Cellular Simulation

Cellular simulation, a method of organizing simulation models to improve experimental efficiency, is described and shown to have the further advantages of reconciling the event based approach with the activity approach to simulation modelling and to have implication for modelling large systems.     

Fuzzy-hybrid modelling of an Ackerman steered electric vehicle

Physical system modelling with known parameters together with 2-D or high order look-up tables (obtained from experimental data), have been the preferred method for simulating electric vehicles. The non-linear phenomena which are present at the vehicle tyre patch and ground interface have resulted in a quantitative understanding of this phenomena. However, nowadays, there is a requirement for a deeper understanding of the vehicle sub-models which previously used look-up tables. In this paper the hybrid modelling methodology used for electric vehicle systems offers a two-stage advantage: firstly, the vehicle model retains a comprehensive analytical formulation and secondly, the ‘fuzzy’ element offers, in addition to the quantitative results, a qualitative understanding of specific vehicle sub-models. In the literature several hybrid topologies are reported, sequential, auxiliary, and embedded.In this paper, the hybrid model topology selected is auxiliary and within the same hybrid model, the first paradigm used is the vehicle dynamics together with the actuator/gearbox system. The second paradigm is the non-linear fuzzy tyre model for each wheel. In particular, conventional physical system dynamic modelling has been combined with the fuzzy logic type-II or type-III methodology. The resulting hybrid-fuzzy tyre models were estimated for a-priori number of rules from experimental data. The physical system modelling required the available vehicle parameters such as the overall mass, wheel radius and chassis dimensions. The suggested synergetic fusion of the two methods, (hybrid-fuzzy), allowed the vehicle planar trajectories to be obtained prior to the hardware development of the entire vehicle. The strength of this methodology is that it requires localised system experimental data rather than global system data. The disadvantage in obtaining global experimental data is the requirement for comprehensive testing of a vehicle prototype which is both time consuming process and requires extensive resources. In this paper the authors have proposed the use of existing experimental rigs which are available from the leading automotive manufacturers. Hence, for the ‘hybrid’ modelling, localised data sets were used. In particular, wheel-tyre experimental data were obtained from the University tyre rig experimental facilities. Tyre forces acting on the tyre patch are mainly responsible for the overall electric vehicle motion. In addition, tyre measurement rigs are a well known method for obtaining localised data thus allowing the effective simulation of more detailed mathematical models. These include, firstly, physical system modelling (conventional vehicle dynamics), secondly, fuzzy type II or III modelling (for the tyre characteristics), and thirdly, electric drive modelling within the context of electric vehicles. The proposed hybrid model synthesis has resulted in simulation results which are similar to piece-wise ‘look-up’ table solutions. In addition, the strength of the ‘hybrid’ synthesis is that the analyst has a set of rules which clearly show the reasoning behind the complex development of the vehicle tyre forces. This is due to the inherent transparency of the type II and type III methodologies. Finally, the authors discussed the reasons for selecting a type-III framework. The paper concludes with a plethora of simulation results.     

A mathematical modelling to extract active ingredients from Oliveria decumbens by supercritical fluid extraction through using ultrasonic process

The experimental feasibility of extracting carvacrol or cymophenol from local and medicinal Oliveria decumbens by supercritical carbon dioxide through using ultrasound waves was investigated in this study. The extraction modelling was carried out in two forms: Internal and external. The internal model was adopted to simulate the concentration distribution of the phytochemicals in the plant particles. In this model, the extracted cell was assumed to be a filled substrate and two mechanisms of molecular permeability and mass transfer had the greatest effect. The comparison of the experimental data obtained from modelling revealed that both the internal and external models are compatible with the experimental data as much as 80%, indicating an acceptable accuracy. Furthermore, the empirical data is completely in line with the model at the final time (40 min), which is the time to reach the equilibrium.     

Dynamic modelling and performance study of solar gas tunnel dryer

The work presented in this article focuses on the analysis and modelling of heat and mass transfers in the tunnel dryer during the drying of agricultural products. The main objective of this work is to establish a global modelling of the studied system based on the bond graph methodology. The pseudo-bond graph methodology was used in modelling the system. Such methodology was very suitable for this thermal process since it allows good management of the nonlinearity present in the system.

The thermal performance of the proposed dryer is analysed by solving the various energy balance equations. An application of drying tomatoes was achieved and a fair agreement was observed between predicted and experimental results.     

Semiqualitative modelling of bioprocesses

This paper deals with semiqualitative modelling of bioprocesses with a view to their supervision. An analysis of several approaches for modelling shows the difficulties involved in taking into account in a same framework, quantitative and qualitative knowledge, generally available about a process that we want to control. We propose an original approach, placed in the context of semiqualitative modelling, that is supported by a knowledge model the variables and parameters of which are defined by intervals. For these semiqualitative models, we study their properties in simulation and prediction, and more precisely, their fitting based on experimental data. We show that pertinent predictions in a short time can be obtained, making of these semiqualitative models interesting tools for the development of systems for bioprocess supervision     

Mathematical and numerical modelling of a circular cross-flow filtration module

We present a computational modelling framework to assess the fluid dynamic behaviour of a circular cross-flow filtration module for water purification. We study two modelling approaches, namely, the Navier-Stokes-Darcy and the one-domain models, that provide a different characterization of the flow in the interfacial region between the feed domain and the membrane surface. Extensive comparison of the numerical results obtained by the two approaches highlights significant differences in the predicted fluid tangential velocity on the membrane surface. Numerical modelling permits to gain a deeper understanding of the flow behaviour than the sole experimental work, e.g., by identifying Dean vortices inside the feed domain and by relating them to geometrical and flow characteristics. This study lays the basis for the optimization of the circular cross-flow filtration module.     

Passive muscle behaviour – experimental and numerical investigations

The material behaviour of skeletal muscles can be decomposed into two parts: an active part, describing the contractile mechanisms, and a passive one, characterising the passive components such as the connective tissue. Computational models are used to support the understanding of complex mechanism inside a muscle. In the present work, we focus on the three-dimensional passive tissue behaviour from the experimental as well as modelling point of view. Therefore, quasi-static experiments have been performed on specimens with regular geometry. By using a three-dimensional optical measurement system the shape of the specimens has been reconstructed at different deformation states. On the modelling side a hyperelastic model with transversal isotropic fibre orientation has been used to describe non-linear stress responses. The model has been validated by performing analyses for different fibre orientations. In summary, it figures out that the proposed modelling approach is able to reflect the experimental results in a satisfying manner. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Using the Relational Paradigm: effects on pupils’ reasoning in solving additive word problems

Pupils’ difficulties in solving word problems continue to attract attention: while researchers highlight the importance of relational reasoning and modelling, school curricula typically use short word problems to develop pupils’ knowledge of arithmetic operations and calculation strategies. The Relational Paradigm attributes the leading role in mathematics learning to the development of relational thinking. Using this perspective, we implemented a new approach to teaching additive word problem-solving in primary school, encouraging relational thinking and modelling. We compared the overall results of additive word problems solved by Grade 2 elementary pupils in the experimental group (N?=?216) and in the control group (N?=?196). Our data show: (a) on average, the experimental group performed significantly better in problem-solving than the control group; and (b) in the control group, there was a considerable lack of success in solving problems that require relational thinking—there was no such effect in the experimental group.     

Modelling of tunnel fires and experimental validation

The relevant scaling parameters for physical modelling of tunnel fires in an experimental test channel are identified. These parameters can be used to correlate the experimental data related to the critical ventilation velocity. Critical ventilation prevents backlayering of smoke during a tunnel fire. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An extended event graph-based modelling method for parallel and distributed discrete-event simulation

The developing logical process (LP)-based parallel and distributed discrete-event simulation (PDES) in the existing PDES programming environments is a difficult and time-consuming process. Event graph is a simple and powerful modelling formalism of discrete-event simulation, whereas this formalism does not support PDES. This article proposes an extension of the event graph to consider the communication of LPs via the events sent, which is called ‘extended event graph (EEG)’, and proposes an EEG-based modelling method for PDES. This modelling method shifts the focus of PDES development from writing code to building models, and the system implementation can be automatically and directly generated from EEG model. The experimental results show that EEG models can successfully execute in the parallel simulator, and this framework can effectively improve the PDES modelling activities.     

Experiments,modelling and simulation of adhesive curing processes in bonded three dimensional curved piezo metal composite structures

This contribution deals with the modelling and simulation of curing phenomena in adhesively bonded piezo metal composites which consists of a piezoelectric module enclosed by an adhesive layer which in turn is surrounded by two metal sheets. A short survey on the neccessary experimental investigations to characterise the adhesive's material behaviour is given and important aspects on the corresponding phenomenological modelling approach are presented. Both steps take into account the curing reaction, changes of volume, like chemical shrinkage, and inelastic mechanical behaviour which is temperature and curing dependent. Finally, the simulation strategy for the modelling within a finite element environment is depicted. By this, residual stresses, secondary deformations and loads on the piezo modules can be predicted, which is exemplified by a comparative study verifying a novel manufacturing strategy. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Discussions on non-probabilistic convex modelling for uncertain problems

Non-probabilistic convex model utilizes a convex set to quantify the uncertainty domain of uncertain-but-bounded parameters, which is very effective for structural uncertainty analysis with limited or poor-quality experimental data. To overcome the complexity and diversity of the formulations of current convex models, in this paper, a unified framework for construction of the non-probabilistic convex models is proposed. By introducing the correlation analysis technique, the mathematical expression of a convex model can be conveniently formulated once the correlation matrix of the uncertain parameters is created. More importantly, from the theoretic analysis level, an evaluation criterion for convex modelling methods is proposed, which can be regarded as a test standard for validity verification of subsequent newly proposed convex modelling methods. And from the practical application level, two model assessment indexes are proposed, by which the adaptabilities of different convex models to a specific uncertain problem with given experimental samples can be estimated. Four numerical examples are investigated to demonstrate the effectiveness of the present study.     

First principles versus artificial neural network modelling of a solar desalination system with experimental validation

ABSTRACT

The present study mainly focuses on enhancing the performance of solar still unit using solar energy through cylindrical parabolic collector and solar panels. A 300 W solar panel is used to heat saline water by thermal elements outside the solar still unit. Solar panels are cooled during the hot hours of the day; thus, reducing their temperature may lead to an increase in solar panel efficiency followed by an increase in the efficiency of the solar still unit. The maximum amount of freshwater used in the experiment was 2.132 kg/day. The experiments were modelled using ANNs. Based on neural network simulation results, there is a significant correlation between experimental data and neural network modelling. This paper compares experimental data with data obtained from mathematical modelling and ANNs. As a conclusion, the artificial neural network prediction has been more accurate than the simplified first principles model presented.     

Simulating economic factors in adjuvant breast cancer treatment

When conducting an experimental study in healthcare systems, two problems are faced, those of uncertainty and complexity. Uncertainty is related to identifying variables for data collection (particularly if there are time and cost constraints on the modelling exercise). Complexity is related to the existence of many interacting variables (including treatment paths for patients, patient illnesses, side effects of treatments, etc.), each of a stochastic nature. This paper reports the usefulness of discrete event simulation modelling in exploring these issues. It focuses on the use of this form of simulation in supporting decision making in a randomised clinical trial (RCT). The objective of using simulation modelling is to help health economists identify the key factors active in the RCT through the development of a model of the healthcare related processes being studied by the RCT. This approach provides an opportunity to allow users to understand the role of these factors in the RCT. This research is carried out in the context of the Adjuvant Breast Cancer RCT.     

Identification of a nonlinear circuit

The identification of a differential equation underlying a measured time series is a prerequisite for numerous types of applications. In the validation of a proposed parameterized model one often faces the dilemma that it is hard to decide whether possible discrepancies between the measured time series and the simulated model output are caused by an inappropriate model or by wrongly specified parameters in a correct type of model. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a nonlinear chaotically oscillating circuit where we finally obtain an extremely accurate reconstruction of the observed attractor.     

A methodological paradigm in plant biology

This study emphasizes some methodological aspects of my research related to the problem of phyllotoxis. It is an epistolological reflection on the particular process of modelling I went through. The intuitive, conceptual, experimental, and metaphysical basis of my model will be discussed, and I will underline its fertility as a deductive system.     

Development of a finite element model of metal powder compaction process at elevated temperature

This paper presents the finite element modelling of metal powder compaction process at elevated temperature. In the modelling, the behaviour of powder is assumed to be rate independent thermo-elastoplastic material where the material constitutive laws are derived based on a continuum mechanics approach. The deformation process of metal powder has been described by a large displacement based finite element formulation. The Elliptical Cap yield model has been used to represent the deformation behaviour of the powder mass during the compaction process. This yield model was tested and found to be appropriate to represent the compaction process. The staggered-incremental-iterative solution strategy has been established to solve the non-linearity in the systems of equations. Some numerical simulation results were validated through experimentation, where a good agreement was found between the numerical simulation results and the experimental data.     

Mathematical modelling and the learning trajectory: tools to support the teaching of linear algebra

In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and span. The objective is to evaluate this didactic proposal by determining the level of match between the hypothetical learning trajectory (HLT) designed in this study with the actual learning trajectory in the second experimental cycle of an investigation design-based research more extensive. The results show a high level of match between the trajectories in more than half of the conjectures, which gives evidence that the HLT has supported, in many cases, the achievement of the learning objective, and that additionally mathematical modelling contributes to the construction of these linear algebra concepts.     

A 3-D coupled Smoothed Particle Hydrodynamics and Coarse-Grained model to simulate drying mechanisms of small cell aggregates

Recently, meshfree-based computational modelling approaches have become popular in modelling biological phenomena due to their superior ability to simulate large deformations, multiphase phenomena and complex physics compared to the conventional grid-based methods. In this article, small plant cell aggregates were simulated using a three dimensional (3-D) Smoothed Particle Hydrodynamics (SPH) and Coarse-Grained (CG) coupled computational approach to predict the morphological behaviour during drying. The model predictions of these cell aggregate models have been compared qualitatively and quantitatively through comparisons with experimental findings. The results show that the shrinkage and wrinkling behaviour of cell cluster models are in fairly good agreement with real cellular structures. The agreement between the cell aggregate model predictions and the experimental findings are closer in the high and medium moisture content values (X/X₀ ≥ 0.3), than highly dried stages (X/X₀ < 0.3). Further, optimisation and sensitivity studies have been conducted on model parameters such as particle resolution, smoothing length, mass transfer characteristics and wall forces. Overall, the 3-D nature of this model allows it to predict real 3-D morphological changes more realistically compared to the previous meshfree based 2-D cellular drying models. The proposed 3-D modelling approach has a higher potential to be used to model larger plant tissues with complicated physical and mechanical interactions as well as their multiscale interactions.