Modelling, Volume 4, Issue 1 (March 2023) – 7 articles

We have developed a three-dimensional hybrid finite-discrete element model to investigate the mode I tensile opening failure of alumina ceramic. This model implicitly considers the flaw system in the material and explicitly shows the macroscopic failure patterns. A single main crack perpendicular to the loading direction is observed during the tensile loading simulation. Some fragments appear near the crack surfaces due to crack branching. The tensile strength obtained by our model is consistent with the experimental results from the literature. Once validated with the literature, the influences of the distribution of the flaw system on the tensile strength and elastic modulus are explored. The simulation results show that the material with more uniform flaw sizes and fewer big flaws has stronger tensile strength and higher elastic modulus. Full article

This paper expands upon the state of the art in nonlinear modeling of automotive air conditioning systems. Prior models considered only the effects of the refrigerant compressor and the condenser fan. There are two new aspects included here. First, we create a mathematical model for front-end underhood airflow, considering vehicle speed, condenser fan rotational speed, and active grille shutter position. In addition, we present a new model for the power consumption of the vehicle associated with aerodynamic drag caused by underhood flow, as well as a fan power model which accounts not only for changes in rotational speed but also changes in flow rate. The models developed in this paper are coded in MATLAB/Simulink and assessed for various vehicle driving conditions against a higher-fidelity vehicle energy management model, showing good agreement. By including the active grille shutters as a controllable actuator and the impact of underhood flow on vehicle drag and fan power consumption, control schemes can be developed to holistically target reduced energy consumption for the air conditioning system and, thus, improve the overall vehicle energy efficiency. Full article

Fouling, caused by the adhesion of fine materials to the blades of the compressor’s last stages, changes the airfoil’s shape and function and the inlet flow angle on the blades. As the fouling increases, the range of influence increases, and the mass flow rate and overall engine efficiency reduce. Therefore, the compressor is choked at lower speeds. This study aims to simulate compressor performance during off-design conditions due to fouling and to present an approach for modeling faults in diagnostic and health monitoring systems. A computational fluid dynamics analysis is carried out to evaluate the proposed method on General Electric’s T700-GE turboshaft engine, and the performance is evaluated at different flight conditions. The results show promising outcomes with an average accuracy of 88% that would help future turboshaft health monitoring systems. Full article

Machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, such as diabetes mellitus, which is classified as an epi-demic due to its high rates of global prevalence. Machine learning techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This study investigates the ability of different classification methods to classify diabetes prevalence rates and the predicted trends in the disease according to associated behavioural risk factors (smoking, obesity, and inactivity) in Saudi Arabia. Classification models for diabetes prevalence were developed using different machine learning algorithms, including linear discriminant (LD), support vector machine (SVM), K -nearest neighbour (KNN), and neural network pattern recognition (NPR). Four kernel functions of SVM and two types of KNN algorithms were used, namely linear SVM, Gaussian SVM, quadratic SVM, cubic SVM, fine KNN, and weighted KNN. The performance evaluation in terms of the accuracy of each developed model was determined, and the developed classifiers were compared using the Classification Learner App in MATLAB, according to prediction speed and training time. The experimental results on the predictive performance analysis of the classification models showed that weighted KNN performed well in the prediction of diabetes prevalence rate, with the highest average accuracy of 94.5% and less training time than the other classification methods, for both men and women datasets. Full article

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modeling capability as well as a high-level of customization and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as being suitable within a number of applications contexts—i.e., as a research/educational tool, communication framework, gamification and participatory modeling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features. Full article

Melby presents a formula to predict damage evolution in rubble-mound breakwaters whose armour layer is made of rock, based on the erosion measured in scale-model tests and the characteristics of the incident sea waves in such tests. However, this formula is only valid for armour layers made of rock and for the range of tested sea states. The present work aims to show how the Melby methodology can be used to establish a similar formula for the armour layer damage evolution in a rubble-mound breakwater where tetrapods are employed. For that, a long-duration test series is conducted with a 1:50 scale model of the quay section of the Ericeira Harbour breakwater. The eroded volume of the armour layer was measured using a Kinect position sensor. The damage parameter values measured in the experiments are lower than those predicted by the formulation for rock armour layers. New $a_p$ and $b$ coefficients for the Melby formula for the tested armour layer were established based on the minimum root mean square error between the measured and the predicted damage. This work shows also that it is possible to assess the damage evolution in scale-model tests with rubble-mound breakwaters by computing the eroded volume and subsequently, the dimensionless damage parameter based on the equivalent removed armour units. Full article