The economic and conceptual advantages of the modeling procedure over coupled, simultaneous solutions of the gasdynamic equations and the vehicle's kinematic equations of motion are discussed. The modeling approach, when valid, eliminates the need for costly repetitive computation of flow field solutions. For the test cases considered, the aerodynamic modeling approach is shown to be valid.

Schiff, L. B.↗