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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 OSTI2014

Detecting and Testing for Structural Error in Computer Models with Application to the Community Atmospheric Model

Bayesian calibration/inversion methods for uncertain input parameters to simulation codes have generated an increasing amount of interest in the climate community in the recent years. This approach uses multiple disparate observational datasets related to the output of the simulator of interest to yield a probabilistic constraint on the uncertain inputs (i.e. probability distribution). A major challenge in applying Bayesian calibration methods to simulation codes is related to the possible presence of a large structural error in the output fields of interest; that is, the presence of discrepancy between the observed and the simulated output that is neither explained by the uncertainty in the inputs nor the observations. We present here methods to test for and visualize structural error in an ensemble of input-perturbed simulations. The methods are based on bootstrap (resmpling) test statistics and well-known metrics to quantify the difference between two probability distributions. The proposed methods are demonstrated using selected precipitation output fields of the Community Atmospheric Model (CAM), which are compared to observations provided by the Global Precipitation Climatology Project (GPCP). The methods are found to be useful in identifying regions and seasons with a large structural error. The proposed structural error detection methodology can be used to screen multiple observational datasets of interest for structural error, making it possible to retain only the datasets with a small structural error for use in Bayesian calibration. In addition, information about the amount of structural error can be valuable to model developers.

58 GEOSCIENCES↗
DOE OSTI2019

Metalearners for estimating heterogeneous treatment effects using machine learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

42 ENGINEERING↗
DOE OSTI2023

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Statistical analysis and modeling of intermittent transport events in the tokamak scrape-off layer

The turbulence observed in the scrape-off-layer of a tokamak is often characterized by intermittent events of bursty nature, a feature which raises concerns about the prediction of heat loads on the physical boundaries of the device. It appears thus necessary to delve into the statistical properties of turbulent physical fields such as density, electrostatic potential, and temperature, focusing on the mathematical expression of tails of the probability distribution functions. The method followed here is to generate statistical information from time-traces of the plasma density stemming from Braginskii-type fluid simulations and check this against a first-principles theoretical model. The analysis of the numerical simulations indicates that the probability distribution function of the intermittent process contains strong exponential tails, as predicted by the analytical theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

The spatial scale dependence of diazotrophic and bacterial community assembly in paddy soil

Aim: The factors driving microbial community β-diversity (variation in composition) at different spatial scales yield fundamental insights into the mechanisms that maintain ecosystem biodiversity, which as yet are uncertain. In this work, we explore whether spatial scale-dependent patterns of β-diversity vary between microbial functional groups and bacterial taxa (i.e., diazotrophic and bacterial communities) across local to regional scales (from metres to hundreds of kilometres). Location: Eastern China. Time period: October and November 2015. Major taxa studied: Diazotrophic and bacterial communities. Methods: We use two complementary statistical tools to unveil biotic mechanisms (i.e., species association) underlying variation in β-diversity of diazotrophic and bacterial communities. We examined distance–decay slopes of both communities at the local (1–113 m), meso- (3.4–39 km) and regional (103–668 km) scales. We used an environmentally constrained checkerboard score and topological features of association networks as indices of species association. We then calculated contributions of species association, abiotic factors and geographical distance to explain community β-diversity. The scale-dependent distance–decay relationships were also examined in ubiquitous (high occupancy across samples) and endemic communities of diazotrophs and bacteria. Results: Diazotrophs displayed steeper distance–decay slopes than bacteria, suggesting that the β-diversity of diazotrophic communities was more variable. The distance–decay slopes were dependent on spatial scales in both communities, owing to different contributions of geographical distance, abiotic factors and species association at three spatial scales. Intriguingly, species association was greater and contributed more to community β-diversity than other forces at the local scale, implying that species association could greatly alter community structures. Main conclusions: Drivers of diazotrophic and bacterial community β-diversity depended on spatial scales, resulting in different distance–decay patterns. Moreover, this was the first study to use two methods to demonstrate that species association played important, but as yet unrecognized, roles in driving spatial scale-dependent β-diversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Anomaly detection in scientific data using joint statistical moments

We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest that principal kurtosis vectors, by analogy to principal component analysis (PCA) vectors, signify the principal directions along which outliers appear. The inception of an anomaly, then, manifests as a change in the principal values and vectors of kurtosis. Obtaining the principal kurtosis vectors requires decomposing a fourth order joint cumulant tensor for which we use a simple, computationally less expensive approach that involves performing a singular value decomposition (SVD) over the matricized tensor. We demonstrate the efficacy of this approach on synthetic data, and develop an algorithm to identify the occurrence of a spatial and/or temporal anomalous event in scientific phenomena. The algorithm decomposes the data into several spatial sub-domains and time steps to identify regions with such events. Feature moment metrics, based on the alignments of the principal kurtosis vectors, are computed at each sub-domain and time step for all features to quantify their relative importance towards the overall kurtosis in the data. Accordingly, spatial and temporal anomaly metrics for each sub-domain are proposed using the Hellinger distance of the feature moment metric distribution from a suitable nominal distribution. Finally, we apply the algorithm to two turbulent auto-ignition combustion cases and demonstrate that the anomaly metrics reliably capture the occurrence of auto-ignition in relevant spatial sub-domains at the right time steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

ABSTRACT Weak lensing convergence maps – upon which higher order statistics can be calculated – can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic – the peak count as a function of signal-to-noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large-scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well-defined confidence levels.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Spatiotemporal Monte Carlo transport methods in x-ray semiconductor detectors: Application to pulse-height spectroscopy in a-Se

Purpose: The authors describe a detailed Monte Carlo (MC) method for the coupled transport of ionizing particles and charge carriers in amorphous selenium (a-Se) semiconductor x-ray detectors, and model the effect of statistical variations on the detected signal. Methods: A detailed transport code was developed for modeling the signal formation process in semiconductor x-ray detectors. The charge transport routines include three-dimensional spatial and temporal models of electron-hole pair transport taking into account recombination and trapping. Many electron-hole pairs are created simultaneously in bursts from energy deposition events. Carrier transport processes include drift due to external field and Coulombic interactions, and diffusion due to Brownian motion. Results: Pulse-height spectra (PHS) have been simulated with different transport conditions for a range of monoenergetic incident x-ray energies and mammography radiation beam qualities. Two methods for calculating Swank factors from simulated PHS are shown, one using the entire PHS distribution, and the other using the photopeak. The latter ignores contributions from Compton scattering and K-fluorescence. Comparisons differ by approximately 2% between experimental measurements and simulations. Conclusions: The a-Se x-ray detector PHS responses simulated in this work include three-dimensional spatial and temporal transport of electron-hole pairs. These PHS were used to calculate the Swank factor and compare it with experimental measurements. The Swank factor was shown to be a function of x-ray energy and applied electric field. Trapping and recombination models are all shown to affect the Swank factor.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Maximum a posteriori Ly α estimator (MAPLE): band power and covariance estimation of the 3D Ly α forest power spectrum

We present a novel maximum a posteriori estimator to jointly estimate band powers and the covariance of the three-dimensional power spectrum (P3D) of Ly $\alpha$ forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Ly $\alpha$ surveys that are sparsely sampled transverse to and densely sampled along the line of sight. We demonstrate our method on idealized Gaussian random fields with two selection functions: (i) a sparse sampling of 30 background sources per square degree designed to emulate the current Dark Energy Spectroscopic Instrument; (ii) a dense sampling of 900 background sources per square degree emulating the upcoming Prime Focus Spectrograph Galaxy Evolution Survey. Our proof-of-principle shows promise, especially since the algorithm can be extended to marginalize jointly over nuisance parameters and contaminants, i.e. offsets introduced by continuum fitting. Our code is implemented in JAX and is publicly available on GitHub.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

PARAMETER ESTIMATION OF THE EXPONENTIALLY DAMPED SINUSOID FOR NOISY SIGNALS

The damped sinusoid equation is a common model for many scientific processes involving damped periodic signals. Here we present two methods for estimating the damped sinusoid parameters for noisy signals. Both methods are based upon an exact, closed form solution to fit the parameters for signals without noise, and they estimate the parameters for the noisy signals by the statistical maximum likelihood criterion. The first method relies on an optimizer to minimize the mean square signal error. The second method estimates the parameters by direct calculation and is suitable when the signal noise is small and the frequencies are sufficiently far from the integer and half integer values.

Pelaia, II, Tom↗
DOE OSTI2011

Comparison of Intraoperatively Built Custom Linked Seeds Versus Loose Seed Gun Applicator Technique Using Real-Time Intraoperative Planning for Permanent Prostate Brachytherapy

Purpose: To report our dosimetric results using a novel push-button seed delivery system that constructs custom links of seeds intraoperatively. Methods and Materials: From 2005 to 2007, 43 patients underwent implantation using a gun applicator (GA), and from 2007 to 2008, 48 patientsunderwent implantation with a novel technique allowing creation of intraoperatively built custom links of seeds (IBCL). Specific endpoint analyses were prostate D90% (pD90%), rV100% > 1.3 cc, and overall time under anesthesia. Results: Final analyses included 91 patients, 43 GA and 48 IBCL. Absolute change in pD90% ({Delta}pD90%) between intraoperative and postoperative plans was evaluated. Using GA method, the {Delta}pD90% was -8.1Gy and -12.8Gy for I-125 and Pd-103 implants, respectively. Similarly, the IBCL technique resulted in a {Delta}pD90% of -8.7Gy and -9.8Gy for I-125 and Pd-103 implants, respectively. No statistically significant difference in {Delta}pD90% was found comparing methods. The GA method had two intraoperative and 10 postoperative rV100% >1.3 cc. For IBCL, five intraoperative and eight postoperative plans had rV100% >1.3 cc. For GA, the mean time under anesthesia was 75 min and 87 min for Pd-103 and I-125 implants, respectively. For IBCL, the mean time was 86 and 98 min for Pd-103 and I-125. There was a statistical difference between the methods when comparing mean time under anesthesia. Conclusions: Dosimetrically relevant endpoints were equivalent between the two methods. Currently, time under anesthesia is longer using the IBCL technique but has decreased over time. IBCL is a straightforward brachytherapy technique that can be implemented into clinical practice as an alternative to gun applicators.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiation of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Statistical theory of deformation distributions in nuclear spectra

The dependence of the nuclear level density on intrinsic deformation is an important input to dynamical nuclear processes such as fission. The auxiliary-field Monte Carlo (AFMC) method is a powerful method for computing state densities. However, the statistical distribution of intrinsic shapes is not readily accessible due to the formulation of the AFMC method in a spherical configuration-interaction shell-model approach. Instead, the theory of deformation has largely relied on a mean-field approximation which breaks rotational symmetry. We show here how the distributions of the intrinsic quadrupole deformation parameters can be calculated within the AFMC method and present results for a chain of even-mass samarium nuclei ( 148 Sm, 150 Sm, 152 Sm, 154 Sm) which includes spherical, transitional, and strongly deformed isotopes. The method relies on a Landau-like expansion of the Helmholtz free energy in invariant polynomials of the quadrupole tensor. Here we find that an expansion to fourth order provides an excellent description of the AFMC results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

The development and deployment of advanced reactors, such as the sodium-cooled fast reactor (SFR), relies on sophisticated modeling tools to ensure the safety of the design under various transients. The predictive capability of these advanced modeling tools requires validation to garner trust in supporting the licensing of the advanced reactors. For this reason, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13.In this study, we present and discuss the benchmarking efforts of the modern system code SAM on the FFTF LOFWOS Test #13. Further, the SAM baseline model was developed according to the benchmark specification, which included a detailed core model with reactivity feedback. Generally, good agreement was observed between the baseline results and benchmark measurements; however, discrepancies persisted, particularly in predicted fuel assembly coolant outlet temperatures. Utilizing the baseline model, uncertainty quantification (UQ) and sensitivity analysis (SA) were conducted with the assistance of various statistical learning and machine learning methods, including kernel density estimation, Gaussian processes, and Sobol indices. Following the baseline model prediction and UQ and SA results, we discuss the reasons for the simulation discrepancies and propose further improvements to the model. This benchmarking effort adheres to the best-estimate plus uncertainty approach and can serve as a valuable example for supporting risk-informed licensing of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning

U—10Zr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States. US research reactors have used and tested this fuel type since the 1960s and accumulated considerable experience and knowledge about the fuel performance. Most of the knowledge, however, remains empirical. The lack of mechanistic understanding of fuel performance puts a large burden on proof through experimental verification for the qualification of U—10Zr fuel for commercial use. Further, this paper proposes an image data-driven machine learning approach, coupled with domain knowledge provided by advanced post irradiation examination, to provide unprecedented quantified insights into the morphology, size, density and the connectivity of fission gas bubbles and their effects on the fission product transportation and thermal conductivity. Specifically, we developed a method to automatically detect, extract statistics, and classify ~19,000 fission gas bubbles into different categories, and quantitatively link the data to lanthanide transportation through connected bubbles and degradation of thermal conductivity along the radial temperature gradient in a neutron irradiated U—10Zr annular fuel. Results indicate the approach can be modified to study other irradiation effects, such as secondary phase redistribution and gaseous fuel swelling in other irradiated nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Treatment approach, delivery, and follow-up evaluation for cardiac rhythm disease management patients receiving radiation therapy: Retrospective physician surveys including chart reviews at numerous centers

In a 2-part study, we first examined the results of 71 surveyed physicians who provided responses on how they address the management of patients who maintained either a pacemaker or a defibrillator during radiation treatment. Second, a case review study is presented involving 112 medical records reviewed at 18 institutions to determine whether there was a change in the radiation prescription for the treatment of the target cancer, the method of radiation delivery, or the method of radiation image acquisition. Statistics are provided to illustrate the level of administrative policy; the level of communication between radiation oncologists and heart specialists; American Joint Committee on Cancer (AJCC) staging and classification; National Comprehensive Cancer Network (NCCN) guidelines; tumor site; patient's sex; patient's age; device type; manufacturer; live monitoring; and the reported decisions for planning, delivery, and imaging. This survey revealed that 37% of patient treatments were considered for some sort of change in this regard, whereas 59% of patients were treated without regard to these alternatives when available. Only 3% of all patients were identified with an observable change in the functionality of the device or patient status in comparison with 96% of patients with normal behavior and operating devices. Documented changes in the patient's medical record included 1 device exhibiting failure at 0.3-Gy dose, 1 device exhibiting increased sensor rate during dose delivery, 1 patient having an irregular heartbeat leading to device reprogramming, and 1 patient complained of twinging in the chest wall that resulted in a respiratory arrest. Although policies and procedures should directly involve the qualified medical physicist for technical supervision, their sufficient involvement was typically not requested by most respondents. No treatment options were denied to any patient based on AJCC staging, classification, or NCCN practice standards.

61 RADIATION PROTECTION AND DOSIMETRY↗