@unpublished {2023, title = {Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review}, year = {2023}, abstract = {

Tumble dryers offer a fast and convenient way of drying textiles independent of weather conditions and therefore are frequently used in ordinary households. However, artificial drying of textiles consumes considerable amounts of energy, approximately 8.2 percent of the residential electricity consumption is for drying of textiles in northern European countries (Cranston et al., 2019). Several authors have investigated the aspects of the clothes drying cycle with experimental and numerical methods to understand and improve the process. The first turning point study on understanding the physics of evaporation for tumble dryers was presented by Lambert et al. (1991) in the early 90s. With the aid of Chilton_Colburn analogy, they introduced the concept of area-mass transfer coefficient to address evaporation rate. Afterwards, several experimental or numerical studies were published based on this concept, and furthermore, the model was then developed into 0-dimensional (Deans, 2001) and 1-dimensional (Wei et al., 2017) to gain more accuracy. The evaporation rate is considered to be the main system parameter for dryers with which other performance parameters including drying time, effectiveness, moisture content and efficiency can be estimated. More recent literature focused on utilizing dimensional analysis or image processing techniques to correlate drying indices with system parameters. However, the validity of these regressed models is machine-specific, and hence, cannot be generalized yet. All the previous models for estimating the evaporation rate in tumble dryers are discussed. The review of the related literature showed that all of the previous models for the prediction of the evaporation rate in the clothes dryers have some limitations in terms of accuracy and applicability.

}, author = {Sajad Salavatidezfouli and Sajad Hajisharifi and Michele Girfoglio and Giovanni Stabile and Gianluigi Rozza} } @article {2022, title = {Finite element based Model Order Reduction for parametrized one-way coupled steady state linear thermo-mechanical problems}, journal = {Finite Elements in Analysis and Design}, volume = {212}, year = {2022}, month = {12/2022}, abstract = {

This contribution focuses on the development of Model Order Reduction (MOR) for one-way coupled\ steady state\ linear thermo-mechanical problems in a\ finite element\ setting. We apply\ Proper Orthogonal Decomposition\ (POD) for the computation of reduced basis space. On the other hand, for the evaluation of the modal coefficients, we use two different methodologies: the one based on the Galerkin projection (G) and the other one based on\ Artificial Neural Network\ (ANN). We aim to compare POD-G and POD-ANN in terms of relevant features including errors and computational efficiency. In this context, both physical and geometrical parametrization are considered. We also carry out a validation of the Full Order Model (FOM) based on customized benchmarks in order to provide a complete computational pipeline. The framework proposed is applied to a relevant industrial problem related to the investigation of thermo-mechanical phenomena arising in\ blast furnace\ hearth walls.

}, keywords = {Artificial Neural Network, Blast furnace, Finite Element Method, Galerkin projection, Geometric and physical parametrization, Proper orthogonal decomposition, Thermo-mechanical problems}, issn = {0168-874X}, doi = {https://doi.org/10.1016/j.finel.2022.103837}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168874X2200110X}, author = {Nirav Shah and Michele Girfoglio and Peregrina Quintela and Gianluigi Rozza and Alejandro Lengomin and Francesco Ballarin and Patricia Barral} } @article {2022, title = {The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations}, journal = {Computer Methods in Applied Mechanics and Engineering}, volume = {392}, year = {2022}, abstract = {

Models with dominant advection always posed a difficult challenge for projection-based reduced order modelling. Many methodologies that have recently been proposed are based on the pre-processing of the full-order solutions to accelerate the Kolmogorov N-width decay thereby obtaining smaller linear subspaces with improved accuracy. These methods however must rely on the knowledge of the characteristic speeds in phase space of the solution, limiting their range of applicability to problems with explicit functional form for the advection field. In this work we approach the problem of automatically detecting the correct pre-processing transformation in a statistical learning framework by implementing a deep-learning architecture. The purely data-driven method allowed us to generalise the existing approaches of linear subspace manipulation to non-linear hyperbolic problems with unknown advection fields. The proposed algorithm has been validated against simple test cases to benchmark its performances and later successfully applied to a multiphase simulation. {\textcopyright} 2022 Elsevier B.V.

}, keywords = {Advection, Computational complexity, Deep neural network, Deep neural networks, Linear subspace, Multiphase simulations, Non linear, Nonlinear hyperbolic equation, Partial differential equations, Phase space methods, Pre-processing, Principal component analysis, reduced order modeling, Reduced order modelling, Reduced-order model, Shifted-POD}, doi = {10.1016/j.cma.2022.114687}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124488633\&doi=10.1016\%2fj.cma.2022.114687\&partnerID=40\&md5=12f82dcaba04c4a7c44f8e5b20101997}, author = {Davide Papapicco and Nicola Demo and Michele Girfoglio and Giovanni Stabile and Gianluigi Rozza} } @article {2022, title = {A POD-Galerkin reduced order model for the Navier{\textendash}Stokes equations in stream function-vorticity formulation}, year = {2022}, month = {2022/06/14/}, pages = {105536}, abstract = {

We develop a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for the efficient numerical simulation of the parametric Navier{\textendash}Stokes equations in the stream function-vorticity formulation. Unlike previous works, we choose different reduced coefficients for the vorticity and stream function fields. In addition, for parametric studies we use a global POD basis space obtained from a database of time dependent full order snapshots related to sample points in the parameter space. We test the performance of our ROM strategy with the well-known vortex merger benchmark and a more complex case study featuring the geometry of the North Atlantic Ocean. Accuracy and efficiency are assessed for both time reconstruction and physical parametrization.

}, keywords = {Galerkin projection, Navier{\textendash}Stokes equations, Proper orthogonal decomposition, Reduced order model, Stream function-vorticity formulation}, isbn = {0045-7930}, url = {https://www.sciencedirect.com/science/article/pii/S0045793022001645}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @conference {2022, title = {Thermomechanical Modelling for Industrial Applications}, booktitle = {Progress in Industrial Mathematics at ECMI 2021}, year = {2022}, month = {11 July 2022}, publisher = {Springer, Cham}, organization = {Springer, Cham}, address = {Online conference hosted by the Bergische Universit{\"a}t Wuppertal}, abstract = {

In this work we briefly present a thermomechanical model that could serve as starting point for industrial applications. We address the non-linearity due to temperature dependence of material properties and heterogeneity due to presence of different materials. Finally a numerical example related to the simplified geometry of blast furnace hearth walls is shown with the aim of assessing the feasibility of the modelling framework.

}, isbn = {978-3-031-11818-0}, doi = {https://doi.org/10.1007/978-3-031-11818-0_28}, url = {https://link.springer.com/chapter/10.1007/978-3-031-11818-0_28}, author = {Nirav Shah and Michele Girfoglio and Gianluigi Rozza} } @unpublished {2021, title = {Consistency of the full and reduced order models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows}, year = {2021}, author = {Maria Strazzullo and Michele Girfoglio and Francesco Ballarin and T. Iliescu and Gianluigi Rozza} } @booklet {PapapiccoDemoGirfoglioStabileRozza2021, title = {The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations}, year = {2021}, author = {Davide Papapicco and Nicola Demo and Michele Girfoglio and Giovanni Stabile and Gianluigi Rozza} } @article {2021, title = {Non-intrusive data-driven ROM framework for hemodynamics problems}, journal = {Acta Mechanica Sinica}, volume = {37}, year = {2021}, pages = {1183{\textendash}1191}, author = {Michele Girfoglio and Leonardo Scandurra and Francesco Ballarin and Giuseppe Infantino and Francesca Nicol{\`o} and Andrea Montalto and Gianluigi Rozza and Roberto Scrofani and Marina Comisso and Francesco Musumeci} } @article {2021, title = {A POD-Galerkin reduced order model for a LES filtering approach}, journal = {Journal of Computational Physics}, volume = {436}, year = {2021}, abstract = {

We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for an implementation of the Leray model that combines a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. The main novelty of the proposed approach relies in applying spatial filtering both for the collection of the snapshots and in the reduced order model, as well as in considering the pressure field at reduced level. In both steps of the EF algorithm, velocity and pressure fields are approximated by using different POD basis and coefficients. For the reconstruction of the pressures fields, we use a pressure Poisson equation approach. We test our ROM on two benchmark problems: 2D and 3D unsteady flow past a cylinder at Reynolds number 0<=Re<=100. The accuracy of the reduced order model is assessed against results obtained with the full order model. For the 2D case, a parametric study with respect to the filtering radius is also presented.

}, doi = {10.1016/j.jcp.2021.110260}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102138957\&doi=10.1016\%2fj.jcp.2021.110260\&partnerID=40\&md5=73115708267e80754f343561c26f4744}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @conference {2020, title = {Advances in reduced order methods for parametric industrial problems in computational fluid dynamics}, booktitle = {Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018}, year = {2020}, abstract = {

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.

}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075395686\&partnerID=40\&md5=fb0b1a3cfdfd35a104db9921bc9be675}, author = {Gianluigi Rozza and M.H. Malik and Nicola Demo and Marco Tezzele and Michele Girfoglio and Giovanni Stabile and Andrea Mola} } @article {2019, title = {A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization}, journal = {Computers \& Fluids}, volume = {187}, year = {2019}, pages = {27-45}, abstract = {

We consider a Leray model with a nonlinear differential low-pass filter for the simulation of incompressible fluid flow at moderately large Reynolds number (in the range of a few thousands) with under-refined meshes. For the implementation of the model, we adopt the three-step algorithm Evolve-Filter-Relax (EFR). The Leray model has been extensively applied within a Finite Element (FE) framework. Here, we propose to combine the EFR algorithm with a computationally efficient Finite Volume (FV) method. Our approach is validated against numerical data available in the literature for the 2D flow past a cylinder and against experimental measurements for the 3D fluid flow in an idealized medical device, as recommended by the U.S. Food and Drug Administration. We will show that for similar levels of mesh refinement FV and FE methods provide significantly different results. Through our numerical experiments, we are able to provide practical directions to tune the parameters involved in the model. Furthermore, we are able to investigate the impact of mesh features (element type, non-orthogonality, local refinement, and element aspect ratio) and the discretization method for the convective term on the agreement between numerical solutions and experimental data.

}, doi = {10.1016/j.compfluid.2019.05.001}, url = {https://arxiv.org/abs/1901.05251}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @article {2019, title = {A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization}, journal = {Computers and Fluids}, volume = {187}, year = {2019}, pages = {27-45}, abstract = {

We consider a Leray model with a nonlinear differential low-pass filter for the simulation of incompressible fluid flow at moderately large Reynolds number (in the range of a few thousands) with under-refined meshes. For the implementation of the model, we adopt the three-step algorithm Evolve-Filter-Relax (EFR). The Leray model has been extensively applied within a Finite Element (FE) framework. Here, we propose to combine the EFR algorithm with a computationally efficient Finite Volume (FV) method. Our approach is validated against numerical data available in the literature for the 2D flow past a cylinder and against experimental measurements for the 3D fluid flow in an idealized medical device, as recommended by the U.S. Food and Drug Administration. We will show that for similar levels of mesh refinement FV and FE methods provide significantly different results. Through our numerical experiments, we are able to provide practical directions to tune the parameters involved in EFR algorithm. Furthermore, we are able to investigate the impact of mesh features (element type, non-orthogonality, local refinement, and element aspect ratio) and the discretization method for the convective term on the agreement between numerical solutions and experimental data.

}, doi = {10.1016/j.compfluid.2019.05.001}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065471890\&doi=10.1016\%2fj.compfluid.2019.05.001\&partnerID=40\&md5=c982371b5b5d4b5664a676902aaa60f4}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} }