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At least 667 records · Page 37

Temperature and Composition Dependence Modeling of Viscosity and Electrical Conductivity of Low-Activity Waste Glass Melts

The development of models that accurately relate the properties of a glass melt to its temperature and composition is important for glass formulation, melter control, and modeling the melt flow, refractory corrosion, and production rate. Using a database consisting of more than 4,000 data points measured between 900 °C and 1250 °C for over 600 unique low-activity waste glass compositions, we developed models for the melt viscosity and electrical conductivity. Models based on the Gaussian process regression approach outperformed models based on the Vogel–Fulcher–Tammann equation according to four standard metrics and yielded reliable prediction intervals. The models found primarily linear effects between properties and individual components, except for the effect of the Na 2 O mass fraction on the electrical conductivity. The effects were found to be consistent with current theories on physical processes involved with those properties.

36 MATERIALS SCIENCE↗

Meso-beta scale numerical simulation studies of terrain-induced jet streak mass/momentum perturbations

Mesoscale model simulations provide insight into the complex jet streak adjustments on 11-12 July 1981 that preceded the first of two significant gravity wave events to have been generated over the Rocky Mountains in Montana. Simulations employing a variety of terrain treatments indicate that prior to wave formation, geostrophic adjustment processes modified the structure of the mid-upper tropospheric jet streak by creating secondary jetlets to the southeast of the polar jet streak in proximity to the gravity wave generation region. This simulated restructuring of the mid-upper tropospheric jet streak is the result of a four stage process. During stage 1, the wind adjusts to the mass field as the jet streak exit region propagates into the inflection point between the upstream trough and downstream ridge in the height field. Stage 2 is initiated as the mass field is forced to adjust to the new ageostrophic wind field created during stage 1. Stage 3 is defined by a second geostrophic adjustment process occurring in a similar manner but to the south and east of the adjustment which occurs during stage 1. A low-level mesoscale jetlet is formed during stage 4 in response to the low-level pressure falls that are established during stage 3. The perturbation of this jetlet, caused by orographically-induced adiabatic and diabatic physical processes, is the likely mechanism responsible for the generation of the first and second episode of observed gravity waves. The dynamics responsible for this wave episode are discussed as differential surface sensible heating inducing an orographically-forced mountain-plains solenoid, resulting in the formation of additional mesoscale jetlets and internal gravity waves. Also discussed is how convective latent heating modifies the numerically simulated terrain-induced internal gravity waves, especially their amplitude and phase velocities, which provide better agreement with those wave characteristics observed in nature. Finally, the three-dimensional linear response of a zonally uniform barotropic flow in a vertically unbounded, continuously stratified, Boussinesq atmosphere which is perturbed from geostrophic equilibrium is investigated.

Lin, Yuh-Lang↗

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗

Characterizing the Mineralogy of Potential Lunar Landing Sites

Many processes active on the early Moon are common to most terrestrial planets, including the record of early and late impact bombardment. The Moon's surface provides a record of the earliest era of terrestrial planet evolution, and the type and composition of minerals that comprise a planetary surface are a direct result of the initial composition and subsequent thermal and physical processing. Lunar mineralogy seen today is thus a direct record of the early evolution of the lunar crust and subsequent geologic processes. Specifically, the distribution and concentration of specific minerals is closely tied to magma ocean products, lenses of intruded or remelted plutons, basaltic volcanism and fire-fountaining, and any process (e.g. cratering) that might redistribute or transform primary and secondary lunar crustal materials. The association of several lunar minerals with key geologic processes is illustrated in Figure 1. The geologic history of potential landing sites on the Moon can be read from the character and context of local mineralogy.

Pieters, Carle↗

Improving inference with matrix elements and machine learning

Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Report of the theory panel

The ultimate goal of this research is to develop an understanding which is sufficiently comprehensive to allow realistic predictions of the behavior of the physical systems. Theory has a central role to play in the quest for this understanding. The level of theoretical description is dependent on three constraints: (1) the available computer hardware may limit both the number and the size of physical processes the model system can describe; (2) the fact that some natural systems may only be described in a statistical manner; and (3) the fact that some natural systems may be observable only through remote sensing which is intrinsically limited by spatial resolution and line of sight integration. From this the report discusses present accomplishments and future goals of theoretical space physics. Finally, the development and use of new supercomputer is examined.

Ashourabdalla, Maha↗

Constitutive Modeling and Testing of Polymer Matrix Composites Incorporating Physical Aging at Elevated Temperatures

Advanced polymer matrix composites (PMC's) are desirable for structural materials in diverse applications such as aircraft, civil infrastructure and biomedical implants because of their improved strength-to-weight and stiffness-to-weight ratios. For example, the next generation military and commercial aircraft requires applications for high strength, low weight structural components subjected to elevated temperatures. A possible disadvantage of polymer-based composites is that the physical and mechanical properties of the matrix often change significantly over time due to the exposure of elevated temperatures and environmental factors. For design, long term exposure (i.e. aging) of PMC's must be accounted for through constitutive models in order to accurately assess the effects of aging on performance, crack initiation and remaining life. One particular aspect of this aging process, physical aging, is considered in this research.

Veazie, David R.↗

Directive giant upconversion by supercritical bound states in the continuum

Photonic bound states in the continuum (BICs), embedded in the spectrum of free-space waves with diverging radiative quality factor, are topologically non-trivial dark modes in open-cavity resonators that have enabled important advances in photonics. However, it is particularly challenging to achieve maximum near-field enhancement, as this requires matching radiative and non-radiative losses. Here we propose the concept of supercritical coupling, drawing inspiration from electromagnetically induced transparency in near-field coupled resonances close to the Friedrich–Wintgen condition. Supercritical coupling occurs when the near-field coupling between dark and bright modes compensates for the negligible direct far-field coupling with the dark mode. This enables a quasi-BIC field to reach maximum enhancement imposed by non-radiative loss, even when the radiative quality factor is divergent. Our experimental design consists of a photonic-crystal nanoslab covered with upconversion nanoparticles. Near-field coupling is finely tuned at the nanostructure edge, in which a coherent upconversion luminescence enhanced by eight orders of magnitude is observed. The emission shows negligible divergence, narrow width at the microscale and controllable directivity through input focusing and polarization. This approach is relevant to various physical processes, with potential applications for light-source development, energy harvesting and photochemical catalysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Understanding Geospace on a Grand Scale: The Global Ionoshphere/Thermosphere Constellation

We present the concept of a constellation of polar orbiting satellites equally spaced in longitude (local time) to systematically sample both the neutral and ionzed gas components of the Earth in circular orbits near 350 km, including their density, temperature, and velocities. The instrumentation would include techniques to measure the height of the ionospheric "F-peak" and its variations along the orbit. The number of satellites (l2? 24? 48?) and their configuration would be determined from modeling analysis and expected geophysical phenomena, including their drivers and characteristic time scales. Together with imaging data from separate satellites, the array of satellites with in situ probes would be expected to provide a new picture of (1) high latitude electrodynamics and atmospheric processes and associated coupling with magnetospheric mass and momentum input, (2) the response of the global ionosphere and thermosphere to magnetic storms, and (3) global neutral wind circulation patterns, neutral density structure, tides, planetary waves, and gravity waves. The comprehensive measurements gathered by the IT-Constellation envisioned here would provide a major leap forward in each of these areas, addressing global physical processes and providing fundamental, new knowledge of Geospace. In particular, by its very nature, the constellation addresses "system science", revealing how the ionosphere-thermosphere connects globally to the magnetosphere above and the troposphere below. We present this concept as the next logical step in observing the "whole" space environment using in situ probes in conjunction with imagers. We invite modelers to not only comment on this concept but also to become actively engaged in helping to define it.

Pfaff, Robert↗

Summary of the 5th IAEA technical meeting on fusion data processing, validation and analysis (FDPVA)

The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.

fusion databases↗

Magnetic Reconnection

Magnetic reconnection is a fundamental plasma physics process ubiquitous in astrophysics, and important in both magnetic confinement fusion and space weather. The MARZ fundamental science program was recently established on Z to enable the first laboratory astrophysics platform able to access and study the strongly radiatively cooled magnetic reconnection regime. Simulations of this system have successfully used a resistive-MHD approach, but in some regions of parameter space Hall physics has the potential to be important. We describe implementation of a Hall method on a staggered grid resistive-MHD method (compatible with the approach used to model MARZ experiments. We then present a different Hall method based on cell-centered field quantities. Both approaches have been implemented in the Sandia KRAKEN code, to enable us to contrast different numerical Hall-MHD methods within the same HED code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Phenomena induced by charged particle beams

The injection of energetic particles along the Earth's magnetic field lines is a possible remote sensing method for measuring the electric fields parallel to the magnetic field with good time resolution over the entire magnetic field. Neutralization processes, return-current effects, dynamics of the beams, triggered instabilities, and waves must be investigated before the fundamental question about proper experimental conditions, such as energy, intensity and divergence of the beams, pitch-angle injection, ion species, proper probes and detectors and their location, and rendezvous conditions, can be resolved. An experiment designed to provide a better understanding of these special physical processes and to provide some answers to questions concerning beam injection techniques is described.

Beghin, C.↗

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

Thin film fuel cell electrodes.

Earlier work shows that fuel cell electrodes prepared by sputtering thin films of platinum on porous vycor substrates avoid diffusion limitations even at high current densities. The presented study shows that the specific activity of sputtered platinum is not unusually high. Performance limitations are found to be controlled by physical processes, even at low loadings. Catalyst activity is strongly influenced by platinum sputtering parameters, which seemingly change the surface area of the catalyst layer. The use of porous nickel as a substrate shows that pore size of the substrate is an important parameter. It is noted that electrode performance increases with increasing loading for catalyst layers up to two microns thick, thus showing the physical properties of the sputtered layer to be different from platinum foil. Electrode performance is also sensitive to changing differential pressure across the electrode. The application of sputtered catalyst layers to fuel cell matrices for the purpose of obtaining thin total cells appears feasible.

Asher, W. J.↗

Texture analysis of radiometric signatures of new sea ice forming in Arctic leads

Analysis of 33.6-GHz, high-resolution, passive microwave images suggests that new sea ice accumulating in open leads is characterized by a unique textural signature which can be used to discriminate new ice forming in this environment from adjacent surfaces of similar radiometric temperature. Ten training areas were selected from the data set, three of which consisted entirely of first-year ice, four entirely of multilayer ice, and three of new ice in open leads in the process of freezing. A simple gradient operator was used to characterize the radiometric texture in each training region in terms of the degree to which radiometric gradients are oriented. New ice in leads has a sufficiently high proportion of well-oriented features to distinguish it uniquely from first-year ice and multiyear ice. The predominance of well-oriented features probably reflects physical processes by which new ice accumulates in open leads. Banded structures, which are evident in aerial photographs of new ice, apparently give rise to the radiometric signature observed, in which the trend of brightness temperature gradients is aligned parallel to lead trends. First-year ice and multiyear ice, which have been subjected to a more random growth and process history, lack this banded structure and therefore are characterized by signatures in which well-aligned elements are less dominant.

Eppler, Duane T.↗

Cirrus Simulations of CRYSTAL-FACE 23 July 2002 Case

A key objective of the Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) is to understand relationships between the properties of tropical convective cloud systems and the properties and lifecycle of the extended cirrus anvils they produce. We report here on a case study of 23 July 2002 where a sequence of convective storms over central Florida produced an extensive anvil outflow. Our approach is to use a suitably-initialized cloud- system simulation with MM5 (Starr et al., companion paper in this volume) to define initial conditions and time-dependent forcing for a simulation of anvil evolution using a two-dimensional fine-resolution (100 m) cirrus cloud model that explicitly accounts for details of cirrus microphysical development (bin or spectra model) and fully interactive radiative processes. The cirrus model follows Lin (1997). The microphysical components are described in Lin et al. (2004) - see Lin et a1 (this volume). Meteorological conditions and observations for the 23 July case are described in Starr et al. (this volume). The goals of the present study are to evaluate how well we can simulate a cirrus anvil lifecycle, to evaluate the importance of various physical processes that operate within the anvil, and to evaluate the importance of environmental conditions in regulating anvil lifecycle. CRYSTAL-FACE produced a number of excellent case studies of anvil systems that will allow environmental factors, such as static stability or wind shear in the upper troposphere, to be examined. In the present study, we strive to assess the importance of propagating gravity waves, likely produced by the deep convection itself, and radiative processes, to anvil lifecycle and characteristics.

Starr, David↗

Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 Model for a Winter Storm

Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations. This study employs this method to evaluate the accuracy of the simulated radiative properties by the MM5 model with different microphysical schemes. It is found that the representations of particle density, size, and mass in the different schemes in the MM5 model determine the model s performance when predicting a winter storm over the eastern Pacific Ocean. Schemes lacking moderate density particles (i.e. graupel), with snow flakes that are too large, or with excessive mass of snow or graupel lead to degraded prediction of the radiative properties as observed by the TRMM satellite. This study demonstrates the uniqueness of the combination of both an active microwave sensor (PR) and passive microwave sensor (TMI) onboard TRMM on assessing the accuracy of numerical weather forecasting. It improves our understanding of the physical and radiative properties of different types of precipitation particles and provides suggestions for better representation of cloud and precipitation processes in numerical models. It would, ultimately, contribute to answering questions like "Why did it not rain when the forecast says it would?"

Han, Mei↗

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗