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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
NASA NTRS2000

Application of Statistical Methods of Rain Rate Estimation to Data From The TRMM Precipitation Radar

The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by the light and heavy rain rates. This is useful in estimating the fraction of rainfall contributed by the rain rates that go undetected by the radar. The results at high rain rates provide a cross-check on the usual attenuation correction methods that are applied at the highest resolution of the instrument.

Meneghini, R.↗
DOE OSTI2017

Beyond assembly bias: exploring secondary halo biases for cluster-size haloes

Secondary halo bias, commonly known as ‘assembly bias’, is the dependence of halo clustering on a halo property other than mass. This prediction of the Λ Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. Lastly, this results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

In this work, we present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation, which are core tasks in Bayesian inference—particularly in the simulation-based (“likelihood-free”) setting. Our methods represent the target conditional distribution as a transformation of a tractable reference distribution. Obtaining such a transformation, chosen here to be an approximation of the COT map, is computationally challenging even in moderate dimensions. To improve scalability, our numerical algorithms use neural networks to parameterize candidate maps and further exploit the structure of the COT problem. Our static approach approximates the map as the gradient of a partially input convex neural network. It uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. Our dynamic approach approximates the conditional optimal transport via the flow map of a regularized neural ODE; compared to the static approach, it is slower to train but offers more modeling choices and can lead to faster sampling. We demonstrate both algorithms numerically, comparing them with competing state-of-the-art approaches, using benchmark datasets and simulation-based Bayesian inverse problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Uncertainty Quantification of Inducer Natural Frequency using Conditional Assessment of Modeling and Modal Testing of Simpler Systems

The low pressure fuel pump inducer of the new Space Launch System RS25 core stage engine operates in a highly complex environment that substantially affects its modal characteristics. Some of the more important effects are fluid-added mass resulting from operation within a light liquid (Hydrogen), and the magnification of this effect due to tight tip clearance. Since higher order cavitation has been identified as a significant harmonic driver, knowledge of the natural frequency of potentially excitable modes is critical for safe operation, but this frequency cannot be measured during the severe operational environment. A comprehensive testing and analysis program has therefore been performed over the last four years to identify the nominal value and uncertainty of the frequency by modeling and testing four simpler structures which share some of the characteristics of the operational inducer. This testing was used to assess and adjust modeling techniques and excellent correlation was achieved. Identification of the uncertainty in the inducer frequency itself was still problematic, however. This difficulty led to an investigation of Bayesian uncertainty quantification techniques, and to the application of the relatively simple technique of Multi-Variate Normal conditional distributions to calculate the inducer natural frequency uncertainty. Assumptions on prior distributions of uncertainty of the fluid-added mass and tip clearance effect are initially applied to models of each of the simple structures and the inducer itself, and these uncertainties are propagated to generate natural frequencies using design of experiments. Simple response surfaces are then created from this data in order to calculate a Covariance Matrix relating all of these natural frequencies. Finally, the results from modal test of the simple structures are considered to be observations and used to calculate the conditional variance of the desired inducer frequencies. As this method is less rigorous than more complicated Bayesian methods reported in the literature, a conservative factor is applied to the result, but the resulting uncertainty is still significantly less than originally estimated and will greatly assist certification of the inducer for use in the engine.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Uncertainty Quantification of Inducer Eigenvalues using Conditional Assessment of Models and Modal Test of Simpler Systems

The low pressure fuel pump inducer of the new Space Launch System RS25 core stage engine operates in a highly complex environment that substantially affects its modal characteristics. Some of the more important effects are fluid-added mass resulting from operation within a light liquid (Hydrogen), and the magnification of this effect due to tight tip clearance. Since higher order cavitation has been identified as a significant harmonic driver, knowledge of the natural frequency of potentially excitable modes is critical for safe operation, but this frequency cannot be measured during the severe operational environment. A comprehensive testing and analysis program has therefore been performed over the last four years to identify the nominal value and uncertainty of the frequency by modeling and testing four simpler structures which share some of the characteristics of the operational inducer. This testing was used to assess and adjust modeling techniques and excellent correlation was achieved. Identification of the uncertainty in the inducer frequency itself was still problematic, however. This difficulty led to an investigation of Bayesian uncertainty quantification techniques, and to the application of the relatively simple technique of Multi-Variate Normal conditional distributions to calculate the inducer natural frequency uncertainty. Assumptions on prior distributions of uncertainty of the fluid-added mass and tip clearance effect are initially applied to models of each of the simple structures and the inducer itself, and these uncertainties are propagated to generate natural frequencies using design of experiments. Simple response surfaces are then created from this data in order to calculate a Covariance Matrix relating all of these natural frequencies. Finally, the results from modal test of the simple structures are considered to be observations and used to calculate the conditional variance of the desired inducer frequencies. As this method is less rigorous than more complicated Bayesian methods reported in the literature, a conservative factor is applied to the result, but the resulting uncertainty is still significantly less than originally estimated and will greatly assist certification of the inducer for use in the engine.

Uncertainty Quantification↗
DOE OSTI2015

Conditional random fields for pattern recognition applied to structured data

Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Management of Irradiated Control Rods: Cost of an Industrial Approach - 18009

This paper concerns the sorting of irradiated control rods that can be done to limit the volume of Intermediate Level Long-lived Waste. More specifically it assesses the cost of an industrial approach. For this we used numerical simulations, penalizing hypotheses and measurements. The cost is given in term of the increased volume of the control rods placed into deep geological storage. To calculate the radioactive inventories, we have developed a calculation scheme which includes a mapping of the neutron flux, a calculation of the isotopic compositions evolution, and a validation of the results. For the state of the art simulations, all operational fuel loading patterns were considered and coupled with all effective power distributions. The analysis of the calculated results shows that the simulations produce a slight overestimation of the measurements. For envelope simulations, penalizing fuel loading patterns and nominal power distribution conditions were used. Most of the radionuclides needed to declare the radiological inventories are difficult to measure. Therefore, for each radionuclide a scaling factor referred to a key radionuclide, can be calculated. Then during the dismantling operations these calculated scaling factors are multiplied by the measured key radionuclide to determine the radiological inventory. The industrial approach consists of combining envelope simulations with scaling factors. Applied to PWR control rods, the industrial approach overestimates the radiological inventories by a factor of 2. As such, the calculated position of the Intermediate Level Long Lived part limit moves +6 cm. Compared to the 1 meter of the control rod pins classified as Intermediate Level Long Lived, the industrial approach would increase by 6 % the volume of the control rods placed into the deep geological storage. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1983

A study of plasmaspheric density distributions for diffusive equilibrium conditions

The plasmaspheric density distribution has been modeled for a range of solar cycle, seasonal and diurnal conditions with a magnetic flux tube dependent diffusive equilibrium model by using experimentally determined values of ionospheric parameters at 675 km as boundary conditions. Data is presented in terms of plasmaspheric H(+) and He(+) density contours, total flux tube content, and equatorial plasma density for a range of L-values from 1.15 to 3.0. The variation of equatorial density with L-value shows good agreement with the 1/L exp 4 dependence observed experimentally. The results show that the model predicts larger solar cycle and diurnal variation in equatorial plasma density than observed using whistler techniques. However, the whistler method requires a model to deduce the equatorial density and is therefore open to interpretation. Seasonal variations are rather artificial since in this general model no attempt has been made to match equatorial densities for flux tubes emanating from the winter and summer hemispheres.

Li, W.↗
DOE OSTI2022

$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.

42 ENGINEERING↗
DOE OSTI2017

Intramolecular Interactions Overcome Hydration to Drive the Collapse Transition of Gly 15

Simulations and experiments show oligo-glycines, polypeptides lacking any side chains, can collapse in water. We assess the hydration thermodynamics of this collapse by calculating the hydration free energy at each of the end points of the reaction coordinate, here taken as the end-to-end distance (r) in the chain. To examine the role of the various conformations for a given r, we study the conditional distribution, P(R g |r), of the radius of gyration for a given value of r. The free energy change versus Rg, -k B T ln P(R g |r), is found to vary more gently compared to the corresponding variation in the excess hydration free energy. Using this observation within a multistate generalization of the potential distribution theorem, we calculate a tight upper bound for the hydration free energy of the peptide for a given r. On this basis, we find that peptide hydration greatly favors the expanded state of the chain, despite primitive hydrophobic effects favoring chain collapse. The net free energy of collapse is seen to be a delicate balance between opposing intrapeptide and hydration effects, with intrapeptide contributions favoring collapse.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

Abstract In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self‐organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion‐mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co‐occurrence of multiple features does not necessarily enhance the EPE probability.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Bayesian-Motivated Probabilistic Model of Hurricane-Induced Multimechanism Flood Hazards

Multimechanism floods (MMFs) are caused by the simultaneous occurrence of more than one flood mechanism such as storm surge, precipitation, tides, and waves. MMFs can lead to more severe or differing impacts than single-mechanism floods. As a result, comprehensive risk assessments require the ability to assess the multivariate probabilistic behaviors of hazards from MMFs. Here this study introduces a novel Bayesian-motivated approach for the probabilistic assessment of hurricane-induced hazards from the combination of the surge, precipitation, tides, and river antecedent flow. A Bayesian network (BN) is developed to capture the physical (conditional) relationship between variables and facilitate the generation of a hazard curve for river discharge that captures the contributions from multiple flood drivers. A case study located along the Delaware River is used to illustrate the proposed approach. Five computationally efficient representative predictive models are developed to estimate the conditional distributions required for the BN as a means of demonstrating the overall framework. The predictive models used in this study act as placeholders and can be replaced with more sophisticated and high-fidelity models depending on the desired accuracy level. While the predictive models are intended to be representative and illustrative, the model performance is evaluated using three historical storms that affected the area. Overall, the proposed framework is shown to be transparent, effective, and adaptable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

graph synthesis↗
DOE OSTI2024

Fate of supernova progenitors in massive binary systems

ABSTRACT How massive stars end their lives depends on the core mass, core angular momentum, and hydrogen envelopes at death. However, these key physical facets of stellar evolution can be severely affected by binary interactions. In turn, the effectiveness of binary interactions itself varies greatly depending on the initial conditions of the binaries, making the situation much more complex. We investigate systematically how binary interactions influence core–collapse progenitors and their fates. Binary evolution simulations are performed to survey the parameter space of supernova progenitors in solar metallicity binary systems and to delineate major evolutionary paths. We first study fixed binary mass ratios ($q=M_2/M_1$ = 0.5, 0.7, and 0.9) to elucidate the impacts of initial mass and initial separation on the outcomes, treating separately Type Ibc supernova, Type II supernova, accretion-induced collapse (AIC), rapidly rotating supernova (Ibc-R), black hole formation, and long gamma ray burst (long GRB). We then conduct 12 binary population synthesis model calculations, varying the initial condition distributions and binary evolution parameters, to estimate various supernova fractions. We obtain a Milky Way supernova rate $R_{\rm SN} = (1.78$–$2.47) \times 10^{-2} \, {\rm yr}^{-1}$ which is consistent with observations. We find the rates of AIC, Ibc-R, and long GRB to be $\sim 1/100$ the rate of regular supernovae. Our estimated long GRB rates are higher than the observed long GRB rate and close to the low luminosity GRB rate, although care must be taken considering our models are computed with solar metallicity. Furthering binary modelling and improving the inputs one by one will enable more detailed studies of these and other transients associated with massive stars.

Astronomy & Astrophysics↗
DOE OSTI2021

Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion

Abstract Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high‐resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time‐steps of simulated and in‐vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.

McDonald, T.↗
DOE OSTI2016

2015 Advanced Site Investigation and Monitoring Report Riverton, Wyoming, Processing Site September 2016

The U.S. Department of Energy conducted initial groundwater characterization of the Riverton, Wyoming, Processing Site in the 1990s. The characterization culminated in a Site Observational Work Plan in 1998 that recommended a natural flushing compliance strategy. Results of verification monitoring indicated that natural flushing was generally progressing as expected until June 2010, when significant increases in contaminant concentrations were measured in several monitoring wells downgradient of the site after the area flooded. In response to the unexpected results following the flood, an enhanced characterization of the surficial aquifer was conducted in 2012, which included installation of 103 boreholes along nine transects with a Geoprobe, collection of 103 water samples and 65 soil samples, laboratory tests on the soil samples, and additional groundwater modeling. This advanced site investigation report summarizes additional investigation in 2015 through the use of backhoe trenching, sonic drilling, multilevel monitoring wells, direct-push drilling, and temporary well points to collect soil and groundwater samples. Additional surface water measurements were made included the installation of a stilling well and the measurement of stream elevation along the Wind River to approximate upgradient groundwater heads. Groundwater sampling included the addition of geochemical constituents and isotopes that have not been sampled in the past to better understand post-flood conditions and the possibility of additional or ongoing contaminant sources. This sampling was performed to (1) better define the contaminant plumes, (2) verify the occurrence of persistent secondary contaminant sources, (3) better understand the reason for the contaminant spikes after a 2010 flood, and (4) assess contaminant plume stagnation near the Little Wind River. This report provides data analyses and interpretations for the 2015 site investigation that addresses these issues and provides recommendations for future efforts. Observations from trenches and sonic drilling indicate the general lithology of the shallow, unconsolidated sediments consists of a silt zone at the surface that ranges from 2.5 to 4.8 feet below ground surface, underlain by sand and gravel, underlain by the top of the weathered bedrock (Wind River Formation). Soil data from trenches and sonic drilling indicate (1) elevated concentrations of several constituents in the silt zone, likely due to the formation of evaporites, (2) uranium is the only measured element that appears to be concentrated in the silt over the groundwater contaminant plume, (3) in the former tailings impoundment area, there may be a thin unsaturated zone with elevated uranium in the native material just below the fill, (4) in the former tailings impoundment area, slightly higher uranium concentrations occur in the underlying saturated sand and gravel, and (5) several bedrock samples have a unique geochemical signature, generally related to a higher silt content. Assessment of groundwater flow included measuring river elevations along the Wind River and installing the temporary well points adjacent to the Little Wind River that provided additional data points to refine contours for water table elevations. These data confirm past interpretations of groundwater flow to the southeast across the site toward the Little Wind River. Hydraulic head elevations between paired surficial and semiconfined aquifer wells indicate variable vertical gradients across the site with the potential for upward and downward flow. Additional direct-push drilling and groundwater sampling confirmed the contaminant plume configuration, but it also revealed a low-sulfate-concentration zone at the edge of the former tailings impoundment. Temporary well points provided better definition of plume concentrations at the bank of the Little Wind River, and data from these wells indicate plume discharge to the river. Additional sampling in an area southwest of the plume that had elevated uranium groundwater concentrations in the past did not have any uranium concentrations above the U.S. Environmental Protection Agency maximum concentration limit for uranium. Results from multilevel monitoring wells indicate some geochemical differences with depth, but overall concentrations are similar to those in nearby conventional monitoring wells in the long-term monitoring program. Geochemistry data from these multilevel monitoring wells confirm the general increase in contaminant concentrations toward the river and toward the plume centerline for chloride, sulfate, and uranium but highlight geochemical controls on calcium. Iron data indicate slightly reducing conditions, especially near water table and bedrock surfaces, with more oxidizing conditions in the middle of the sand and gravel. Uranium activity ratios (234U/238U) confirm the uranium plume in the surficial aquifer as being mill related, and the area to the southwest outside the plume as natural, non-mill related. In the semiconfined aquifer, evidence of aquifer connection and impacts from the mill is inconclusive. Values of δD and δ 18 O suggest water is derived from different sources and tritium data confirm that the semiconfined aquifer water is generally older than the surficial aquifer water. However, these data do indicate some groundwater communication from the surficial aquifer into the semiconfined aquifer, which resulted in δD and δ 18 O and tritium values in the semiconfined aquifer that are more similar to those in the surficial aquifer. Values of δ 34 S sulfate in the semiconfined aquifer combined with sulfate concentrations indicate the potential for some mill- related sulfate in the semiconfined aquifer, albeit limited to an area near and beneath the former tailings impoundment. Uranium and molybdenum concentrations in the semiconfined aquifer are below groundwater standards in all wells. However, the elevated molybdenum concentrations at one semiconfined aquifer well underneath the former tailings impoundment suggests a mill- derived source for the uranium and molybdenum in that well. It is possible that aquifer cross- communication occurred when the tailings impoundment was active and created a higher head. Current cross-communication appears unlikely given the large differences in tritium values and an upward hydraulic head at this location. Nine domestic wells are located within the institutional control boundary (eight in the confined aquifer and one in the semiconfined aquifer). Uranium and molybdenum concentrations in all samples collected from these wells were one or two orders of magnitude below the groundwater standards. Surface water flow in the Little Wind River in September 2015 was low compared to historical averages for that time of year. As a result, the uranium concentration measured in the Little Wind River was at a historical maximum at the sampling location upstream of the site. However, the impact of uranium discharge from the groundwater plume into the Little Wind River was not measureable. Elevated sulfate concentrations were observed in an outfall ditch related to an active sulfuric acid plant. Uranium concentrations in the oxbow lake remain at concentrations above the groundwater standard. Plume contaminant concentrations had returned to levels found prior to the 2010 flood by the end of 2015. However, these concentrations still exceed model predictions for natural flushing, and the current data indicate that natural flushing to achieve remediation goals within the 100-year time period is not likely, especially with the high potential for additional floods in the update to the conceptual site model (CSM), soil data indicate additional contaminant sources, specifically uranium, in evaporites within the silt layer over the uranium plume and in naturally reduced zones (NRZs). Additional zones of slightly elevated uranium concentration are in the native sediments just above the water table but below the fill layer in the former tailings impoundment area. This area also has slightly elevated uranium in the sand and gravel below the water table. Mass balance calculations indicate that small amounts of dissolution in any of these zones with increased uranium in the solid phase can produce groundwater uranium concentrations above the groundwater standard and could account for the post-flood uranium spike. The additional uranium near the former tailings impoundment provides a mechanism for a continuing source for the uranium plume that was not considered in earlier natural flushing models. In addition, uranium released from the silt layer or the NRZs seasonally and during flooding may add uranium to the groundwater plume near the Little Wind River. These mechanisms provide a possible explanation for plume persistence, along with spikes in concentrations during floods, that creates the current plume configuration. Additional updates to the CSM include (1) chloride flushes more rapidly than uranium beneath the former mill site, (2) chloride in the silt layer provides a scenario in which chloride cannot be used as a conservative tracer (especially in areas prone to flooding), (3) uranium concentrations with depth can be variable (especially below NRZs), and (4) calcite and gypsum solubility limits appear to provide important geochemical controls on groundwater geochemistry. The conclusion of this study provides several recommendations for additional work to refine the CSM and continue assessment of the natural flushing compliance strategy. Recommendations for additional work include targeted soil and groundwater sampling to assess geochemical conditions, distribution of contaminants, and groundwater/surface water interaction; additional column tests to provide data for geochemical modeling; and development of an updated groundwater flow model, which will be used in conjunction with a geochemical model to assess the viability of the natural flushing compliance strategy.

2015 Monitoring Riverton, WY↗
DOE OSTI2017

A local structure model for network analysis

The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending on neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.

97 MATHEMATICS AND COMPUTING↗