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At least 1,729 records · Page 96

Process Modeling of U-10wt% Mo Alloys Using Integrated Computational Materials Engineering

Low-enriched uranium alloyed with 10 wt% molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium as fuel for the nation’s high-performance research reactors. Manufacturing the U-10Mo alloy consists of multiple complex thermomechanical processes, which is highly challenging for computational modeling. The integrated computational materials engineering (ICME) concept supports building a microstructure-based framework to investigate the effect of manufacturing processes on the material microstructure. In this report, an ICME model that combines homogenization, hot rolling, annealing, and cold rolling is presented as an example of implementing the ICME concept for modeling the manufacturing processes of the U-10Mo alloy. The integrated model enables information to be passed between the different model components, leading to an improved prediction and a better understanding of microstructure across multiple processes. The integrated model was also implemented to investigate the variation of chemical composition, thickness of the zirconium interlayer, grain growth, and carbide redistribution and fracture of U-10Mo during the thermomechanical processes. With the homogenization model, a molybdenum concentration map can be reconstructed from any given microstructure, and the homogenization time needed to reach a desired homogenization level can also be approximated. The microstructure-based finite-element rolling model, shows that the homogenization process reduces the nonuniformity in the thickness of the zirconium interlayers on the U-10Mo. With the output from the rolling model, a statistical analysis of second-phase hard particles was conducted to quantify the particle redistribution and interparticle spacing between carbides during the deformation. Also, new stringer identification criteria were proposed to predict the correlation between stringer volume fractions and hot-rolling reduction. A macro-finite-element method hot-rolling model showed that higher reduction passes can be achieved within the mill separation force range, and a new “aggressive” rolling schedule was proposed. The model also showed the importance of can material on the quality of rolled foils, with stronger can materials resulting in hot-rolled U-10Mo free of defects (waviness, dog-boning). The deformation-induced recrystallization model demonstrated capability to use inputs from deformation simulations of strain accumulation in U-10Mo samples to predict grain growth behavior of the fuel during thermal treatment stages. Finally, a plane-strain compression mode simulated particle fracture and calculates the void volume fraction generated during cold rolling. The macro-finite-element method cold-rolling model showed that smaller diameter rolls are necessary to achieve the desired foil thickness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Statistically conditioned least squares retrievals planned for the VAS demonstration experiment

Problems in the retrieval of atmospheric profiles from passively observed satellite radiances are reviewed. Plans are described for statistically conditioned least squares retrievals of temperature and moisture profiles around mesoscale events, based upon the expected sensitivity of the VAS channels to the atmospheric variations. Simulated soundings for a global data set and for the 1976 National Storm Laboratory (NSSL) severe storm data set are also examined. This combination of radiance modeling and statistical conditioning should yield reliable mesoscale soundings and provide a test bed for sounding research and development with the VAS instrument.

Chester, D.↗

Prediction of coronary artery disease in patients undergoing operations for mitral valve degeneration

OBJECTIVES: We sought to develop and validate a model that estimates the risk of obstructive coronary artery disease in patients undergoing operations for mitral valve degeneration and to demonstrate its potential clinical utility. METHODS: A total of 722 patients (67% men; age, 61 +/- 12 years) without a history of myocardial infarction, ischemic electrocardiographic changes, or angina who underwent routine coronary angiography before mitral valve prolapse operations between 1989 and 1996 were analyzed. A bootstrap-validated logistic regression model on the basis of clinical risk factors was developed to identify low-risk (< or =5%) patients. Obstructive coronary atherosclerosis was defined as 50% or more luminal narrowing in one or more major epicardial vessels, as determined by means of coronary angiography. RESULTS: One hundred thirty-nine (19%) patients had obstructive coronary atherosclerosis. Independent predictors of coronary artery disease include age, male sex, hypertension, diabetes mellitus,and hyperlipidemia. Two hundred twenty patients were designated as low risk according to the logistic model. Of these patients, only 3 (1.3%) had single-vessel disease, and none had multivessel disease. The model showed good discrimination, with an area under the receiver-operating characteristic curve of 0.84. Cost analysis indicated that application of this model could safely eliminate 30% of coronary angiograms, corresponding to cost savings of $430,000 per 1000 patients without missing any case of high-risk coronary artery disease. CONCLUSION: A model with standard clinical predictors can reliably estimate the prevalence of obstructive coronary atherosclerosis in patients undergoing mitral valve prolapse operations. This model can identify low-risk patients in whom routine preoperative angiography may be safely avoided.

Non-NASA Center↗

The energy dependence of GRB minimum variability timescales

We constrain the minimum variability timescales for 938 gamma-ray bursts (GRBs) observed by the Fermi/Gamma-ray Burst Monitor instrument prior to 2012 July 11. The tightest constraints on progenitor radii derived from these timescales are obtained from light curves in the hardest energy channel. In the softer bands—or from measurements of the same GRBs in the hard X-rays from Swift—we show that variability timescales tend to be a factor of two to three longer. Applying a survival analysis to account for detections and upper limits, we find median minimum timescale in the rest frame for long-duration and short-duration GRBs of 45 and 10 ms, respectively. Less than 10% of GRBs show evidence for variability on timescales below 2 ms. These shortest timescales require Lorentz factors ≳400 and imply typical emission radii R≈1×10{sup 14} cm for long-duration GRBs and R≈3×10{sup 13} cm for short-duration GRBs. We discuss implications for the GRB fireball model and investigate whether or not GRB minimum timescales evolve with cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗

Automatic Responses of Crop Stocks and Policies Buffer Climate Change Effects on Crop Markets and Price Volatility

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

54 ENVIRONMENTAL SCIENCES↗

GEM: Statistical weather forecasting procedure

The objective of the Generalized Exponential Markov (GEM) Program was to develop a weather forecast guidance system that would: predict between 0 to 6 hours all elements in the airways observations; respond instantly to the latest observed conditions of the surface weather; process these observations at local sites on minicomputing equipment; exceed the accuracy of current persistence predictions at the shortest prediction of one hour and beyond; exceed the accuracy of current forecast model output statistics inside eight hours; and be capable of making predictions at one location for all locations where weather information is available.

Robert G. Miller↗

Compositional Effects on Nickel-Base Superalloy Single Crystal Microstructures

Fourteen nickel-base superalloy single crystals containing 0 to 5 wt% chromium (Cr), 0 to 11 wt% cobalt (Co), 6 to 12 wt% molybdenum (Mo), 0 to 4 wt% rhenium (Re), and fixed amounts of aluminum (Al) and tantalum (Ta) were examined to determine the effect of bulk composition on basic microstructural parameters, including gamma' solvus, gamma' volume fraction, volume fraction of topologically close-packed (TCP) phases, phase chemistries, and gamma - gamma'. lattice mismatch. Regression models were developed to describe the influence of bulk alloy composition on the microstructural parameters and were compared to predictions by a commercially available software tool that used computational thermodynamics. Co produced the largest change in gamma' solvus over the wide compositional range used in this study, and Mo produced the largest effect on the gamma lattice parameter and the gamma - gamma' lattice mismatch over its compositional range, although Re had a very potent influence on all microstructural parameters investigated. Changing the Cr, Co, Mo, and Re contents in the bulk alloy had a significant impact on their concentrations in the gamma matrix and, to a smaller extent, in the gamma' phase. The gamma phase chemistries exhibited strong temperature dependencies that were influenced by the gamma and gamma' volume fractions. A computational thermodynamic modeling tool significantly underpredicted gamma' solvus temperatures and grossly overpredicted the amount of TCP phase at 982 C. Furthermore, the predictions by the software tool for the gamma - gamma' lattice mismatch were typically of the wrong sign and magnitude, but predictions could be improved if TCP formation was suspended within the software program. However, the statistical regression models provided excellent estimations of the microstructural parameters based on bulk alloy composition, thereby demonstrating their usefulness.

MacKay, Rebecca A.↗

Artificial spin ice: Paths forward

Artificial spin ice systems are lithographically prepared arrays of interacting nanoscale magnetic moments with collective behavior resulting from the chosen array geometry. These many-body systems are unusual in that their simple constituent elements can be configured to design their interactions with exquisite control. They can then be probed experimentally over a remarkably large range of time scales and length scales, including imaging of the individual moments. The study of artificial spin ice has broadened well beyond the original focus on systems with connection to spin ice and ice models, and now the term is used in reference to many other structures that exhibit a much wider range of physical phenomena. Here in this perspective, we review progress in the field of artificial spin ice since its inception more than a decade ago. We then discuss prospects for future directions – continuing the exploration of nanomagnetism, statistical spin models, and the potential for technological devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Identification of blind geothermal resources in Surprise Valley, CA, using publicly available groundwater well water quality data

Geothermal resource exploration is generally limited to areas with surface expressions of thermal activity (fumaroles and hot springs), or relies on expensive geophysical exploration techniques. The hidden subsurface distribution of geothermal fluids has been identified using a free and publicly available water quality dataset from agricultural and domestic water wells in Surprise Valley, northeastern California. Thermally evolved waters in Surprise Valley have element ratios that vary in response to Ca carbonate and Mg silicate mineral precipitation, and have elevated total dissolved solids (TDS). The arid climate in Surprise Valley leads to surface water evaporation in a closed basin, producing high TDS Na-Cl-CO 3 -SO 4 brines in three ephemeral alkali lakes and in shallow groundwater under elevated soil CO 2 conditions. Evaporated fluids in Surprise Valley follow a chemical divide that leads to Ca carbonate and Mg silicate mineral precipitation. Plots of dissolved element ratios can be used to distinguish groundwater affected by evaporation from water affected by thermal water-rock interaction, however it is challenging to select components for plotting that best illustrate different fluid evolution mechanisms. Here, we use a principal component analysis of centered log-ratio transformed data, coupled with geochemical models of fluid evaporation and thermal mixing pathways, to identify components to plot that distinguish between groundwater samples influenced by evaporation from those influenced by thermal processes. We find that groundwater samples with a thermal signature come from wells that define a coherent, linear geographical distribution that closely matches the location of known and inferred faults. Finally, modification of the general approach employed here provides promise for identifying blind geothermal resources in other locations, by applying low-cost geochemical modeling and statistical techniques to areas where large groundwater quality geochemical datasets are available.

15 GEOTHERMAL ENERGY↗

A hierarchical Bayesian model for background variation in radiation source localization

In this paper, we apply a new model to account for varying background radiation in radiological source localization. We present a hierarchical Bayesian model that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. We employ a simplified photon transport model to reduce the computational expense of Bayesian model calibration. Furthermore, we demonstrate the model accuracy by localizing a cesium-137 source in a simulated city block, and we analyze experimental field measurements with varying background. In both cases, the model provides sufficient fidelity that we can locate the source while simultaneously estimating background radiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Neutron and Thermal Embrittlement of RPV Steels: An Overview

Because the reactor pressure vessel (RPV) represents the first structural line of defense against the release of radiation to the public, the design and fabrication of the RPV for any nuclear reactor facility is performed at very high standards in accordance with consensus codes that are based on mechanical and physical properties of the steels used to construct the vessel. Nuclear RPVs may weigh up to 800 tons with wall thicknesses up to approximately 330 mm and are clad on the inside with stainless-steel weld metal and given a final post-weld heat treatment. The RPV is a unique structural component in that it operates under high pressures and temperatures and is exposed to relatively high neutron radiation. Although typical RPV steels and welds have excellent fracture toughness at room temperature and above when put into service, the degrading effects of high-energy neutron irradiation can cause levels of irradiation-induced embrittlement in radiation-sensitive materials of concern for the structural integrity of the RPV. In recent decades, remarkable progress has been made in developing a mechanistic understanding of irradiation embrittlement. This progress includes developing physically based and statistically calibrated models of Charpy V-notch-indexed transition temperature shifts based on results from RPV surveillance programs complemented by significant results from comprehensive research experiments performed in test reactors. In addition, advances in elastic-plastic fracture mechanics allow for a relatively small number of relatively small specimens to characterize the fracture toughness of RPV steels with statistical confidence. This paper presents a review of the primary mechanical properties, test procedures, examples of applicable codes and standards, and specimen types used to characterize RPV steels and welds, the effects of neutron irradiation on those most relevant mechanical properties, and a brief review of the effects of thermal aging on RPV materials. The paper closes with a summary.

Nanstad, Randy K.↗

Comparison of the Performance of Various Correlated Fission Multiplicity Monte Carlo Codes

When measuring special nuclear material for a large array of national security needs, it is often important to accurately predict outcomes of the physical experiment using Monte Carlo radiation transport codes. Historically, radiation transport codes have uncorrelated fission emissions. In reality, both spontaneous and induced fissions release particles that are correlated in time, energy, and multiplicity. This work investigates the performance of various current Monte Carlo codes that take into account the correlated physics of fission neutrons. Only fission neutrons are of interest and the physics of gamma production in fission is ignored. Because of their large impact on correlated neutron results, underlying fission neutron multiplicity distributions utilized by the different codes are also compared. The codes currently being compared include MCNP{sup R}6, MCNP{sup R}6/FREYA, and MCNPX-PoliMi. By default, MCNP{sup R}6 uses a bounded integer treatment to sample the number of neutrons emitted from each fission event. The FMULT card, an optional input in MCNP that allows for user definition of spontaneous and induced fission parameters, can be utilized to call either built-in or user-specified multiplicities to replace the bounded integer treatment. Similarly, MCNPX-PoliMi utilizes one of a few different built-in multiplicity sets. MCNPX-PoliMi also models the multiplicity dependence of emitted neutron energy in spontaneous fission. It is possible to use an anisotropic model for emitted fission neutrons in MCNPXPoliMi, but this option was not used. For MCNP{sup R}6/FREYA, the FREYA fission event generator determines the number of particles emitted for each fission event and gives the results to MCNP{sup R}6 for transport. The fission event generator uses fission fragment mass and kinetic energy distributions, unbounded statistical evaporation models, and conservation of energy and momentum to generate the number, energy, and direction of neutrons released by each fission event. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Revised Perturbation Statistics for the Global Scale Atmospheric Model

Magnitudes and scales of atmospheric perturbations about the monthly mean for the thermodynamic variables and wind components are presented by month at various latitudes. These perturbation statistics are a revision of the random perturbation data required for the global scale atmospheric model program and are from meteorological rocket network statistical summaries in the 22 to 65 km height range and NASA grenade and pitot tube data summaries in the region up to 90 km. The observed perturbations in the thermodynamic variables were adjusted to make them consistent with constraints required by the perfect gas law and the hydrostatic equation. Vertical scales were evaluated by Buell's depth of pressure system equation and from vertical structure function analysis. Tables of magnitudes and vertical scales are presented for each month at latitude 10, 30, 50, 70, and 90 degrees.

Justus, C. G.↗

Extended optical model for fission

A comprehensive formalism to calculate fission cross sections based on the extension of the optical model for fission is presented. It can be used for description of nuclear reactions on actinides featuring multi-humped fission barriers with partial absorption in the wells and direct transmission through discrete and continuum fission channels. The formalism describes the gross fluctuations observed in the fission probability due to vibrational resonances, and can be easily implemented in existing statistical reaction model codes. The extended optical model for fission is applied for neutron induced fission cross-section calculations on 234,235,238 U and 239 Pu targets. A triple-humped fission barrier is used for 234,235 U(n,f), while a double-humped fission barrier is used for 238 U(n,f) and 239 Pu(n,f) reactions as predicted by theoretical barrier calculations. The impact of partial damping of class-II/III states, and of direct transmission through discrete and continuum fission channels, is shown to be critical for a proper description of the measured fission cross sections for 234,235,238 U(n,f) reactions. The 239 Pu(n,f) reaction can be calculated in the complete damping approximation. Calculated cross sections for 235,238 U(n,f) and 239 Pu(n,f) reactions agree within 3% with the corresponding cross sections derived within the Neutron Standards least-squares fit of available experimental data. Lastly, the extended optical model for fission can be used for both theoretical fission studies and nuclear data evaluation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.

Sumanth Nethi↗

Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing

Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. Here, in this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.

36 MATERIALS SCIENCE↗

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗