investigation of particle dynamics and classification mechanism in a spiral jet mill through computational fluid dynamics and discrete element methods

Abstract Predicting the outcome of jet-milling based on the knowledge of process parameters and starting material properties is a task still far from being accomplished. Given the technical difficulties in measuring thermodynamics, flow properties and particle statistics directly in the mills, modelling and simulations constitute alternative tools to gain insight in the process physics and many papers have been recently published on the subject. An ideal predictive simulation tool should combine the correct description of non-isothermal, compressible, high Mach number fluid flow, the correct particle-fluid and particle-particle interactions and the correct fracture mechanics of particle upon collisions but it is not currently available. In this paper we present our coupled CFD-DEM simulation results; while comparing them with the recent modelling and experimental works we will review the current understating of the jet-mill physics and particle classification. Subsequently we analyze the missing elements and the bottlenecks currently limiting the simulation technique as well as the possible ways to circumvent them towards a quantitative, predictive simulation of jet-milling

Medial temporal lobe atrophy and posterior atrophy scales normative values

OBJECTIVES: The medial temporal lobe atrophy (MTA) and the posterior atrophy (PA) scales allow to assess the degree hippocampal and parietal atrophy from magnetic resonance imaging (MRI) scans. Despite reliable, easy and widespread employment, appropriate normative values are still missing. We aim to provide norms for the Italian population. // METHODS: Two independent raters assigned the highest MTA and PA score between hemispheres, based on 3D T1-weighted MRI of 936 Italian Brain Normative Archive subjects (age: mean ± SD: 50.2 ± 14.7, range: 20-84; MMSE>26 or CDR = 0). The inter-rater agreement was assessed with the absolute intraclass correlation coefficient (aICC). We assessed the association between MTA and PA scores and sociodemographic features and APOE status, and normative data were established by age decade based on percentile distributions. // RESULTS: Raters agreed in 90% of cases for MTA (aICC = 0.86; 95% CI = 0.69-0.98) and in 86% for PA (aICC = 0.82; 95% CI = 0.58-0.98). For both rating scales, score distribution was skewed, with MTA = 0 in 38% of the population and PA = 0 in 52%, while a score ≥ 2 was only observed in 12% for MTA and in 10% for PA. Median denoted overall hippocampal (MTA: median = 1, IQR = 0-1) and parietal (PA: median = 0, IQR = 0-1) integrity. The 90th percentile of the age-specific distributions increased from 1 (at age 20-59) for both scales, to 2 for PA over age 60, and up to 4 for MTA over age 80. Gender, education and APOE status did not significantly affect the percentile distributions in the whole sample, nor in the subset over age 60. // CONCLUSIONS: Our normative data for the MTA and PA scales are consistent with previous studies and overcome their main limitations (in particular uneven representation of ages and missing percentile distributions), defining the age-specific norms to be considered for proper brain atrophy assessment

Numerical Simulations of Heterogeneous Nucleate Boiling from a Single Site

In this paper we analyze the growth and detachment of well-separated bubbles from a single nucleation site. This approach is the basic step in the simulation of nucleate-boiling and can be considered as the starting point in the study of complex heat flux mechanisms such as the Departure from Nucleate Boiling (DNB). Most of the published work in this area are empirical correlations based on experimental data and physical models which rely on simplified balances among the different forces acting on the bubble. Different numerical approaches, which are based on the Lattice Boltzmann Method (LBM) and finite-difference discretization of the macroscopic conservation equations, have been proposed more recently in the literature. However, most of the published papers consider a two-dimensional geometry. In this work we present three-dimensional simulations of the nucleate boiling process performed with the code Trio_U. We follow in time the growth of the bubble up to its departure and measure its diameter and the frequency of the process. In particular we analyze the dependence of the departure diameter from a number of physical quantities which appear in several correlations, such as gravity, surface tension and contact angle. The preliminary results we have obtained are in agreement with the experimental correlations and the two-dimensional LBM numerical results

Heat Transfer Numerical Simulations with the Four Parameter k-w-kt-et Model for Low-Prandtl Number Liquid Metals

The present work addresses a new effort towards the improvement of turbulence models for low-Prandtl number fluids, like heavy liquid metals, whose interest arises in many fields such as in the study of innovative nuclear fission reactors. The commonly used turbulence models are based on a similarity between the modeling of the velocity and energy Reynolds stress tensors that relies on the constant turbulent Prandtl number hypothesis. Unfortunately, for low-Prandtl number fluids, this assumption fails to reproduce the available experimental correlations and a rather different convective heat transfer behavior is observed. In order to simulate accurately liquid metal turbulent flows, in this work we consider the algebraic flux model (AFM) together with the four parameter k-w-kt-et model. We show some finite element numerical results by investigating the case of a vertical annular geometry. These results are compared with the simple eddy diffusivity (SED) and the generalized gradient diffusion hypothesis (GGDH) models. For a large range of forced flows the k-w-kt-et model is a powerful tool for predicting the heat transfer in flows with large dissimilarity between velocity and thermal fields

Effects of Buoyancy on Mixed Turbulent Heat Transfer to Heavy liquid Metals in Vertical Annuli

Turbulent mixed convection in low Prandtl number fluids, such as heavy liquid metals, is studied because of the interest in such materials as possible coolants for nuclear power plants. Liquid metals show a behavior similar to that of ordinary fluids, such as water or air, when buoyancy effects are negligible in turbulent flows

Thermo-Hydraulic Analysis of a LFR Generation IV Reactor with a Porous Medium Approach

This paper analyzes the stationary behaviour of the LFR European reactor design ELSY with a porous medium approach by computing the three-dimensional average distribution of temperature, pressure and velocity fields. Knowledge of these physical quantities is of particular importance for the computation of reactor reactivity and heat stresses on the fuel structural elements. The numerical simulations of the flow in the lower and upper plena of the reactor are performed with a FEM code that solves the full three-dimensional set of the incompressible Navier-Stokes equations with energy and turbulence model. Due to the reactor huge size the code uses a simple LES (Large Eddy Simulation) turbulence model for the viscosity which takes into account the turbulence vorticity effects at scales greater than the typical grid spacing. In the core region the reactor complexity requires the adoption of a spatial scale greater than the assembly transverse dimension. This approximation implies the reduction of the core to a sort of {it porous medium} through which the lead coolant flows at the subchannel level. This approach leads to a two-scale model where the phenomena that occur at the high-resolution level may have impact on the lower resolution

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification