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DOE OSTI2022

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗

DOE OSTI2020

Quantitative assessment of environmental phenomena on maximum pit size predictions in marine environments

Maximum pit sizes were predicted for dilute and concentrated NaCl and MgCl 2 solutions as well as sea-salt brine solutions corresponding to 40% relative humidity (RH) (MgCl 2 -rich) and 76% RH (NaCl-rich) at 25 °C. A quantitative method was developed to capture the effects of various cathode evolution phenomena including precipitation and dehydration reactions. Additionally, the sensitivity of the model to input parameters was explored. Despite one's intuition, the highest chloride concentration (roughly 10.3 M Cl – ) did not produce the largest predicted pit size as the ohmic drop was more severe in concentrated MgCl 2 solutions. Therefore, the largest predicted pits were calculated for saturated NaCl (roughly 5 M Cl – ). Next, it was determined that pit size predictions are most sensitive to model input parameters for concentrated brines. However, when the effects of cathodic reactions on brine chemistry are considered, the sensitivity to input parameters is decreased. Although there was not one main input parameter that influenced pit size predictions, two main categories were identified. Under similar chloride concentrations (similar RH), the water layer thickness (WL), and pit stability product, (i · x) sf , are the most influential factors. When varying chloride concentrations (RH), changes in WL, the brine specific cathodic kinetics on the external surface (captured in the equivalent current density (i eq )), and conductivity (k o ) are the most influential parameters. Finally, it was noted that dehydration reactions coupled with precipitation in the cathode will have the largest effect on predicted pit size, and cause the most significant inhibition of corrosion damage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Dark Field Proton Radiography (Los Alamos LDRD Report)

Proton radiography at LANL (pRad) takes advantage of the high flux, high proton energy, and flexible timing structure of LANSCE's 800-MeV proton beam to visualize dense, dynamic systems under extreme strain. With recent needs identified for the development of the W93, the pRad team has been actively searching for ways to increase the facility's radiographic capabilities. Recently, we discovered that utilizing two magnetic lenses (one upstream and one downstream of the object), and specific combinations of collimator choices within the two lenses, that the signal-to-noise ratio was improved by more than a factor of two. However, we did not fully understand the mechanism, and found that results did not match forward modeling predictions. This work specifically sought to implement an engineering solution to solve this problem, through the development of an actuated collimator that reduced the time required to swap collimator settings from a typical 4-8 hours, to less than 30 seconds. This allowed, in combination with a unique multi-material, multi-resolution stepwedge, to acquire an unprecedently enormous dataset, with in total 24 collimation combinations, and multiple diffuser settings and pulse patterns, equivalent to almost a full run cycle's worth of data in a few days. This massive dataset is currently being harnessed to make a fully-predictive, ultra-fast Python-based model of the system, to dial in specific settings for any future experiments that fully optimize that radiographic sensitivity to the material of interest.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Crystal field splittings and magnetic ground state of the square-lattice antiferromagnets YbBi 2 ⁢ClO 4 and YbBi 2 ⁢IO 4 with J eff = $\frac{1}{2}$

Here, we report on the crystal field level splitting and magnetic ground state of the J eff = $\frac{1}{2}$ square lattice antiferromagnets YbBi 2 ⁢ClO 4 and YbBi 2 ⁢IO 4 using powder inelastic neutron scattering (INS) and neutron diffraction measurements. Both compounds exhibit a well-isolated Γ 7 doublet ground state under a tetragonal crystal field environment, confirming a robust J eff = $\frac{1}{2}$ picture with slight XY-type anisotropic character in the g-tensor. Notably, the ground state wave functions closely resemble the Γ 7 doublet expected in the perfect cubic limit, consistent with the nearly cubic ligand configuration of eight O 2- ions surrounding Yb 3+ . Below T N = 0.21 K, YbBi 2 ⁢IO 4 exhibits a stripe long-range magnetic order characterized by an ordering wave vector q m = (1/2, 0, 0) or its symmetry-equivalent (0, 1/2, 0), with magnetic moments aligned along q m . The ordered moment is approximately 79% of the classical prediction, significantly larger than expected from the isotropic J 1 -J 2 model, suggesting the possible involvement of exchange anisotropy in explaining this observation. We show that symmetry-allowed XXZ and bond-dependent anisotropic exchange terms in a square lattice can play a critical role in stabilizing the stripe order and suppressing the moment reduction as observed. These findings establish YbBi 2 ⁢ClO 4 and YbBi 2 ⁢IO 4 as unique platforms for exploring rich J eff = $\frac{1}{2}$ magnetism from two less investigated perspectives: (i) on a square lattice and (ii) within a (nearly) cubic ligand environment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Autoignition of cyclohexane at low-to-intermediate temperatures: Rapid compression machine experiments and improved comprehensive chemical kinetic model

Cycloalkanes are an important class of hydrocarbons found in conventional and alternative transportation fuels. Being one of the simplest practical cycloalkanes, cyclohexane is often used as a surrogate in studies to understand the oxidation, ignition, and sooting characteristics of cycloalkanes more broadly. Although cyclohexane has been extensively studied, very limited datasets for cyclohexane oxidation at low-to-intermediate temperatures are available in the literature. Moreover, the literature autoignition data from rapid compression machines (RCMs) did not provide information on volume histories or experimental heat transfer characteristics which can significantly impact model predictions, thereby hindering the detailed validation of chemical kinetic models. It has also been observed that the cyclohexane kinetic models developed so far are not able to predict the literature data across a wide range of operating and mixture conditions. Hence, further studies are recommended to understand and improve model predictions for cyclohexane oxidation. In this study, RCM experiments were conducted using cyclohexane/air mixtures at varying compressed pressures (P C =15, 20, and 30 bar), equivalence ratios (ϕ=0.5, 1.0, and 2.0), and compressed temperatures (T C =670–960 K). The current RCM results of cyclohexane were found to complement well with the literature data, with the first-stage IDTs at high pressures and volume histories at all test conditions being first reported for better development and validation of chemical kinetic models. Additionally, a detailed chemical kinetic model for the cyclohexane oxidation has been developed and validated using both newly acquired experimental IDTs in this study and literature experimental studies (IDTs in RCMs and STs, species profiles in jet-stirred reactors, and laminar burning velocities). Unlike literature models, the proposed model was observed to provide an overall better prediction for experimental results over a wide range of conditions. Chemical kinetic analysis of the developed model was further conducted to identify the reaction pathways controlling the autoignition of cyclohexane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

14 SOLAR ENERGY↗

DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Understanding Transient Combustion Phenomena in Low-NO x Gas Turbines

The goal of this work was to understand, quantify, and eventually predict combustion instability during operational transients in low-NO x gas turbine combustors. Transient operation is defined as a mode where one or more key operating parameters, including equivalence ratio, fuel composition, or fuel splitting between nozzles in a combustor, are varying in time. The timescale over which this variation occurs will be referred to as the transient timescale, the magnitude of the change in the transient parameter will be referred to as the transient amplitude, and the direction of the variation will be referred to as the transient direction. Transients occur regularly in power generation gas turbine operation. For example, load-following gas turbines vary load to meet power demands while accounting for variation in unsteady grid contributors like wind and solar power. Operators achieve this by varying equivalence ratio and fuel splitting inside the combustors to produce more or less power. Another example of transient operation is during fuel-switching operations, a particular issue for turbines operating with syngas fuels in a co-generation facility. In this case, the composition of the fuel stream may vary in time either as a result of fluctuations in the pipeline, or in the case of a co-generation plant, variations in feedstock or gasification processes. This fluctuation in fuel composition can have significant implications for combustion processes, particularly with high-hydrogen fuels. A small number of studies have investigated transient behavior in gas turbine combustors, particularly in the context of flame blow-off; here, investigators have typically focused on issues of transient amplitude, though without the depth and physics-based experimental design that is proposed in this program. To our knowledge, there have been no studies that have quantified the impact of the transient timescale on combustion. The work helped us understand the impact of several types of transients on combustion stability. Both static and dynamic combustion stability will be considered. The transients will be quantified using their amplitude, direction, and timescale, and we will consider transients in equivalence ratio, fuel composition, and fuel splitting. There were two main objectives of this work: 1. Provide a fundamental understanding of combustion stability during transient operation using data obtained from a number of high-speed diagnostic techniques on experimental configurations that capture important gas turbine combustor features and operating conditions. This goal was met in conjunction with our industrial partner, GE Global Research, with whom we worked closely throughout the program. 2. Develop a stability prediction framework that will allow for the prediction, and possible control, of instabilities as a function of the transient amplitude, direction, and timescale. This input/output model should be physics-based and allow for screening of operating conditions and prediction of unstable behavior at the end of a transient by monitoring combustor health and precursor signals during operation.

03 NATURAL GAS↗

DOE OSTI2022

Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Dry deposition of atmospheric aerosols in large-scale models is a critical, but highly uncertain, sink process with a strong dependence on particle size, meteorological conditions, and land surface properties. This study investigates the particle dry deposition scheme implemented in the standard Community Multiscale Air Quality (CMAQ) model v5.2.1, characterizes its underlying parameterized components with comparison to a similar scheme in a contemporary regional-scale model, and proposes two updated schemes that are then evaluated with available ambient particle deposition velocity (V d ) measurements. Both updated schemes reduce the surprisingly strong dependence of deposition velocity on the aerosol mode width, with one scheme further introducing a dependence on vegetation coverage that is broadly consistent with variability in observations between vegetated and non-vegetated surfaces. Compared to the base scheme, the updated scheme with vegetation dependence increases V d for submicron particles and decreases it for larger particles by an average of 37% and –66%, respectively. This scheme performs statistically better than the base scheme, reducing fractional biases by 56%–97% for vegetated land-use types and has roughly equivalent performance over water. The base and updated schemes are tested with three annual CMAQ (v5.2.1) simulations for the year 2011; predicted ambient aerosol concentrations are evaluated with routine monitoring network observations and predicted dry deposition fluxes are evaluated with data from the Clean Air Status and Trends Network (CASTNET). The updated scheme with vegetation dependence reduces negative fractional biases for PM 10 by 41% and positive fractional biases for PM 2.5 organic carbon by 15%. This scheme has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3 and beyond) to replace the scheme used in previous versions of CMAQ (v4.5 through v5.2.1).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

An experimental and modeling study on autoignition of 2-phenylethanol and its blends with n-heptane

2-Phenylethanol (2-PE) is an aromatic alcohol with high research octane number, high octane sensitivity, and a potential to be produced using biomass. Considering that 2-PE can be used as a fuel additive for boosting the anti-knocking quality of gasoline in spark-ignition engines and as the low reactivity fuel or fuel component in dual-fuel reactivity controlled compression ignition (RCCI) engines, it is of fundamental and practical interest to understand the autoignition chemistry of 2-PE, especially at low-to-intermediate temperatures (<1000 K). Based upon the experimental ignition delay time (IDT) results of neat 2-PE obtained from our previous rapid compression machine (RCM) investigation and the literature shock tube study, a detailed chemical kinetic model of 2-PE is developed herein, covering low-to-high temperature regimes. Besides, RCM experiments using binary fuel blends of 2-PE and n-heptane (nC7) are conducted in this work to investigate the nC7/2-PE blending effects, as they represent a dual-fuel system for RCCI operations. Furthermore, the newly developed 2-PE model is merged with a well-validated nC7 kinetic model to generate the current nC7/2-PE binary blend model. Overall, the consolidated model reasonably predicts the experimental IDT data of neat 2-PE and nC7/2-PE blends, as well as captures the experimental effects of pressure, equivalence ratio, and blending ratio on autoignition. Finally, model-based chemical kinetic analyses are carried out to understand and identify the controlling chemistry accounting for the observed blending effects in RCM experiments. In conclusion, the analyses reveal that nC7 enhances 2-PE autoignition via providing extra $\dot{O}$H radicals to the shared radical pool, while the diminished nC7 promoting effect on 2-PE autoignition with increasing temperature is due to the negative temperature coefficient characteristics of nC7.

2-phenlyethanol↗

DOE OSTI2024

Effect of helium bubbles on the mobility of edge dislocations in copper

Abstract Helium bubbles can form in materials upon exposure to irradiation. It is well known that the presence of helium bubbles can cause changes in the mechanical behavior of materials. To improve the lifetime of nuclear components, it is important to understand deformation mechanisms in helium-containing materials. In this work, we investigate the interactions between edge dislocations and helium bubbles in copper using molecular dynamics (MD) simulations. We focus on the effect of helium bubble pressure (equivalently, the helium-to-vacancy ratio) on the obstacle strength of helium bubbles and their interaction with dislocations. Our simulations predict significant differences in the interaction mechanisms as a function of helium bubble pressure. Specifically, bubbles with high internal pressure are found to exhibit weaker obstacle strength as compared to low-pressure bubbles of the same size due to the formation of super-jogs in the dislocation. Activation energies and rate constants extracted from the MD data confirm this transition in mechanism and enable upscaling of these phenomena to higher length-scale models.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Critical Shear Stress for Erosion Under Laminar Jet Flow

Particle erosion is an important process in both natural and manmade environments. For example, in natural environments particle or sediment erosion rates determine the course of rivers, depth of streams, and fate of deltas. The interplay between erosion rates and flow patterns can have dramatic impact on shorelines. Where hydrodynamic jet flows or rip currents change direction, sediments may accumulate or disperse altering the shape of coastlines and the ecosystems and economies that rely on them. In man-made systems, jet flows may be used to mobilize particles from surfaces or within industrial scale mixing tanks. For example, in the nuclear industry, impinging jet mixers may be used to mobilize and suspend sludges at the bottom of waste tanks. In each of these examples, the Shields diagram is useful to determine the conditions under which erosion occurs. This diagram plots the scaled critical shear stress for erosion as a function of the particle size as embedded in particle Reynolds numbers, Archimedes numbers, or their equivalents. Although the data, that carries a large spread, is largely in agreement with mathematical models, for the smallest particles, model predictions diverge from each other and from experimental data. Indeed, there is substantial uncertainty about the Shields diagrams at relatively small particle sizes and flow rates. Here we evaluate critical shear stresses for erosion under laminar jet flow. To date critical shear stresses for erosion, as embodied in Shields diagrams, have largely been developed for turbulent flow both experimental and mathematically. However, curves on the Shields diagrams diverge among various models at lower particle Reynolds numbers. Indeed, how critical shear stresses for erosion develop under fully laminar jet flow conditions (not simply laminar boundary layers under turbulent flow) remains unclear. Here we address this gap, which is particularly important for mobilization of the smallest particles. We find the Shields parameter to be inversely proportional to the particle Reynolds numbers under laminar jet flow conditions similar to turbulent conditions.

critical shear stresses, erosion rates, effective ↗

DOE OSTI2023

(U) A General-Purpose Code for Correlated Sampling Using Batch Statistics with MCNP6 for Fixed-Source Problems

Correlated sampling can be used to reduce the uncertainty of a difference of tallies by taking advantage of the negative covariance term in the sandwich formula. Booth first showed how correlated sampling can be applied with batch statistics using MCNP’s tally fluctuation chart (TFC) to reduce the uncertainty of a difference of tallies in fixed-source problems. Booth presented a problem in which a 1273% uncertainty in a difference was reduced to 8% by accounting for correlations. Researchers He and Su recently studied correlated sampling using the TFC in MCNP version 5. They determined that the code did not print enough digits in the TFC tally means for accurate batch statistics in some cases. After modifying the source code, they concluded that “correlated sampling can yield a standard deviation of about one magnitude smaller than that predicted by the direct, un-correlated simulation when the changes in system response are small (say about 1%), which is equivalent to saving in CPU time by a factor of 100. Such saving [sic] becomes less significant as the change in system response becomes larger.” He and Su provided the formulas needed to apply batch statistics to compute the correlated uncertainty of a difference of tallies. In this report, we follow up on their work by providing the formulas needed to apply batch statistics to compute the correlated uncertainty of a ratio of tallies and of a difference of two tallies divided by a third tally. We extend these formulas to differences and ratios of ratios. These formulas are applied to reduce the uncertainty associated with calculating a relative sensitivity. He and Su did not investigate the accuracy of their correlated sampling uncertainty estimates. We use their test problems and evaluate the accuracy of the uncertainty estimates by comparing with results obtained from random sampling, and, in simple cases, with theoretical values of the “exact” uncertainties. We find that the uncertainties obtained from batch statistics are accurate as long as at least 100 batches are used. We present a new computer code, COSUBS (COrrelated Sampling Using Batch Statistics), that reads MCNP6 TFCs and applies correlated sampling using batch statistics for the tally combinations that the user specifies. COSUBS is a very general tool that compares all TFCs for a base case and one or two perturbed cases. It computes uncertainties for ratios if given only a base case. This report is organized as follows. The equations to apply batch statistics to the difference of random tallies are reviewed in Sec. II. Section III presents the equations for applying batch statistics to a ratio of random tallies; this is useful for computing relative sensitivities using a one-sided finite difference and the relative sensitivity using the differential operator method. Section IV presents the equations for applying batch statistics to a difference of two random tallies divided by a third; this is useful for computing a relative sensitivities using a central difference. Section V presents the equations for applying batch statistics to a difference of two ratios with four random tallies. Section VI presents the equations for applying batch statistics to a one-sided finite difference estimate of the relative sensitivity of a ratio (this uses four random tallies). Section VII presents the equations for applying batch statistics to a central difference estimate of the relative sensitivity of a ratio (this uses six random tallies). Section VIII presents the equations for applying batch statistics to a sum of random tallies. Section IX discusses how to apply batch statistics using MCNP6. Section X presents COSUBS, describing its command-line options and logic. Sections XI through XVI present numerical results for various test problems. Section XVII is a summary and conclusions. Appendix A derives the theoretical Monte Carlo tally variance given certain assumptions; these variances are used to verify the batch statistics for some of the problems. Appendix B lists the MCNP6 input for the unperturbed example problem. Appendix C presents modifications made to MCNP6.3 to support this work.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The Concentration–Mass relation of massive, dynamically relaxed galaxy clusters: agreement between observations and ΛCDM simulations

ABSTRACT The relationship linking a galaxy cluster’s total mass with the concentration of its mass profile and its redshift is a fundamental prediction of the Cold Dark Matter (CDM) paradigm of cosmic structure formation. However, confronting those predictions with observations is complicated by the fact that simulated clusters are not representative of observed samples where detailed mass profile constraints are possible. In this work, we calculate the symmetry-peakiness-alignment (SPA) morphology metrics for maps of X-ray emissivity from the three hundred project hydrodynamical simulations of galaxy clusters at four redshifts, and thereby select a sample of morphologically relaxed, simulated clusters, using observational criteria. These clusters have on average earlier formation times than the full sample, confirming that they are both morphologically and dynamically more relaxed than typical. We constrain the concentration–mass–redshift relation of both the relaxed and complete sample of simulated clusters, assuming power-law dependences on mass (κm) and 1 + z (κζ), finding κm = −0.12 ± 0.07 and κζ = −0.27 ± 0.19 for the relaxed subsample. From an equivalently selected sample of massive, relaxed clusters observed with Chandra, we find κm = −0.12 ± 0.08 and κζ = −0.48 ± 0.19, in good agreement with the simulation predictions. The simulated and observed samples also agree well on the average concentration at a pivot mass and redshift providing further validation of the ΛCDM paradigm in the properties of the largest gravitationally collapsed structures observed. This also represents the first clear detection of decreasing concentration with redshift, a longstanding prediction of simulations in data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Wear of Wave Energy Converters Mooring Lines Belts

Using a belt as a replacement for a rope on a rotary power take-offs (PTOs) system has become more common for wave energy converters, improving cyclic bend over sheave performance with a smaller bending thickness for belts. However, the service life predictions of PTOs are a major concern in design, because belt performance under harsh underwater environments is largely less studied. Here, in this work, the effect of fleet and twist angles on wear life is being investigated both experimentally and numerically. Two three-dimensional equivalent static finite element models are constructed to evaluate the complex stress state of polyurethane-steel belts around steel drums. The first is to capture the response of the experimental investigation performed on the wear life, and the second to predict the wear life of an existing functional PTO. The results show a significant effect for fleet and twist angles on stress concentrations and estimated service life.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Kinetic simulation of a 50 mTorr capacitively coupled argon discharge over a range of frequencies and comparison to experiments

The effect of driving frequency in the range of 13.56–73 MHz on electron energy distribution and electron heating modes in a 50 mTorr capacitively coupled argon plasma discharge is studied using 1D-3V particle-in-cell simulations. Calculated electron energy probability functions exhibit three distinct “temperatures” for low-, mid-, and high-energy electrons at all the studied driving frequencies. When compared to published experimental data, the calculated probability functions show a reasonable agreement for the energy range resolved in the measurements (about 2–10 eV). Discrepancies due to limitations in experimental energy resolution outside this range lead to differences between computational and experimental values of the electron number density determined from the distribution functions, and the predicted effective electron temperature is within 25% of experimental values. The impedance of the discharge is interpreted in terms of a homogeneous equivalent circuit model, and the driving frequency dependence of the inferred combined sheath thickness is found to obey a known, theoretically derived, power law. The average power transferred from the field to the electrons (electron heating) is computed, and a region of negative heating near the sheath edge, particularly at higher driving frequencies, is identified. Analysis of the electron momentum equation shows that electron inertia, which on temporal averaging would be zero in a linear regime, is responsible for negative values of power deposition near the sheath edge at high driving frequencies due to the highly nonlinear behavior of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Verification and validation of detonation-shock-dynamics relations for explosives described by general equation of state and chemical reaction models

Detonation shock dynamics is a powerful method to model the behaviour of High Explosives (HE). However in order to use this method, the underlying relationship between the local radius of curvature and the detonation speed must be known. Previous work has developed methods to calculate this effect using simple, single-step Arrhenius and polytropic gas, models for the chemical reaction and the equation of state, respectively. In recent years, more complex models for both reaction rates and equations of state have been developed which show better agreement with experimental data than these simple models, especially when considering condensed phase explosives.. This work presents the governing equations for solving these problems in a way that is generalised to use arbitrary equations of state as well as reaction models which may have more than a single step and multiple product species. This implementation is verified against exact solutions, demonstrating that the equations were implemented properly. The verified algorithm is then validated against experimental data and high fidelity simulations, showing that it is able to make accurate predictions in a regime where the underlying assumptions of the governing equations are valid. Importantly, this approach has many applications: from creating equivalent detonation shock dynamics models for existing reactive burn calibrations for HE; to developing new functional forms and calibrations of reactive burn models for condensed phase high explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗