MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1251 products
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37/2022 - 05/31/2022
Boon, W. M.; Fumagalli, A.
A Reduced Basis Method for Darcy flow systems that ensures local mass conservation by using exact discrete complexes | Abstract | | A solution technique is proposed for flows in porous media that guarantees local conservation of mass. We first compute a flux field to balance the mass source and then exploit exact co-chain complexes to generate a solenoidal correction. A reduced basis method based on proper orthogonal decomposition is employed to construct the correction and we show that mass balance is ensured regardless of the quality of the reduced basis approximation. The method is directly applicable to mixed finite and virtual element methods, among other structure-preserving discretization techniques, and we present the extension to Darcy flow in fractured porous media. |
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36/2022 - 05/17/2022
Vaccaro, F.; Brivio, S.; Perotto, S.; Mauri, A.G.; Spiga, S.
Physics-based Compact Modelling of the Analog Dynamics of HfOx Resistive Memories | Abstract | | Resistive random access memories (RRAMs) constitute a class of memristive devices particularly appealing for bio-inspired computing schemes. In particular, the possibility of achieving analog control of the electrical conductivity of RRAM devices can be exploited to mimic the behaviour of biological synapses in neuromorphic systems. With a view to neuromorphic computing applications, it turns out to be crucial to guarantee some features, among which a detailed device characterization, a mathematical modelling comprehensive of all the key features of the device both in quasi-static and dynamic conditions, a description of the variability due to the inherently stochasticity of the processes involved in the switching transitions. In this paper, starting from experimental data, we provide a modelling and simulation framework to reproduce the operative analog behaviour of HfO$_x$-based RRAM devices under train of programming pulses both in the analog and binary operation mode. To this aim, we have calibrated
the model by using a single set of parameters for the quasi-static current-voltage characteristics as well as switching kinetics and device dynamics. The physics-based compact model here settled captures the difference between the SET and the RESET processes in the I-V characteristics, as well as the device memory window both for strong and weak programming conditions. Moreover, the model reproduces the correct slopes of the highly non-linear kinetics curves over several orders of magnitudes in time, and the dynamic device response including the inherent device variability.
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35/2022 - 05/17/2022
Perotto, S.; Bellini, G.; Ballarin, F.; Calò, K.; Mazzi, V.; Morbiducci, U.
Isogeometric hierarchical model reduction for advection-diffusion process simulation in microchannels | Abstract | | Microfluidics proved to be a key technology in various applications, allowing to reproduce large-scale laboratory settings
at a more sustainable small-scale. The current effort is focused on enhancing the mixing process of different passive species at the micro-scale, where a laminar flow regime damps turbulence effects. Chaotic advection is often used to improve mixing effects also at very low Reynolds numbers. In particular, we focus on passive micromixers, where chaotic advection is mainly achieved by properly selecting the geometry of microchannels. In such a context, reduced order modeling can play a role, especially in the design of new geometries.
In this chapter, we verify the reliability and the computational benefits lead by a Hierarchical Model (HiMod) reduction when modeling the transport of a passive scalar in an S-shaped microchannel. Such a geometric configuration provides an ideal setting where to apply a HiMod approximation, which exploits the presence of a leading dynamics to commute the original three-dimensional model into a system of one-dimensional coupled problems.
It can be proved that HiMod reduction guarantees a very good accuracy when compared with a high-fidelity model, despite a drastic reduction in terms of number of unknowns. |
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34/2022 - 05/17/2022
Antonietti, P.F.; Vacca, G.; Verani, M.
Virtual Element method for the Navier-Stokes equation coupled with the heat equation | Abstract | | We consider the Virtual Element discretization of the Navier-Stokes equations coupled with the heat equation where the viscosity depends on the temperature. We present the Virtual Element discretization of the coupled problem, show its well-posedness, and prove optimal error estimates. Numerical experiments which confirm the theoretical error bounds are also presented. |
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33/2022 - 05/11/2022
Africa, P.C.; Salvador, M.; Gervasio, P.; Dede', L.; Quarteroni, A.
A matrix-free high-order solver for the numerical solution of cardiac electrophysiology | Abstract | | We propose a matrix-free solver for the numerical solution of the cardiac electrophysiology model consisting of the monodomain nonlinear reaction-diffusion equation coupled with a system of ordinary differential equations for the ionic species. Our numerical approximation is based on the high-order Spectral Element Method (SEM) to achieve accurate numerical discretization while employing a much smaller number of Degrees of Freedom than first-order Finite Elements. We combine sum-factorization with vectorization, thus allowing for a very efficient use of high-order polynomials in a high performance computing framework. We validate the effectiveness of our matrix-free solver in a variety of applications and perform different electrophysiological simulations ranging from a simple slab of cardiac tissue to a realistic four-chamber heart geometry. We compare SEM to SEM with Numerical Integration (SEM-NI), showing that they provide comparable results in terms of accuracy and efficiency. In both cases, increasing the local polynomial degree p leads to better numerical results and smaller computational times than reducing the mesh size h. We also implement a matrix-free Geometric Multigrid preconditioner that entails better performance in terms of linear solver iterations than state-of-the-art matrix-based Algebraic Multigrid preconditioners. As a matter of fact, the matrix-free solver here proposed yields up to 50× speed-up with respect to a conventional matrix-based solver. |
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32/2022 - 05/09/2022
Barbi, C.; Menafoglio, A; Secchi, P.
An object-oriented approach to the analysis of spatial complex data over stream-network domains | Abstract | | We address the problem of spatial prediction for Hilbert data, when their spatial domain of observation is a river network. The reticular nature of the domain requires to use geostatistical methods based on the concept of Stream Distance, which captures the spatial connectivity of the points in the river induced by the network branching. Within the framework of Object Oriented Spatial Statistics (O2S2), where the data are considered as points of an appropriate (functional) embedding space, we develop a class of functional moving average models based on the Stream Distance. Both the geometry of the data and that of the spatial domain are thus taken into account. A consistent definition of covariance structure is developed, and associated estimators are studied. Through the analysis of the summer water temperature profiles in the Middle Fork River (Idaho, USA), our methodology proved to be effective, both in terms of covariance structure characterization and forecasting performance. |
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31/2022 - 05/09/2022
Bortolotti, T; Peli, R.; Lanzano, G; Sgobba, S.; Menafoglio, A
Weighted functional data analysis for the calibration of ground motion models in Italy | Abstract | | Motivated by the crucial implications of Ground Motion Models (GMM) in terms of seismic hazard analysis and civil protection planning, this work extends a scalar ground motion model for Italy to the framework of Functional Data Analysis. The inherent characteristic of seismic data to be incomplete over the observation domain entails embedding the analysis in the context of partially observed functional data. This work proposes a novel methodology that combines pre-existing techniques of data reconstruction with the definition of observation-specific functional weights, which enter the estimation process to reduce the impact that the reconstructed parts of the curves have on the final estimates. The classical methods of smoothing and concurrent functional regression are extended to include weights. The advantages of the proposed methodology are assessed on synthetic data. Eventually, the weighted functional analysis performed on seismological data is shown to provide a natural smoothing and stabilization of the spectral estimates of the GMM. |
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30/2022 - 05/09/2022
Bonetti S.; Botti M.; Antonietti P.F.
Discontinuous Galerkin approximation of the fully-coupled thermo-poroelastic problem | Abstract | | We present and analyze a discontinuous Galerkin method for the numerical modelling of the fully-coupled quasi-static thermo-poroelastic problem. In particular, for the space discretization we introduce a discontinuous Galerkin method over polygonal and polyhedral grids and we present the stability analysis via two different approaches: first exploiting the Poincarè's inequality and second using the generalized inf-sup condition. Error estimates are derived for the resulting semi-discrete formulation in a suitable mesh dependent energy norm. Numerical simulations are presented in order to validate the theoretical analysis and to show the application of the model to a realistic case test. |
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