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DOE OSTI2026

Determination of proton PDF uncertainties with Markov chain Monte Carlo

We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ⁢𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.

Risse, Peter Clemens [Universität Münster (Germany↗

DOE OSTI2024

Second harmonic generation for estimating state of charge of lithium-ion batteries

This study applied the nonlinear ultrasonic method, second harmonic generation, to precisely estimate the state of charge (SoC) in lithium-ion batteries. The second harmonic of the longitudinal wave is generated on a pouch cell battery at 5 MHz with a through-transmission setup. The relative nonlinear parameter β' is determined by analyzing the amplitudes at the fundamental and second harmonic frequencies. To enhance the nonlinear parameter's measurement accuracy, multiple excitation amplitudes are employed. Two separate charge/discharge tests (four-cycle and eight-cycle) are conducted on the battery at a rate of C/10. The nonlinear parameter is measured periodically during the charge/discharge process, and temperature compensation is applied to the measurement. The correlation curves between the nonlinear parameter and the actual SoC align well for the four-cycle and eight-cycle tests, and a robust linear relationship is observed for both correlation curves. A linear model and a second-order polynomial model are applied to fit the correlation using all data points from both tests. The two models are employed to validate the SoC prediction on a second battery by using a four-cycle test. Furthermore, the results indicate that both models can predict the SoC with an accuracy of approximately 3%, whereas the polynomial model demonstrates smaller errors in the regions near 0% and 100% SoC. Therefore, the nonlinear parameter β', measured through the second harmonic generation, can effectively predict lithium-ion battery SoC with an accuracy of less than 3%.

25 ENERGY STORAGE↗

DOE OSTI2016

A model-based scatter artifacts correction for cone beam CT

Purpose: Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. Methods: The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated. Results: Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from −21.8 to −0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction. Conclusions: The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Random Forests as a Viable Method to Select and Discover High-redshift Quasars

We present a method of selecting quasars up to redshift ≈6 with random forests, a supervised machine-learning method, applied to Pan-STARRS1 and WISE data. We find that, thanks to the increasing set of known quasars, we can assemble a training set that enables supervised machine-learning algorithms to become a competitive alternative to other methods up to this redshift. We present a candidate set for the redshift range 4.8–6.3, which includes the region around z = 5.5 where selecting quasars is difficult due to their photometric similarity to red and brown dwarfs. We demonstrate that, under our survey restrictions, we can reach a high completeness (66% ± 7% below redshift 5.6/83{sub -9}{sup +6}% above redshift 5.6) while maintaining a high selection efficiency (78{sub -8}{sup +10}%/94{sub -8}{sup +5}%). Our selection efficiency is estimated via a novel method based on the different distributions of quasars and contaminants on the sky. The final catalog of 515 candidates includes 225 known quasars. We predict the candidate catalog to contain additional 148{sub -33}{sup +41} new quasars below redshift 5.6 and 45{sub -8}{sup +5} above, and we make the catalog publicly available. Spectroscopic follow-up observations of 37 candidates led us to discover 20 new high redshift quasars (18 at 4.6 ≤ z ≤ 5.5, 2 z ~ 5.7). These observations are consistent with our predictions on efficiency. We argue that random forests can lead to higher completeness because our candidate set contains a number of objects that would be rejected by common color cuts, including one of the newly discovered redshift 5.7 quasars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

SU-D-207-04: GPU-Based 4D Cone-Beam CT Reconstruction Using Adaptive Meshing Method

Purpose: Due to the limited number of projections at each phase, the image quality of a four-dimensional cone-beam CT (4D-CBCT) is often degraded, which decreases the accuracy of subsequent motion modeling. One of the promising methods is the simultaneous motion estimation and image reconstruction (SMEIR) approach. The objective of this work is to enhance the computational speed of the SMEIR algorithm using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the tetrahedral mesh based on the features of a reference phase 4D-CBCT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. After the mesh generation, the updated motion model and other phases of 4D-CBCT can be obtained by matching the 4D-CBCT projection images at each phase with the corresponding forward projections of the deformed reference phase of 4D-CBCT. The entire process of this 4D-CBCT reconstruction method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its tremendous parallel computing ability. Results: A 4D XCAT digital phantom was used to test the proposed mesh-based image reconstruction algorithm. The image Result shows both bone structures and inside of the lung are well-preserved and the tumor position can be well captured. Compared to the previous voxel-based CPU implementation of SMEIR, the proposed method is about 157 times faster for reconstructing a 10 -phase 4D-CBCT with dimension 256×256×150. Conclusion: The GPU-based parallel 4D CBCT reconstruction method uses the feature-based mesh for estimating motion model and demonstrates equivalent image Result with previous voxel-based SMEIR approach, with significantly improved computational speed.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Process-oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction

Weather forecasting nowadays often requires some estimation of uncertainties associated with the output of meteorological models, in order to better inform decision making, especially in the context of intense weather events. Ensemble prediction systems provide such information through sets of possible scenarios which are designed to represent various uncertainty sources, including model uncertainties. A wide variety of methods have been proposed to estimate model uncertainties, among which perturbation methods targeting uncertain processes are a promising research field. Here, we focus on the representation of small-scale variability by process-oriented perturbation schemes applied to two key physical processes, namely turbulence and shallow convection. The perturbations are applied to a single-column version of the convection-permitting AROME model, in three idealized boundary-layer cases. Large-eddy simulations (LESs) of the same cases serve as a reference for the subgrid variability that has to be represented, and the results are also compared to those given by the Stochastically Perturbed Parametrization Tendencies (SPPT) method, which is a method commonly used by weather forecast centres to represent model uncertainty. The spread produced by our process-oriented perturbations of turbulence and shallow convection does not represent all the small-scale variability implied by the LESs for temperature and humidity. However, it is of a similar order of magnitude for the wind, thanks to perturbations generated by the stochastic turbulence scheme. The dispersion is structurally different from what is obtained with SPPT. It is non-negligible in the lower levels, where SPPT perturbations are usually suppressed because of numerical instabilities, indicating a possible complementarity between the schemes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Eddy Current Loss Model Unifying the Effects of Reaction Field and Non-Homogeneous 3-D Magnetic Field

In this article, a new analytical method is proposed to estimate eddy currents inside linear conductive materials. The novel closed-form formulation takes into account the effects of both conductor reaction field that dominates at high frequencies as well as the spatially non-homogeneous nature of the magnetic field penetrating the material. Although the model is suitable for most kinds of permanent magnet machines, it is particularly useful for Surface-mounted Permanent Magnet (SPM) machines where both of these phenomena are prominent. The numerical implementation of the proposed model, which consists of a combination of the method of images and 4-D Fourier transform, is presented. The model is then validated against 2-D and 3-D finite element models (FEMs) for a simple magnetic circuit, showing good agreement. Finally, the eddy current magnet loss in an SPM synchronous machine is evaluated and results are discussed.

Sahu, Raj↗

DOE OSTI2019

Estimation of Evapotranspiration of Amazon Rainforest Using the Maximum Entropy Production Method

Energy budget of Amazonian forests has a large influence on regional and global climate, but relevant data are scarce. A novel energy partition method based on the maximum entropy production (MEP) theory is applied to simulate evapotranspiration in Amazonia. Using site-level eddy flux data, the MEP method shows high skill at the hourly, daily, and monthly scales. Consistent performance under different levels of land surface dryness is revealed, hinting that drought signal is appropriately resolved. The site-level MEP-based estimates outperform the estimates of the Moderate Resolution Imaging Spectroradiometer evapotranspiration product, which is commonly used for large-scale assessments. At the Amazon basin scale, the two series yield similar averages but exhibit spatial differences. The parameter parsimony and demonstrated skill of the MEP method make it an attractive approach for environments with diverse strategies of water flux control.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Adaptive hybrid simulations for multiscale stochastic reaction networks

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Conservative Estimation of Tail Probabilities from Limited Sample Data

Several sparse-sample uncertainty quantification (UQ) methods are compared for conservative but not overly conservative estimation of small tail probabilities involving responses that lay beyond specified thresholds in the tails of probability distributions. Sixteen very differently shaped distributions (or probability density functions, PDFs) and tail probability magnitudes ranging from 10 -5 to 10 -1 are considered in order for the study to be relevant to a wide range of risk analysis and quantification of margins and uncertainty (QMU) problems. The emphasis of the study is on limited data regimes ranging from N = 2 to 20 samples, reflective of most experimental and some expensive computational situations. Relatively simple sparse-sample UQ methods tested for this regime involve statistical tolerance interval "Equivalent Normal and related "Ensemble of Normals" and "Superdistribution (SD) approaches. (The independently derived SD is effectively equivalent to the Bayesian posterior predictive distribution given the assumptions of the derivation.) The performance of the methods was generally improved for N ≥ 5 samples with a generalized Jackknife resampling technique, which determines a tail probability estimate by averaging estimates from smaller sub-samples. Several quantitative metrics for method conservatism and accuracy of tail probability estimation are used to assess and rank the methods' performance over many random trials for each test PDF and probability magnitude. A variant of Bootstrap resampling was also tried, but did not significantly improve tail probability estimates in most cases. Detailed results are presented from over 100-million tests over the above factors that provide useful granular information on which methods or combination of methods perform best in various areas of the factor space.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Congruity of genomic and epidemiological data in modelling of local cholera outbreaks

Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Estimation of volume-weighted average grain size in Fe-based nanocrystalline soft magnetic materials by autocorrelation function

Highlights: • Normalized ACF of TEM images estimates the volume-weighted average grain size. • Grain size estimation by normalized ACF shows good accuracy and time efficiency. • Normalized ACF can reveal grain size difference in different areas of a TEM sample. • Normalized ACF helps modelling of grain size distributions in Fe-B nanostructures. - Abstract: The coercivity of Fe-based nanocrystalline soft magnetic materials depends critically on the grain size of bcc-Fe grains. Accurately estimating the volume-weighted mean grain size is extremely important to develop new Fe-based nanocrystalline soft magnetic materials with low coercivity. This article shows that autocorrelation analysis of dark-field transmission electron microscope (TEM) images provides a very good estimation of bcc-Fe grain size in nanocrystalline soft magnetic materials. This method can be used to efficiently estimate the volume-weighted average grain size from multiple TEM images with much less inherent bias than manual measurement techniques. The estimated grain sizes show good consistency to the grain sizes estimated by X-ray diffraction (XRD) and manual measurement. In contrast to XRD, this method can also reveal the differences in grain size from different micron-sized regions of a sample.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Worldwide Population Estimates for Small Geographic Areas

This chapter discusses the basis for these population estimates, scope, and limitations based on experiences in development of massive global population datasets and usage of these datasets as a basis for sampling design. It presents tools and approaches for using these georeferenced population estimates for complex household survey sampling. The chapter provides a unified resource for understanding current gridded population datasets, their use in survey research, and promising areas of future work toward improved population estimates. It includes an overview of popular gridded population datasets, common methodological frameworks for developing gridded population estimates, and discusses pros and cons for selecting a gridded population dataset from a survey perspective. The chapter focuses on survey methods that use gridded population estimates specifically. It highlights differences between census population estimates and gridded population estimates that may be relevant when planning data collection. The chapter presents a case study of gridded population data and sampling methods in Nigeria.

Amer, Safaa↗

DOE OSTI2018

A Data-Driven Framework for Residential Electric Vehicle Charging Load Profile Generation

Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

SU-E-T-569: Neutron Shielding Calculation Using Analytical and Multi-Monte Carlo Method for Proton Therapy Facility

Purpose: To evaluate the shielding wall design to protect patients, staff and member of the general public for secondary neutron using a simply analytic solution, multi-Monte Carlo code MCNPX, ANISN and FLUKA. Methods: An analytical and multi-Monte Carlo method were calculated for proton facility (Sumitomo Heavy Industry Ltd.) at Samsung Medical Center in Korea. The NCRP-144 analytical evaluation methods, which produced conservative estimates on the dose equivalent values for the shielding, were used for analytical evaluations. Then, the radiation transport was simulated with the multi-Monte Carlo code. The neutron dose at evaluation point is got by the value using the production of the simulation value and the neutron dose coefficient introduced in ICRP-74. Results: The evaluation points of accelerator control room and control room entrance are mainly influenced by the point of the proton beam loss. So the neutron dose equivalent of accelerator control room for evaluation point is 0.651, 1.530, 0.912, 0.943 mSv/yr and the entrance of cyclotron room is 0.465, 0.790, 0.522, 0.453 mSv/yr with calculation by the method of NCRP-144 formalism, ANISN, FLUKA and MCNP, respectively. The most of Result of MCNPX and FLUKA using the complicated geometry showed smaller values than Result of ANISN. Conclusion: The neutron shielding for a proton therapy facility has been evaluated by the analytic model and multi-Monte Carlo methods. We confirmed that the setting of shielding was located in well accessible area to people when the proton facility is operated.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Predicting Effective Diffusion Coefficients in Mudrocks Using a Fractal Model and Small-Angle Neutron Scattering Measurements

The determination of effective diffusion coefficients of gases or solutes in the water–saturated pore space of mudrocks is time consuming and technically challenging. Yet, reliable values of effective diffusion coefficients are important to predict migration of hydrocarbon gases in unconventional reservoirs, dissipation of (explosive) gases through clay barriers in radioactive waste repositories, mineral alteration of seals to geological CO 2 storage reservoirs and contaminant migration through aquitards. In this study, small angle and very small angle neutron scattering techniques have been utilized to determine a range of transport properties in mudrocks, including porosity, pore size distributions and surface and volume fractal dimensions of pores and grains, from which diffusive transport parameters can be estimated. Using a fractal model derived from Archie's Law, we calculate effective diffusion coefficients from these parameters and compare them to laboratory–derived effective diffusion coefficients for CO 2 , H 2 , CH 4 and HTO on either the same or related mudrock samples. The samples include Opalinus Shale from the underground laboratory in Mont Terri, Switzerland; Boom Clay from a core drilled in Mol, Belgium and a marine claystone cored in Utah, USA. The predicted values were compared to laboratory diffusion measurements. The measured and modeled diffusion coefficients show good agreement, differing generally by less than factor 5. Furthermore, neutron or X–ray scattering analysis is therefore proposed as a novel method for fast, accurate estimation of effective diffusion coefficients in mudrocks, together with simultaneous measurement of multiple transport parameters including porosity, pore size distributions and surface areas, important for (reactive) transport modelling.

58 GEOSCIENCES↗