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DOE OSTI2022

Determination of Molecular Structure and Dynamics of Molten Salts by Advanced Neutron and X-ray Scattering Measurements and Computer Modeling

The design and development of fully functional Molten-Salt Reactors (MSR) require detailed knowledge of the molten salt properties in order to understand and predict the salt’s behavior. Fundamental properties of interest include molecular structure, speciation, and dynamics (such as diffusion coefficients) of salt components and dissolved corrosion and fission products. Computer modeling is necessary to predict changes in physical and chemical properties due to irradiation, burning of dissolved fuel, and corrosion. The modeling requires experimental data, and advanced neutron and x-ray scattering and spectroscopy provide the most reliable and direct determination of the structure (Pair-Distribution Functions, PDF), and dynamics of ions in the melt. This project dealt with both fluoride and chloride salts. The PDFs have been measured by a combination of neutron and x-ray diffraction. We utilized the techniques of isotope substitutions, a very powerful tool available for neutron-scattering, to extract the details of the liquid structure. Although similar measurements have been done before, modern advanced neutron and x-ray-scattering techniques allow collecting the data at much higher resolution and in a wider range of temperatures. Importantly, we were among the first to study fluoride salts by neutron scattering. The importance of impurities and their effects on salt properties have become apparent recently and so new methods of salt purification were developed. We took advantage of these developments to produce reliable data, which have been used for computer simulations of both clean salts and those with added fission and corrosion products most relevant for MSRs. Ab initio molecular dynamics simulations have been performed to understand the multi-component liquid solution, in particular solubility of impurities and thermodynamic interactions in relation to the ionic-cluster structure of the fluid. We applied machine learning to regress from the simulation and experimental data in order to develop a fast-acting model that can handle molten salt with an arbitrary (≥ 10) number of chemical elements and be able to predict chemical potential as a function of composition and temperature. This project resulted in a number of experimental and computer-simulation publications, a patent application, and numerous conference presentations (American Physical Society, American Chemical Society, and The Electrochemical Society among others). Multiple students and postdocs participated and collaborated on aspects of this project. This project seeded new collaborations between MIT and other institutions, such as the University of Massachusetts Lowell, the University of Illinois Urbana-Champaign, the University of California Berkeley, and Oak Ridge and Los Alamos National Labs. As such, this project has had a broad and lasting impact beyond its original scientific scope.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions

Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation-/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the data-intensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPUs. The evaluation demonstrates MICCO outperforms other state-of-art works, achieving up to 2.25× speedup in synthesized datasets, and 1.49× speedup in real-world correlation functions.

Wang, Qihan↗

DOE OSTI2017

Impact of cardiac reverse remodeling after cardiac resynchronization therapy assessed by myocardial perfusion imaging on ventricular arrhythmia

Background: Although cardiac resynchronization therapy (CRT) has been a useful treatment of heart failure, patients with CRT are still in risk of sudden cardiac death due to ventricular arrhythmia. The aim of this study was to investigate the impact of cardiac reverse remodeling after CRT on the prevalence of ventricular tachycardia or fibrillation (VT/VF). Methods and Results: Forty-one heart failure patients (26 men, age 66 ± 10 years), who were implanted with CRT for at least 12 months, were enrolled. All patients received myocardial perfusion imaging (MPI) under CRT pacing to evaluate left ventricle (LV) function, dyssynchrony, and scar. VT/VF episodes during the follow-up period after MPI were recorded by the CRT devices. Sixteen patients (N = 16/41, 39%) were found to have VT/VF. Multivariate Cox regression analysis and receiver operating characteristic curve analysis showed that five risk factors were significant predictors of VT/VF, including increased left ventricle ejection fraction (LVEF) by ≤7% after CRT, low LVEF after CRT (≤30%), change of intrinsic QRS duration (iQRSd) by ≤7 ms, wide iQRSd after CRT (≥121 ms), and high systolic dyssynchrony after CRT (phase standard deviation ≥45.6°). For those patients with all of the 5 risk factors, 85.7% or more developed VT/VF. Conclusions: The characteristics of cardiac reverse remodeling after CRT as assessed by MPI are associated with the prevalence of ventricular arrhythmia.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Developing a space-filling mixture experiment design when the components are subject to linear and nonlinear constraints

This article presents a case study of developing a space-filling design (SFD) for a constrained mixture experiment when the experimental region is specified by single-component constraints (SCCs), linear multiple-component constraints (LMCCs), and nonlinear multiple-component constraints (NMCCs). Traditional methods and software for designing constrained mixture experiments with SCCs and LMCCs (using either optimal design or SFD approaches) are not directly applicable because of the NMCCs. A SFD algorithm in the JMP software was modified to accommodate the nonlinear MCCs; the modification is described in this article. The case study involves high-level waste (HLW) glass that is subject to the formation of nepheline crystals as the glass cools. This can significantly reduce the durability of HLW glass (which is undesirable). The goal of the study was to develop a SFD for the HLW glass compositional region where nepheline may form, and generate data for modeling nepheline formation as a function of HLW glass composition. The HLW glass composition region was specified in terms of eight components with SCCs, two LMCCs, and two NMCCs. The NMCCs were based on a nonlinear logistic regression model for a binary-response nepheline response that was developed from previous data. This article discusses the HLW glass example, the constraints specifying the experimental composition region, and how an existing algorithm for generating SFDs was modified to accommodate the NMCCs. The methodology discussed in this article can be applied to any example in which the experimental region is specified by one or more nonlinear constraints in addition to linear constraints on mixture components and/or non-mixture variables.

Mixture experiment, space-filling experimental des↗

DOE OSTI2024

Maximum Likelihood Estimation: Some Basics

The maximum likelihood estimation is a general estimation procedure. It is often compared to estimation procedures like the ordinary least squares regression or generalized method of moments, to name a few. We discuss some basics about the maximum likelihood estimation, its advantages and disadvantages, and provide an example application to a gamma distribution function.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Maximum Likelihood Estimation: Some Basics

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1984

A rotor optimization using regression analysis

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Giansante, N.↗

NASA NTRS1999

Time-on-task decrements in "steer clear" performance of patients with sleep apnea and narcolepsy

Loss of attention with time-on-task reflects the increasing instability of the waking state during performance in experimentally induced sleepiness. To determine whether patients with disorders of excessive sleepiness also displayed time-on-task decrements indicative of wake state instability, visual sustained attention performance on "Steer Clear," a computerized simple RT driving simulation task, was compared among 31 patients with untreated sleep apnea, 16 patients with narcolepsy, and 14 healthy control subjects. Vigilance decrement functions were generated by analyzing the number of collisions in each of six four-minute periods of Steer Clear task performance in a mixed-model analysis of variance and linear regression equations. As expected, patients had more Steer Clear collisions than control subjects (p=0.006). However, the inter-subject variability in errors among the narcoleptic patients was four-fold that of the apnea patients, and 100-fold that of the controls volunteers; the variance in errors among untreated apnea patients was 27-times that of controls. The results of transformed collision data revealed main effects for group (p=0.006), time-on-task (p=0.001), and a significant interaction (p=0.022). Control subjects showed no clear evidence of increasing collision errors with time-on-task (adjusted R2=0.22), while apnea patients showed a trend toward vigilance decrement (adjusted R2=0.42, p=0.097), and narcolepsy patients evidenced a robust linear vigilance decrement (adjusted R2=0.87, p=0.004). The association of disorders of excessive somnolence with escalating time-on-task decrements makes it imperative that when assessment of neurobehavioral performance is conducted in patients, it involves task durations and analyses that will evaluate the underlying vulnerability of potentially sleepy patients to decrements over time in tasks that require sustained attention and timely responses, both of which are key components in safe driving performance.

NASA Discipline Space Human Factors↗

NASA NTRS2016

Dynamic Filtering Improves Attentional State Prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Harrivel, Angela R.↗

DOE OSTI2015

Kidney function and blood pressure in preschool-aged children exposed to cadmium and arsenic - potential alleviation by selenium

Background: Early-life exposure to toxic compounds may cause long-lasting health effects, but few studies have investigated effects of childhood exposure to nephrotoxic metals on kidney and cardiovascular function. Objectives: To assess effects of exposure to arsenic and cadmium on kidney function and blood pressure in pre-school-aged children, and potential protection by selenium. Methods: This cross-sectional study was part of the 4.5 years of age (range: 4.4–5.4 years) follow-up of the children from a supplementation trial in pregnancy (MINIMat) in rural Bangladesh, and nested studies on early-life metal exposures. Exposure to arsenic, cadmium and selenium from food and drinking water was assessed by concentrations in children's urine, measured by ICP-MS. Kidney function was assessed by the estimated glomerular filtration rate (eGFR, n=1106), calculated from serum cystatin C, and by kidney volume, measured by ultrasound (n=375). Systolic and diastolic blood pressure was measured (n=1356) after five minutes rest. Results: Multivariable-adjusted regression analyzes showed that exposure to cadmium, but not arsenic, was inversely associated with eGFR, particularly in girls. A 0.5 µg/L increase in urinary cadmium among the girls (above spline knot at 0.12) was associated with a decrease in eGFR of 2.6 ml/min/1.73 m{sup 2}, corresponding to 0.2SD (p=0.022). A slightly weaker inverse association with cadmium was also indicated for kidney volume, but no significant associations were found with blood pressure. Stratifying on children's urinary selenium (below or above median of 12.6 µg/L) showed a three times stronger inverse association of U-Cd with eGFR (all children) in the lower selenium stratum (B=−2.8; 95% CI: −5.5, −0.20; p=0.035), compared to those with higher selenium (B=−0.79; 95% CI: −3.0, 1.4; p=0.49). Conclusions: Childhood cadmium exposure seems to adversely affect kidney function, but not blood pressure, in this population of young children in rural Bangladesh. Better selenium status appears to be protective. However, it is important to follow up these children to assess potential long-term consequences of these findings. - Highlights: • Bangladesh has high levels of arsenic and cadmium in drinking water and food • We assessed toxic exposure, kidney and cardiovascular function in Bangladeshi children • Cadmium appeared to decrease estimated glomerular filtration rate in girls • These effects were somewhat alleviated by selenium.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Microbial community diversity changes during voltage reversal repair in a 12-unit microbial fuel cell

Microbial fuel cell stacks (MFC-Stack) are often confronted with voltage reversals, likely due to an interplay between microbial community dynamics and insufficient electric circuit balancing. Herein, we provide new insight into voltage reversals by examining the microbiomes of twelve MFC units of a 12-liter Pilot-MFC-Stack during repair. Different biofilm repair methods (self-healing, electrostimulation, and re-acclimatization upon cross-inoculation) were used to evaluate the microbial community response. In addition, MFC-Stack simulation was performed based on Kirchhoff’s Second Law to predict values for source potentials and post-evaluate internal resistances. Analysis of the 16S rRNA amplicon sequencing data suggests that the biofilm repair methods could slowly heal damaged biofilms. Notably, severely voltage reversed MFC units had low electrogen relative abundances (18%) and positive anode potentials, while strong bioanodes and contained more than 50% electrogens and had negative anode potentials. Between-community analyses (beta diversity ordination and multinomial regression) of the voltage reversed MFC units revealed differences among biofilms in contrast to healthy/strong MFC units. Permutational multivariate analysis of variance (PERMANOVA) confirmed that reversed biofilms were, indeed, significantly (p < 0.05) different from stronger ones. Overall, these analyses demonstrated the utility of combining electrotechnical and microbial community analyses, especially beta diversity ordination and multinomial regression, to understand problematic MFC units and the potential success of a biofilm repair method. Finally, thicker biofilms were usually healthier and stronger, although thickness was no guarantee for proper structure and power function as all factors were interdependent. There was an evolutionary trend that strong anodes became stronger/healthier and others weaker. This spontaneous trend has to be considered to avoid irreversible voltage reversals and to repair electrogenic biofilms in an MFC-Stack.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Dynamic calibration of differential equations using machine learning, with application to turbulence models

We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Dosimetric characterization of the GammaClip™{sup 169}Yb low dose rate permanent implant brachytherapy source for the treatment of nonsmall cell lung cancer postwedge resection

Purpose: A novel {sup 169}Yb low dose rate permanent implant brachytherapy source, the GammaClip™, was developed by Source Production and Equipment Co. (New Orleans, LA) which is designed similar to a surgical staple while delivering therapeutic radiation. In this report, the brachytherapy source was characterized in terms of “Dose calculation for photon-emitting brachytherapy sources with average energy higher than 50 keV: Report of the AAPM and ESTRO” by Perez-Calatayud et al. [Med. Phys. 39, 2904–2929 (2012)] using the updated AAPM Task Group Report No. 43 formalism.Methods: Monte Carlo calculations were performed using Monte Carlo N-Particle 5, version 1.6 in water and air, the in-air photon spectrum filtered to remove photon energies below 10 keV in accordance with TG-43U1 recommendations and previously reviewed {sup 169}Yb energy cutoff levels [D. C. Medich, M. A. Tries, and J. M. Munro, “Monte Carlo characterization of an Ytterbium-169 high dose rate brachytherapy source with analysis of statistical uncertainty,” Med. Phys. 33, 163–172 (2006)]. TG-43U1 dosimetric data, including S{sub K}, D-dot (r,θ), Λ, g{sub L}(r), F(r, θ), φ{sub an}(r), and φ{sub an} were calculated along with their statistical uncertainties. Since the source is not axially symmetric, an additional set of calculations were performed to assess the resulting axial anisotropy.Results: The brachytherapy source's dose rate constant was calculated to be (1.22 ± 0.03) cGy h{sup −1} U{sup −1}. The uncertainty in the dose to water calculations, D-dot (r,θ), was determined to be 2.5%, dominated by the uncertainties in the cross sections. The anisotropy constant, φ{sub an}, was calculated to be 0.960 ± 0.011 and was obtained by integrating the anisotropy factor between 1 and 10 cm using a weighting factor proportional to r{sup −2}. The radial dose function was calculated at distances between 0.5 and 12 cm, with a maximum value of 1.20 at 5.15 ± 0.03 cm. Radial dose values were fit to a fifth order polynomial and dual exponential regression. Since the source is not axially symmetric, angular Monte Carlo calculations were performed at 1 cm which determined that the maximum azimuthal anisotropy was less than 8%.Conclusions: With a higher photon energy, shorter half-life and higher initial dose rate {sup 169}Yb is an interesting alternative to {sup 125}I for the treatment of nonsmall cell lung cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Harnessing Uncertainty through Functional Data Analysis in Gas Breakthrough Data

Detecting subsurface explosions from radionuclide gas migration through rock fractures is an effective way to identify nuclear activity. Los Alamos National Laboratory (LANL) has developed simulation methods, based on data from the 1962 Hardhat underground nuclear test, to predict gas breakthrough times at the surface. However, these methods rely on an imperfect understanding of the relationship between rock damage and fracture permeability. Our clinic project studies methods for predicting breakthrough curves that characterize total mass produced as a function of time, as well as quantifying the uncertainty associated with these predictions. The model that is currently employed to relate damage to permeability uses an empirically motivated power-law expression, with a range of parameter values that are compatible with the experimental Hardhat data. We develop emulators, built from functional data analysis techniques and trained on simulation data, that rapidly predict the gas breakthrough curve given a damage field and given the parameter values of the power-law equation. Using Bayesian regression, we address the problem of uncertainty quantification in our emulators. Finally, in order to test the robustness of the model, we further validate it on a damage field representing different physical conditions.

58 GEOSCIENCES↗

DOE OSTI2018

Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures

The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

Microwave scatterometer measurements of oceanic wind vector

Results from Seasat-A scatterometer (SASS) remote sensing of oceanic wind vectors are reviewed, together with the theory of radar backscattering. The SASS was designed to measure the surface wind stress and neutral stability wind vector at 19.5 m altitude. Requirements included windspeeds from 4-24 m/sec to within 2 m/sec or 10%, a 1000 km swath, directions from 0-360 deg to within 20 deg, a 50 km resolution cell, and cross-track and along-track spacing between resolution cells of 100 km. The method was based on Bragg scattering of microwaves from cm-length capillary ocean waves. The strength of the backscatter is proportional to the capillary wave amplitude, which is in equilibrium with the ocean surface wind speed. The measurement grouping, least-squares estimator, and dB deviation model function table for the SASS geophysical algorithm are discussed. Comparisons of SASS, GOASEX, and ground truth wind data are made, and a 0.92 correlation between SASS and ground truth data is determined from regression analysis.

Jones, W. L.↗

NASA NTRS1998

Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Laudeman, I. V.↗