Effects of Wavelength-Shifting Plates on the Light Collection and Vertex Reconstruction of a Large-Volume Water-Cherenkov Detector
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Spatial Transferability of Machine Learning Based Volume Estimation Models
High-quality traffic volume data is essential for efficient transportation planning and operations. However, such high-quality data is expensive to collect, owing primarily to the high capital cost of installing and maintaining continuous counting stations (CCSs). Recent availability of probe-based vehicle data offers a cost-effective solution for increasing the observability of traffic volumes. However, having ample ground truth traffic data is a prerequisite for developing robust volume estimation models. Though this might not be a big issue in many states, states with scarce CCS data might be able to benefit from robust volume estimation models developed in (adjacent) data-rich states. While there is a reasonable amount of spatial transferability research in the transportation domain, there is a dearth of knowledge on the spatial transferability of probe-based volume estimation models. To address this gap, this paper explores spatial transferability of volume estimation models developed from data in three states (Colorado, North Carolina, and Pennsylvania). Results indicate that it is extremely important to maintain temporal consistency when attempting spatial transferability of volume estimation models. It was also found that models trained on regions with lower peak traffic volumes will limit the performance of models transferred to states with higher peak hourly traffic volumes. Corroborating findings from existing spatial transferability research on other topics, it was found that a meta-model (developed using data from multiple states) performs better than volume estimation models developed within any one of the states.
ADVANCED PROPULSION SYSTEMS↗
Investigating the Relative Roles of INPs and CCN in a Simulated Thunderstorm Using a New Immersion Freezing Algorithm
Microphysical processes in deep convective clouds are sensitive to the number concentrations of cloud condensation nuclei (CCN) and ice nucleating particles (INPs), but the effects of INPs are less studied. Modeling studies investigating the effects of INPs and/or CCN on deep convection typically retain a volume‐dependent raindrop freezing relation. The resulting neglect of aerosol accumulation in raindrops via drop collisions has likely produced unrealistic storm responses to INPs in past studies. To address this deficiency, a new immersion freezing algorithm was developed and embedded in a bulk microphysics scheme that freezes both cloud drops and raindrops using the same immersion freezing INP (IF‐INP) activity spectrum based on measurements. Multiple idealized simulations of a single case of deep convection observed during the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign were conducted, with microphysical differences produced by independently altering IF‐INP temperature dependencies and CCN number concentrations from their observed values. Surface precipitation in all simulations resulted almost exclusively from riming graupel that melted upon descending to the surface. Rainfall and cold pools were substantially and systematically weakened with increased CCN due to decreased graupel riming rates but were relatively insensitive to variations in the magnitude and slope of IF‐INP spectra due to compensating depletion of supercooled liquid water. These compensating processes were a consequence of the accumulation of IF‐INPs in raindrops, encouraging caution in studying IF‐INP effects upon thunderstorms using traditional volume‐dependent drop freezing relationships.
CCN↗
Screening Ionic Liquids Based on Ionic Volume and Electrostatic Potential Analyses
Ionic liquids are known to have tunable solvation properties, based on the pairing of different anions and cations, but the compositional landscape is vast and challenging to navigate efficiently. Some computational screening protocols are available, but they can be either time-consuming or difficult to implement. Herein, we perform a detailed investigation of the fundamental role of electrostatic interactions in these systems. We effectively develop a bridge between the previous volume-based approach (VBA) with a quantum structure–property relationship (QSPR) approach to create fast, simple screening guidelines. We propose a new parameter that is applicable to both monovalent and multivalent ions, the ionic polarity index (IPI), which is defined as the ratio of the average electrostatic surface potential ($\bar{V}$) of the ion to the net charge of the ion (q). The IPI correlation has been tested on a diverse dataset of 121 ions and reliable predictions can be obtained within a homologous series of ionic liquid compounds.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Promoting CO 2 Release from CO 3 2- -Containing Solvents during Water Electrolysis for Direct Air Capture
The pH swings from water electrolysis are leveraged to condition OH – -based facile CO 2 capture solvents using an electrochemical flow cell for direct air capture (DAC). Besides demonstrating the DAC using a membrane contactor, promoting CO 2 release from a CO 3 2– solution at the anode is specifically studied by adjusting the volumetric flow rate, anode chamber volume, residence time, and K 2 CO 3 concentration. Through case-by-case comparisons coupled with modeled results, increasing current, reducing volumetric flow rate, and/or reducing CO 3 2– concentration are the effective methods to promote CO 2 release from a CO 3 2– -containing solvent, whereas enlarging the anode chamber volume poses a minor effect. Moreover, the discrepancies between the experimental and modeled results may be caused by H + crossover rather than K + transport through the Nafion membrane during water electrolysis based upon the total alkalinity measurements for the K 2 CO 3 solutions gleaned from the anode. Here, it is believed that such results will provide guidance to design and operate an electrochemical flow cell for electrochemistry-assisted DAC and point source CO 2 capture.
20 FOSSIL-FUELED POWER PLANTS↗
Atomistic Simulations of the Elastic Compression of Platinum Nanoparticles
Abstract The elastic behavior of nanoparticles depends strongly on particle shape, size, and crystallographic orientation. Many prior investigations have characterized the elastic modulus of nanoscale particles using experiments or simulations; however their reported values vary widely depending on the methods for measurement and calculation. To understand these discrepancies, we used classical molecular dynamics simulation to model the compression of platinum nanoparticles with two different polyhedral shapes and a range of sizes from 4 to 20 nm, loaded in two different crystal orientations. Multiple standard methods were used to calculate the elastic modulus from stress-vs-strain data for each nanoparticle. The magnitudes and particle-size dependence of the resulting moduli varied with calculation method and, even for larger nanoparticles where bulk-like behavior may be expected, the effective elastic modulus depended strongly on shape and orientation. Analysis of per-atom stress distributions indicated that the shape- and orientation-dependence arise due to stress triaxiality and inhomogeneity across the particle. When the effective elastic modulus was recalculated using a representative volume element in the center of a large nanoparticle, the elastic modulus had the expected value for each orientation and was shape independent. It is only for single-digit nanoparticles that meaningful differences emerged, where even the very center of the particle had a lower modulus due to the effect of the surface. These findings provide better understanding of the elastic properties of nanoparticles and disentangle geometric contributions (such as stress triaxiality and spatial inhomogeneity) from true changes in elastic properties of the nanoscale material.
36 MATERIALS SCIENCE↗
Pitch Variation Experiments in Water-Moderated Square-Pitched U(6.90)O 2 Fuel Rod Lattices with Fuel to Water Volume Ratios Spanning 0.08 to 0.67
The US Department of Energy (DOE) Nuclear Energy Research Initiative funded the design and construction of the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories. The start-up of the experiment facility and the execution of the experiments described here were funded by the DOE Nuclear Criticality Safety Program. The 7uPCX is designed to investigate critical systems with fuel for light water reactors in the enrichment range above 5 % 235 U. The 7uPCX assembly is a water-moderated and -reflected array of aluminum-clad square-pitched U(6.90 %)O 2 fuel rods. Other critical experiments performed in the 7uPCX assembly are documented in LEU-COMP-THERM-078, LEU-COMP-THERM-080, LEU-COMPTHERM- 096, LEU-COMP-THERM-097, and LEU-COMP-THERM-101. The fuel used in these experiments was fabricated using unirradiated 6.90 % enriched UO 2 fuel pellets from fuel elements designed to be used in the internal nuclear superheater section of the Pathfinder boiling water reactor operated in South Dakota by the Northern States Power Company in the 1960s. The fuel elements were obtained from The Pennsylvania State University where they had been stored for many years. The fuel pellets in those fuel elements were removed from the original Incoloy cladding and reclad in 3003 aluminum tubes and end caps for use in the experiments reported here. The purpose of these experiments was to measure the effects of decreasing the fuel-to-water volume ratio on the critical array size. This was accomplished by removing fuel rods from fully fueled configurations, effectively increasing the pitch of the fuel arrays in the assembly. The fuel rod pitch variations provided assembly configurations that ranged from strongly undermoderated to slightly overmoderated.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Design Principles to Govern Electrode Fabrication for the Lithium Trivanadate Cathode
A full depth of discharge mathematical model for the lithium trivanadate cathode, considering lithiation of the layered α-phase, phase change, and lithiation of the rock-salt like β-phase at lower potentials, is developed. The coupled electrode-scale and crystal-scale model is fit to electrochemical data, and additionally validated with operando EDXRD. There is good agreement between the simulated and measured spatial variation of the volume fraction of the β-phase. This mathematical model is used to guide electrode fabrication, accounting for both ionic and electronic transport effects. Values of three design parameters—electrode thickness, porosity, and volume fraction of conductor—are identified, and the sensitivity of the energy density to these design parameters is quantified. The model is also used to investigate electrode design to create electrodes that deliver the maximum achievable energy density under the constraint that the α to β-phase transition is avoided, since phase change has been demonstrated to reduce cycle life. The energy density sacrificed to avoid phase change decreases at higher discharge rates, but the target values for electrode fabrication remain the same as those when optimizing the electrode for the full depth of discharge.
25 ENERGY STORAGE↗
Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data
Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.
Severino, Joseph↗
Uncertainty-Informed Volume Visualization using Implicit Neural Representation
The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MC-Dropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.
Saklani, Shanu↗
A Thermo‐Flow‐Mechanics‐Fracture Model Coupling a Phase‐Field Interface Approach and Thermo‐Fluid‐Structure Interaction
This work proposes a novel approach for coupling non-isothermal fluid dynamics with fracture mechanics to capture thermal effects within fluid-filled fractures accurately. This method addresses critical aspects of calculating fracture width in enhanced geothermal systems, where the temperature effects of fractures are crucial. The proposed algorithm features an iterative coupling between an interface-capturing phase-field fracture method and interface-tracking thermo-fluid-structure interaction using arbitrary Lagrangian–Eulerian coordinates. We use a phase-field approach to represent fractures and reconstruct the geometry to frame a thermo-fluid-structure interaction problem, resulting in pressure and temperature fields that drive fracture propagation. We developed a novel phase-field interface model accounting for thermal effects, enabling the coupling of quantities specific to the fluid-filled fracture with the phase-field model through the interface between the fracture and the intact solid domain. We provide several numerical examples to demonstrate the capabilities of the proposed algorithm. In particular, we analyze mesh convergence of our phase-field interface model, investigate the effects of temperature on crack width and volume in a static regime, and highlight the method's potential for modeling slowly propagating fractures.
fracture↗
Data Center Facility Monitoring with Physics Aware Approach
U.S. Department of Energy's National Renewable Energy Laboratory (NREL) hosts one of the world's most energy-efficient HPC data centers; this system uses component-level warm-water liquid cooling to efficiently remove heat from the data center and capture it for reuse in the building or rejection to the atmosphere. Given the complexity of this system, building data-driven tools for holistically monitoring and operating the entire data center is a priority for ensuring maximal efficiency and resiliency. In this advanced smart facility, over one million metrics are recorded per minute using state-of-the-art streaming data architecture and software to capture and process the state of the system in real time. Here we detail two efforts to effectively analyze, visualize, and interpret this large volume streaming data. We have developed a novel, flexible system for identifying and visualizing individual metric anomalies and component performance across the data center through automatic metadata extraction and physically-motivated visualization for quick interpretation. Additionally, to directly connect system maintenance to data stream processing we explore a physics informed multi-metric drift and anomaly detection application to detect scale-build up in heat exchangers.
anomaly detection↗
Role of side-chain length on gas transport of CO 2 /CH 4 mixtures in polymers with side-chain porosity
Polymers of intrinsic microporosity (PIMs) are traditionally formed from ladder backbones, but recent synthetic advances have allowed for the formation of non-traditional PIMs using a poly(ladder) motif, whereby rigid PIM-like sidechains are appended onto more flexible backbones. The effect of side-chain length on free volume and gas transport properties was recently evaluated for a methoxy-functionalized poly(ladder) (OMe-ROMP). In this study, we elaborate on the role of side-chain length and its influence on mixed-gas performance and plasticization stability for OMe-ROMP. Pure-gas sorption results are also reported, including hysteresis effects from CO 2 conditioning. Taken together, this study reveals the role of side-chain length on gas sorption, diffusion, and plasticization for CO 2 /CH 4 separations. Finally, we conclude that the length of a rigid side chain in the poly(ladder) motif is a valuable structural parameter to control sorption, diffusion, and stability towards plasticization for gas separation membrane materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Synthesis of High-Quality Mg-MOF-74 Thin Films via Vapor-Assisted Crystallization
The unique features of metal-organic frameworks (MOFs), such as their large surface areas and diversity of structures, makes them suitable to a broad range of applications. One of the most critical enabling technologies for implementing MOF films is the preparation of dense and continuous films that would maximize the performance of the configurations. While formation of crystalline Mg-MOF-74 thin films presents significant challenges, demonstrating large-scale coatings of the MOF, which exhibits the highest CO2 uptake among MOFs at 1 bar and 25 °C, would be particularly beneficial for CO2- related sensing applications. In this work, we demonstrate for the first time high-quality Mg-MOF-74 films synthesized via a vapor-assisted crystallization (VAC) process. The VAC process described herein produces dense and highly crystalline layers of Mg-MOF-74 with a low coefficient of variation (COV) of film thickness below 7%. We first optimized a precursor solution for the VAC process, and then investigated the effects of synthesis temperature, time and droplet volume on the growth, crystallinity and thickness of Mg-MOF-74 films. The porosity of the film was assessed by measuring the CO2 uptake at room temperature and 1 bar. The obtained Mg-MOF-74 films possess a well-defined microporosity, as deduced from CO2 adsorption studies via quartz crystal microbalance (QCM) and comparison with bulk Mg MOF-74 reference data. Furthermore, CO2 cyclic adsorption-desorption experiments on the Mg-MOF-74
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Comparison of the Mechanical Properties of a Conjugated Polymer Deposited Using Spin Coating, Interfacial Spreading, Solution Shearing, and Spray Coating
The mechanical properties of π-conjugated (semiconducting) polymers are a key determinant of the stability and manufacturability of devices envisioned for applications in energy and healthcare. These properties—including modulus, extensibility, toughness, and strength—are influenced by the morphology of the solid film, which depends on the method of processing. To date, the majority of work done on the mechanical properties of semiconducting polymers has been performed on films deposited by spin coating, a process not amenable to the manufacturing of large-area films. Here, we compare the mechanical properties of thin films of regioregular poly(3-heptylthiophene) (P3HpT) produced by three scalable deposition processes—interfacial spreading, solution shearing, and spray coating—and spin coating (as a reference). Our results lead to four principal conclusions. (1) Spray-coated films have poor mechanical robustness due to defects and inhomogeneous thickness. (2) Sheared films show the highest modulus, strength, and toughness, likely resulting from a decrease in free volume. (3) Interfacially spread films show a lower modulus but greater fracture strain than spin-coated films. (4) The trends observed in the tensile behavior of films cast using different deposition processes held true for both P3HpT and poly(3-butylthiophene) (P3BT), an analogue with a higher glass transition temperature. Grazing incidence X-ray diffraction and ultraviolet–visible spectroscopy reveal many notable differences in the solid structures of P3HpT films generated by all four processes. Herein, while these morphological differences provide possible explanations for differences in the electronic properties (hole mobility), we find that the mechanical properties of the film are dominated by the free volume and surface topography. In field-effect transistors, spread films had mobilities more than 1 magnitude greater than any other films, likely due to a relatively high proportion of edge-on texturing and long coherence length in the crystalline domains. Overall, spread films offer the best combination of deformability and charge-transport properties.
36 MATERIALS SCIENCE↗
Chemical influence of ammonia on suppressing soot formation pathways
There is interest in mixing ammonia (NH3) with hydrocarbons, as this decreases overall CO2 emissions from the fuel mixture when burned. Co-firing NH3 with hydrocarbons like methane (CH4) also enables stable combustion over a larger set of operating conditions in engines and gas turbines than firing only NH3 permits. When NH3 is co-fired with hydrocarbons, the influence of NH3 on soot formation chemistry becomes relevant. In this work, soot concentrations in laminar nonpremixed co-flow flames of NH3/CH4 and N2/CH4 were analyzed using color-ratio pyrometry. The concentrations of combustion species (CH4, CO2, H2O, O2, C2H2, C6H6, HCN) across the different flames were also determined using on-line electron-impact mass spectrometry. The flames were also interrogated with 2D CFD simulations, which employ a detailed chemical mechanism tailored to capturing fuel-nitrogen chemistry. Addition of NH3 to CH4 flames was found to decrease soot concentrations more significantly than N2-addition (Fig. 1). The NH3/CH4 flames were found to have higher flame temperatures than the N2/CH4 flames, implying the suppression of soot afforded by NH3 is due to a chemical influence of NH3, as opposed to thermal or dilution effects. Based on experimental and simulated soot volume fractions and species concentrations, soot formation pathways affected by NH3 are postulated.
10 SYNTHETIC FUELS↗
Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin, California
Abstract Subsidence induced by groundwater depletion is a grave problem in many regions around the world, leading to a permanent loss of groundwater storage within an aquifer and even producing structural damage at the Earth’s surface. California’s Tulare Basin is no exception, experiencing about a meter of subsidence between 2015 and 2020. However, understanding the relationship between changes in groundwater volumes and ground deformation has proven difficult. We employ surface displacement measurements from Interferometric Synthetic Aperture Radar (InSAR) and gravimetric estimates of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellite pair to characterize the hydrological dynamics within the Tulare basin. The removal of the long-term aquifer compaction from the InSAR time series reveals coherent short-term variations that correlate with hydrological features. For example, in the winter of 2018–2019 uplift is observed at the confluence of several rivers and streams that drain into the southeastern edge of the basin. These observations, combined with estimates of mass changes obtained from the orbiting GRACE satellites, form the basis for imaging the monthly spatial variations in water volumes. This approach facilitates the quick and effective synthesis of InSAR and gravimetric datasets and will aid efforts to improve our understanding and management of groundwater resources around the world.
54 ENVIRONMENTAL SCIENCES↗
Coulomb correlation in noncollinear antiferromagnetic 𝛼-Mn
Here, we discuss the interplay between magnetic and structural degrees of freedom in elemental Mn. The equilibrium volume is shown to be sensitive to magnetic interactions between the Mn atoms. While the standard generalized gradient approximation underestimates the equilibrium volume, a more accurate treatment of the effects of electronic localization and magnetism is found to solve this longstanding problem. Our calculations also reveal the presence of a magnetic phase in strained $\alpha$-Mn that has been reported previously in experiments. This new phase of strained $\alpha$-Mn exhibits a noncollinear spin structure with large magnetic moments.
36 MATERIALS SCIENCE↗