The key parameter that affects the consolidation process of soil is the coefficient of permeability. The common assumption in the consolidation analysis is that the coefficient of permeability is porosity-dependent. However, various authors suggest that the strain-dependency of the coefficient of permeability should also be taken into account. In this paper, we present results of experimental and numerical analyses, with an aim to determine the strain-dependency of the coefficient of permeability. We present in detail both the experimental procedure and the finite element formulation of the two-dimensional axisymmetric numerical model of the oedometer test (standard and modified). We perform a set of experimental standard and modified oedometer tests. We use these experimental results to validate our numerical model and to define the model input parameter. Finally, by combining the experimental and numerical results, we propose the expression for the strain-dependent coefficient of permeability.

The comprehensive understanding of the fiber reinforced polymer behavior requires the use of advanced non-destructive testing methods due to its heterogeneous microstructure and anisotropic mechanical proprieties. In addition, the material response under load is strongly associated with manufacturing defects (e.g., voids, inclusions, fiber misalignment, debonds, improper cure and delamination). Such imperfections and microstructures induce various damage mechanisms arising at different scales before macrocracks are formed. The origin of damage phenomena can only be fully understood with the access to underlying microstructural features. This makes X-ray Computed Tomography an appropriate imaging tool to capture changes in the bulk of fibrous materials. Moreover, Digital Volume Correlation (DVC) can be used to measure kinematic fields induced by various loading histories. The correlation technique relies on image contrast induced by microstructures. Fibrous composites can be reinforced by different fiber architectures that may lead to poor natural contrast. Hence, a priori analyses need to be performed to assess the corresponding DVC measurement uncertainties. This study aimed to evaluate measurement resolutions of global and regularized DVC for glass fiber reinforced polymers with different fiber architectures. The measurement uncertainties were evaluated with respect to element size and regularization lengths. Even though FE-based DVC could not reach the recommended displacement uncertainty with low spatial resolution, regularized DVC enabled for the use of fine meshes when applying appropriate regularization.

Quite often we have a lot of measurement data and would like to find some relation between them. One common task is to see whether some measured data or a curve of known shape fit into the cumulative measured data. The problem can be visualized since data could generally be presented as curves or planes in Cartesian coordinates where each curve could be represented as a vector. In most cases we have measured the cumulative 'curve', we know shapes of other 'curves' and would like to determine unknown coefficients that multiply the known shapes in order to match the measured cumulative 'curve'. This problem could be presented in more complex variants, e.g., a constant could be added, some missing (unknown) data vector could be added to the measured summary vector, and instead of constant factors we could have polynomials, etc. All of them could be solved with slightly extended version of the procedure presented in the sequel. Solution procedure could be devised by reformulating the problem as a measurement problem and applying the generalized inverse of the measurement matrix. Measurement problem often has some errors involved in the measurement data but the least squares method that is comprised in the formulation quite successfully addresses the problem. Numerical examples illustrate the solution procedure.

In order to analyse the thermal performance of battery systems in electric vehicles complex simulation models with high computational cost are necessary. Using reduced order methods, real-time applicable model can be developed and used for on-board monitoring. In this work a data driven model of the cooling plate as part of the battery system is built and derived from a computational fluid dynamics (CFD) model. The aim of this paper is to create a meta model of the cooling plate that estimates the temperature at the boundary for different heat flow rates, mass flows and inlet temperatures of the cooling fluid. In order to do so, the cooling plate is simulated in a CFD software (ANSYS Fluent ®). A data driven model is built using the design of experiment (DOE) and various approximation methods in Optimus ®. The model can later be combined with a reduced model of the thermal battery system. The assumption and simplification introduced in this paper enable an accurate representation of the cooling plate with a real-time applicable model.

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

Models for water treatment processes include simulation, i.e., modelling of water quality, flow hydraulics, process controls and design. Water treatment processes are inherently dynamic because of the large variations in the influent water flow rate, concentration and composition. Moreover, these variations are to a large extent not possible to control. Mathematical models and computer simulations are essential to describe, predict and control the complicated interactions of the water treatment processes. An accurate description of such systems can therefore result in highly complex models, which may not be very useful from a practical, operational point of view. The main objective is to combine knowledge of the process dynamics with mathematical methods for processes estimation and identification. Good modelling practice is way to obtain this objective and to improve water treatment processes(its understanding, design, control and performance- efficiency). By synthesize of existing knowledge and experience on good modelling practices and principles the aim is to help address the critical strategic gaps and weaknessesin water treatment models application.