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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Global Food Demand Model for the Assessment of Complex Human-Earth Systems

Demand for agricultural products is an important problem in climate change economics. Food consumption will shape and shaped by climate change and emissions mitigation policies through interactions with bioenergy and afforestation, two critical issues in meeting international climate goals such as two-degrees. We develop a model of food demand for staple and nonstaple commodities that evolves with changing incomes and prices. The model addresses a long-standing issue in estimating food demands, the evolution of demand relationships across large changes in income and prices. We discuss the model, some of its properties and limitations. We estimate parameter values using pooled cross-sectional-time-series observations and the Metropolis Monte Carlo method and cross-validate the model by estimating parameters using a subset of the observations and test its ability to project into the unused observations. Finally, we apply bias correction techniques borrowed from the climate-modeling community and report results.

climate change, fo, in↗
DOE OSTI2020

The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Linear Time-Invariant Models of a Large Cumulus Ensemble

Abstract Methods in system identification are used to obtain linear time-invariant state-space models that describe how horizontal averages of temperature and humidity of a large cumulus ensemble evolve with time under small forcing. The cumulus ensemble studied here is simulated with cloud-system-resolving models in radiative–convective equilibrium. The identified models extend steady-state linear response functions used in past studies and provide accurate descriptions of the transfer function, the noise model, and the behavior of cumulus convection when coupled with two-dimensional gravity waves. A novel procedure is developed to convert the state-space models into an interpretable form, which is used to elucidate and quantify memory in cumulus convection. The linear problem studied here serves as a useful reference point for more general efforts to obtain data-driven and interpretable parameterizations of cumulus convection.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Monsoonal MCS Initiation, Rainfall, and Diurnal Gravity Waves over the Bay of Bengal: Observation and a Linear Model

Abstract Previous observational studies have indicated that mesoscale convective systems (MCSs) contribute the majority of precipitation over the Bay of Bengal (BoB) during the summer monsoon season, yet their initiation and propagation remain incompletely understood. To fill this knowledge gap, we conducted a comprehensive study using a combination of 20-yr satellite observations, MCS tracking, reanalysis data, and a theoretical linear model. Satellite observations reveal clear diurnal propagation signals of MCS initiation frequency and rainfall from the west coast of the BoB toward the central BoB, with the MCS rainfall propagating slightly slower than the MCS initiation frequency. Global reanalysis data indicate a strong association between the offshore-propagating MCS initiation frequency/rainfall and diurnal low-level wind perturbations, implying the potential role of gravity waves. To verify the hypothesis, we developed a 2D linear model that can be driven by realistic meteorological fields from reanalysis. The linear model realistically reproduces the characteristics of offshore-propagating diurnal wind perturbations. The wind perturbations, as well as the offshore propagation signals of MCS initiation frequency and rainfall, are associated with diurnal gravity waves emitted from the coastal regions, which in turn are caused by the diurnal land–sea thermal contrast. The ambient wind speed and vertical wind shear play crucial roles in modulating the timing, propagation, and amplitude of diurnal gravity waves. Using the linear model and satellite observations, we further show that the stronger monsoonal flows lead to faster offshore propagation of diurnal gravity waves, which subsequently control the offshore propagation signals of MCS initiation and rainfall. Significance Statement Rainfall over the Bay of Bengal (BoB) is primarily contributed by large and organized rainfall systems in the summer monsoon season. During this season, these systems are commonly observed over the east coast of India around midnight, the western BoB in the morning, and the central BoB in the afternoon. This eastward rainfall propagation is confirmed by observations, reanalysis data, and a theoretical model to have a strong association with atmospheric diurnal gravity waves. These waves are caused by land–sea thermal contrast and can trigger rainfall systems over the offshore regions. We also found that the diurnal gravity waves, as well as their triggered rainfall systems, can be greatly modulated by the large-scale monsoonal flows. All the above findings improve our understanding of diurnal rainfall cycle over the BoB and may contribute to the future improvement of rainfall forecast over the region.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

A thermodynamics-based damage model for the non-linear mechanical behavior of SiC/SiC ceramic matrix composites in irradiation and thermal environments

A damage model is developed and validated with experimental data for the non-linear mechanical behavior of SiC/SiC composite materials in nuclear applications. Cyclic thermal and mechanical loading associated with neutron irradiation effects of these composites leads to wide-spread and progressive micro-cracking that leads to loss of thermal conductivity and further enhancement of thermo-mechanical damage. A physics-based model of wide-spread micro-cracking is developed within the thermodynamic framework of continuum damage mechanics. Evolution equations for damage parameters that describe the growth of continuum damage are developed, where the material variables are obtained from experiments. The model novelty is in coupling mechanical, thermal, and irradiation damage through a consistent thermodynamic framework, including loss of thermal conductivity due to the evolution of mechanically induced micro-cracks. A number of thermo-mechanical experiments were conducted to confirm model assumptions. The model is shown to be validated with out-of-pile experiments, and then implemented using commercial finite element code COMSOL to the fuel cladding problem with normal and high radiation dose cases.

Materials Science↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2025

Manuscript model outputs, model source code, and figure scripts: The role of geomorphology in mediating biomass allocation impacts on salt-marsh resilience and carbon accumulation

This data package provides model source code (C++), model outputs, and figure generation scripts (R) needed to reproduce the following manuscript: Bruns, Nicholas E., Genevieve L. Noyce, and Matthew L. Kirwan. "The Role of Geomorphology in Mediating Biomass Allocation Impacts on Salt-Marsh Resilience and Carbon Accumulation." Estuarine, Coastal and Shelf Science 327 (December 2025): 109549. https://doi.org/10.1016/j.ecss.2025.109549. This manuscript investigates how geomorphology mediates the impact of biomass allocation shifts on salt marsh persistence and carbon (C) sequestration under sea level rise. We use a 1-D soil-column model (Kirwan and Mudd 2012) to perform experiments across a range of static root:shoot ratios (RSR = 1-4) spanning observed values. The model explicitly simulates interactions between tidal inundation, productivity, inorganic sediment deposition, and organic matter accumulation. Experiments determine whether geomorphic feedbacks amplify, dampen, or leave unchanged the ecosystem response to biomass allocation shifts. A first experiment uses constant sea level rise (2.5 mm/yr) to examine equilibrium dynamics and their influence on carbon accumulation rates across different suspended sediment concentrations (SSC). A second set of experiments calculates threshold sea level rise rates for marsh drowning across RSR and SSC combinations. Final experiments apply accelerating sea level rise scenarios (2000-2200) derived from NOAA projections (Sweet et al. 2022) to generate an envelope of expected responses, quantifying the importance of biomass allocation shifts on marsh carbon accumulation and persistence. All experiments are repeated across SSC ranging from 5-50 mg/L to investigate how these interactions vary in micro-tidal marshes with different sediment supplies. Package contents: * README.txt with detailed description of package contents and instructions for reproducing manuscript figures and model outputs * R scripts for generating all manuscript figures * C++ baseline model code from Kirwan and Mudd (2012) * C++ source code for 4 experimental model variants used in the manuscript, extending above baseline code * Model input files (.csv, .txt) including sea level rise scenarios * Model output files (.txt) used in manuscript analyses Temporal coverage: Model simulations span years 1900-2200, with accelerating sea level rise scenarios for 2000-2200. Key variables: Root:shoot ratio, suspended sediment concentration, carbon accumulation rate, vertical accretion rate, marsh elevation, inundation depth, threshold sea level rise rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

ABSTRACT The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray-path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground-motion simulations. Toward this goal, we introduce the probability graphical model (PGM), combining velocity models with heterogeneous resolutions and nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM’s adaptive weight selection facilitates preserving and learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

Geochemistry & Geophysics↗
DOE OSTI2014

Evaluation of INL Supplied MOOSE/OSPREY Model: Modeling Water Adsorption on Type 3A Molecular Sieve

The purpose of this study was to evaluate Idaho National Lab’s Multiphysics Object-Oriented Simulation Environment (MOOSE) software in modeling the adsorption of water onto type 3A molecular sieve (3AMS). MOOSE can be thought-of as a computing framework within which applications modeling specific coupled-phenomena can be developed and run. The application titled Off-gas SeParation and REcoverY (OSPREY) has been developed to model gas sorption in packed columns. The sorbate breakthrough curve calculated by MOOSE/OSPREY was compared to results previously obtained in the deep bed hydration tests conducted at Oak Ridge National Laboratory. The coding framework permits selection of various options, when they exist, for modeling a process. For example, the OSPREY module includes options to model the adsorption equilibrium with a Langmuir model or a generalized statistical thermodynamic adsorption (GSTA) model. The vapor solid equilibria and the operating conditions of the process (e.g., gas phase concentration) are required to calculate the concentration gradient driving the mass transfer between phases. Both the Langmuir and GSTA models were tested in this evaluation. Input variables were either known from experimental conditions, or were available (e.g., density) or were estimated (e.g., thermal conductivity of sorbent) from the literature. Variables were considered independent of time, i.e., rather than having a mass transfer coefficient that varied with time or position in the bed, the parameter was set to remain constant. The calculated results did not coincide with data from laboratory tests. The model accurately estimated the number of bed volumes processed for the given operating parameters, but breakthrough times were not accurately predicted, varying 50% or more from the data. The shape of the breakthrough curves also differed from the experimental data, indicating a much wider sorption band. Model modifications are needed to improve its utility and predictive capability. Recommended improvements include: greater flexibility for input of mass transfer parameters, time-variable gas inlet concentration, direct output of loading and temperature profiles along the bed, and capability to conduct simulations of beds in series.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Assessing the Validity of the Simplified Potential Energy Clock Model for Modeling Glass-Ceramics

Glass-ceramic seals may be the future of hermetic connectors at Sandia National Laboratories. They have been shown capable of surviving higher temperatures and pressures than amorphous glass seals. More advanced finite-element material models are required to enable model-based design and provide evidence that the hermetic connectors can meet design requirements. Glass-ceramics are composite materials with both crystalline and amorphous phases. The latter gives rise to (non-linearly) viscoelastic behavior. Given their complex microstructures, glass-ceramics may be thermorheologically complex, a behavior outside the scope of currently implemented constitutive models at Sandia. However, it was desired to assess if the Simplified Potential Energy Clock (SPEC) model is capable of capturing the material response. Available data for SL 16.8 glass-ceramic was used to calibrate the SPEC model. Model accuracy was assessed by comparing model predictions with shear moduli temperature dependence and high temperature 3-point bend creep data. It is shown that the model can predict the temperature dependence of the shear moduli and 3- point bend creep data. Analysis of the results is presented. Suggestions for future experiments and model development are presented. Though further calibration is likely necessary, SPEC has been shown capable of modeling glass-ceramic behavior in the glass transition region but requires further analysis below the transition region.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Material Model Testing in the DYNA3D Code: Material Models 1 through 5

This report is the first volume in an effort to study the material model implementations in DYNA3D, using the latest developmental version of the code. Input files were derived from a template supplied by Jerry Lin to perform an equation-of-state study. Most of the simulations use 10 cubes, each 1 cm on a side with a density of 8.03 (steel). Each of the cubes tests a different aspect of the material model. The first three cubes are testing normal stresses and normal strains (uniaxial stresses and strains) placed on the cube parallel to the x-axis, y-axis and z-axis, respectively. The x (or y or z) = 1 face of the cube is moved along the x-axis (or y-axis or z-axis) to test the model, while the opposite side remains in the x (or y or z) = 0 plane. For some of the tests, the nodes are free to move perpendicular to the applied strain to allow the cross-sectional area of the cube to change in response to being placed under pressure or tension (to test Poisson’s Ratio), while in others the nodes are frozen to eliminate dynamic effects resulting from the code. The cube is placed in tension along the axis being tested and then released, followed by a cycle of compression and release. This tension/compression cycle is repeated 3 more times. The additional cycles allow for the model to be tested under different loads. For these calculations, the amount by which the cube is stretched and compressed is varied from cycle to cycle to allow testing of model features. Cubes number 4 and 5 are intended to test xy shear stresses/strains. This is done by sliding one face of the cube in its plane parallel to the axis of interest. For example, for cube 4, the upper face of the cube is slid in the y=1 plane in a direction parallel to the x-axis (so there are no changes in the y and z coordinates of the points in the plane) and then returned to its starting position. This preserves the volume of the cube, so there is no change in density or pressure. The cycle is repeated 8 times. For cube 5, the plane of the cube satisfying x=1 is slid in this plane in a direction parallel to the y-axis and then returned to its starting position. This preserves the volume of the cube, so there is no change in density or pressure. The cycle is repeated 8 times. Cubes number 6 through 9 repeat these tests for yz shear stresses/strains (Cubes 6 and 7) and zx shear stresses and strains (Cubes 8 and 9). For reasons discussed in the next paragraph, it is unclear if these tests represent a state of pure shear stress and shear strain in the code. Application of shear stresses and strains should not, in general, result in the appearance of normal stresses and strains in Cubes 4 - 9. In the infinitesimal limit, this result might be obtained. However, DYNA3D is a finite deformation code and in the real deformations used in the code, this result is not achieved and the individual components of the normal stresses and strains are non-zero during the cycling. However, the sums of the normal strains are zero in these tests and, except in the case of thermally dependent models, so are the sums of the normal stresses. Cube 10 tests the response of the material model to a hydrostatic loading of the cube. The load curve used for the first three tests is used again and applied to each of the 8 nodes in a manner causing the cube to be either compressed or expanded. This compression/tension cycle is repeated 4 times. The additional cycles allow for the model to be tested under different loads. The amount by which the cube is stretched and compressed is varied from cycle to cycle to allow testing of model features. This document will discuss the testing of Material Models 1 through 5. Relevant results from the test cubes will be discussed. A discussion of the results from all 10 test cubes will be presented only if necessary.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Load Composition Analysis in Support of the NERC Load Modeling Task Force. 2019-2020 Field Test of the Composite Load Model

In 2015, NERC’s reliability standards were revised to require the use of dynamic load models in transmission planning studies. To comply with the standards, planners must use load models that explicitly represent the dynamic behavior of the different constituents of load at each load bus within their transmission planning models. The most important of these constituents are motor-driven and power electronics-based loads. Collectively, these representations are known as composite load models. In anticipation of the compliance date for the new standards, NERC’s Load Modeling Task Force (LMTF), in 2019, initiated a field test of composite load models involving the regional reliability planning entities. In support of the field test, DOE and BPA researchers developed region-specific composite load models that could be assigned to each non-industrial load bus in the planning models for each of the North American interconnections. Separate models were developed for each hour of a summer peak day, a winter peak day, and a spring light-load day. This report is the technical documentation for the load composition analysis that was conducted to develop these non-industrial composite load models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Improvements to CTF Closure Models for Modelling of Two-Phase Flow

This report documents the efforts to improve the CTF prediction of void fraction and two-phase pressure drop by improving two-phase closure models. Previous validation activities have revealed that CTF tends to overpredict void fraction and two-phase pressure drop. In response to this, two approaches were taken to improve CTF’s predictive capabilities. Based on findings that the interfacial drag and subcooled boiling models significantly impact void prediction, alternative closure models for these physical effects were found and implemented into the code. An extensive assessment was performed by using the existing and newly added two-phase experimental data, which showed that void prediction, wall temperature, and two-phase pressure-drop results are improved by using the newly implemented models. A second approach for improving CTF modeling accuracy involved the use of a Bayesian calibration process that uses CTF validation data to optimize selected modeling coefficients and closure model multipliers to achieve a more accurate prediction of experimental results. A similar assessment was performed with calibrated models that improved the void and pressure-drop prediction for test cases that were both included and not included in the calibration dataset. Results of this study will be used to change the models used in CTF to achieve more accurate BWR analyses moving forward. This study also identified opportunities for improving additional closure models and other opportunities to use calibration techniques to improve CTF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Extended Modeling of DOE Sealed Canisters with Updated Chemistry Models

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel currently specifies storage within helium backfilled DOE sealed standard canisters. The aluminum cladding of the ATR fuel contains an oxyhydroxide layer of boehmite/bayerite that generates hydrogen when subjected to irradiation. Understanding the effect of this hydrogen buildup over time to important for long term storage considerations. Previous modeling efforts have built a coupled CFD-chemical model to simulate the temperature gas phase concentrations within the DOE sealed standard canisters. These models have coupled the temperature conditions to both the gas phase radiolysis chemistry and the surface chemistry associated with the oxyhydroxide layer. The previous iteration of the model utilized constant G-values for the hydrogen generation a 50-year period. This new iteration of the model utilizes new experimental data to update the hydrogen generation rate, as well as increase the simulated time to a 200-year period. Given the half-life assumed for the primary Cs-137 isotope responsible for gamma radiation in the ATR spent fuel, the 200-year period decreases the decay heat and dose rate of the spent fuel by a factor of 100, which combined with the updated chemistry substantially decreases new hydrogen generation. Continued experimental work has identified trends for aluminum surrogate samples with oxyhydroxide layers for tests done at higher dose rates. At low initial doses a fast generation rate of hydrogen occurs which starts to roll over to a lower generation as the total dose applied increase. A small-scale chemical model was built to replicate as mini-canister surrogate system at SRNL as well as for the smaller capsule tests performed at INL. A variety of chemical models to capture this effect were tested, and a back reaction of H radical absorbing onto the surface, or inhibition of the reaction by significant H 2 cover gas were both able to fit both the mini-canister and the small capsule test data. Both kinetic fits were used to generate data from the new 200-year simulation. As additional experiments continue, the kinetic fits may be adjusted to adapt to new data. However, updated data shows that hydrogen atmosphere has little effect on actual generation data, so the model was reverted to use a star-stepped G-value for low-dose and high-dose regions. For the undried fuel case, the three models differ significantly with an end concentration of 1% for back reaction, 4.2% for inhibition, and 13.1% for constant G-value for the nominal scenario. For the dried fuel case, the nominal cases showed end concentrations of 0.23% for back reaction, 1.9% for inhibition, and 4.2% for const G-value for the nominal scenario. For the constant G-value case that is less conservative than the other two, the pressure for undried fuel increases to 1.94 atm over 200 years for the nominal case and 2.11 atm for the high decay heat case. The total hydrogen concentration after 200 years is 4.92% for the low decay heat case and 27.4% for the high decay heat case for undried fuel and is 1.4% and 9.6% for low and high decay heat fuel for the dried fuel case. If small amount of residual air is present, the potential for nitric acid formation of 595, 1568, and 2816 ppm for the lower, nominal, and upper fuel decay heat can occur. At long-term timeframes no significant shift in major species present occurs, so no appreciable amount of oxygen is present in the system. The continued rate of hydrogen generation in the canister occurs at an increase of 0.04% by mole over the final 10 years. Increasing the model range out to 1000 years continues to drop the hydrogen generation rate through decreasing dose rate.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Quantifying model prediction sensitivity to model-form uncertainty

Computational and mathematical models are essential to understanding complex systems and phenomena. However, when developing such models, limited knowledge and/or resources necessitates the use of simplifying assumptions. It is therefore crucial to quantify the impact of such simplifying assumptions on the reliability and accuracy of resulting model predictions. This work develops a first-of-its-kind approach to quantify the impact of physics modeling assumptions on predictions. Here, we leverage the emerging field of model-form uncertainty (MFU) representations, which are parameterized modifications to modeling assumptions, in combination with grouped Sobol’ indices to quantitatively measure an assumption’s importance. Specifically, we compute the grouped Sobol’ index for the MFU representation’s parameters as a single importance measure of the assumption for which the MFU representation characterizes uncertainty. To ensure this approach is robust to the subjective choice of how to parameterize a MFU representation, we establish bounds for the difference between sensitivity results for two different MFU representations based on differences in model prediction statistics. The capabilities associated with this approach are demonstrated on three exemplar problems: an upscaled subsurface contaminant transport problem, ablation modeling for hypersonic flight, and nuclear waste repository modeling. We found that our grouped approach is able to assess the impact of modeling assumptions on predictions and offers computational advantages over classical Sobol’ index computation while providing more interpretable results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

DETECTION OF SOLAR-LIKE OSCILLATIONS, OBSERVATIONAL CONSTRAINTS, AND STELLAR MODELS FOR θ CYG, THE BRIGHTEST STAR OBSERVED BY THE KEPLER MISSION

θ Cygni is an F3 spectral type magnitude V = 4.48 main-sequence star that was the brightest star observed by the original Kepler spacecraft mission. Short-cadence (58.8 s) photometric data using a custom aperture were first obtained during Quarter 6 (2010 June–September) and subsequently in Quarters 8 and 12–17. We present analyses of solar-like oscillations based on Q6 and Q8 data, identifying angular degree l = 0, 1, and 2 modes with frequencies of 1000–2700 μ Hz, a large frequency separation of 83.9 ± 0.4 μ Hz, and maximum oscillation amplitude at frequency ν {sub max} = 1829 ± 54 μ Hz. We also present analyses of new ground-based spectroscopic observations, which, combined with interferometric angular diameter measurements, give T {sub eff} = 6697 ± 78 K, radius 1.49 ± 0.03 R {sub ⊙}, [Fe/H] = -0.02 ± 0.06 dex, and log g = 4.23 ± 0.03. We calculate stellar models matching these constraints using the Yale Rotating Evolution Code and the Asteroseismic Modeling Portal. The best-fit models have masses of 1.35–1.39 M {sub ⊙} and ages of 1.0–1.6 Gyr. θ Cyg’s T {sub eff} and log g place it cooler than the red edge of the γ Doradus instability region established from pre- Kepler ground-based observations, but just at the red edge derived from pulsation modeling. The pulsation models show γ Dor gravity modes driven by the convective blocking mechanism, with frequencies of 1–3 cycles per day (11 to 33 μ Hz). However, gravity modes were not seen in Kepler data; one signal at 1.776 cycles per day (20.56 μ Hz) may be attributable to a faint, possibly background, binary.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗