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DOE OSTI2023

Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Theoretical Study of NH 2 Radical Reactions with Propane and Its Kinetic Implications in NH 3 -Propane Blends’ Oxidation

The reaction of NH 2 radicals with C 3 H 8 is crucial for understanding the combustion behavior of NH 3 /C 3 H 8 blends. In this study, we investigated the temperature dependence of the rate coefficients for the hydrogen abstraction reactions of C 3 H 8 by NH 2 radicals using high-level theoretical approaches. The potential energy surface was constructed at the CCSD(T)/cc-pV(T, Q)//M06-2X/aug-cc-pVTZ level of theory, and the rate coefficients were computed using conventional transition state theory, incorporating the corrections for quantum tunneling and hindered internal rotors (HIR). The computed rate coefficients showed a strong curvature in the Arrhenius behavior, capturing the experimental literature data well at low temperatures. However, at T > 1500 K, the theory severely overpredicted the experimental data. The available theoretical studies did not align with the experiment at high temperatures, and the possible reasons for this discrepancy are discussed. At 300 K, the reaction of NH 2 with C 3 H 8 predominantly occurs at the secondary C-H site, which accounts for approximately 95% of the total reaction flux. However, the hydrogen abstraction reaction at the primary C-H site becomes the dominant reaction above 1700 K. A composite kinetic model was built, which incorporated the computed rate coefficients for NH 2 + C 3 H 8 reactions. The importance of NH 2 + C 3 H 8 reactions in predicting the combustion behavior of NH 3 /C 3 H 8 blends was demonstrated by kinetic modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Measurements of jet cross-section ratios in 13 TeV proton-proton collisions with ATLAS

Measurements of jet cross-section ratios between inclusive bins of jet multiplicity are performed in 140 fb -1 of proton-proton collisions with √s = 13 TeV center-of-mass energy, recorded with the ATLAS detector at CERN’s Large Hadron Collider. These ratios are constructed from double-differential cross-section measurements that are made in bins of jet multiplicity and other observables that are sensitive the energy scale and angular distribution of radiation due to the strong interaction in the final state. Additionally, the scalar sum of the two leading jets’ transverse momenta is measured triple differentially, in bins of the third jet’s transverse momentum and of jet multiplicity. These measurements are unfolded to account for acceptance and detector-related effects. The measured distributions are used to construct ratios of the inclusive jet-multiplicity bins, which have been shown to be sensitive to the strong coupling Formula Presented while being less sensitive than other observables to systematic uncertainties and parton distribution functions. The measured distributions are compared with state-of-the-art QCD calculations, including next-to-next-to-leading-order predictions for two- and three-jet events. These predictions are generally found to model the data well and perform best in bins with a modest requirement on the third jet’s transverse momentum. Significant differences between data and Monte Carlo predictions are observed in events with large rapidity gaps and invariant masses of the leading jet pair. Studies leading to reduced jet energy scale uncertainties significantly improve the precision of this work and are documented herein.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Satellite Enveloped with STITCHED Engineering Sensors for Detection of Approaching Objects

Today as well as tomorrows spaceborne assets impact almost all areas of national and nuclear security. Spaceborne assets can not only collect and disseminate valuable data, well beyond just the visual, but also track terrestrial-based mobile assets in real-time, and active spaceborne platforms potentially pose serious risk to vulnerable earth-based systems and infrastructures. The capability to defend national spaceborne assets from attack/interference is critical for security interests. This effort supports this mission through the cost-effective preeminent detection of approaching threats to our nation’s vital resources, in order to help secure and trust these high-value assets against the threats of tomorrow. This project develops novel fabrication techniques for conformal, low-profile and lightweight leakywave antenna (LWA) detection/imaging systems, which fuses technical embroidery (TE) and laser ablation (LA) processes with LWA design. Technical embroidery is an emerging field in additive textile manufacturing where flexible materials and functionalized fabrics are created for a wide variety of uses and purposes, while laser ablation is the process of removing material from a solid surface by irradiating it with a laser beam. Here, thin, conformal antenna designs are designed, modeled and fabricated using both TE and LA, to create lightweight, flexible and conformal object detection and imaging radars. This novel development ensures our nation’s ability to field advanced lightweight and conformal technologies to protect spaceborne assets.

42 ENGINEERING↗

DOE OSTI2020

Probing the Deuteron at Very Large Internal Momenta

We measure H-2(e, e' p)n cross sections at 4-momentum transfers of Q(2) = 4.5 +/- 0.5 (GeV/c)(2) over a range of neutron recoil momenta p(r), reaching up to similar to 1.0 GeV/c. We obtain data at fixed neutron recoil angles theta(nq) = 35 degrees, 45 degrees, and 75 degrees with respect to the 3-momentum transfer (q)over-right-arrow. The new data agree well with previous data, which reached p(r) similar to 500 MeV/c. At theta(nq) = 35 degrees and 45 degrees, final state interactions, meson exchange currents, and isobar currents are suppressed and the plane wave impulse approximation provides the dominant cross section contribution. We compare the new data to recent theoretical calculations, where we observe a significant discrepancy for recoil momenta P-r > 700 MeV/c.

Yero, C.↗

DOE OSTI2024

Jurassic deserts and CO2 sequestration: the Glen Canyon Group in Utah, western USA

The Early Jurassic in the Western Interior of USA consisted of an extensive desert environment, including one of the largest ergs in geologic history. The Navajo erg has been estimated to extend as much as 2.2 million square kilometers, though the preserved extent is somewhat smaller. The Mesozoic was a global greenhouse phase, and during the Jurassic this region experienced fluctuations in climate aridity, reflected in the depositional environment. In addition to extensive dunefields, interdune deposits (lakes and oases) and fluvial systems are also documented within formations of the Glen Canyon Group. The Navajo Sandstone has received much attention as a potential CO2 injection target in recent years. It consists of thick, aeolian sandstones with high porosity and permeability, occurs in both outcrop and subcrop, and has industry data. Mapping of stratigraphic and sedimentologic changes within the Navajo Sandstone has been undertaken in localized areas, however piecing together these studies and developing regional models for CO2 potential has not received as much attention. There is significant industry data across Utah, and utilizing this data to pivot from a focus on hydrocarbon extraction to CO2 injection is an effective way to move forward with green energy, and to meet carbon neutral emission goals. Using legacy well data, we are developing a more comprehensive study of the Navajo Sandstone as a potential CO2 reservoir, in addition to identifying other zones of interest within the Glen Canyon Group, and improving understanding of stratigraphic complexity within one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗

DOE OSTI2022

Lost Circulation Materials in Geothermal Drilling: Thermal Degradation, Viscosity, Compression, and Flow Loop Tests

This dataset provides a set of experimental data on the performance of lost circulation materials (LCMs) used in geothermal drilling operations. It includes results from four distinct tests: thermal degradation, viscosity measurements, compression tests, and high-temperature flow loop experiments. The materials tested, such as cedar fiber, cotton seed hulls, magma fiber, microcellulose, and sawdust, were analyzed under a range of conditions to assess their effectiveness in mitigating fluid losses in geothermal wells. Data on thermal degradation explores mass loss, water loss, and gas release for each material when subjected to temperatures between 90C and 250C. Viscosity measurements capture the behavior of LCM mixtures under different temperatures, while compression tests investigate the structural integrity of both undegraded and thermally degraded materials. The high-temperature flow loop experiments simulate geothermal well conditions, analyzing how different materials and mixtures affect fluid flow and sealing performance in a fractured reservoir environment. In addition to the experimental data, the dataset includes photos documenting the materials, schematics of test setups, and several published manuscripts that provide detailed insights into the experimental methods and findings.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Time-lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse vertical seismic profiling (VSP) integration. The selected study site is a region around the injector well located within Farnsworth field unit (FWU), where there is an ongoing CO 2 -enhanced oil recovery (EOR) operation. In our study, a site-specific rock physics model was created from extensive geological, geophysical, and geomechanical characterization through 3D seismic data, well logs, and core assessed as part of the 1D MEM conducted on the characterization well within the study area. Here, the Biot-Gassmann workflow was utilized to combine the rock physics and reservoir simulation outputs to determine the seismic velocity change due to fluid substitution. Modeled seismic velocities attributed to mean effective stress were determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from ultrasonic seismic velocity measurements. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) was utilized to minimize a penalty function created between the modeled seismic velocities and the observed time-lapse VSP dataset. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

58 GEOSCIENCES↗

DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Adjoint-Based Inversion of Geodetic Data for Sources of Deformation and Strain

An adjoint-based formulation leads to a particularly efficient approach for inverting geodetic measurements for the source of the deformation. Specifically, the quantities necessary to iteratively improve the fit to the observations can be computed with just three forward calculations, one to obtain the current residuals, another to solve the adjoint problem, and a third to compute the step length. An inversion algorithm utilizing the adjoint-based gradient is applied to a set of Interferometric Synthetic Aperture Radar (InSAR) data gathered between 2016 and 2018 over the Tulare Basin in California's Central Valley. Because the measured deformation is due to groundwater withdrawal, a penalty function is included in the inversion to avoid placing aquifer volume change in locations that are far from any documented wells. The solution of the inverse problem provides estimates of aquifer compaction that provide a match to the observed range changes while honoring the well data. The solution indicates an average aquifer volume loss of 2.17 km 3 /year over the two year period from January 2016 to January 2018, encompassing one drought year (2016) and one wet year (2017). Finally, this magnitude of lost volume is compatible with the 3.1 km 3 /year decrease in water volume for the entire Central Valley, estimated from GRACE satellite gravity data.

58 GEOSCIENCES↗

DOE OSTI2021

The Ultra-High-Energy Source MGRO J1908+06

The TeV gamma-ray source MGRO J1908+06 is one of the highest-energy sources known, with observed emission by the High Altitude Water Cherenkov (HAWC) Observatory extending well past 100 TeV. The source exhibits both energy-dependent morphology and a spatially-dependent spectral index. The emission is likely to be dominantly leptonic, and associated with the radio-quiet PSR J1907+0602. However, one-population models do not describe the data well; a second particle population is needed to explain the shape of the spectral energy distribution at the highest energies. This component can be well-described by either leptonic or hadronic hypotheses. We discuss this feature and implications for detection by multi-wavelength and multi-messenger experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗

DOE OSTI2022

Electrical Conductivity of Clayey Rocks and Soils: A Non-Linear Model

It is well-accepted that Archie's law is only applicable to “clean” rocks and soils but fails in “dirty” ones where clay minerals possess an additional component of surface conductivity. Although several models, for example, Waxman-Smits model, were presented to account for this phenomenon, surface conductivity is always inappropriately treated as constant, which actually only holds at high salinities. The essential non-linear characteristic differing over fluid salinities has not been physically or mathematically explained well in those models. Here, we scrutinize the conduction mechanism of clayey rocks and soils and ascribe this non-linear feature to (a) variation of the electrical double layer and (b) the intrinsic clay-and-water conduction pattern. With effective medium theory, we develop an easy-to-use non-linear model that both reflects electrochemical theories and explains the measurement data well. Our model can be used to produce more accurate results for laboratory- and field-scale petrophysical parameter evaluations than the previous models.

58 GEOSCIENCES↗

DOE OSTI2024

Deep energy-pressure regression for a thermodynamically consistent EOS model

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data

ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well ( R 2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

Ogle, Kiona [School of Informatics, Computing, and↗

DOE OSTI2023

Long-term sinking of nuclear waste canisters in salt formations by low-stress creep at high temperature

Abstract Rock salt has a self-sealing capacity, low permeability, and high thermal conductivity, making it a potential host for heat-generating nuclear waste. The feasibility of nuclear waste disposal within salt formations has been investigated mostly for small-sized canisters. Geologic disposal of larger-sized canisters originally designed for spent fuel storage and transportation has lately been examined as a cost-effective alternative. This raises questions about their long-term vertical movement due to their weight and high decay heat. Low-stress creep governs this movement; however, most salt constitutive models do not incorporate it. In this paper, the Norton and the WIPP creep models are compared with the Lux/Wolters/Lerche (LWL) model and a simpler model that combines linear and Norton creep laws (named combined creep model). The LWL and combined creep models consider pressure solution creep, though all incorporate dislocation creep. The models are first applied to creep tests under various stress levels. The LWL and the combined creep models results fit the experimental data well in both high and low stress ranges, whereas the Norton and WIPP models results only fit in higher-stress ranges. The different models are further applied for analyzing long-term canister movement. A sinking rate of $$-4.4\times 10^{-7}$$ - 4.4 × 10 - 7 mm/year was predicted using the Norton and WIPP models versus $$-2.1\times 10^{-2}$$ - 2.1 × 10 - 2 mm/year and $$-3.1\times 10^{-2}$$ - 3.1 × 10 - 2 mm/year using the LWL and the combined creep models, respectively. This comparative study confirms that creep models calibrated exclusively against high-deviatoric stress data might result in an inaccurate estimation of waste packages sinking rate in salt formations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗