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At least 235 records · Page 13

Modeling aerosol bolus inhalations in the human lung with the multiple path particle deposition model: Comparison with experimental data

Existing one-dimensional (1D) models of aerosol dosimetry often ignore mixing mechanisms of inhaled aerosols during their transport in the lung. This mixing or aerosol dispersion results from different physical mechanisms in different regions of the lung. It is a higher order effect, which cannot be directly captured in 1D modeling approaches, and thus is sometimes modeled as a diffusive process. Here, in this study, we improved our recently developed alveolar mixing module incorporated in the multiple path particle dosimetry model (MPPD) to account for flow irreversibility and particle trapping in the alveolar spaces, as well as mixing occurring in the tracheobronchial region. This new version of MPPD was coupled with CFPD-based predictions of aerosol bolus dispersion in the oral airway. The model was used to predict the deposition, dispersion, and mode shift of aerosol bolus inhaled at different penetration depths within the lung for breathing patterns and particle size matching those used in a previous experimental study (Darquenne et al., 2016). Even though a quite simplified approach was used, the computations appear to describe subject-specific and test-specific experimental data reasonably well. The proposed combined dispersion-deposition model can be a useful tool for targeted drug delivery and also for exposure health risk assessment.

MPPD↗

A Data-driven, Physics-based Transport Model of Solar Energetic Particles Accelerated by Coronal Mass Ejection Shocks Propagating through the Solar Coronal and Heliospheric Magnetic Fields

Abstract In an effort to develop computational tools for predicting radiation hazards from solar energetic particles (SEPs), we have created a data-driven physics-based particle transport model to calculate the injection, acceleration, and propagation of SEPs from coronal mass ejection (CME) shocks traversing through the solar corona and interplanetary magnetic fields. The model runs on an input of corona and heliospheric plasma and magnetic field configuration from a magnetohydrodynamic model driven by solar photospheric magnetic field measurements superposed with observed CME shocks determined from coronagraph images. SEP source particles are injected at the shock using the result of diffusive shock acceleration formulation from a characteristic obliquity-dependent injection from a heated solar wind thermal tail population. With several advanced computation techniques involving stochastic simulation and integration, the model obtains the particle intensity at any location in interplanetary space through the rigorous solution to the time-dependent 5D focus transport equation in the phase space that includes perpendicular diffusion. We apply the model to the 2011 November 3 CME event. The calculation results reproduce multispacecraft SEP observations at Earth and STEREO-B reasonably well without normalization of particle flux. The observations at STEREO-A can be reproduced by rescaling particle energy or modified energy dependence of particle diffusion coefficients. This circumsolar SEP event seen by spacecraft at Earth, STEREO-A, and STEREO-B at widely separated longitudes can be explained by diffusive shock acceleration by a single CME shock with a moderate speed.

79 ASTRONOMY AND ASTROPHYSICS↗

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗

A precise measurement of the jet energy scale derived from single-particle measurements and in situ techniques in proton–proton collisions at $\sqrt{s}=$ 13 TeV with the ATLAS detector

The jet energy calibration and its uncertainties are derived from measurements of the calorimeter response to single particles in both data and Monte Carlo simulation using proton–proton collisions at $\sqrt{s} = 13$ TeV collected with the ATLAS detector during Run 2 at the Large Hadron Collider. The jet calibration uncertainty for anti-$k_T$ jets with a jet radius parameter of R$_\textrm{jet} = 0.4$ and in the central jet rapidity region is about 2.5% for transverse momenta ($p_{\text {T}}$) of 20 $\text {GeV}$ , about 0.5% for $p_{\text {T}} = 300$ GeV and 0.7% for $p_{\text {T}} = 4$ TeV . Excellent agreement is found with earlier determinations obtained from -balance based in situ methods ($Z/\gamma$ +jets). The combination of these two independent methods results in the most precise jet energy measurement achieved so far with the ATLAS detector with a relative uncertainty of 0.3% at $p_\textrm{T} = 300$ GeV and 0.6% at 4 TeV. The jet energy calibration is also derived with the single-particle calorimeter response measurements separately for quark- and gluon-induced jets and furthermore for jets with R jet varying from 0.2 to 1.0 retaining the correlations between these measurements. Differences between inclusive jets and jets from boosted top-quark decays, with and without grooming the soft jet constituents, are also studied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at $\sqrt{s} = 13$ TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton–proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015–2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across these generations, and the nonzero neutrino masses. In this report, the results of searches targeting vectorlike quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton-proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015-2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across the generations, and the nonzero neutrino masses. In this report, the results of searches targeting vector-like quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

FOS: Physical sciences↗

Novel data interpretation method for DIII-D divertor retarding field energy analyzer with 3-D particle-in-cell simulations

A novel data interpretation process that utilizes comprehensive particle-in-cell (PIC) simulations is developed for the new retarding field energy analyzer (RFEA) currently being constructed at DIII-D for the lower divertor using the Divertor Material Evaluation System. Furthermore, this probe is expected to survive a heat load of up to 100 MW/m 2 for up to 5 s and reliably measure the main ion temperature (T i ) on the divertor target ranging from 10 to 200 eV. These extreme conditions posed significant engineering limitations on the probe geometry, thus extensive validation work has been performed. The conventional fitting method for the RFEA I–V characteristics is based on a simplified 1-D model without considering the ion space charge inside the probe cavity and may not be sufficient for probes designed for the DIII-D divertor environment. In this article, a more realistic description of the particle propagation process within the RFEA cavity is achieved by including both 3-D geometric effects and ion space charge in the PIC simulations, and the capability to reconstruct the ion energy distribution functions is demonstrated with reasonable consistency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A model for oblique impacts on material surfaces

Many practical situations of material damage, wear, and erosion involve collisions between small particles and surfaces at inclined angles. While there are many well-validated models of normal incidence impact situations, elastic-plastic models for oblique incidence impact events are lacking. Here the finite element method is used to predict the normal and tangential coefficient of restitution in oblique impacts for hard, elastic spheres impacting an elastic-perfectly plastic material surface. The proposed model covers various impact angles ranging from 0° to 45°, within a limiting impact velocity below which the effects of heating are negligible. The normal coefficient of restitution follows power-laws with respect to normalized values of the impact velocity. Interestingly, the tangential coefficient of restitution follows a linear relationship with impact velocity. Together, these results provide a semi-empirical set of equations predicting oblique impact rebounds (both velocity and trajectory) for a wide range of conditions and material properties, with which experimental results can be rapidly interpreted. As a result, Laser-Induced Particle Impact Test (LIPIT) data are also presented for aluminum particles impacting aluminum substrates, at impact angles of 25° and 40°; the results compare favorably with the model and validate the general use of such models for the analysis of experimental data.

Coefficient of restitution↗

Nuclear Data for High Energy Ion Interactions and Secondary Particle Production

Understanding the harmful effects of galactic cosmic rays (GCRs) on space exploration requires a substantial amount of nuclear data. Specifically, the interaction of energetic GCR charged particles with spacecraft materials generates secondary radiations that, through energy deposition, can harm astronauts and electronic systems. By identifying the gaps in our knowledge of the relevant nuclear data – interaction cross sections – and identifying ways to fill those gaps – with measurements, compilations, evaluations, disseminations, reaction modeling, sensitivity studies, and uncertainty quantification – the safety and viability of space exploration can be improved. This work surveys the state of the art in this interdisciplinary field and identifies promising collaborative research topics that have significant potential to advance our understanding of the effects of the space radiation environment on space exploration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

CFD-DEM study of bubble properties in a cylindrical fluidized bed of Geldart Group D particles and comparison with prior MRI data

Numerical simulations of a freely bubbling cylindrical fluidized bed are carried out using a coupled computational fluid dynamics and discrete element method (CFD-DEM) model and compared to recent experimental data. The experiments were conducted using high-resolution and high-frequency magnetic resonance imaging providing high-fidelity data of the bubbling within a central 10 mm slice of the bed. Qualitatively, we nd more regular (i.e., less chaotic) structures in the simulated beds than observed experimentally. Quantitatively, however, the bubble diameter and number of bubbles as a function of height within the bed is predicted well by the base model. Unfortunately, the regularity in the simulations manifests as a considerable discrepancy in the speed of the (dense) emulsion phase. The simulated velocity probability distribution functions show an accumulation of low-speed regions and deficiency of high-speed regions. A simple parametric study of the base model is also carried out considering many of the most common CFD-DEM modeling parameters. It is found that the fluid grid size, geometry resolution, transfer kernel and drag law did not have a significant effect on bubble or particle dynamics.

42 ENGINEERING↗

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,200 new measurements from 903 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Most of the 118 reviews are updated, including many that are heavily revised.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Data-driven assessment of magnetic charged particle confinement parameter scaling in magnetized liner inertial fusion experiments on Z

In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the α-particle Larmor radius $ϱ_α$ is a critical parameter setting the scale of electron thermal-conduction loss and charged burn-product confinement. Here, using a previously developed deep-learning-based Bayesian inference tool, we obtain the magnetic-field fuel-radius product BR ∝ R / $ϱ_α$ from an ensemble of 16 magnetized liner inertial fusion (MagLIF) experiments. Observations of the trends in BR are consistent with relative trade-offs between compression and flux loss as well as the impact of mix from 1D resistive radiation magneto-hydrodynamics simulations in all but two experiments, for which 3D effects are hypothesized to play a significant role. Finally, we explain the relationship between BR and the generalized Lawson parameter χ. Our results indicate the ability to improve performance in MagLIF through careful tuning of experimental inputs, while also highlighting key risks from mix and 3D effects that must be mitigated in scaling MagLIF to higher currents with a next-generation driver.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

3Flex,BET specific surface area and pore volume, A↗

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Cryogenic Refrigerator Applied to Freezing Test

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

Solid-phase Mn speciation in suspended particles along meltwater-influenced fjords of West Greenland

Manganese (Mn) is an essential micro-nutrient that can limit or, along with iron (Fe), co-limit phytoplankton growth in the ocean. Glacier meltwater is thought to be a key source of trace metals to high latitude coastal systems, but little is known about the nature of Mn delivered to glacially-influenced fjords and adjacent coastal waters. In this work, we combine in-situ dissolved Mn (dMn) measurements of surface waters with Mn K-edge X-ray absorption spectroscopy (XAS) data of suspended particles in four fjords of West Greenland. Data were collected from transects of up to 100 km in fjords with different underlying bedrock geology from 64 to 70°N. We found that dMn concentrations generally decreased conservatively with increasing salinity (from 80 to 120 nM at salinity < 8 to < 40 nM at salinities > 25). Dissolved Fe (dFe) trends in these fjords similarly declined with increasing distance from glacier outflows (declining from > 20 nM to < 8 nM). However, the dMn/dFe ratio increased rapidly likely due to the greater stability of dMn at intermediate salinities (i.e. 10–20) compared to rapid precipitation of dFe across the salinity gradient. The XAS data indicated a widespread presence of Mn(II)-rich suspended particles near fjord surfaces, with structures akin to Mn(II)-bearing phyllosilicates. However, a distinct increase in Mn oxidation state with depth and the predominance of birnessite-like Mn(IV) oxides was observed for suspended particles in a fjord with tertiary basalt geology. The similar dMn behaviour in fjords with different suspended particle Mn speciation (i.e., Mn(II)-bearing phyllosilicates and Mn(IV)-rich birnessite) is consistent with the decoupling of dissolved and particulate Mn and suggests that dMn concentrations on the scale of these fjords are controlled primarily by dilution of a freshwater dMn source rather than exchange between dissolved and particle phases. This work provides new insights into the Mn cycle in high latitude coastal waters, where small changes in the relative availabilities of dMn, dFe and macronutrients may affect the identity of the nutrient(s) proximally limiting primary production.

58 GEOSCIENCES↗