Modelling

Based on the solutions of deterministic fracture mechanics and the methods of probability theory, the algorithm for calculating the probabilistic strength characteristics of plate elements of structures with an arbitrary stochastic distribution of surface defects is outlined. On the plate surface, there are uniformly distributed cracks that do not interact with each other, the plane of which is normal to the surface, and the depth is much less than its length on the surface. The cracks’ depth and angle of orientation are random values, and their joint distribution density is specified. Plates made of this material are under the influence of biaxial loading. The probability of failure, along with the mean value, the dispersion, and the variation coefficient of the plate’s strength, taking into account the surface defects under different types of stress, were determined. Their dependence on the type of loading, the size of the plate, and the surface structural heterogeneity of the material were studied graphically. Joint consideration of the influence of the interrelated properties of real materials, such as defectiveness and stochasticity, on strength and fracture, opens up new opportunities in creating a theory of strength and fracture of deformable solids. Full article

This research aims to study the mechanical behavior of the materials most commonly used in crankshaft manufacturing by designing a four-piston crankshaft, analyzing the stresses and displacements resulting from the applied load, and determining vibration frequencies. Additionally, this study examines the thermal behavior of the crankshaft. For this purpose, a three-dimensional model of the crankshaft was designed using CATIA V5 R18 software, and finite element analysis was subsequently performed using ANSYS 2019 R1 software under static, dynamic, and thermal conditions with four different materials in various orientations. To verify the effectiveness of the proposed design, it was compared with a reference design in terms of stresses and displacements. This study also explores improvements in crankshaft geometry and shape. The results indicate that selecting the appropriate material for the working conditions and optimizing the geometry and shape enhance engine performance and reduce the crankshaft’s weight by 20%. The findings were validated by comparing the designs, which support increased productivity and improved durability. Full article

Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade. The blade is regarded as a one-dimensional prismatic cantilever beam that is exposed to triangular loading, and comprehending its mechanical behavior is of utmost importance in the aerospace field. PINNs utilize the physical information, including differential equations and boundary conditions, within the loss function of the neural network to approximate the solution. Our approach determines the overall loss by aggregating the losses from the differential equation, boundary conditions, and data. We employed a physics-informed neural network (PINN) and an artificial neural network (ANN) with equivalent hyperparameters to solve a fourth-order differential equation. By comparing the performance of the PINN model against the analytical solution of the equation and the results obtained from the ANN model, we have conclusively shown that the PINN model exhibits superior accuracy, robustness, and computational efficiency when addressing high-order differential equations that govern physics-based problems. In conclusion, the study demonstrates that PINN offers a superior alternative for addressing solid mechanics problems with applications in the aerospace industry. Full article

This paper investigates the influence of harmonic content on the root mean square value of electromagnetic fields emitted by overhead power lines. The paper presents a methodology to assess the intensity of electric field and magnetic flux density, incorporating both fundamental frequencies and harmonics. The results of our calculations indicate that harmonic distortion in current waveforms can significantly increase the RMS value of magnetic flux density but its effect on electric field intensity is minimal. Additionally, our findings highlight a potential increase in induced voltages on buried or overhead steel pipelines in the vicinity of OPLs, which could pose risks such as pipeline damage and increased corrosion. This underscores the importance of considering harmonic content in EMF exposure evaluations to address both health risks and potential infrastructure impacts comprehensively. Effective harmonic management and rigorous infrastructure monitoring are essential to prevent potential hazards and ensure the reliability of protective systems. Full article

This research aims to present a case study on the use of simulation to support operational decision-making and improve the patient experience in outpatient clinics. A simulation model was developed to represent patient flow through the endocrine clinics of the internal medicine department in a large hospital in Saudi Arabia. The research evaluated the impact of using simulation models on different aspects of healthcare facility operations, such as patient flow, resource utilization, and staffing. Potential bottlenecks and inefficiencies in the clinic’s processes were identified. Furthermore, improvements were suggested and evaluated that could significantly reduce patient waiting times and increase the number of patients served. Different scenarios and strategies were evaluated without the need for real-world implementation, which can be costly and time consuming. The model can also be easily modified and adapted to accommodate changes in patient demand, staffing levels, or other factors that may impact clinic operations. The findings demonstrate the utility of simulation models in healthcare management. Overall, the use of simulation models in healthcare management has the potential to revolutionize the way clinics and hospitals operate, leading to improved patient outcomes and more efficient use of resources. Full article

We investigate the Hele-Shaw flow of fluids whose viscosity depends on pressure, i.e., piezo-viscous fluids, near the tip of a sharp edge. In particular, we consider both cases of two-dimensional symmetric and antisymmetric flows. To obtain the pressure field, we provide a procedure that is based on the method of separation of variables and does not depend on a specific choice of the expression for the pressure-dependent viscosity. Therefore, we show the existence of a general procedure to investigate the behavior of piezo-viscous fluids in Hele-Shaw flow and its solution near a sharp corner. The results are applied to the case of an exponential dependence of viscosity on pressure as an example of exact solutions for the pressure field. Full article

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy. Full article

Predictive numerical models in the study of ground-borne vibrations generated by railway systems have traditionally relied on the subsystem partition approach (segmented). In such a method, loads are individually applied, and the cumulative effect of the rolling stock is obtained through superposition. While this method serves to mitigate computational costs, it may not fully capture the complex interactions involved in ground-borne vibrations—especially in the frequency domain. Recent advancements in computation and software have enabled the development of more sophisticated vibrational contamination prediction models that encompass the entire dynamics of the system, from the rolling stock to the terrain, allowing continuous simulations with a defined time step. Furthermore, the incorporation of computational contact mechanics tools between various elements not only ensures accuracy in the time domain but also extends the analysis into the frequency domain. In this novel approach, the segmented models are shifted to continuous simulations where the nonlinear problem of a rigid–flexible multibody system is fully considered. The model can predict the impact of a high-speed rail (HSR) vehicle passing, capturing the key intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain. Full article

Hydrogen technologies have the potential to reduce aviation’s CO₂ emissions but come with many challenges. This paper introduces a scalable hydrogen fuel cell model tailored for system integration analysis in early aircraft design phases. The model focuses on Proton Exchange Membrane Fuel Cells (PEMFCs) and is based on thermodynamic equations and empirical data to simulate performance under different ambient and operating conditions; it also includes a simplified model of the Balance of Plant (BOP) systems and is implemented in OpenModelica. The model performance is validated through a comparison of the simulated polarization curves with real datasheet data. A case study highlights the peculiarities of this model by studying the sizing of the fuel cell stacks for a modified ATR 72 aircraft. The developed model effectively supports the early design exploration of the aircraft with a greater level of detail for system integration studies, essential to better explore the potential of aircraft featuring hydrogen-based power systems. Full article

This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values. Full article

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point. Full article

This paper explores the features of numerical simulation used to analyze the dynamic behaviour of drives controlled by pulse-width modulators. Modern motor control systems commonly employ pulse-width modulation. Effective numerical modelling of such systems presents unique challenges because the models employed are continuous-event and have hybrid behaviour due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. This paper shows how these problems are solved when modelling an electric drive with a DC motor using the SimInTech software. Full article

Finite element modelling of multi-bolted foundation connections used for the foundation of heavy machinery or equipment is presented. Connections made using different types of shims, with particular emphasis on polymer–steel shims, are investigated. The stiffness characteristics for the adopted models of multi-bolted foundation connections at the installation stage are described and compared. It is shown that the use of polymer–steel shims can result in a significant improvement in the stiffness of a multi-bolted foundation connection compared to a connection with a polymer shim, and in achieving a multi-bolted foundation connection with a stiffness similar to that of a connection with a steel shim (at a sufficiently low polymer layer thickness). Full article

In the oil industry, the leakage of pipelines containing hydrocarbon fluids causes significant environmental and economic damage. Recently, there has been a growing trend in employing data mining techniques for detecting leaks. Among these methods is the Hidden Markov Model, which, despite good results with stationary data, becomes inefficient when a leak causes a drop in the pressure or flow, reducing its accuracy. This paper presents an adaptive Hidden Markov method. Previous methods had low accuracy due to insufficient information for accurate leak detection. They often classified the size and location of leaks broadly. In contrast, the proposed model extracts hidden features to accurately identify the location and size of leaks, even in noisy conditions. Simulating a leak in a section of an oil pipeline in the Iranian Oil Export Corridor demonstrates the proposed method’s superiority over common methods like K-NN, SVM, Naive Bayes, and logistic regression. Full article

In recent decades, reinforced concrete (RC) shear walls have been one of the best structural solutions to resist lateral load in high-rise buildings. Shear wall openings are essential for preparations and architectural requirements, which weaken the wall, reducing bearing capacity, energy absorption, and stiffness while also causing stress concentrations. This paper presents a comprehensive finite element (FE) investigation of the behavior and performance of RC shear walls with openings and subjected to lateral loads. The study aims to evaluate the influence of various parameters, such as opening location, size, wall aspect ratio, axial load, and concrete strength, which affect the performance of shear walls. FE models were developed to simulate the seismic response of RC shear walls under the combined effect of constant axial and lateral loads. The obtained results from the FE model showed a successful validation using the experimental data available in the literature. The FE analysis results demonstrate that the inclusion of lower openings leads to a 25% decrease in the bearing capacity of the wall when compared to the upper openings. Moreover, it was observed that augmenting the sizes of the openings and the aspect ratios of the wall resulted in declines in the strength, stiffness, and energy absorption capacity of the wall while simultaneously enhancing the ductility and displacement of the RC shear walls. Full article

This study investigates the transformative impact of smart intelligence, leveraging the Internet of Things and edge-cloud platforms in smart urban development. Smart urban development, by integrating diverse digital technologies, generates substantial data crucial for informed decision-making in disaster management and effective urban well-being. The edge-cloud platform, with its dynamic resource allocation, plays a crucial role in prioritizing tasks, reducing service delivery latency, and ensuring critical operations receive timely computational power, thereby improving urban services. However, the current method has struggled to meet the strict quality of service (QoS) requirements of complex workflow applications. In this study, these shortcomings in edge-cloud are addressed by introducing a multi-objective resource optimization (MORO) scheduler for diverse urban setups. This scheduler, with its emphasis on granular task prioritization and consideration of diverse makespans, costs, and energy constraints, underscores the complexity of the task and the need for a sophisticated solution. The multi-objective makespan–energy optimization is achieved by employing a deep reinforcement learning (DRL) model. The simulation results indicate consistent improvements with average makespan enhancements of 31.6% and 70.09%, average cost reductions of 62.64% and 73.24%, and average energy consumption reductions of 25.02% and 17.77%, respectively, by MORO over-reliability enhancement strategies for workflow scheduling (RESWS) and multi-objective priority workflow scheduling (MOPWS) for SIPHT workflow. Similarly, consistent improvements with average makespan enhancements of 37.98% and 74.44%, average cost reductions of 65.53% and 74.89%, and average energy consumption reductions of 29.52% and 24.73%, respectively, by MORO over RESWS and MOPWS for CyberShake workflow, highlighting the proposed model’s efficiency gains. These findings substantiate the model’s potential to enhance computational efficiency, reduce costs, and improve energy conservation in real-world smart urban scenarios. Full article

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations. Full article

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. Full article

Agriculture drones face important challenges regarding autonomy and construction, as flying time below the 9-minute mark is the norm, and their manufacture requires several tests and research before reaching proper flight dynamics. Therefore, correct design, analysis, and manufacture of the structure are imperative to address the aforementioned problems and ensure a robust build that withstands the tough environments of this application. In this work, the analysis and implementation of a Nylamid motor bracket, aluminum sandwich-type skeleton, and carbon fiber tube arm in a 30 kg agriculture drone is presented. The mechanical response of these components is evaluated using the finite element method in ANSYS Workbench, and the material behavior assumptions are assessed using a universal testing machine before their implementations. The general description of these models and the numerical results are presented. This early prediction of the behavior of the structure allows for mass optimization and cost reductions. The fast dynamics of drone applications set important restrictions in ductile materials such as this, requiring extensive structural analysis before manufacture. Experimental and numerical results showed a maximum variation of 8.7% for the carbon fiber composite and 13% for the Nylamid material. The mechanical properties of polyamide nylon allowed for a 51% mass reduction compared to a 6061 aluminum alloy structure optimized for the same load case in the motor brackets design. The low mechanical complexity of sandwich-type skeletons translated into fast implementation. Finally, the overall performance of the agriculture drone is evaluated through the data gathered during the flight test, showing the adequate design process. Full article