COMPLAS 2021 is the 16th conference of the COMPLAS Series.
The COMPLAS conferences started in 1987 and since then have become established events in the field of computational plasticity and related topics. The first fifteen conferences in the COMPLAS series were all held in the city of Barcelona (Spain) and were very successful from the scientific, engineering and social points of view. We intend to make the 16th edition of the conferenceanother successful edition of the COMPLAS meetings.
The objectives of COMPLAS 2021 are to address both the theoretical bases for the solution of nonlinear solid mechanics problems, involving plasticity and other material nonlinearities, and the numerical algorithms necessary for efficient and robust computer implementation. COMPLAS 2021 aims to act as a forum for practitioners in the nonlinear structural mechanics field to discuss recent advances and identify future research directions.
Scope
COMPLAS 2021 is the 16th conference of the COMPLAS Series.
Most of the Unreinforced Masonry (URM) buildings are quite old in Europe based on 'Building stock inventory to assess seismic vulnerability across Europe' [1] report. Following the earthquakes (Albania, Italy, etc.) that occurred in Europe, it was revealed that masonry buildings are extremely vulnerable. While probabilistic and deterministic approaches are important for examining a small number of buildings, they do not offer the opportunity to examine a large building stock in a short period of time. Rapid Visual Screening (RVS) methods are used to identify building preand post-earthquake vulnerability. Several RVS techniques have been presented in literature over last 30 years. Recent earthquakes have highlighted critical necessity of a rapid vulnerability assessment method for pre-earthquake warning, mitigation, preparedness, and post-earthquake damage state assessment of existing buildings. These findings demonstrate the importance of using an accurate RVS technique to inspect buildings. Due to the subjectivity of the screener, these RVS methods contain uncertainty and vagueness. Fuzzy Inference System (FIS) overcomes nonrandom uncertainty and vagueness by considering building characteristics in terms of their degree of truth. This paper introduces a FIS-based SRVS case implementation and compares FIS-based Soft-RVS (S-RVS) to traditional RVS methods for identifying building damage state taking into account rapid visual assessment reports about damage caused by the 2019 Albania earthquake. To determine the damage states of URM buildings, 40 buildings damaged in the 2019 Albania earthquake were analyzed and processed to use in the applied fuzzy logic mathematical model. Initial findings demonstrate that the site-specific FIS-based S-RVS method is capable of accurately determining the damage states of at least half of the buildings.
Abstract Most of the Unreinforced Masonry (URM) buildings are quite old in Europe based on 'Building stock inventory to assess seismic vulnerability across Europe' [1] report. [...]
This contribution is related to the key-note lecture `Space-time fluid-structure interaction with adjoint-based methods for error estimation and optimization' given in the Minisymposium `Innovative Methods for Fluid-Structure Interaction'. The main objective is twofold. First, we design function spaces and a space-time variational-monolithic formulation of fluid-structure interaction in arbitrary Lagrangian-Eulerian coordinates. Second, we apply a Galerkin-time discretization using discontinuous finite elements of degree r = 0. Therein, the main emphasis is on the correct derivation of the jump terms and the integration of nonlinear time derivatives, as the latter arise due to the arbitrary Lagrangian-Eulerian transformation.
Abstract This contribution is related to the key-note lecture `Space-time fluid-structure interaction with adjoint-based methods for error estimation and optimization' given in [...]
In this contribution, the displacement-based scaled-boundary finite element method (SBFEM) is extended to a mixed displacement-pressure formulation for the geometrically and materially nonlinear analysis of nearly-incompressible solids. The displacements and pressures are both parameterized by a scaled-boundary approach, for which an interpolation in the scaling direction is used. Here, higher-order interpolation functions may be employed. It is shown that by introducing the pressure as a field variable, volumetric locking is no longer present. The approach is valid for arbitrary scaling center locations, which can be either inside or outside of the element domain. Other than that, the formulation is valid for non-star-convex element geometries. Numerical examples show that the method is capable of alleviating volumetric locking for arbitrary polygonal meshes and is beneficial in comparison to the displacement-based method when it comes to modeling nearly-incompressible materials.
Abstract In this contribution, the displacement-based scaled-boundary finite element method (SBFEM) is extended to a mixed displacement-pressure formulation for the geometrically and [...]
Most epidemiological models are rooted in the pioneering work proposed by Kermack and McKendrick and are based on systems of deterministic ODEs, which describe the temporal evolution of the spread of an infectious disease assuming population and territorial homogeneity. Generally, the concept of the average behavior of a population is sufficient to have a first reliable description of an epidemic development, but the inclusion of the spatial component becomes crucial when it is necessary to consider spatially heterogeneous interventions, as in the case of the COVID-19 pandemic. Moreover, any realistic data-driven model must take into account the large uncertainty in the values reported by official sources such as the amount of infectious individuals. In this work, drawing inspiration from kinetic theory, recent advances on the development of stochastic multiscale kinetic transport models for the spread of epidemics under uncertain data are presented. The propagation of the infectious disease is described by the spatial movement and interactions of individuals divided into commuters moving in the territory on a wide scale and non-commuters acting only on urban scales. The resulting models are solved numerically through a suitable stochastic Asymptotic-Preserving IMEX Runge-Kutta Finite Volume Collocation Method, which ensures a consistent treatment of the system of equations, without loss of accuracy when entering in the stiff, diffusive regime. Application studies concerning the spread of the COVID-19 pandemic in Italy assess the validity of the proposed methodology.
Abstract Most epidemiological models are rooted in the pioneering work proposed by Kermack and McKendrick and are based on systems of deterministic ODEs, which describe the temporal [...]
A. Kubik, A. Ramos, B. Ivorra, M. Vela-Pérez, M. Ferrández
eccomas2022.
Abstract
In December 2019, a new coronavirus, the SARS-CoV-2, was detected in the Chinese city of Wuhan. Since then, many mathematical models have been developed to study the possible evolution of the COVID-19 disease and shed some light on the different biological processes of concern. On 14 December 2020, the United Kingdom reported a potentially more contagious and lethal variant of the virus, at the same time that different vaccines were being tested in order to prevent severe forms of the disease. In the following lines, we revisit a model proposed by our team, which took into account these two determining facts, showing its performance with real Italian data.
Abstract In December 2019, a new coronavirus, the SARS-CoV-2, was detected in the Chinese city of Wuhan. Since then, many mathematical models have been developed to study the possible [...]
The virtual element method (VEM), is a stabilized Galerkin scheme deriving from mimetic finite differences, which allows for very general polygonal meshes, and does not require the explicit knowledge of the shape functions within the problem domain. In the VEM, the discrete counterpart of the continuum formulation of the problem is defined by means of a suitable projection of the virtual shape functions onto a polynomial space, which allows the decomposition of the bilinear form into a consistent part, reproducing the polynomial space, and a correction term ensuring stability. In the present contribution, we outline an extended virtual element method (X-VEM) for two-dimensional elastic fracture problems where, drawing inspiration from the extended finite element method (X-FEM), we extend the standard virtual element space with the product of vector-valued virtual nodal shape functions and suitable enrichment fields, which reproduce the singularities of the exact solution. We define an extended projection operator that maps functions in the extended virtual element space onto a set spanned by the space of linear polynomials augmented with the enrichment fields. Numerical examples in 2D elastic fracture are worked out to assess convergence and accuracy of the proposed method for both quadrilateral and general polygonal meshes.
Abstract The virtual element method (VEM), is a stabilized Galerkin scheme deriving from mimetic finite differences, which allows for very general polygonal meshes, and does not require [...]
Driven by the challenging task of finding robust discretization methods for Galbrun's equation, we investigate conditions for stability and different aspects of robustness for different finite element schemes on a simplified version of the equations. The considered PDE is a second order indefinite vector-PDE which remains if only the highest order terms of Galbrun's equation are taken into account. A key property for stability is a Helmholtz-type decomposition which results in a strong connection between stable discretizations for Galbrun's equation and Stokes and nearly incompressible linear elasticity problems.
Abstract Driven by the challenging task of finding robust discretization methods for Galbrun's equation, we investigate conditions for stability and different aspects of robustness [...]
A hybrid FV/DG framework is developed for the simulation of compressible multispecies flows on unstructured meshes with a five-equation Diffuse-Interface Model [1]. The high order DG method is employed for the purpose of limiting the material interface smearing typical of the diffuse-interface models resulting from excessive numerical dissipation [2, 3]. In order to ensure high-order accuracy in smooth flow regions and non-oscillatory behaviour near shocks or material interfaces, the hybrid scheme resorts to the underlying FV method when invalid cells are detected by a troubled cell indicator checking the unlimited DG solution, and enables a highorder non-linear CWENOZ reconstruction [4,5] if the solution present oscillations. The CWENO and CWENOZ type reconstruction uses a high-order polynomial for the central stencil and a lower-order polynomial for the directional stencils enhancing robustness and efficiency of classic WENO schemes. To achieve consistency in advecting material interfaces at constant pressure and velocity, the source term from the non-conservative equation is discretised compatibly with the Riemann solver, following the work of Johnsen and Colonius [6].
Abstract A hybrid FV/DG framework is developed for the simulation of compressible multispecies flows on unstructured meshes with a five-equation Diffuse-Interface Model [1]. The high [...]
M. Lahooti, G. Vivarelli, F. Montomoli, S. Sherwin
eccomas2022.
Abstract In this work a high-order spectral-h/p element solver is employed to efficiently but accurately resolve the flow field around the NACA0012 aerofoil.
In this work, we present an application of modern deep learning methodologies to the numerical solution of two dimensional hyperbolic partial differential equations in transport models. More specifically, we employ a supervised deep neural network that takes into account of initial-boundary value problems for a scalar, 2D inviscid Burgers model including the case with Riemann data, whose solutions develop discontinuity, containing both shock wave and rarefaction wave. We also apply the proposed PINN approach to the linear advection equation with periodic sinusoidal initial condition. Our results suggest that a relatively simple deep learning model was capable of achieving promising results in the linear advection and inviscid Burgers equation with rarefaction, providing numerical evidence of good approximation of weakentropy solutions to the case of nonlinear 2D inviscid Burgers model. For the Riemann problems, the neural network performed better when rarefaction wave is predominant. The premises underlying these preliminary results as an integrated physics-informed deep learning approach are promising. However, there are hints of evidence suggesting specific fine tuning on the PINN methodology for solving hyperbolic-transport problems in the presence of shock formation in the solutions.
Abstract In this work, we present an application of modern deep learning methodologies to the numerical solution of two dimensional hyperbolic partial differential equations in transport [...]