Browse ORBi

- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?

ORBi

Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs Dehghani, Hamidreza ; Zilian, Andreas in Computational Mechanics (2023) This contribution introduces and discusses a formulation of poro-hyperelasticity at finite strains. The prediction of the time-dependent response of such media requires consideration of their ... [more ▼] This contribution introduces and discusses a formulation of poro-hyperelasticity at finite strains. The prediction of the time-dependent response of such media requires consideration of their characteristic multi-scale and multi-physics parameters. In the present work this is achieved by formulating a non-dimensionalised fluid–solid interaction problem (FSI) at the pore level using an arbitrary Lagrange–Euler description (ALE). The resulting coupled systems of PDEs on the reference configuration are expanded and analysed using the asymptotic homogenisation technique. This approach yields three partially novel systems of PDEs: the macroscopic/effective problem and two supplementary microscale problems (fluid and solid). The latter two provide the microscopic response fields whose average value is required in real-time/online form to determine the macroscale response (a concurrent multi-scale approach). In order to overcome the computational challenges related to the above multi-scale closure, this work introduces a surrogate approach for replacing the direct numerical simulation with an artificial neural network. This methodology allows for solving finite strain (multi-scale) porohyperelastic problems accurately using direct automated differentiation through the strain energy. Optimal and reliable training data sets are produced from direct numerical simulations of the fully-resolved problem by including a simple real-time output density check for adaptive sampling step refinement. The data-driven approach is complemented by a sensitivity analysis of the RVE response. The significance of the presented approach for finite strain poro-elasticity/poro-hyperelasticity is shown in the numerical benchmark of a multi-scale confined consolidation problem. Finally, to show the robustness of the method, the system response is dimensionalised using characteristic values of soil and brain mechanics scenarios. [less ▲] Detailed reference viewed: 32 (3 UL)AI-supported Modelling of Brain tissue as Soft Multiscale Multiphysics (Poroelastic) medium Dehghani, Hamidreza ; Zilian, Andreas Presentation (2022, January) Detailed reference viewed: 12 (2 UL)AI-aided, incremental numerical approach for fi nite strain poroelasticity: On the brain tissue deformation Dehghani, Hamidreza ; Zilian, Andreas Scientific Conference (2021, May 21) Detailed reference viewed: 53 (3 UL)ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity Dehghani, Hamidreza ; Zilian, Andreas in Computational Mechanics (2021) Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical ... [more ▼] Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing a more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy’s law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under the uniaxial cyclic test is simulated and studied. [less ▲] Detailed reference viewed: 76 (6 UL)Data science meets computational mechanics Dehghani, Hamidreza ; Zilian, Andreas Report (2021) Detailed reference viewed: 83 (4 UL)Modelling of residually stressed, extended and inflated cylinders with application to aneurysms ; ; Dehghani, Hamidreza et al in Mechanics Research Communications (2021), 111 The paper presents the localized bifurcation abnormal enlargement associated with certain human diseases such as abdominal aortic aneurysms (AAA), among others. The constitutive framework herewith ... [more ▼] The paper presents the localized bifurcation abnormal enlargement associated with certain human diseases such as abdominal aortic aneurysms (AAA), among others. The constitutive framework herewith proposed is constructed relying on the modelling of non-linear elastic materials under the action of residual stresses. The suitable incorporation on the mechanical response of residual stresses in the analysis is regarded important for the formation of aneurysms in soft tissues. From a mechanical perspective, the onset of aneurysms formation can be interpreted through bifurcation conditions, whose localization is relatively sensitive to different material and geometrical parameters as it is shown here. In order to reduce the risk and interpret aneurysm formation, we perform a thorough sensitivity analysis of the effect of design parameters such as tube diameter, length, thickness and strength of the residual stress field on bifurcation of a tube under inflation and extesion. A consistent residually stressed material model is formulated in terms of invariants for a general elastic strain-energy function. The dependence of applied pressure, axial stretch and different geometrical and constitutive parameters on bulging and bending bifurcation is illustrated. The numerical procedure to analyse the bifurcation of the finite deformation boundary-value problem at hand is developed based on the modified Riks method. The proposed formulation is implemented in the general-purpose finite element code ABAQUS using user-defined material subroutines.For a given material model, bulging bifurcation is expected for sufficiently large values of the axial stretch while the onset of bifurcation is found to be the bending mode for small values of the axial stretch. This transition zone from bending bifurcation to bulging bifurcation is analyzed for the different parameters considered. [less ▲] Detailed reference viewed: 133 (2 UL)A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs Dehghani, Hamidreza ; Zilian, Andreas E-print/Working paper (2020) We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems ... [more ▼] We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems described, in strong-form, by PDEs via ANNs (Artificial Neural Networks). This presented approach allows the selection of a number of locations of interest at which the state variables are expected to fulfil the governing equations associated with a physical problem. Unlike classical PDE approximation methods such as finite differences or the finite element method, there is no need to establish and reconstruct the physical field quantity throughout the computational domain in order to predict the mechanical response at specific locations of interest. The basic idea of MGA-MSGD is the manipulation of the learnable parameters’ components responsible for the error explosion so that we can train the network with relatively larger learning rates which avoids trapping in local minima. The proposed training approach is less sensitive to the learning rate value, training points density and distribution, and the random initial parameters. The distance function to minimise is where we introduce the PDEs including any physical laws and conditions (so-called, Physics Informed ANN). The Genetic algorithm is modified to be suitable for this type of ANN in which a Coarse-level Stochastic Gradient Descent (CSGD) is exploited to make the decision of the offspring qualification. Employing the presented approach, a considerable improvement in both accuracy and efficiency, compared with standard training algorithms such classical SGD and Adam optimiser, is observed. The local displacement accuracy is studied and ensured by introducing the results of Finite Element Method (FEM) at sufficiently fine mesh as the reference displacements. A slightly more complex problem is solved ensuring the feasibility of the methodology [less ▲] Detailed reference viewed: 85 (3 UL)Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio Dehghani, Hamidreza ; Zilian, Andreas in Computational Mechanics (2020), 66 Predictive analysis of poroelastic materials typically require expensive and time-consuming multiscale and multiphysics approaches, which demand either several simplifications or costly experimental tests ... [more ▼] Predictive analysis of poroelastic materials typically require expensive and time-consuming multiscale and multiphysics approaches, which demand either several simplifications or costly experimental tests for model parameter identification. This problem motivates us to develop a more efficient approach to address complex problems with an acceptable computational cost. In particular, we employ artificial neural network (ANN) for reliable and fast computation of poroelastic model parameters. Based on the strong-form governing equations for the poroelastic problem derived from asymptotic homogenisation, the weighted residuals formulation of the cell problem is obtained. Approximate solution of the resulting linear variational boundary value problem is achieved by means of the finite element method. The advantages and downsides of macroscale properties identification via asymptotic homogenisation and the application of ANN to overcome parameter characterisation challenges caused by the costly solution of cell problems are presented. Numerical examples, in this study, include spatially dependent porosity and solid matrix Poisson ratio for a generic model problem, application in tumour modelling, and utilisation in soil mechanics context which demonstrate the feasibility of the presented framework. [less ▲] Detailed reference viewed: 136 (5 UL)Continuous solution of poroelastic problems using Artificial Neural Networks Dehghani, Hamidreza ; Zilian, Andreas Presentation (2020, January 13) Detailed reference viewed: 68 (4 UL)The role of microscale solid matrix compressibility on the mechanical behaviour of poroelastic materials Dehghani, Hamidreza ; ; et al in European Journal of Mechanics. A, Solids (2020), 83 We present the macroscale three-dimensional numerical solution of anisotropic Biot's poroelasticity, with coefficients derived from a micromechanical analysis as prescribed by the asymptotic ... [more ▼] We present the macroscale three-dimensional numerical solution of anisotropic Biot's poroelasticity, with coefficients derived from a micromechanical analysis as prescribed by the asymptotic homogenisation technique. The system of partial differential equations (PDEs) is discretised by finite elements, exploiting a formal analogy with the fully coupled thermal displacement systems of PDEs implemented in the commercial software Abaqus. The robustness of our computational framework is confirmed by comparison with the well-known analytical solution of the one-dimensional Therzaghi's consolidation problem. We then perform three-dimensional numerical simulations of the model in a sphere (representing a biological tissue) by applying a given constant pressure in the cavity. We investigate how the macroscale radial displacements (as well as pressures) profiles are affected by the microscale solid matrix compressibility (MSMC). Our results suggest that the role of the MSMC on the macroscale displacements becomes more and more prominent by increasing the length of the time interval during which the constant pressure is applied. As such, we suggest that parameter estimation based on techniques such as poroelastography (which are commonly used in the context of biological tissues, such as the brain, as well as solid tumours) should allow for a sufficiently long time in order to give a more accurate estimation of the mechanical properties of tissues. [less ▲] Detailed reference viewed: 71 (0 UL)Poroelastic material characterisation by means of Artificial Neural Network Dehghani, Hamidreza ; Zilian, Andreas Presentation (2019, November 13) Poroelastic problems require multiscale and multiphysics techniques that are expensive and time-consuming, which result in either several simplifications or costly experimental tests. The latter motivates ... [more ▼] Poroelastic problems require multiscale and multiphysics techniques that are expensive and time-consuming, which result in either several simplifications or costly experimental tests. The latter motivates us to develop a more efficient approach to address more complex problems with an acceptable computational cost. In this manuscript, first, the necessary equations derived from Asymptotic homogenisation for poroelastic media are mentioned. Then, the variational formulation of the cell problems is carried out and solved by the open-source FE package FEniCS. This is followed by presenting the advantages and downsides of macroscale properties identification via asymptotic homogenisation and the application of Artificial Neural Network (ANN) to solve the issues stated as its downsides by means of bypassing the process of solving the cell problems. Finally, we study a practical example, namely, spatial dependent porosity (in macroscale) to demonstrate the feasibility of using the provided framework to include more details. Further applications, including growth and remodelling, are subjects of future articles. [less ▲] Detailed reference viewed: 97 (6 UL) |
||