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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

42 ENGINEERING↗
DOE OSTI2017

Considerations and Lessons Learned from the Use of Gamma Spectroscopy Systems at FUSRAP Project Sites - 17459

This paper will discuss the application of gamma spectroscopy systems for analysis of environmental media samples for parents and daughter progeny of the Natural Uranium and Thorium Decay Series radionuclides. The concentrations and relative ratios of naturally occurring isotopes are used to confirm the presence of FUSRAP-related wastes and for understanding the associated potential dose and health risks. Focus will be given to lessons learned from the set-up and operation of onsite gamma spectroscopy laboratories used to provide stakeholders with defensible and timely results to support FUSRAP site investigations and remediation including adaptive survey design implementation, physical remediation and waste transport, and confirmatory final status survey activities. This paper focuses on the potential impacts to gamma spectroscopy measurement accuracy from the following: - Bias caused by sample matrix effects as well as use of different library reference values between performance evaluation study providers and testing laboratories; - Use of calibration sources with limited energy ranges, and; - Selection of available alternative isotopic decay reference data for the input of line energies and their intensities. These lessons learned include discussing how inter-laboratory measurement differences from blind performance evaluation samples are affected by the manufacturer's measurement method, including equilibrium assumptions; the isotope decay information source library used by the study provider and participating laboratories; and calibration source geometry/density variances from actual sample conditions. Any of these factors can significantly impact the accuracy of reported results data. Deepening the reader's understanding of the various factors that impact the accuracy, interpretation, and overall usability of gamma spectroscopy results data from laboratories will help ensure defensible decision-making and cost-effective mission implementation within FUSRAP and similar remediation programs. (authors)

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

Multitaper Magnitude‐Squared Coherence for Time Series With Missing Data: Understanding Oscillatory Processes Traced by Multiple Observables

To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic data sets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman α flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.

Astro-statistics techniques (1886)↗
DOE OSTI2017

Measurement of Nuclear Power Plant Construction Performance Using EVMS Model

The Earned Value Management System (EVMS) method integrates three critical elements of project management: scope, cost and time management. It requires the periodic monitoring of actual expenditures and physical scope accomplishments and allows calculation of cost and schedule variances along with performance indices. It allows forecasting of project cost and schedule at completion and highlights the possible need for corrective action. Application of earned value management techniques is scalable to individual projects as determined by their size and complexity. Effective management of quality, cost, and time is the utmost objective for any type of construction project. An EVMS model provides an advanced systematic method of controlling these three performance measures in an integrated way. However, the additional management effort required to develop and maintain an effective Work Breakdown Structure (WBS) has been highlighted as a major barrier to application of this concept. This paper examines the influencing attributes that would facilitate effective EVMS implementation including a restructuring of the WBS for nuclear power plant construction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Application of a Novel Heat Pump Model for Estimating Economic Viability and Barriers of Heat Pumps in Dairy Applications in the United States

Heat pumps represent an important opportunity for energy savings and decarbonization. This work investigates the techno-economic performance of high-temperature heat pumps (HTHPs) for use in the U.S. dairy industry. The studied heat pump performs a 50 °C temperature lift on a waste heat stream of cleaning water and applies the upgraded heat stream to a fluid milk pasteurization process. This work involved the creation of a HTHP model that estimated the coefficient of performance (COP), internal rate of return (IRR), net present value (NPV), and payback period (PBP), and emissions saved for a heat pump replacing a natural gas boiler. Capital costs, operations, and maintenance (O&M) cost, heat pump lifetime, electricity prices, natural gas prices, and a cost of carbon were varied to perform a parametric study on the factors affecting the break-even price of HTHPs. The results show that HTHP economics are highly sensitive to COP and energy price environment, and less sensitive to capital and O&M cost variance, leading to a large scatter of positive and negative NPVs based on U.S. location. PBPs demonstrate a defined threshold, based on energy price environment, below which favorable two-to-three-year PBPs predominate. This work is focused on the U.S. dairy industry, but international application in relation to fossil vs. electricity price regimes. Heat pumps have seen wider adoption in regions with a high ratio of fossil energy to electricity prices ($/MMBTU vs. $/kWh). The U.S. has plentiful natural gas resulting in lower fossil energy prices which has reduced heat pump adoption. This paper identifies potential first mover industries for HTHP adoption and their associated price regimes even in regions with lower ratios of fossil energy to electricity prices that exist many places globally.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2019

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO 2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.

58 GEOSCIENCES↗
DOE OSTI2013

A novel method for modeling the neutron time of flight (nTOF) detector response in current mode to inertial confinement fusion experiments.

There are several machines in this country that produce short bursts of neutrons for various applications. A few examples are the Z machine, operated by Sandia National Laboratories in Albuquerque, NM ; the OMEGA Laser Facility at the University of Rochester in Rochester, NY ; and the National Ignition Facility (NIF) operated by the Department of Energy at Lawrence Livermore National Laboratory in Livermore, California . They all incorporate neutron time of flight (nTOF) detectors which measure neutron yield, and the shapes of the waveforms from these detectors contain germane information about the plasma conditions that produce the neutrons. However, the signals can also be %E2%80%9Cclouded%E2%80%9D by a certain fraction of neutrons that scatter off structural components and also arrive at the detectors, thereby making analysis of the plasma conditions more difficult. These detectors operate in current mode - i.e., they have no discrimination, and all the photomultiplier anode charges are integrated rather than counted individually as they are in single event counting. Up to now, there has not been a method for modeling an nTOF detector operating in current mode. MCNP PoliMi was developed in 2002 to simulate neutron and gamma ray detection in a plastic scintillator, which produces a collision data output table about each neutron and photon interaction occurring within the scintillator; however, the post processing code which accompanies MCNP PoliMi assumes a detector operating in single event counting mode and not current mode. Therefore, the idea for this work had been born: could a new post processing code be written to simulate an nTOF detector operating in current mode? And if so, could this process be used to address such issues as the impact of neutron scattering on the primary signal? Also, could it possibly even identify sources of scattering (i.e., structural materials) that could be removed or modified to produce %E2%80%9Ccleaner%E2%80%9D neutron signals? This process was first developed and then applied to the axial neutron time of flight detectors at the Z Facility mentioned above. First, MCNP PoliMi was used to model relevant portions of the facility between the source and the detector locations. To obtain useful statistics, variance reduction was utilized. Then, the resulting collision output table produced by MCNP PoliMi was further analyzed by a MATLAB postprocessing code. This converted the energy deposited by neutron and photon interactions in the plastic scintillator (i.e., nTOF detector) into light output, in units of MeVee%D1%84 (electron equivalent) vs time. The time response of the detector was then folded into the signal via another MATLAB code. The simulated response was then compared with experimental data and shown to be in good agreement. To address the issue of neutron scattering, an %E2%80%9CIdeal Case,%E2%80%9D (i.e., a plastic scintillator was placed at the same distance from the source for each detector location) with no structural components in the problem. This was done to produce as %E2%80%9Cpure%E2%80%9D a neutron signal as possible. The simulated waveform from this %E2%80%9CIdeal Case%E2%80%9D was then compared with the simulated data from the %E2%80%9CFull Scale%E2%80%9D geometry (i.e., the detector at the same location, but with all the structural materials now included). The %E2%80%9CIdeal Case%E2%80%9D was subtracted from the %E2%80%9CFull Scale%E2%80%9D geometry case, and this was determined to be the contribution due to scattering. The time response was deconvolved out of the empirical data, and the contribution due to scattering was then subtracted out of it. A transformation was then made from dN/dt to dN/dE to obtain neutron spectra at two different detector locations.

Nelson, Alan J.↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploration of a Novel Technique for Waste Heat Recovery Through Molecular Dynamics: Influence of Wettability and Electric Field on Water and Water-Based Nanofluids

Most of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle). Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water. Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes. In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Cluster Tails for Critical Power-Law Inhomogeneous Random Graphs

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299–2361, 2012). It was proved that when the degrees obey a power law with exponent τ∈(3,4), the sequence of clusters ordered in decreasing size and multiplied through by n{sup −(τ−2)/(τ−1)} converges as n→∞ to a sequence of decreasing non-degenerate random variables. Here, we study the tails of the limit of the rescaled largest cluster, i.e., the probability that the scaling limit of the largest cluster takes a large value u, as a function of u. This extends a related result of Pittel (J Combin Theory Ser B 82(2):237–269, 2001) for the Erdős–Rényi random graph to the setting of rank-1 inhomogeneous random graphs with infinite third moment degrees. We make use of delicate large deviations and weak convergence arguments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Bayesian calibration of irradiated graphite property models under high temperatures

Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2013

The impact of camera optical alignments on weak lensing measures for the Dark Energy Survey

Telescope point spread function (PSF) quality is critical for realizing the potential of cosmic weak lensing observations to constrain dark energy and test general relativity. In this paper, we use quantitative weak gravitational lensing measures to inform the precision of lens optical alignment, with specific reference to the Dark Energy Survey (DES). We compute optics spot diagrams and calculate the shear and flexion of the PSF as a function of position on the focal plane. For perfect optical alignment, we verify the high quality of the DES optical design, finding a maximum PSF contribution to the weak lensing shear of 0.04 near the edge of the focal plane. However, this can be increased by a factor of approximately 3 if the lenses are only just aligned within their maximum specified tolerances. We calculate the E- and B-mode shear and flexion variance as a function of the decentre or tilt of each lens in turn. We find tilt accuracy to be a few times more important than decentre, depending on the lens considered. Finally, we consider the compound effect of decentre and tilt of multiple lenses simultaneously, by sampling from a plausible range of values of each parameter. We find that the compound effect can be around twice as detrimental as when considering any one lens alone. Furthermore, this combined effect changes the conclusions about which lens is most important to align accurately. For DES, the tilt of the first two lenses is the most important.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Analysis of the Tank 6F Final Characterization Samples-2012 (U)

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (SRR) to provide sample preparation and analysis of the Tank 6F final characterization samples to determine the residual tank inventory prior to grouting. Fourteen residual Tank 6F solid samples from three areas on the floor of the tank were collected and delivered to SRNL between May and August 2011. These Tank 6F samples were homogenized and combined into three composite samples based on a proportion compositing scheme and the resulting composite samples were analyzed for radiological, chemical and elemental components. Additional measurements performed on the Tank 6F composite samples include bulk density and water leaching of the solids to account for water soluble components. The composite Tank 6F samples were analyzed and the data reported in triplicate. Sufficient quality assurance standards and blanks were utilized to demonstrate adequate characterization of the Tank 6F samples. The main evaluation criteria were target detection limits specified in the technical task request document. While many of the target detection limits were met for the species characterized for Tank 6F some were not met. In a few cases, the relatively high levels of radioactive species of the same element or a chemically similar element precluded the ability to measure some isotopes to low levels. The isotopes whose detection limits were not met in all cases included Sn-126, Sb-126, Sb-126m, Eu-152, Cm-243 and Cf-249. SRNL, in conjunction with the customer, reviewed all of these cases and determined that the impacts of not meeting the target detection limits were acceptable. Based on the analyses of variance (ANOVA) for the inorganic constituents of Tank 6F, all the inorganic constituents displayed heterogeneity. The inorganic results demonstrated consistent differences across the composite samples: lowest concentrations for Composite Sample 1, intermediate-valued concentrations for Composite Sample 2, and highest concentrations for Composite Sample 3. The Hg and Mo results suggest possible measurement outliers. However, the magnitudes of the differences between the Hg 95% upper confidence limit (UCL95) results with and without the outlier and the magnitudes of the differences between the Mo UCL95 results with and without the outlier do not appear to have practical significance. It is recommended to remove the potential measurement outliers. Doing so is conservative in the sense of producing a higher UCL95 for Hg and Mo than if the potential outliers were included in the calculations. In contrast to the inorganic results, most of the radionuclides did not demonstrate heterogeneity among the three Tank 6F composite sample characterization results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Analysis of the Tank 6F Final Characterization Samples-2012

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (SRR) to provide sample preparation and analysis of the Tank 6F final characterization samples to determine the residual tank inventory prior to grouting. Fourteen residual Tank 6F solid samples from three areas on the floor of the tank were collected and delivered to SRNL between May and August 2011. These Tank 6F samples were homogenized and combined into three composite samples based on a proportion compositing scheme and the resulting composite samples were analyzed for radiological, chemical and elemental components. Additional measurements performed on the Tank 6F composite samples include bulk density and water leaching of the solids to account for water soluble components. The composite Tank 6F samples were analyzed and the data reported in triplicate. Sufficient quality assurance standards and blanks were utilized to demonstrate adequate characterization of the Tank 6F samples. The main evaluation criteria were target detection limits specified in the technical task request document. While many of the target detection limits were met for the species characterized for Tank 6F some were not met. In a few cases, the relatively high levels of radioactive species of the same element or a chemically similar element precluded the ability to measure some isotopes to low levels. The isotopes whose detection limits were not met in all cases included Sn-126, Sb-126, Sb-126m, Eu-152, Cm- 243 and Cf-249. SRNL, in conjunction with the customer, reviewed all of these cases and determined that the impacts of not meeting the target detection limits were acceptable. Based on the analyses of variance (ANOVA) for the inorganic constituents of Tank 6F, all the inorganic constituents displayed heterogeneity. The inorganic results demonstrated consistent differences across the composite samples: lowest concentrations for Composite Sample 1, intermediate-valued concentrations for Composite Sample 2, and highest concentrations for Composite Sample 3. The Hg and Mo results suggest possible measurement outliers. However, the magnitudes of the differences between the Hg 95% upper confidence limit (UCL95) results with and without the outlier and the magnitudes of the differences between the Mo UCL95 results with and without the outlier do not appear to have practical significance. It is recommended to remove the potential measurement outliers. Doing so is conservative in the sense of producing a higher UCL95 for Hg and Mo than if the potential outliers were included in the calculations. In contrast to the inorganic results, most of the radionuclides did not demonstrate heterogeneity among the three Tank 6F composite sample characterization results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Preliminary RAM-RODD results for the MUSiC subcritical configurations

The Measurement of Uranium Subcritical and Critical (MUSiC) was performed at the DOE’s National Criticality Experiments Research Center (NCERC) located in the Nevada National Security Site (NNSS). The measurement utilized the Rocky Flats shells to perform benchmark measurements of similar highly enriched uranium (HEU) systems that span a wide range of reactivities. The Rocky Flats (RF) shells are 93.16% U-235 enriched metal hemishells that can be stacked concentrically. Ten configurations were measured with effective multiplication factors spanning between deeply subcritical (~ 0.64) through delayed critical. Details of the measured configurations are listed in Table 1. This unique set of measurements with its large span of reactivies is being used to determine the range over which neutron noise techniques such as Feynman variance-to-mean, Rossi-alpha, and pulsed neutron source techniques can be accurately employed for a bare HEU system. The results of the measurements will be published as a benchmark in The International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The results will support the growing amount of subcritical benchmark data, such as the SCRaP measurements, that is available to the community. The measurements were performed using several different detector systems for the purposes of cross-validation and obtaining detector independent results. Four detector systems were deployed, three by the NCERC team and one from the University of Michigan. The detector systems included a Neutron Multiplicity Array Detector (NoMAD) system (similar to the MC-15), four small volume 0.635 cm (Ø) × 7.59 cm 3 He detectors (ideal for measuring prompt neutron decay constants due to their fast recovery speed), the Rossi Alpha Measurements – Rapid Organic (n, γ) Discrimination Detector (RAM-RODD), and the Organic Scintillator Array (OSCAR). RAM-RODD is an array of eight 5.08 cm (Ø) × 5.08 cm EJ-309 organic scintillator detectors. OSCAR is a University of Michigan system and is an array of twelve 5.08 cm (Ø) × 5.08 cm stilbene detectors. Details of measurements performed with OSCAR will be discussed in a separate talk. The focus of this work is preliminary results obtained by RAM-RODD for the 8 subcritical configurations. Additional details on the measurements and on the setup and deployment of RAM-RODD will be discussed. Preliminary neutron noise analysis results including Rossi-alpha and Feynman-alpha will also be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Simplified predictive models for CO 2 sequestration performance assessment

CO2 sequestration in deep saline formations is increasingly being considered as a viable strategy for the mitigation of greenhouse gas emissions from anthropogenic sources. In this context, detailed numerical simulation based models are routinely used to understand key processes and parameters affecting pressure propagation and buoyant plume migration following CO2 injection into the subsurface. As these models are data and computation intensive, the development of computationally-efficient alternatives to conventional numerical simulators has become an active area of research. Such simplified models can be valuable assets during preliminary CO2 injection project screening, serve as a key element of probabilistic system assessment modeling tools, and assist regulators in quickly evaluating geological storage projects. We present three strategies for the development and validation of simplified modeling approaches for CO2 sequestration in deep saline formations: (1) simplified physics-based modeling, (2) statisticallearning based modeling, and (3) reduced-order method based modeling. In the first category, a set of full-physics compositional simulations is used to develop correlations for dimensionless injectivity as a function of the slope of the CO2 fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. Furthermore, the dimensionless average pressure buildup after the onset of boundary effects can be correlated to dimensionless time, CO2 plume footprint, and storativity contrast between the reservoir and caprock. In the second category, statistical “proxy models” are developed using the simulation domain described previously with two approaches: (a) classical Box-Behnken experimental design with a quadratic response surface, and (b) maximin Latin Hypercube sampling (LHS) based design with a multidimensional kriging metamodel fit. For roughly the same number of simulations, the LHS-based metamodel yields a more robust predictive model, as verified by a k-fold cross-validation approach (with data split into training and test sets) as well by validation with an independent dataset. In the third category, a reduced-order modeling procedure is utilized that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) in order to represent system response at new control settings from a limited number of training runs. Significant savings in computational time are observed with reasonable accuracy from the PODTPWL reduced-order model for both vertical and horizontal well problems – which could be important in the context of history matching, uncertainty quantification and optimization problems. The simplified physics and statistical learning based models are also validated using an uncertainty analysis framework. Reference cumulative distribution functions of key model outcomes (i.e., plume radius and reservoir pressure buildup) generated using a 97-run full-physics simulation are successfully validated against the CDF from 10,000 sample probabilistic simulations using the simplified models. The main contribution of this research project is the development and validation of a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Performance of Solar-Induced Chlorophyll Fluorescence in Estimating Water-Use Efficiency in a Temperate Forest

Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

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