Summer School on Reduced Order Methods in Computational Fluid Dynamics

Session

Poster blitz

Jul 13, 2022, 4:45 PM

SISSA, International School for Advanced Studies, Main Campus, Trieste, Italy

102. Non-linear reduction for data-driven non-intrusive reduced-order models with parallel autoencoders

Aly Karim Yehia

7/13/22, 4:45 PM

103. A continuous trainable filter for convolution with unstructured data

Dario Coscia

7/13/22, 4:48 PM

91. Comparing Different Nonlinear Dimensionality Reduction Techniques for Data-Driven Unsteady Fluid Flow Modeling

Hunor Csala

7/13/22, 4:51 PM

Poster

104. SHOPROMs: SHip Optimization with Reduced Order Methods

Lorenzo Fabris

7/13/22, 4:54 PM

Poster

105. A Digital Twin of an Operating Theater

Giovanna Gargiulo

7/13/22, 4:57 PM

106. Adjacency-based DMD on Deforming Grids for Vortex-Induced Vibrations

Leonidas Konstantinos Gkimisis

7/13/22, 5:00 PM

107. A novel deep learning architecture for the model reduction of parametric time-dependent problems

Isabella Carla Gonnella

7/13/22, 5:03 PM

108. A data-driven reduced order modeling framework for shape optimization of marine propellers

Anna Ivagnes

7/13/22, 5:06 PM

109. ROM for Large-scale Modelling of Urban Air Pollution

Moaad Khamlich

7/13/22, 5:09 PM

110. A reduced order model for segregated FSI solvers based on a ALE approach

Valentin Nkana

7/13/22, 5:12 PM

Poster

111. A Numerical Proof of Shell Model Turbulence Closure

Giulio Ortali

7/13/22, 5:15 PM

112. Machine Learning and Optimal Transport for shape parametrisation

Guglielmo Padula

7/13/22, 5:18 PM

Poster

113. Data Driven Methods for Delay-Differential Equations

Alessandro Pecile

7/13/22, 5:21 PM

114. A reduced order model for the optimisation-based domain decomposition algorithm for the incompressible Navier-Stokes equations

Ivan Prusak

7/13/22, 5:24 PM

Poster

115. Reduced order model of airflow in a room with two unidirectional air diffusers

Dario Pupic

7/13/22, 5:27 PM

Poster

116. A machine learning-based reduced order model for the investigation of the haemodynamics in coronary artery bypass grafts

Piefrancesco Siena

7/13/22, 5:30 PM

88. A data-driven reduced order modeling framework for shape optimization of marine propellers

Ms Anna Ivagnes (SISSA)

Poster

The project here presented is in collaboration with Fincantieri S.p.a. and aims to find the optimal shape of the propellers' blades in a cruise ship. The goal of the project is to improve the efficiency of the propeller, to reduce noise, vibration and consumption, and to avoid the cavitation phenomenon, which can lead to erosion and damage of the machinery.
In this work, we extend the...

79. A Digital Twin of an Operating Theater

Giovanna Gargiulo (Università di Roma Tor Vergata)

Poster

This study is an example of the possible use of the Reduced Order Model (ROM) in air quality management in an operating theater. We present a steady-state CFD (Computer Fluid Dynamics) simulation with the aim of obtaining, in real-time, the variables of interest associated with any change in the room conditions. This is possible by means of a Digital Twin (DT), obtained by a ROM using a...

90. A machine learning-based reduced order model for the investigation of the haemodynamics in coronary artery bypass grafts

Pierfrancesco Siena (SISSA)

Poster

The diffusion of coronary artery diseases and the increasing demand from the medical community for quantitative and patient-specific investigations are the main motivations which gave a strong impulse in recent time to the development of fast and accurate numerical models. The final goal is a better understanding of the blood flow behaviour in grafts and graft junctions so as to aid in...

94. A novel deep learning architecture for the model reduction of parametric time-dependent problems

Isabella Carla Gonnella (SISSA)

Poster

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs – built, e.g., exclusively through proper orthogonal decomposition (POD) – when applied to nonlinear time-dependent parametrized PDEs.
In this work, thanks to a prior dimensionality reduction through POD, a two-step DL-based prediction framework has been...

89. A Numerical Proof of Shell Model Turbulence Closure

Giulio Ortali (SISSA)

Poster

In this work we define a Subgrid Closure model that, employed in a Large Eddy Simulation approach, exhibits correct scaling laws in high order Structure Functions, encompassing intermittent effects and energy cascade dynamics. Due to the massive amount of data needed to reach converged statistics of high order statistical moments, we consider the setting of Shell Models of Turbulence. Our...

96. A reduced order model for segregated FSI solvers based on a ALE approach

Valentin Nkana (SISSA)

Interactions between fluid and moving (deforming) structures are essential in many fields such as aerospace, civil, and mechanical engineering. Numerical simulation has been one of the few available methods for studying this problem. The flow past a circular cylinder is a benchmark problem in fluid dynamics that serves to illustrate a large variety of applications. Therefore, flow around a...

95. A reduced order model for the optimisation-based domain decomposition algorithm for the incompressible Navier-Stokes equations

Mx Ivan Prusak (SISSA)

Poster

Keywords: Reduced Order Modelling, Proper Orthogonal Decomposition, Domain Decomposition, Optimal Control, Computational Fluid Dynamics

In the last few decades, there has been an explosion in the numerical analysis for Computational Fluid Dynamics, and many different methods have been proposed to solve Navier-Stokes equations numerically. Nevertheless, there is still an immense need to...

78. Adjacency-based DMD on Deforming Grids for Vortex-Induced Vibrations

Leonidas Konstantinos Gkimisis (Max Planck Institute for the Dynamics of Complex Technical Systems)

Poster

Vortex-induced vibrations (VIV) of structures pose computationally expensive problems of high practical interest to several engineering fields. In this work, we develop a methodology for non-intrusive, reduced-order modelling of two-dimensional VIV simulations. We consider the problem of an elliptical non-deformable solid mounted on vertical and horizontal springs, subject to a low-Reynolds...

80. Comparing Different Nonlinear Dimensionality Reduction Techniques for Data-Driven Unsteady Fluid Flow Modeling

Mr Hunor Csala (Northern Arizona University)

Poster

Computational fluid dynamics (CFD) data are usually high dimensional, with millions of degrees of freedom in space and thousands of timesteps. The first step of reduced order modeling is to identify a new set of coordinates, a low dimensional embedding, that describe the underlying dominant structures in the data. This is usually done via Proper Orthogonal Decomposition (POD), which gives the...

92. Machine Learning and Optimal Transport for shape parametrisation

Guglielmo Padula (Internship at Sissa MathLab)

Poster

This work focuses on the application of methods of 3D mesh deformation in order to preserve properties of interest like volume or curvature. Results regarding Variational Autoencoders, Semi-Discrete Optimal Transport applied to simple geometries will be presented. The extension of Semi Discrete Optimal Transport to three meshes will also be discussed.

97. Non-linear reduction for data-driven non-intrusive reduced-order models with parallel autoencoders

Mr Karim Yehia Aly (SISSA), Nicola Demo (SISSA), Giovanni Stabile (SISSA), Gianluigi Rozza (Full professor)

Poster

Projection-based intrusive reduced-order models (ROMs) have demonstrated exciting results across a wide range of applications [1-2]; nevertheless, implementing such intrusive techniques requires significant modifications to the underlying numerical codes. Non-Intrusive Reduced-Order Models (NIROM) based on Proper Orthogonal Decomposition (POD) have been proposed for providing a reduced-order...

100. prova

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81. Reduced order model of airflow in a room with two unidirectional air diffusers

Dario Pupić

As part of a broader study on optimal control of ventilation systems based on reduced order models (ROM) for ventilation effectiveness and thermal comfort assessment, a linear ROM was constructed on the example of a room with two unidirectional air diffusers. The reduced order model includes different flows per air diffuser. Airflow and C02 distribution within the room were reproduced under...

93. ROM for Large-scale Modelling of Urban Air Pollution

Moaad Khamlich (SISSA)

Poster

Urban air pollution is a major global challenge that is responsible for damage to climate, ecosystems, and health. Recent research conducted by the World Health Organization (WHO) has shown that 9 out of 10 people breathe polluted air and this indirectly translates into a human life cost (over 3 million deaths per year) and an economic cost (estimated at 5 trillion dollars per year). For these...

82. SHOPROMs: SHip Optimization with Reduced Order Methods

Lorenzo Fabris (SISSA)

Poster

The SHOPROMs project aims to integrate new optimization techniques in the structural design process of passenger cruise ships.
Many sections of the ship hull are parameterized by their thickness, with the structural behaviour of the configuration being subjected to complex regulatory and industrial constraints.
From simulations run with a commercial solver, reduced order surrogates are built...