A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach

This article presents a Galerkin projection model order reduction approach for fluid-structure interaction (FSI) problems in the Finite Volume context. The reduced-order model (ROM) is based on proper orthogonal decomposition (POD), where a reduced basis is formed using energy-dominant POD modes. The reduced basis also consists of characteristics of the POD time modes derived from the POD time modes coefficients. In addition, the solution state vector comprises the mesh deformation, considering the structural motion in FSI. The results are obtained by applying the proposed method to time-dependent problems governed by the 2D incompressible Navier-Stokes equations. The main objective of this work is to introduce a hybrid technique mixing up the classical Galerkin-projection approach with a data-driven method to obtain a versatile and accurate algorithm for resolving FSI problems with moving meshes. The effectiveness of this approach is demonstrated in the case study of vortex-induced vibrations (VIV) of a cylinder at Reynolds number Re = 200. The results show the stability and accuracy of the proposed method with respect to the high-dimensional model by capturing transient flow fields and, more importantly, the forces acting on the moving objects

air permeability of naturally ventilated italian classrooms

Abstract The study is focused on the evaluation of air permeability and ventilation rate in Italian classrooms. Measurements were performed in 16 naturally ventilated classrooms located in Cassino, Central Italy. Classrooms' airtightness was evaluated through the fan pressurization method. Air exchange rates where both estimated from the blower door results and measured using a CO2 decay test method. The effect of the periodic manual airing of the classrooms (through window and door opening) was also investigated performing CO2 and particle number concentration measurements during the school time

Pressure Data-Driven Variational Multiscale Reduced Order Models

In this paper, we develop data-driven closure/correction terms to increase the pressure and velocity accuracy of reduced order models (ROMs) for fluid flows. Specifically, we propose the first pressure-based data-driven variational multiscale ROM, in which we use the available data to construct closure/correction terms for both the momentum equation and the continuity equation. Our numerical investigation of the two-dimensional flow past a circular cylinder at Re=50000 in the marginally-resolved regime shows that the novel pressure data-driven variational multiscale ROM yields significantly more accurate velocity and pressure approximations than the standard ROM and, more importantly, than the original data-driven variational multiscale ROM (i.e., without pressure components). In particular, our numerical results show that adding the closure/correction term in the momentum equation significantly improves both the velocity and the pressure approximations, whereas adding the closure/correction term in the continuity equation improves only the pressure approximation

Hybrid Data-Driven Closure Strategies for Reduced Order Modeling

In this paper, we propose hybrid data-driven ROM closures for fluid flows. These new ROM closures combine two fundamentally different strategies: (i) purely data-driven ROM closures, both for the velocity and the pressure; and (ii) physically based, eddy viscosity data-driven closures, which model the energy transfer in the system. The first strategy consists in the addition of closure/correction terms to the governing equations, which are built from the available data. The second strategy includes turbulence modeling by adding eddy viscosity terms, which are determined by using machine learning techniques. The two strategies are combined for the first time in this paper to investigate a two-dimensional flow past a circular cylinder at Re=50000. Our numerical results show that the hybrid data-driven ROM is more accurate than both the purely data-driven ROM and the eddy viscosity ROM.Comment: arXiv admin note: text overlap with arXiv:2205.1511

Ventilation strategies to minimise the airborne virus transmission in indoor environments

A key challenge to fight the Covid-19 pandemic is to minimise the airborne transmission of the SARS-CoV-2 virus. Highly crowded indoor environments, such as schools, become possible hotspots for virus spreading because the basic non-pharmaceutical mitigation measures applied until now are not effective in reducing the virus airborne transmission mode, which is the principal one in indoor environments and requires improved ventilation. In the present study, a mass balance equation was applied to typical school scenarios to evaluate (i) required air exchange rates for mechanically-ventilated classrooms and (ii) adequate airing procedures for naturally ventilated classrooms. In the case of naturally ventilated classrooms, a feedback control strategy was evaluated using the measurements of indoor CO2. Our results show how these procedures can be applied in real life to support continued in-person instruction during a pandemic.publishedVersio

Organic Light‐Emitting Nanofibers by Solvent‐Resistant Nanofluidics

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Idiopathic infratentorial superficial siderosis of the central nervous system: case report and review of literature

The superficial siderosis (SS) of the central nervous system (CNS) is a rare condition characterized by a wide range of neurological manifestations directly linked to an acquired iron-mediated neurodegeneration. First described more than 100 years ago, only recently SS has been divided into two distinct entities, according to the distribution of iron deposition in the CNS: cortical superficial siderosis (cSS) and infratentorial superficial siderosis (iSS). Here we describe an adult case of iSS, with detailed clinical and radiological features. Moreover, we extensively review the literature of SS, particularly focusing on the pathogenesis, clinical-radiological classification, diagnostic algorithm and treatment options of this rare condition

Advances in reduced order methods for parametric industrial problems in computational fluid dynamics

Reduced order modeling has gained considerable attention in recent decades owing to the advantages offered in reduced computational times and multiple solutions for parametric problems. The focus of this manuscript is the application of model order reduction techniques in various engineering and scientific applications including but not limited to mechanical, naval and aeronautical engineering. The focus here is kept limited to computational fluid mechanics and related applications. The advances in the reduced order modeling with proper orthogonal decomposition and reduced basis method are presented as well as a brief discussion of dynamic mode decomposition and also some present advances in the parameter space reduction. Here, an overview of the challenges faced and possible solutions are presented with examples from various problems

Nuovi strumenti per la gestione della salute e sicurezza sul lavoro con i cobot

Abstract non present