Modelling

Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick–slip transition of Coulomb’s approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why dynamic friction models were developed in recent decades. The friction force depends herein not only on the sliding velocity but also on internal states. The probably best-known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too-simple approximation. The recently published second-order dynamic friction model describes the dynamics of a fictitious bristle more accurately. It is based on a regularized friction force characteristic, which is continuous and smooth but can maintain long-term stick due to an appropriate shift in the regularization. Its performance is compared here to stick–slip friction models, developed and launched not long ago by commercial multibody software packages. The results obtained by a virtual friction test-bench and by a more practical festoon cable system are very promising. Thus, the second-order dynamic friction model may serve not only as an alternative to the LuGre model but also to commercial stick–slip models. Full article

Human-exoskeleton misalignment could lead to permanent damages upon the targeted limb with long-term use in rehabilitation. Hence, achieving proper alignment is necessary to ensure patient safety and an effective rehabilitative journey. In this study, a joint-based and task-based exoskeleton for upper limb rehabilitation were modeled and assessed. The assessment examined and quantified the misalignment present at the elbow joint as well as its effects on the main flexor and extensor muscles’ tendon length during elbow flexion-extension. The effects of the misalignments found for both exoskeletons resulted to be minimal in most muscles observed, except the anconeus and brachialis. The anconeus muscle demonstrated a relatively higher variation in tendon length with the joint-based exoskeleton misalignment, indicating that the task-based exoskeleton is favored for tasks that involve this particular muscle. Moreover, the brachialis demonstrated a significantly higher variation with the task-based exoskeleton misalignment, indicating that the joint-based exoskeleton is favored for tasks that involve the muscle. Full article

The reliable and repeatable fabrication of complex geometries with predetermined homogeneous properties is still a major challenge in electron beam powder bed fusion (PBF-EB). Although previous research identified a variety of process parameter–property relationships, the underlying end-to-end approach, which directly relates process parameters to material properties, omits the underlying thermal conditions. Since the local properties are governed by the local thermal conditions of the melt pool, the end-to-end approach is insufficient to transfer predetermined properties to complex geometries and different processing conditions. This work utilizes high-throughput thermal simulation for the identification of fundamental relationships between process parameters, processing conditions, and the resulting melt pool geometry in the quasi-stationary state of line-based hatching strategies in PBF-EB. Through a comprehensive study of over 25,000 parameter combinations, including beam power, velocity, line offset, preheating temperature, and beam diameter, process parameter-melt pool relationships are established, processing boundaries are identified, and guidelines for the selection of process parameters to the achieve desired properties under different processing conditions are derived. Full article

The prediction of the equilibrium and metastable morphologies during the solidification of Ni-based superalloys on the mesoscopic scale can be performed using phase-field modeling. In the present paper, we apply the phase-field model to simulate the evolution of solidification microstructures depending on undercooling in a quasi-binary approximation. The results of modeling are compared with experimental data obtained on samples of the alloy Inconel 718 (IN718) processed using the electromagnetic leviatation (EML) technique. The final microstructure, concentration profiles of niobium, and the interface-velocity–undercooling relationship predicted by the phase field modeling are in good agreement with the experimental findings. The simulated microstructures and concentration fields can be used as inputs for the simulation of the precipitation of secondary phases. Full article

This paper deals with the development of a novel mesoscopic model of polycrystalline diamond compact (PDC) drill bits that can be implemented in complex drill string models for simulations to analyse the influence of rock inhomogeneities or the impact of anti-whirl bits on drill string dynamics. In contrast to existing modelling approaches, the model is developed at a mesoscopic level, where the basic bit–rock interaction is taken from the macroscopic bit model and the cutting characteristics are summarised at a microscopic cutting level into a simplified configuration via cutting blades. This model can therefore effectively describe asymmetries and thus interactions between the torsional and lateral dynamics of the drill bit, and is particularly suitable for investigating the effects of drilling into rock inhomogeneities and fault zones on drilling dynamics. By integration into a complex drill string model, simulation studies of drilling through a sandwich formation were carried out. The simulation results allow detailed stability statements and show the influence of formation properties and bit design on torsional and lateral drill string dynamics. Full article

The behaviour of polymeric pipes in transient flows has been proved to be viscoelastic. Generalized Kelvin–Voigt (GKV) models perform very well when simulating the experimental pressure. However, in the literature, no general indications on the evaluation of the model parameters are given. In the present study, the calibration of GKV model parameters is carried out using a micro-genetic algorithm for experimental tests of transient flows in polymeric pipes taken from the literature. The results confirm that the higher the number of Kelvin–Voigt elements, the better the reproduction of experimental tests, but it is difficult to search for general rules for parameter characterization. Assuming a Kelvin–Voigt (KV) model with a single element, it is shown that the retardation time is related to the oscillation period that can be obtained from the elastic modulus and from easily evaluable pipe characteristics. A simple procedure is then proposed for the characterization of the viscoelastic parameters that can be used by manufacturers and technicians. Considering the limits of such a model, the procedure has to be considered as a first step for the characterization of the viscoelastic parameters of more complex models. Full article

The transportation industry’s transition to carbon neutrality is essential for addressing sustainability concerns. This study details a model for calculating the carbon footprint of the transportation sector as it progresses towards carbon neutrality. The model aims to support policymakers in estimating the potential impact of various decisions regarding transportation technology and infrastructure. It accounts for energy demand, technological advancements, and infrastructure upgrades as they relate to each transportation market: passenger vehicles, commercial vehicles, aircraft, watercraft, and trains. A technology roadmap underlies this model, outlining anticipated advancements in batteries, hydrogen storage, biofuels, renewable grid electricity, and carbon capture and sequestration. By estimating the demand and the technologies that comprise each transportation market, the model estimates carbon emissions. Results indicate that based on the technology roadmap, carbon neutrality can be achieved by 2070 for the transportation sector. Furthermore, the model found that carbon neutrality can still be achieved with slippage in the technology development schedule; however, delays in infrastructure updates will delay carbon neutrality, while resulting in a substantial increase in the cumulative carbon footprint of the transportation sector. Full article

Public organizations must provide high-quality services at a lower cost. In order to accomplish this goal, they need to apply well accepted cost methods and evaluate the efficiency of their processes using Business Process Management (BPM). However, only a few studies have evaluated the addition of cost information to a process model in a public organization. The aim of the research is to evaluate the combination of cost data to process modeling in an academic library. Our research suggests a new and easy to implement process analysis in three phases. We have combined qualitative (i.e., interviews with the library staff) and quantitative research methods (i.e., estimation of time and cost for each activity and process) to model two important processes of the academic library of the University of Macedonia (UoM). We have modeled the lending and return processes using Business Process Model and Notation (BPMN) in an easy-to-understand format. We have evaluated the costs of each process and sub process with the use of Time-Driven Activity-Based Costing (TDABC) method. The library’s managers found our methodology and results very helpful. Our analysis confirmed that the combination of workflow and cost analysis may significantly improve the decision-making procedure and the efficiency of an organization’s processes. However, we need to further research and evaluate the appropriateness of the combination of various cost and BPM methods in other public organizations. Full article

Healthcare systems in low-resource settings need effective methods for managing their scant resources, especially people and equipment. Digital technologies may provide means for circumventing the constraints hindering low-income economies from improving their healthcare services. Although analytical and simulation techniques, such as queuing theory and discrete event simulation, have already been successfully applied in addressing various optimisation problems across different operational contexts, the literature reveals that their application in optimisation of healthcare maintenance systems remains relatively unexplored. This study considers the problem of maintenance workflow optimisation with respect to labour, equipment availability and cost. The study aims to provide objective means for forecasting resource demand, given a set of task requests with varying priorities and queue characteristics that flow from multiple queues, and in parallel, into the same maintenance process for resolution. The paper presents how discrete event simulation is adopted in combination with simulated annealing to develop a decision-support tool that helps healthcare asset managers leverage operational performance data to project future asset-performance trends objectively, and thereby determine appropriate interventions for optimal performance. The study demonstrates that healthcare facilities can achieve efficiency in a cost-effective manner through tool-generated maintenance strategies, and that any future changes can be expeditiously re-evaluated and addressed. Full article

The functionalization of nanoporous ceramics for applications in healthcare and defence necessitates the study of the effects of geometric structures on their fundamental mechanical properties. However, there is a lack of research on their stiffness and fracture strength along diverse directions under multi-axial loading conditions, particularly with the existence of typical voids in the models. In this study, accurate atomic models and corresponding properties were meticulously selected and validated for further investigation. Comparisons were made between typical material geometric and elastic properties with measured results to ensure the reliability of the selected models. The mechanical behavior of nanoporous alumina under multiaxial stretching was explored through molecular dynamics simulations. The results indicated that the stiffness of nanoporous alumina ceramics under uniaxial tension was greater, while the fracture strength was lower compared to that under multiaxial loading. The fracture of nanoporous ceramics under multi-axial stretching, was mainly dominated by void and crack extension, atomic bond fracture, and cracking with different orientations. Furthermore, the effects of increasing strain rates on the void volume fraction were found to be similar across different initial radii. It was also found that the increasing tension loading rates had greater effects on decreasing the fracture strain. These findings provide additional insight into the fracture mechanisms of nanoporous ceramics under complex loading states, which can also contribute to the development of higher-scale models in the future. Full article

The structural health monitoring (SHM) technique is a highly competent operative process dedicated to improving the resilience of an infrastructure by evaluating its system state. SHM is performed to identify any modification in the dynamic properties of an infrastructure by evaluating the acceleration, natural frequencies, and damping ratios. Apart from the vibrational measurements, SHM is employed to assess the displacement. Consequently, sensors are mounted on the investigated framework aiming to collect frequent readings at regularly spaced time intervals during and after being induced. In this study, a LabVIEW program was developed for vibrational monitoring and system evaluation. In a case study reported herein, it calculates the natural frequencies as well as the damping and displacement parameters of a cantilever steel beam after being subjected to excitation at its free end. For that purpose, a Bridge Diagnostic Inc. (BDI) accelerometer and a displacement transducer were parallelly mounted on the free end of the beam. The developed program was capable of detecting the eigenfrequencies, the damping properties, and the displacements from the acceleration data. The evaluated parameters were estimated with the ARTeMIS modal analysis software for comparison purposes. The reported response confirmed that the proposed system strongly conducted the desired performance as it successfully identified the system state and modal parameters. Full article

The automatic character recognition of historic documents gained more attention from scholars recently, due to the big improvements in computer vision, image processing, and digitization. While Neural Networks, the current state-of-the-art models used for image recognition, are very performant, they typically suffer from using large amounts of training data. In our study we manually built our own relatively small dataset of 404 characters by cropping letter images from a popular historic manuscript, the Electronic Beowulf. To compensate for the small dataset we use ImageDataGenerator, a Python library was used to augment our Beowulf manuscript’s dataset. The training dataset was augmented once, twice, and thrice, which we call resampling 1, resampling 2, and resampling 3, respectively. To classify the manuscript’s character images efficiently, we developed a customized Convolutional Neural Network (CNN) model. We conducted a comparative analysis of the results achieved by our proposed model with other machine learning (ML) models such as support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and XGBoost. We used pretrained models such as VGG16, MobileNet, and ResNet50 to extract features from character images. We then trained and tested the above ML models and recorded the results. Moreover, we validated our proposed CNN model against the well-established MNIST dataset. Our proposed CNN model achieves very good recognition accuracies of 88.67%, 90.91%, and 98.86% in the cases of resampling 1, resampling 2, and resampling 3, respectively, for the Beowulf manuscript’s data. Additionally, our CNN model achieves the benchmark recognition accuracy of 99.03% for the MNIST dataset. Full article

Socio-cyber-physical systems (SCPSs) have emerged as networked heterogeneous systems that incorporate social components (e.g., business processes and social networks) along with physical (e.g., Internet-of-Things devices) and software components. Model-driven techniques for building SCPSs need actor and goal models to capture social concerns, whereas system issues are often addressed with the Systems Modeling Language (SysML). Comprehensive traceability between these types of models is essential to support consistency and completeness checks, change management, and impact analysis. However, traceability management between these complementary views is not well supported across SysML tools, particularly when models evolve because SysML does not provide sophisticated out-of-the-box goal modeling capabilities. In our previous work, we proposed a model-based framework, called CGS4Adaptation, that supports basic traceability by importing goal and SysML models into a leading third-party requirement-management system, namely IBM Rational DOORS. In this paper, we present the framework’s traceability management method and its use for automated consistency and completeness checks. Traceability management also includes implicit link detection, thereby, improving the quality of traceability links while better aligning designs with requirements. The method is evaluated using an adaptive SCPS case study involving an IoT-based smart home. The results suggest that the tool-supported method is effective and useful in supporting the traceability management process involving complex goal and SysML models in one environment while saving development time and effort. Full article

Copulas are well-known tools for describing the relationship between two or more quantitative variables. They have recently received a lot of attention, owing to the variable dependence complexity that appears in heterogeneous modern problems. In this paper, we offer five new copulas based on a common original ratio form. All of them are defined with a single tuning parameter, and all reduce to the independence copula when this parameter is equal to zero. Wide admissible domains for this parameter are established, and the mathematical developments primarily rely on non-trivial limits, two-dimensional differentiations, suitable factorizations, and mathematical inequalities. The corresponding functions and characteristics of the proposed copulas are looked at in some important details. In particular, as common features, it is shown that they are diagonally symmetric, but not Archimedean, not radially symmetric, and without tail dependence. The theory is illustrated with numerical tables and graphics. A final part discusses the multi-dimensional variation of our original ratio form. The contributions are primarily theoretical, but they provide the framework for cutting-edge dependence models that have potential applications across a wide range of fields. Some established two-dimensional inequalities may be of interest beyond the purposes of this paper. Full article

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