Modelling, Volume 5, Issue 4 (December 2024) – 38 articles

Surrogate modeling technology is used to create lightweight analogs of resource- and calculation-intensive software, provided that the problem can be reduced to the regression problem. In this article, we construct a surrogate model for predicting annual energy consumption using the open-source EnergyPlus software and various sampling techniques. A general algorithm for an error-based adaptive sampling technique to build the surrogate model is presented. The best results were shown by the composite Mixed Sampling method with a data refining window the size of 70% and a LightGBM regression model. The best attained metrics values are as follows: MSE = 7.76, RMSE = 1.47, MAE = 0.98 and R² = 0.99. For a small number of iterations, an error-based adaptive sampling technique with hyperparameter tuning is preferable to the static sampling approach. For a large number of iterations, both techniques show approximately good predictive results of the built surrogate model. After hyperparameter tuning was performed, the average value of the MSE metric decreased from 43.43 to 7.76. A gas thickness feature greater than 0.015 had no positive effect on energy-saving optimization. For temperatures on a summer day of 30 degrees and above, there was a sharp increase in energy consumption. The maximum dry bulb temperature on a winter and summer day and the wind speed on a winter day were the most important features of the built surrogate model with 492, 483 and 443 gain values of the feature importance method, respectively. Full article

The relevance of creating specialized computer programs that convert a virtual 3D model of an object into machine code (G-code) for controlling the process of 3D printing products from wire raw materials is substantiated. It is shown that for wire-based additive technologies, a fundamentally important requirement is to ensure the continuity of the surfacing trajectory within one section. A method for determining a continuous surfacing trajectory is proposed, the implementation of which requires two stages: performing a numerical analysis of a two-dimensional region with boundary conditions describing this section; and running a heuristic algorithm for the movement of the surfacing head, in which the direction of movement is selected based on the results of the analysis. The procedure for setting boundary conditions and an algorithm for numerically solving the boundary value problem of determining the field of the “height” function for each section are described. The principles of operation of the heuristic algorithm for selecting the direction of head movement based on the calculated height field and continuous determination of the proximity of adjacent layers and section boundaries are disclosed. An analysis of the algorithm operation is carried out using a section with holes as an example, and the potential of using numerical methods to calculate the change in the temperature field during the surfacing process is shown. Full article

Efficient management of hospital evacuations and pharmaceutical supply chains is a critical challenge in modern healthcare, particularly during emergencies. This study addresses these challenges by proposing a novel bi-objective optimization framework. The model integrates a Mixed-Integer Linear Programming (MILP) approach with advanced machine learning techniques to simultaneously minimize total costs and maximize patient satisfaction. A key contribution is the incorporation of a Gated Recurrent Unit (GRU) neural network for accurate drug demand forecasting, enabling dynamic resource allocation in crisis scenarios. The model also accounts for two distinct patient destinations—receiving hospitals and temporary care centers (TCCs)—and includes a specialized pharmaceutical supply chain to prevent medicine shortages. To enhance system robustness, probabilistic demand patterns and disruption risks are considered, ensuring supply chain reliability. The solution methodology combines the Grasshopper Optimization Algorithm (GOA) and the ɛ-constraint method, efficiently addressing the multi-objective nature of the problem. Results demonstrate significant improvements in cost reduction, resource allocation, and service levels, highlighting the model’s practical applicability in real-world scenarios. This research provides valuable insights for optimizing healthcare logistics during critical events, contributing to both operational efficiency and patient welfare. Full article

Currently, about a thousand micro gas turbine units of small and medium capacity are in operation in the Russian Federation, which are used as an autonomous power source at critical infrastructure facilities. During long-term operation, the component parts of the micro GTU may fail and require replacement or repair. The lack of spare parts and design documentation for their production makes it impossible to operate. As a way to solve the problem, the reverse engineering process can be used to produce components. One of the stages of reverse engineering is to determine the geometric parameters of the object. The fastest and most accurate way to obtain geometric characteristics in the reverse engineering process is 3D scanning. Three-dimensional scanning technology is used to obtain a solid 3D model of the prototype surface, based on which design documentation is subsequently developed. This article presents the results of a study of the influence of the parameters of the distance between polygonal grid points and the scanner exposure on the detailing of the outer surface and the geometric parameters of the resulting polygonal model. As a result of this study, the dependence of the final file size and the time spent on scanning and processing on the distance between the points of the polygonal grid and the model was established. Based on the dependence of the parameters, recommendations were obtained for choosing the distance between the points of the polygonal grid of laser 3D scanning. Also, after performing the stages of reverse engineering, the methodology for creating solid models and design documentation of parts of power equipment units using 3D scanning technology was improved. Full article

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R²) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes. Full article

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality. Full article

This paper presents a model of the bending behavior of a bi-layer cantilever composed of titanium nickelide and a magnetoactive elastomer embedded with magnetically hard particles. The cantilever is initially subjected to an external magnetic field in its high-temperature state, followed by cooling to a low-temperature state before the magnetic field is removed. This sequence results in residual bending deformation. Basic relations describing the material behavior of titanium nickelide and the magnetoactive elastomer are presented. A variational formulation for the problem under consideration is written down. The problem is solved numerically using the finite element method. The influence of the applied magnetic field magnitude and the thickness of the titanium nickelide layer on the cantilever deflection magnitude is studied. The dependence of the residual cantilever deflection on the applied magnetic field is obtained. The possibility of this structure as a controllable gripping element for applications in robotics and micro-manipulation is demonstrated. Full article

This paper presents a finite-difference-based method for numerically deriving the $DQ$ impedance model of power electronics-based power systems, specifically tailored for stability analysis. The proposed method offers a computationally efficient alternative to traditional approaches by directly applying finite-difference approximations to the large-signal dynamic system, without relying on repetitive time-domain simulations or small-signal analytical models. This method eliminates the need for additional models or complex procedures to compute the steady-state solution, streamlining the impedance modeling process. The accuracy, efficiency, and precision of the proposed method are evaluated through comparative studies with analytical and time-domain perturbation methods. Results demonstrate that the proposed approach provides accuracy comparable to analytical models while significantly reducing computational effort, outperforming perturbation methods in both speed and precision. These findings highlight the practical value of the proposed method for real-time and large-scale system analysis, making it a robust tool for power systems stability assessment. Full article

This work presents a non-linear mathematical model of a machining system subjected to a non-ideal vibration source. Computer simulations have shown chaotic behavior for specific parameters of the proposed mathematical model. The chaotic behavior is proven using time histories, phase diagrams, bifurcation diagrams, and the Lyapunov exponent. Considering that cutting tool vibration in the machining process is one of the main problems of productivity and machining accuracy, the introduction of a magnetorheological damper was considered in the proposed model to reduce the vibration amplitudes of the cutting tool and suppress the chaotic behavior. Hysteresis was considered in the magnetorheological damper model and its application in the system as both a passive and active absorber. The active control strategy considered the application of two non-linear control signals: feedforward to maintain the vibration with a desired behavior and state feedback to drive the system to the desired behavior. The numerical results demonstrated that the proposed controls efficiently reduced the vibration amplitude by introducing the MR damper. Active control has proven effective in controlling the force of the MR damper by varying the electrical voltage applied to the damper coil. Full article

The development of digital twins for power transformers has become increasingly important to predict possible operating modes and reduce the likelihood of faults. The accuracy of these predictions relies heavily on the numerical models used, which must be both simple and computationally efficient. This work focuses on creating a simplified numerical model for a template oil-immersed power transformer (100 MVA, 230/69 KV). The study investigates how the number of elements and the strategies used to set up the mesh in the domain of interest influence the results, aiming to identify the key parameters that affect the outcomes. Furthermore, a significant effect of resolving thermal boundary layers on the accurate identification of hot spots is demonstrated. Two approaches to resolving thermal boundary layers are explored in this work. This study presents a comprehensive analysis of three numerical models for conjugate heat transfer simulations, each with distinct features and computational domain compositions. The results show that the addition of extra calculation domains leads to the emergence of new vortex structures, affecting the velocity profile at the channel inlet and altering the location of hot spots. This study provides valuable insights into the configuration and composition of calculated domains in numerical models of oil-immersed power transformers, essential for the accurate prediction of hot spot temperatures and ensuring reliable operation. Full article

In the study, the combined molecular dynamics and Monte Carlo (MD/MC) simulation was used to investigate the short-range ordering effect on tensile deformation of bicrystals with grain boundaries (GBs) Σ3(1$\overline{1}$2)[110]. Three different equiatomic high-entropy alloys, namely, ZrTiNbV, ZrTiNbTa and ZrTiNbHf, were considered. The tensile loading at 300K was applied in the direction perpendicular to the GBs’ planes. The stress–strain response as well as the structure evolution of the alloys with initial random distribution of atoms were compared with results obtained for the corresponding materials relaxed during the MD/MC procedure. It was revealed that the distribution of atoms in the alloys significantly affects the deformation process. Ordered clusters of Nb atoms are able to suppress the dislocation sliding and twin formation increasing the yield strength of ZrTiNbV. On the contrary, in ZrTiNbTa, the twinning mechanism is dominant in the case of the ordered structure due to the absence of Nb clusters and the presence of areas enriched with Zr atoms, which ease nucleation of dislocations and twins. Since Hf decreases the stability of the body-centered cubic lattice, the main deformation mechanism of ZrTiNbHf is the stress-induced phase transition; however, Nb clusters inside grains of the relaxed alloy slightly delay this process. Full article

In this paper, we address the modeling, simulation, and control of a rotary inverted pendulum (RIP). The RIP model assembled via the MATLAB (Matlab 2021a)^®/Simulink (Simulink 10.3) Simscape (Simscape 7.3)™ environment demonstrates a high degree of fidelity in its capacity to capture the dynamic characteristics of an actual system, including nonlinear friction. The mathematical model of the RIP is obtained via the Euler–Lagrange approach, and a parameter identification procedure is carried out over the Simscape model for the purpose of validating the mathematical model. The usefulness of the proposed Simscape model is demonstrated by the implementation of a variety of control strategies, including linear controllers as the linear quadratic regulator (LQR), proportional–integral–derivative (PID) and model predictive control (MPC), nonlinear controllers such as feedback linearization (FL) and sliding mode control (SMC), and artificial intelligence (AI)-based controllers such as FL with adaptive neural network compensation (FL-ANC) and reinforcement learning (RL). A design methodology that integrates RL with other control techniques is proposed. Following the proposed methodology, a FL-RL and a proportional–derivative control with RL (PD-RL) are implemented as strategies to achieve stabilization of the RIP. The swing-up control is incorporated into all controllers. The visual environment provided by Simscape facilitates a better comprehension and understanding of the RIP behavior. A comprehensive analysis of the performance of each control strategy is conducted, revealing that AI-based controllers demonstrate superior performance compared to linear and nonlinear controllers. In addition, the FL-RL and PD-RL controllers exhibit improved performance with respect to the FL-ANC and RL controllers when subjected to external disturbance. Full article

Well placement optimization refers to the identification of optimal locations for wells (producers and injectors) to maximize net present value (NPV) and oil recovery. It is a complex challenge in all phases of production (primary, secondary and tertiary) of a reservoir. Reservoir simulation is primarily used to solve this intricate task by analyzing numerous scenarios with varied well locations to determine the optimum location that maximizes the targeted objective functions (e.g., NPV and oil recovery). Proxy models are a computationally less expensive alternative to traditional reservoir simulation techniques since they approximate complex simulations with simpler models. Previous review papers have focused on analyzing various optimization algorithms and techniques for well placement. This article explores various types of proxy models that are the most suitable for well placement optimization due their discrete and nonlinear natures and focuses on recent advances in the area. Proxy models in this article are sub-divided into two primary classes, namely data-driven models and reduced order models (ROMs). The data-driven models include statistical- and machine learning (ML)-based approximations of nonlinear problems. The second class, i.e., a ROM, uses proper orthogonal decomposition (POD) methods to reduce the dimensionality of the problem. This paper introduces various subcategories within these two proxy model classes and presents the successful applications from the well placement optimization literature. Finally, the potential of integrating a data-driven approach with ROM techniques to develop more computationally efficient proxy models for well placement optimization is also discussed. This article is intended to serve as a comprehensive review of the latest proxy model techniques for the well placement optimization problem. In conclusion, while proxy models have their own challenges, their ability to significantly reduce the complexity of the well placement optimization process for huge reservoir simulation areas makes them extremely appealing. With active research and development occurring in this area, proxy models are poised to play an increasingly central role in oil and gas well placement optimization. Full article

This study investigates the steady, two-dimensional boundary layer flow of a Casson fluid over an inclined nonlinear stretching surface embedded within a Forchheimer porous medium. The governing partial differential equations are transformed into a set of ordinary differential equations through similarity transformations. The analysis incorporates the effects of an external uniform magnetic field, gravitational forces, thermal radiation modeled by the Rosseland approximation, and first-order homogeneous chemical reactions. We consider several dimensionless parameters, including the Casson fluid parameter, magnetic parameter, Darcy and Forchheimer numbers, Prandtl and Schmidt numbers, and the Eckert number to characterize the flow, heat, and mass transfer phenomena. Analytical solutions for the velocity, temperature, and concentration profiles are derived under simplifying assumptions, and expressions for critical physical quantities such as the skin friction coefficient, Nusselt number, and Sherwood number are obtained. Full article

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed. Full article

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line’s resilience against environmental conditions. Full article

The article formulates the state of the problem of improving the theoretical calculation of the nanofiltration kinetic characteristics in the time cycle of separation of industrial solutions containing copper(II), iron(III), trisodium phosphate and OP-10 (a wetting agent used in electroplating, a product of treating a mixture of mono- and dialkylphenols with ethylene oxide) using the equations of convective diffusion, hydrodynamics and mass transfer. To calculate the kinetic characteristics of the nanofiltration process, the mathematical model was improved by numerically solving the equations of convective diffusion, the Navier–Stokes equation and the flow continuity equation in a polar coordinate system with initial and boundary conditions. The theoretical results obtained in the process of an analytical solution of the system of equations allow calculating changes in concentrations in the permeate and retentate tracts and the permeate volume during nanofiltration separation. The acceptability of the developed nanofiltration method for separating solutions is assessed by comparing the calculated data according to the mathematical model with the experimental data obtained on the nanofiltration unit during separation of solutions containing copper(II), iron(III), trisodium phosphate and OP-10. Full article

Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from premature convergence and may become stuck in local optima. The primary goal is accelerating convergence and preventing solutions from falling into these local optima. This paper introduces a new approach to address these shortcomings and improve overall performance: utilizing a reinforcement deep learning method to carry out online adjustments of parameters in a homogeneous Particle Swarm Optimization, where all particles exhibit identical search behaviors inspired by models of social influence among uniform individuals. The present method utilizes an online parameter control to analyze and adjust each primary PSO parameter, particularly the acceleration factors and the inertia weight. Initially, a partially observed Markov decision process model at the PSO level is used to model the online parameter adaptation. Subsequently, a Hidden Markov Model classification, combined with a Deep Q-Network, is implemented to create a novel Particle Swarm Optimization named DPQ-PSO, and its parameters are adjusted according to deep reinforcement learning. Experiments on different benchmark unimodal and multimodal functions demonstrate superior results over most state-of-the-art methods regarding solution accuracy and convergence speed. Full article

The current engineering theories on bending vibrations and the stability of beam structures are based on solving eigenvalue problems through similarly formulated differential equations. Solving the eigenvalue problem for engineering calculations is particularly laborious, especially for non-classical supports, where factors like the stiffness of supports, axial forces, or temperature must be considered. In this case, the solution can be obtained only by numerical methods using specially created programs, which makes it difficult to select supports for a given planar beam structure in engineering practice. This work utilizes established solutions from eigenvalue problems in the theory of vibrations and stability of beams, incorporating factors such as axial forces, temperature, and support stiffness. This combined solution is applicable to beam structures of any type and cross-section, as it is determined solely by the selected support conditions (stiffness) and loading (axial force, temperature). Approximation of eigenvalue problem solutions through continuous functions allows the readers to use them for the analytical solution of the design problem of choosing a support system to ensure the frequency of vibrations and stability of the planar beam structure. At the same time, the known solutions given in the reference books on bending vibrations and stability become their particular solutions. This approach is applicable to solving problems of vibrations and loss of stability of various types (torsional, longitudinal, etc.), and is also applicable in other disciplines where solving problems for eigenvalues is required. Full article

Due to the failure of string seals, gas can leak and result in the abnormal annulus pressure in gas wells, so it is necessary to relieve the pressure in gas wells. In the process of pressure relief, the leaked gas enters the annulus, causes a the great disturbance to the annulus flow field, and thus reduces the protection performance of the annular protection fluid in the string. In order to investigate the influence of gas leakage on the annular flow field, a VOF finite element model of the gas-liquid two-phase flow disturbed by gas leakage in a casing was established to simulate the transient flow field in the annular flow disturbed by gas leakage, and the influences of leakage pressure differences, leakage direction, and leakage time on annular flow field disturbance and wall shear force were analyzed. The analysis results showed that the larger leakage pressure difference corresponded to the faster diffusion rate of the leaked gas in the annulus, the faster the flushing rate of the leaked gas against the casing wall, and a larger shear force on the tubing wall was detrimental to the formation of the corrosion inhibitor film on the tubing wall and casing wall. Under the same conditions, the shear action on the outer wall of tubing in the leakage direction of 90° was stronger than that in the leakage directions of 135° and 45° and the diffusion range was also larger. With the increase in leakage time, leaked gas further moved upward in the annulus and the shear effect on the outer wall of tubing was gradually strengthened. The leaked acid gas flushed the outer wall of casing, thus increasing the peeling-off risk of the corrosion inhibitor film. The study results show that the disturbance law of gas leakage to annular protection fluid is clear, and it was suggested to reduce unnecessary pressure relief time in the annulus to ensure the safety and integrity of gas wells. Full article

This paper investigates the production of nanoparticles via detonation. To extract valuable knowledge regarding this route, a phenomenological model of the process is developed and simulated. This framework integrates the mathematical description of the detonation with a model representing the particulate phenomena. The detonation process is simulated using a combination of a thermochemical code to determine the Chapman–Jouguet (C-J) conditions, coupled with an approximate spatially homogeneous model that describes the radial expansion of the detonation matrix. The conditions at the C-J point serve as initial conditions for the detonation dynamic model. The Mie–Grüneisen Equation of State (EoS) is used, with the “cold curve” represented by the Jones–Wilkins–Lee Equation of State. The particulate phenomena, representing the formation of metallic oxide nanoparticles from liquid droplets, are described by a Population Balance Equation (PBE) that accounts for the coalescence and coagulation mechanisms. The variables associated with detonation dynamics interact with the kernels of both phenomena. The numerical approach employed to handle the PBE relies on spatial discretization based on a fixed-pivot scheme. The dynamic solution of the models representing both processes is evolved with time using a Differential-Algebraic Equation (DAE) implicit solver. The strategy is applied to simulate the production of alumina nanoparticles from Ammonium Nitrate Fuel Oil aluminized emulsions. The results show good agreement with the literature and experience-based knowledge, demonstrating the tool’s potential in advancing understanding of the detonation route. Full article

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach. Full article

Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings. Full article

The ongoing industrial revolution emphasizes the importance of precise machinery monitoring. Among these machines, induction motors (IMs) stand out due to their large numbers, which imply a significant part of industrial energy consumption. To achieve accurate in-service IM monitoring, robust modelling is required, with a particular emphasis on in situ constraints. In this study, we create a precise digital model for squirrel cage induction motors (SCIMs) that can be used in Industry 4.0 digital twin applications. To achieve this, we survey the existing literature, describe the main modelling techniques, identify the best models in terms of ease of implementation, and ensure the accuracy of our digital representation. We develop four methods, namely finite element analysis (FEA), thermal modelling, circuit-based models, and quantum-based fuzzy logic control, as a crucial first step in implementing digital twin (DT) technology for IMs. The quantum fuzzy logic is based on the transition from classical equations to the quantum equation determining the speed of the motor in the quantum world by passing through the Schrödinger equation. We propose the DT level of integration architecture for IMs based on the industry 4.0 reference architecture model. Finally, the main tools used to successfully implement DT for IMs are revealed. Full article

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