Divergence and convergence of inertial particles in high Reynolds number turbulence

Inertial particle data from three-dimensional direct numerical simulations of particle-laden homogeneous isotropic turbulence at high Reynolds number are analyzed using Voronoi tessellation of the particle positions, considering different Stokes numbers. A finite-time measure to quantify the divergence of the particle velocity by determining the volume change rate of the Voronoi cells is proposed. For inertial particles the probability distribution function (PDF) of the divergence deviates from that for fluid particles. Joint PDFs of the divergence and the Voronoi volume illustrate that the divergence is most prominent in cluster regions and less pronounced in void regions. For larger volumes the results show negative divergence values which represent cluster formation (i.e. particle convergence) and for small volumes the results show positive divergence values which represents cluster destruction/void formation (i.e. particle divergence). Moreover, when the Stokes number increases the divergence takes larger values, which gives some evidence why fine clusters are less observed for large Stokes numbers. Theoretical analyses further show that the divergence for random particles in random flow satisfies a PDF corresponding to the ratio of two independent variables following normal and gamma distributions in one dimension. Extending this model to three dimensions, the predicted PDF agrees reasonably well with Monte-Carlo simulations and DNS data of fluid particles.Comment: 23 pages, 9 figure

Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology

We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN). The CNN is trained using pairs of high-resolution (HR) and low-resolution (LR) images created from meteorological simulation results for different resolutions so that it can map LR simulation images to HR ones. The proposed SR simulation system, which performs LR simulations, can provide HR prediction results in much shorter operating cycles than those required for corresponding HR simulation prediction system. We apply the SR simulation system to urban micrometeorology, which is strongly affected by buildings and human activity. Urban micrometeorology simulations that need to resolve urban buildings are computationally costly and thus cannot be used for operational real-time predictions even when run on supercomputers. We performed HR micrometeorology simulations on a supercomputer to obtain datasets for training the CNN in the SR method. It is shown that the proposed SR method can be used with a spatial scaling factor of 4 and that it outperforms conventional interpolation methods by a large margin. It is also shown that the proposed SR simulation system has the potential to be used for operational urban micrometeorology predictions

Three-Dimensional Super-Resolution of Passive-Scalar and Velocity Distributions Using Neural Networks for Real-Time Prediction of Urban Micrometeorology

In future cities, micrometeorological predictions will be essential to various services such as drone operations. However, the real-time prediction is difficult even by using a super-computer. To reduce the computation cost, super-resolution (SR) techniques can be utilized, which infer high-resolution images from low-resolution ones. The present paper confirms the validity of three-dimensional (3D) SR for micrometeorology prediction in an urban city. A new neural network is proposed to simultaneously super-resolve 3D temperature and velocity fields. The network is trained using the micrometeorology simulations that incorporate the buildings and 3D radiative transfer. The error of the 3D SR is sufficiently small: 0.14 K for temperature and 0.38 m s-1for velocity. The computation time of the 3D SR is negligible, implying the feasibility of real-time predictions for the urban micrometeorology

Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques

Atmospheric simulations for urban cities can be computationally intensive because of the need for high spatial resolution, such as a few meters, to accurately represent buildings and streets. Deep learning has recently gained attention across various physical sciences for its potential to reduce computational cost. Super-resolution is one such technique that enhances the resolution of data. This paper proposes a convolutional neural network (CNN) that super-resolves instantaneous snapshots of three-dimensional air temperature and wind velocity fields for urban micrometeorology. This super-resolution process requires not only an increase in spatial resolution but also the restoration of missing data caused by the difference in the building shapes that depend on the resolution. The proposed CNN incorporates gated convolution, which is an image inpainting technique that infers missing pixels. The CNN performance has been verified via supervised learning utilizing building-resolving micrometeorological simulations around Tokyo Station in Japan. The CNN successfully reconstructed the temperature and velocity fields around the high-resolution buildings, despite the missing data at lower altitudes due to the coarseness of the low-resolution buildings. This result implies that near-surface flows can be inferred from flows above buildings. This hypothesis was assessed via numerical experiments where all input values below a certain height were made missing. This research suggests the possibility that building-resolving micrometeorological simulations become more practical for urban cities with the aid of neural networks that enhance computational efficiency

Scale-dependent statistics of inertial particle distribution in high Reynolds number turbulence

Multiscale statistical analyses of inertial particle distributions are presented to investigate the statistical signature of clustering and void regions in particle-laden incompressible isotropic turbulence. Three-dimensional direct numerical simulations of homogeneous isotropic turbulence at high Reynolds number ($Re_\lambda \gtrsim 200$) with up to $10^9$ inertial particles are performed for Stokes numbers ranging from $0.05$ to $5.0$. Orthogonal wavelet analysis is then applied to the computed particle number density fields. Scale-dependent skewness and flatness values of the particle number density distributions are calculated and the influence of Reynolds number $Re_\lambda$ and Stokes number $St$ is assessed. For $St \sim 1.0$, both the scale-dependent skewness and flatness values become larger as the scale decreases, suggesting intermittent clustering at small scales. For $St \le 0.2$, the flatness at intermediate scales, i.e. for scales larger than the Kolmogorov scale and smaller than the integral scale of the flow, increases as $St$ increases, and the skewness exhibits negative values at the intermediate scales. The negative values of the skewness are attributed to void regions. These results indicate that void regions at the intermediate sales are pronounced and intermittently distributed for such small Stokes numbers. As $Re_\lambda$ increases, the flatness increases slightly. For $Re_\lambda \ge 328$, the skewness shows negative values at large scales, suggesting that void regions are pronounced at large scales, while clusters are pronounced at small scales.Comment: 26 pages, 9 figure

Scale-similar clustering of heavy particles in the inertial range of turbulence.

Heavy particle clustering in turbulence is discussed from both phenomenological and analytical points of view, where the -4/3 power law of the pair-correlation function is obtained in the inertial range. A closure theory explains the power law in terms of the balance between turbulence mixing and preferential-concentration mechanism. The obtained -4/3 power law is supported by a direct numerical simulation of particle-laden turbulence

Idiopathic Pneumonia Syndrome Refractory to Ruxolitinib after Post-Transplant Cyclophosphamide-based Haploidentical Hematopoietic Stem Cell Transplantation: Lung Pathological Findings from an Autopsy Case

A 69-year-old Japanese man with acute leukemia received post-transplant cyclophosphamide-based haploidentical stem cell transplantation (PTCY-haplo-SCT) but was readmitted with dyspnea and ground-glass-opacities of the lungs. Bronchoscopy showed inflammatory changes with no signs of infection. He received steroids but required intubation as his condition deteriorated. In addition to antithymocyte globulin and cyclophosphamide, we administered ruxolitinib but failed to save him. Autopsy findings revealed fibrotic nonspecific interstitial pneumonia (NSIP) without evidence of organizing pneumonia or infection. Thus, we diagnosed idiopathic pneumonia syndrome (IPS). As far as our knowledge, this is the first case of IPS with NSIP histology after PTCY-haplo-SCT

Molecular analysis of afibrinogenemic mutations caused by a homozygous FGA1238 bp deletion, and a compound heterozygous FGA1238 bp deletion and novel FGA c.54+3A > C substitution

We identified two afibrinogenemic girls in two Japanese families and performed molecular analysis to clarify the mechanisms of fibrinogen defects. Genetic analyses were performed by PCR amplification of the fibrinogen gene and DNA sequence analysis. To analyze the mechanisms of mature fibrinogen defects in plasma, we cloned minigenes from the proposita's PCR-amplified DNA, transfected them into CHO cells, and sequenced the cDNA amplified with the RT reaction followed by PCR. Sequence analyses indicated that one was caused by a homozygous 1238 bp deletion of the fibrinogen A alpha-chain gene (FGA Delta 1238) and the other was a compound heterozygous FGA Delta 1238 and novel FGA c.54+3A > C substitution. The minigene corresponding to FGA Delta 1238 generates two aberrant mRNAs, both of which may induce a frameshift and terminate prematurely. In contrast, the minigene corresponding to FGA c.54+3A > C generates two aberrant mRNAs, one of which may induce a frameshift and terminate prematurely, and the other uses a cryptic 5' splice site in exon 1, resulting in the deletion of six amino acids in signal peptides. Molecular analyses of both genetic variants suggest that the lack of a mature A alpha-chain, impaired assembly, and/or secretion of the fibrinogen molecule may lead to afibrinogenemia.ArticleINTERNATIONAL JOURNAL OF HEMATOLOGY. 96(1):39-46 (2012)journal articl

Influence of gravitational settling on turbulent droplet clustering and radar reflectivity factor

This study investigates the influence of gravitational settling of droplets on turbulent clustering and the radar reflectivity factor. A three-dimensional direct numerical simulation (DNS) of particle-laden isotropic turbulence is performed to obtain turbulent droplet clustering data. The turbulent clustering data are then used to calculate the power spectrum of droplet number density fluctuations. The results show that the gravitational settling modulates the power spectrum more significantly as the settling becomes larger. The gravitational settling weakens the intensity of clustering at large wavenumbers for St ≦ 1, whereas it significantly enlarges the intensity for St > 1. The dependence on the Taylor-microscale-based Reynolds number is also investigated to discuss the contribution of large-scale eddies to the settling influence. The results show that large-scale eddies modulate the small scale clustering structure of large St droplets. The increment of radar reflectivity factor due to turbulent clustering is estimated from the power spectrum for the case of St = 1.0. The result shows that the influence of gravitational settling on the radar reflectivity factor can be significant for the case of large settling velocity droplets

Application of Kampo Medicines for Treatment of General Fatigue Due to Long COVID

Evidence regarding treatment for the acute phase of COVID-19 has been accumulating, but specific treatment for long COVID/post-COVID-19 condition has not yet been established. Treatment with herbal medicine might be one treatment option for long COVID, but there has been little research on the effectiveness of herbal medicine for long COVID. The aim of this study was to clarify the prescription patterns of Kampo medicines, which are herbal medicines that originated in China and were developed in Japan, for the treatment of general fatigue due to long COVID. A retrospective descriptive study was performed for patients who visited a COVID-19 aftercare clinic established in Okayama University Hospital during the period from Feb 2021 to Dec 2021 with a focus on symptoms accompanying general fatigue and prescriptions of Kampo medicine. Among the clinical data obtained from medical records of 195 patients, clinical data for 102 patients with general fatigue and accompanying symptoms were analyzed. The patients had various symptoms, and the most frequent symptoms accompanying general fatigue were dysosmia, dysgeusia, headache, insomnia, dyspnea, and hair loss. Prescriptions of Kampo medicine accounted for 24.1% of the total prescriptions (n = 609). The most frequently prescribed Kampo medicine was hochuekkito (71.6%) and other prescribed Kampo medicines were tokishakuyakusan, ryokeijutsukanto, juzentaihoto, hangekobokuto, kakkonto, ninjin'yoeito, goreisan, rikkunshito, and keishibukuryogan. Since the pathophysiology of general fatigue after an infectious disease is, in general, considered a qi deficiency in Kampo medicine, treatments with such compensation agents can be the major prescription as a complement for the qi. In conclusion, Kampo medicine can be one of the main pharmacological treatments for long COVID accompanying general fatigue