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DOE OSTI2023

Novel ammonia recovery from anaerobic digestion by integrating biogas stripping and gypsum absorption

Recovering ammonia from anaerobic digestion (AD) effluent offers the opportunity to produce a renewablefertilizer and reduce environmental impact. A novel process developed for such an application was evaluatedduring this study. The feature of the process is integrating biogas stripping and gypsum (CaSO 4 •2H 2 O) absorption. The result revealed that the lower CO 2 content and the absorption temperature contributed the higherefficiency of ammonia recovery. The gypsum solution recovered around 95% of the stripped ammonia when thestripping gas contained 10% CO 2 and it rose to 100% when the gas contained more than 30% CO 2 . Furthermore,a statistical model and genetic algorithm were applied to estimate the total ammonia recovery and optimize theoperating parameters. The experimental and modeling results indicated that integrating biogas stripping withgypsum absorption can effectively harvest ammonia nitrogen and reduce CO 2 emissions while producing(NH 4 ) 2 SO 4 fertilizer from AD effluent.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Abstract Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge–Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

First Leptophobic Dark Matter Search from the Coherent–CAPTAIN-Mills Liquid Argon Detector

We report the first results of a search for leptophobic dark matter (DM) from the Coherent–CAPTAIN-Mills (CCM) liquid argon (LAr) detector. An engineering run with 120 photomultiplier tubes (PMTs) and 17.9 × 10 20 protons on target (POT) was performed in fall 2019 to study the characteristics of the CCM detector. The operation of this 10-ton detector was strictly light based with a threshold of 50 keV and used coherent elastic scattering off argon nuclei to detect DM. Despite only 1.5 months of accumulated luminosity, contaminated LAr, and nonoptimized shielding, CCM’s first engineering run has already achieved sensitivity to previously unexplored parameter space of light dark matter models with a baryonic vector portal. With an expected background of 115 005 events, we observe 115 005 + 16.5 events which is compatible with background expectations. For a benchmark mediator-to-DM mass ratio of m V B =m χ = 2.1, DM masses within the range 9 MeV ≲ m χ ≲ 50 MeV are excluded at 90% C. L. in the leptophobic model after applying the Feldman-Cousins test statistic. CCM’s upgraded run with 200 PMTs, filtered LAr, improved shielding, and 10 times more POT will be able to exclude the remaining thermal relic density parameter space of this model, as well as probe new parameter space of other leptophobic DM models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Interaction models and configurational entropies of binary MoTa and the MoNbTaW high entropy alloy

We introduce a simplified method to model the interatomic interactions of high entropy alloys based on a lookup table of cluster energies. Furthermore, these interactions are employed in replica exchange Monte Carlo simulations with histogram analysis to obtain thermodynamic properties across a broad temperature range. Kikuchi's cluster variation method entropy formalism is applied to directly calculate entropy from statistics on short- and long-range chemical order, and we discuss the convergence of the entropy as clusters of differing size are included. A high temperature series expansion aids in our understanding of the convergence. Computer codes implementing these methods, and supporting data, are freely available on the internet.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Optode Images, and Dissolved Oxygen Data from Sand and Sediment Flow-through Column Experiments associated with: "2-D Imaging of Dissolved Oxygen Concentration in Flow-Through Sediment Columns"

This data package is associated with the publication "2-D Imaging of Dissolved Oxygen Concentration in Flow-Through Sediment Columns" submitted to Frontiers in Water (Garayburu-Caruso et al., 2022). This work presents a novel application of planar optode technology to sediment columns that allows the study of well-constrained flow fields without the need to assume completely homogeneous flow. We introduce a flow-through column with the interior coated in an oxygen sensing film and a series of sampling ports as a unique tool capable of capturing changes in dissolved oxygen (DO) at high frequency (10 s) and spatial (< 1 mm) resolution across the length of a column. In addition, we provide custom scripts that allows image processing steps to be automated and consistently reproduced over a variety of experimental conditions. This dataset is comprised of four folders (1) 01_Riverbed_Sediment_Experiment, (2) 02_Sand_Experiment, (3) 01_Riverbed_Sediment_Raw_Images, and (4) 02_Sand_Experiment_Raw_Images. 01_Riverbed_Sediment_Experiment contains (1) a subfolder with csv files that contain statistical parameters from the linear regressions applied to each optode image, (2) a subfolder with csv files from DO data measured by in-line sensors, (3) a subfolder with csv files containing column and injection specific input parameters for the image processing script, and (4) a subfolder with scripts used to process the images and create associated figures. 02_Sand_Experiment contains (1) a subfolder with csv files containing column and injection-specific input parameters for the image processing script, and (2) a subfolder with scripts used to process the images and create associated figures. 01_Riverbed_Sediment_Raw_Images (spited in 3 parts due to size) contains a subfolder with raw red, and green images from the optode system during the MilliQ Injection, Vanillin Injection or Vanillin + Sampling Injection respectively. 02_Sand_Experiment_Raw_Images contains a subfolder with raw red, and green images from the optode system. Outside of the main folders there is a csv containing file-level metadata and a csv data dictionary defining column headers for all csv files contained in the data package.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Uncertainty in Annual Energy Resulting from Uncertain Irradiance Measurements

We report an analysis quantifying the contribution to uncertainty in annual energy projections from uncertainty in ground-measured irradiance. Uncertainty in measured irradiance is quantified for eight instruments by the difference from a well maintained, secondary standard pyranometer which is regarded as truthful. We construct a statistical model of irradiance uncertainty and apply the model to generate a sample of 100 annual time series of irradiance for each instrument. The sample is propagated through a common performance model for a reference photovoltaic system to quantify variation in annual energy. Although the measured irradiance varies from the reference by a few percent (standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.

14 SOLAR ENERGY↗

DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗

DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗

DOE OSTI2020

Assessing Current and Future Infrastructure Hazards

Project Objective Execute intelligent analytics via an advanced analytical framework, to assess the current state of offshore infrastructure, evaluate infrastructure life, and identify technologies to reduce infrastructure hazards, costs, and extend infrastructure life. Approach & Results thus Far• Build comprehensive dataset• Perform data-driven analytics to evaluate infrastructure integrity 1. Remaining lifespan 2. Likelihood of future risk• Apply data-driven advanced spatial, statistical, and Machine Learning (ML) models to quantify existing infrastructure integrity• Release data and models through a smart, online platform hosted by Energy Data eXchange (EDX)

Romeo, Lucy F.↗

DOE OSTI2023

Examining Light-Induced Degradation with Combined-Accelerated Stress Testing

While further understanding is sought, the results suggest that a module can show no degradation in performance through a qualification test light soak, (IEC 61215: 2021 MQT 19.1 requirement of 10 kW minimum on one side). Yet, such cells may degrade significantly by LID under conditions within the statistical extremes of the natural environment applied in combined-accelerated stress testing (CAST).

combined-accelerated stress testing↗

DOE OSTI2025

Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in an Earth system model. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO 2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO 2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model based on a 9-day lag had significantly fewer nodes than a graph based on monthly means. Furthermore, this demonstrates our method’s ability to perform dimension reduction while still uncovering source-to-impact pathways.

Changepoint detection↗

DOE OSTI2024

Complete Development of Critical Capabilities for TRISO Fission Product Source Term Calculations and Quantify Mechanisms for Pd Penetration of SiC

Overall fission product (FP) release will be an important consideration for the licensing and deployment of advanced reactors utilizing tristructural isotropic (TRISO) fuels. This work focuses on enhancing and applying the BISON models needed to predict FP transport within TRISO particles and particle failure probability, both of which factor directly into release predictions. Specifically, this report details (1) the development of the models needed to predict palladium (Pd) conservation at the engineering scale and the application of those models to characterize Pd fluxes for input into a mechanistic multiscale model for Pd penetration; (2) the refinement of sorption mass transfer models and the development of models for trapping in porous layers, which were applied and compared to particle scans from AGR-2 to provide proof of concept for a method of particle-scale validation that may reduce uncertainties compared to compact-scale validation using data from integral effects tests; (3) the development of a failure-statistics-informed, mesh-independent methodology for applying smeared cracking, enabling further study of the localized multiphysics behaviors associated with cascading particle failure mechanisms; and (4) the preliminary characterization of those coupled multiphysics particle failure behaviors using smeared, nonretentive diffusivities to provide a baseline for future study and to guide ongoing engineering applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Comparative Assessment of Data-driven Process Models in Health Information Technology

Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Expanded method for the determination of burnup in nuclear fuels using multiple neodymium isotopes

Established methods for the determination of burnup in nuclear fuels commonly rely on the measurement of 148Nd in the spent fuel in concert with known fission product yields to determine the atom percent of fissions in the fuel. This isotope of neodymium is used for various reasons, including chemical and radioactive stability, ease of measurement, and low rates of formation and destruction due to neutron flux apart from fission. However, careful calculation of effective cumulative fission yields and correction factors for (n,γ) capture reactions allows for additional stable and long-lived isotopes of neodymium to be used to provide additional independent measurements of burnup, reducing statistical uncertainty. This method was developed and successfully applied to measure the burnup of compacts from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program. The mean burnup measured using 143Nd, 145Nd, 146Nd, 148Nd, and 150Nd was statistically observed to be the same as that measured using 148Nd alone, but the statistical uncertainty in the measurement was reduced by a factor of 2, providing a tighter confidence interval in the final results.

Helmreich, Grant [ORNL] (ORCID:0000000330464394)↗

DOE OSTI2022

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A coupled vacancy diffusion-dislocation dynamics model for the climb-glide motion of jogged screw dislocations

Here, we develop a novel model to study the climb/glide motion of jogged screw dislocations within the discrete dislocation dynamics (DDD) framework. We present results for the dependence of the climb velocity on the applied stress and on the jog size and distribution statistics. We show that the model predictions are consistent with experimental data in both γ-TiAl and Zircaloy-4. The ranges of the applied stress and jog spacing that determine the dominance of one of three dislocation mechanisms are identified. These are the jog dragging, dipole dragging, and dipole bypass mechanisms, respectively. The overall dislocation motion in the jog dragging regime is composed of glide of screw segments and climb of jogs, controlling the plastic strain and the creep rate, respectively. Based on current simulations and on a detailed examination of the predicted jog heights compared to experiments, we advance the hypothesis that a combination of jog dragging and dipole bypass mechanisms is necessary to reproduce the high creep rate observed in some experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Optimal Frequency-domain Analysis for Spacecraft Time Series: Introducing the Missing-data Multitaper Power Spectrum Estimator

While the Lomb–Scargle periodogram is foundational to astronomy, it has a significant shortcoming: the variance in the estimated power spectrum does not decrease as more data are acquired. Statisticians have a 60 yr history of developing variance-suppressing power spectrum estimators, but most are not used in astronomy because they are formulated for time series with uniform observing cadence and without seasonal or daily gaps. Here we demonstrate how to apply the missing-data multitaper power spectrum estimator to spacecraft data with uniform time intervals between observations but missing data during thruster fires or momentum dumps. The F-test for harmonic components may be applied to multitaper power spectrum estimates to identify statistically significant oscillations that would not rise above a white noise–based false alarm probability. Multitapering improves the dynamic range of the power spectrum estimate and suppresses spectral window artifacts. We show that the multitaper–F-test combination applied to Kepler observations of KIC 6102338 detects differential rotation without requiring iterative sinusoid fitting and subtraction. Significant signals reside at harmonics of both fundamental rotation frequencies and suggest an antisolar rotation profile. Next we use the missing-data multitaper power spectrum estimator to identify the oscillation modes responsible for the complex "scallop-shell" shape of the K2 light curve of EPIC 203354381. We argue that multitaper power spectrum estimators should be used for all time series with regular observing cadence.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The look-elsewhere effect from a unified Bayesian and frequentist perspective

When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high signi ficance will be found. This is known as the look-elsewhere effect and is prevalent throughout cosmology, (astro)particle physics, and beyond. To avoid making false claims of detection, one must account for this effect when assigning the statistical significance of an anomaly. This is typically accomplished by considering the trials factor, which is generally computed numerically via potentially expensive simulations. In this paper we develop a continuous generalization of the Bonferroni and Sidak corrections by applying the Laplace approximation to evaluate the Bayes factor, and in turn relating the trials factor to the prior-to-posterior volume ratio. Here, we use this to define a test statistic whose frequentist properties have a simple interpretation in terms of the global p-value, or statistical significance. We apply this method to various physics-based examples and show it to work well for the full range of p-values, i.e. in both the asymptotic and non-asymptotic regimes. We also show that this method naturally accounts for other model complexities such as additional degrees of freedom, generalizing Wilks' theorem. This provides a fast way to quantify statistical significance in light of the look-elsewhere effect, without resorting to expensive simulations.

79 ASTRONOMY AND ASTROPHYSICS↗