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 1253 products
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51/2017 - 10/16/2017
Gerbi, A.; Dede', L.; Quarteroni, A.
A monolithic algorithm for the simulation of cardiac electromechanics in the human left ventricle | Abstract | | In this paper, we propose a monolithic algorithm for the numerical solution of an electromechanics model of the left ventricle in the human heart. We consider the monodomain equation together with the Bueno-Orovio minimal ionic model for the description of the electrophysiology and the Holzapfel-Ogden strain energy function within the active strain framework for the mechanics of the myocardium. For the latter, we use for the first time in the context of electromechanics a transmurally variable active strain formulation. The Finite Element Method is used for the space discretization, while Backward Differentiation Formulas are used for the time discretization. Both implicit and semi-implicit schemes are addressed in this paper: the Newton method is used to solve the nonlinear system arising in the implicit scheme, while the semi-implicit scheme (corresponding to extrapolation of nonlinear terms from previous timesteps) yields a linear problem at each timestep. In the latter case, stability constraints may pose limitations in the timestep size. Much emphasis is laid into on the preconditioning strategy, which is based on the factorization of a block Gauss-Seidel preconditioner combined with the use of parallel preconditioners for each of the single core models composing the full electromechanics model. This monolithic preconditioner can be easily extended to cases where other ionic models are adopted and,
besides heart models, to other integrated problems arising in different multiphysics applications in engineering and applied sciences. Several numerical simulations are carried out in a high performance computing framework for both idealized and patient-specific left ventricle geometries. The latter are obtained from medical MRI images through suitable segmentation procedures to generate the computational mesh. Personalized pressure-volume loops are produced by means of the computational procedure and used to synthetically interpret and analyze the outputs of the electromechanics model. |
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50/2017 - 10/16/2017
Formaggia, F.; Vergara, C.
Defective boundary conditions for PDEs with applications in haemodynamics | Abstract | | This works gives an overview of the mathematical treatment of state-of-the-art techniques for partial differential problems where boundary data are provided only in
terms of averaged quantities. A condition normally indicated as ``defective boundary condition''. We present and analyze several procedures by which this type of problems can be handled |
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49/2017 - 09/13/2017
Antonietti, P. F.,; Pennesi, G.
V-cycle multigrid algorithms for discontinuous Galerkin methods on non-nested polytopic meshes | Abstract | | In this paper we analyse the convergence properties of V-cycle multigrid algorithms for the numerical solution of the linear system of equations arising from discontinuous Galerkin discretization of second-order elliptic partial differential equations on polytopal meshes. Here, the sequence of spaces that stands at the basis of the multigrid scheme is possibly non nested and is obtained based on employing agglomeration with possible edge/face coarsening. We prove that the method converges uniformly with respect to the granularity of the grid and the polynomial approximation degree p, provided that the number of smoothing steps, which depends on p, is chosen sufficiently large. |
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48/2017 - 08/30/2017
Regazzoni, F.; Dedè, L.; Quarteroni, A.
Active contraction of cardiac cells: a reduced model for sarcomere dynamics with cooperative interactions | Abstract | | We propose a reduced ODE model for the mechanical activation of cardiac myofilaments, which is based on explicit spatial representation of nearest-neighbour interactions. Our model is derived from the cooperative Markov Chain model of Washio et al. 2012, under the assumption of conditional independence of specific sets of events. This physically motivated assumption allows to drastically reduce the number of degrees of freedom, thus resulting in a significantly large computational saving. Indeed, the original Markov Chain model involves a huge number of degrees of freedom (order of 10^21) and is solved by means of the Monte Carlo method, which notoriously reaches statistical convergence in a slow fashion. With our reduced model, instead, numerical simulations can be carried out by solving a system of ODEs, reducing the computational time by more than 10000 times. Moreover, the reduced model is accurate with respect to the original Markov Chain model. We show that the reduced model is capable of reproducing physiological steady-state force-calcium and force-length relationships with the observed asymmetry in apparent cooperativity near the calcium level producing half activation. Finally, we also report good qualitative and quantitative agreement with experimental measurements under dynamic conditions. |
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47/2017 - 08/29/2017
Menghini, F.; Dede, L.; Forti, D.; Quarteroni, A.
Hemodynamics in a left atrium based on a Variational Multiscale-LES numerical model | Abstract | | Standard studies of the cardiovascular system are based on advanced experimental and imaging techniques, however, in the past few years, they are being complemented by computational fluid dynamics simulations of blood
flows with increasing level of details. The vast majority of works dealing with the heart hemodynamics focus on the left ventricle, both in patient-specific and idealized geometries, while the fluid dynamics of the left atrium is much less investigated. In this work we propose a computational model of a left atrium suitable to provide physically meaningful fluid dynamics indications and other outputs as the velocity profile at the mitral valve. A Variational Multiscale model is used to obtain a stable formulation of the Navier-Stokes equations discretized by means of the Finite Element method and to account for turbulence modeling within the framework of Large Eddy Simulation (LES). We present and discuss numerical results regarding the fluid dynamics of the left atrium with the focus on possible transitions to turbulence. We also provide a comparison with the results obtained using a SUPG formulation. |
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46/2017 - 08/08/2017
Agosti, A.; Gower, A.L.; Ciarletta, P.
The constitutive relations of initially stressed incompressible Mooney-Rivlin materials | Abstract | | Initial stresses originate in soft materials by the occurrence of misfits in the undeformed microstruc-
ture. Since the reference configuration is not stress-free, the effects of initial stresses on the hyperelastic
behavior must be constitutively addressed. Notably, the free energy of an initially stressed material
may not possess the same symmetry group as the one of the same material deforming from a naturally
unstressed configuration. This work assumes an explicit dependence of the hyperelastic strain energy
density on both the deformation gradient and the initial stress tensor, taking into account for their inde-
pendent invariants. Using this theoretical framework, a constitutive equation is derived for an initially
stressed body that naturally behaves as an incompressible Mooney-Rivlin material. The strain energy
densities for initially stressed neo-Hookean and Mooney materials are derived as special sub–cases. By
assuming the existence of a virtual state that is naturally stress-free, the resulting strain energy functions
are proved to fulfill the required frame–independence constraints. In the case of plane strain condition,
great simplifications arise in the expression of the constitutive relations. Finally, the resulting constitu-
tive relations prove useful guidelines for designing non-destructive methods for the quantification of the
underlying initial stresses in naturally isotropic materials.
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45/2017 - 08/06/2017
Gasperoni, F.; Ieva, F.; Paganoni, A.M.; Jackson C.H.; Sharples L.D.
Nonparametric frailty Cox models for hierarchical time-to-event data | Abstract | | In this work we propose a novel model for dealing with hierarchical time-to-event data, which is a common structure in healthcare research field (i.e., healthcare providers, seen as groups of patients). The most common statistical model for dealing with this kind of data is the Cox proportional hazard model with shared frailty term, whose distribution has to be specified a priori.
The main objective of this work consists in overcoming this limit by avoiding any a priori hypothesis on the frailty distribution. In order to do it, we introduce a nonparametric discrete frailty, through which we are not just guaranteeing a very good level of flexibility, but we are also building a probabilistic clustering technique, which allows to detect a clustering structure of groups, where each cluster is named latent population.
A tailored Expectation-Maximization algorithm, combined with model selection tech- niques, is proposed for estimating model’s parameters.
Beyond the new methodological contribution, we propose a useful tool for exploring big hierarchical time-to-event data, where it is very difficult to explain all the phenomenon variability through explanatory covariates. We show the power of this model by applying it to a clinical administrative database, where several information of patients suffering from Heart Failure is collected, like age, comorbidities, procedures etc. In this way, we are able to detect a latent clustering structure among healthcare providers. |
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44/2017 - 08/01/2017
Martino, A.; Ghiglietti, A.; Ieva, F.; Paganoni, A.M.
A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data | Abstract | | This paper proposes a clustering procedure for samples of multivariate functions in (L^2(I))^J, with J>=1. This method is based on a k-means algorithm in which the distance between the curves is measured with a metrics that generalizes the Mahalanobis distance in Hilbert spaces,
considering the correlation and the variability along all the components of the functional data. The proposed procedure has been studied in simulation and compared with the k-means based on other distances typically adopted for clustering multivariate functional data. In these simulations, it is shown that the k-means algorithm with the generalized Mahalanobis distance provides the best clustering performances, both in terms of mean and standard deviation of the number of misclassified curves. Finally, the proposed method has been applied to two real cases studies, concerning ECG signals and growth curves, where the results obtained in simulation are confirmed and strengthened. |
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