Abstract

En algunos problemas relevantes de la ingeniería, el campo mecánico está fuertemente acoplado con el de temperatura y el de transporte de masa. La solución de estos problemas es compleja sobre todo cuando las deformaciones son grandes. Por ello, a menudo se acude a aproximaciones numéricas que pueden encontrar todos los campos involucrados en el problema acoplado, sus efectos y sus interacciones. En este artículo se describe una discretización por el método de los elementos finitos del problema acoplado de difusión, temperatura y deformación incluyendo el rango no lineal de deformaciones. La formulación tiene en cuenta todos los acoplamientos posibles entre los tres campos de estudio y se proporcionan todos los detalles necesarios para su completa implementación llenando un hueco de la literatura de estos métodos. Asimismo, el artículo describe la fundamentación termodinámica de los problemas acoplados termo-difusivo-mecánicos, insistiendo en la derivación de las ecuaciones de balance y restricciones que siguen de la segunda ley de la termodinámica. Los resultados del artículo serán de interés para investigadores que necesiten implementar las ecuaciones de problemas acoplados en códigos de elementos finitos y, en particular, para aplicaciones en el modelado de baterías, del comportamiento de metales bajo los efectos del hidrógeno, de geles, etc.

Abstract

This study presents a novel fractional semi-analytical iterative approach for solving nonlinear fractional Fisher’s equations using the Caputo fractional operator. The primary objective is to provide a method that yields exact solutions to nonlinear fractional equations without requiring assumptions about nonlinear terms. By applying the Temimi-Ansari Method (TAM) with fractional calculus, this approach offers a robust solution to the time-fractional nonlinear Fisher’s equation, a model relevant in fields such as population dynamics, tumor growth, and gene propagation. In this work, tables and graphical illustrations show that the proposed method minimizes computational complexity and delivers significant accuracy across multiple cases of Fisher’s equations. The findings indicate that TAM with fractional order derivatives provides accurate, efficient approximations with reduced computational workload, showcasing the technique’s potential for addressing a wide range of nonlinear fractional differential equations.OPEN ACCESS Received: 19/07/2024 Accepted: 01/11/2024

Abstract

Measuring and determining the parameters and characteristics of multilayer structures have become an important subject for several recent studies. This importance is due to industry needs and structural health requirements. The eddy current inspection is considered an important practical tool that ensures the safety and efficiency of multilayer structures and responds to the above necessities. The diversity of multilayer structure characteristics is one of the principal problems that must be solved. These physical and electromagnetic parameters are not always available or provided by the suppliers. Another problem that arises in the development of different models related to these structures is the difficulty of obtaining a satisfactory diagnosis. This difficulty returns to the complexity of geometry, the presence of small dimensions and sizes, and the existence of various parameters. In this context, it is necessary to achieve a strategy for the development of software and hardware tools concerning the characterization of multilayer structures. These tools must be applied to surmount the above problems and improve the technical advantages of the eddy current inspection. The principal objective of this work is to investigate the efficacy of the eddy current method applied to aeronautical materials, particularly low thickness multilayer structures. The modeling was performed using the finite element method. A software program was developed to investigate changes in the coil impedance. Results are initially validated and compared against the analytical and computational results given for simple cases. They are very similar, and they present a good agreement for both situations. The error is 5% for the calculus of the induction magnetic B. It also varies from 0.17% to 5.32% for impedance responses that enable the application of the developed code to carry out simulations for complex geometries. For various values of parameters and a wide range of applications, the parameters and properties of the problem can easily be introduced into the code. This permits the analysis and calculation of changes in impedance versus the effect of any variation in parameters. The developed approach is sufficiently general. It can simulate various potential cases of defects in low thickness multilayer structures due to its adequate design. It can generate interpretable results for differentOPEN ACCESS Received: 14/07/2024 Accepted: 02/10/2024

Abstract

Urban underground public spaces are relatively enclosed, with high daily passenger flow and diverse directions. Enhancing evacuation capabilities through dynamic guidance systems is a crucial solution. Therefore, this paper proposes a double-layer computational framework based on meta-heuristic integration of a bi-objective evacuation path optimization method. Firstly, the outer layer computation explores the population by combining the genetic algorithm and simulated annealing. It uses temperature parameters to probabilistically accept worse solutions, enhancing global search capability and avoiding local optima. Secondly, the inner layer calculates the fitness value of each solution from the outer layer, considering both the shortest path and minimum risk objectives. The path optimization is carried out by A∗ algorithm on the basis of constructing the safety matrix by using breadth-first search algorithm, and the distance matrix by using Dijkstra’s algorithm. Finally, this method can search for the shortest safe path, effectively guiding people safely from the starting point to the exit. The experiments show that compared to traditional evacuation path planning methods, the proposed method significantly improves path optimization capabilities, quickly planning the shortest and safest evacuation route. It can provide guidance for fire safety and emergency plans for evacuations.

Abstract

The problem of uneven ventilation in pig houses must be solved to effectively improve the winter environmental quality of pig nurseries in cold regions. In this study, the airflow field and airflow uneven coefficients of pig nurseries with duct ventilation were simulated and calculated using computational fluid dynamics, and compared with pig nurseries with different duct diameters, inlet and outlet air velocities and air supply angles. The average relative error between the simulated and measured values was 12%. Comparison of simulation results and airflow uneven coefficients showed that the airflow uneven coefficients of the fences were reduced, and the airflow field was uniformly distributed with a duct diameter of 0.3 m, inlet and outlet air velocities of 1.5 and 2.0 m/s, respectively, and an air supply angle of 45°. These improvements resulted in a more homogeneous ventilation, which led to more uniform ventilation and contributed to discharging dirty air outdoors. Then the test pig nursery was modified based on the simulation and analysis results. Difference analyses were carried out between the control and the test pig nurseries. Comparative analyses showed the differences between the test data and the monitoring data were smaller, and the duct ventilation was more uniform, which was suitable for the healthy growth of piglets.

Abstract

Nowadays, with the rise of computing power, control-relevant identification methods have gained attention in various industrial applications, incorporating the requirements for control design into the process of system identification. Mathematical identification of stable linear dynamical systems is a widely studied problem in the literature, and it is prevalently performed in open-loop structures that may lead to high-order models suitable for control system design with imposed control objectives. However, in the case of unstable systems identification can become a challenge task, and usually is performed using closed-loop identification techniques. This paper presents the control-relevant identification approach for two kinds of unstable processes. The contribution focuses on establishing a well-fitted identified model by using a strategy that involves collecting data from the closed-loop system’s operation with a proportional controller when the system achieves an underdamped step response. In addition, a proportional-integral-derivative (PID) controller for each process was synthesized using maximal stability degree method. Concerning identification, the simulation results were compared with those of the genetic algorithm and offered better model estimation than the genetic algorithm. On the other hand, it is also demonstrated that the designed control algorithm offered a high degree of stability to the system and is more reliable in stabilizing the behavior of the unstable system than the genetic algorithm and the parametric optimization method.

Abstract

Urban underground public spaces are relatively enclosed, with high daily passenger flow and diverse directions. Enhancing evacuation capabilities through dynamic guidance systems is a crucial solution. Therefore, this paper proposes a double-layer computational framework based on meta-heuristic integration of a bi-objective evacuation path optimization method. Firstly, the outer layer computation explores the population by combining the genetic algorithm and simulated annealing. It uses temperature parameters to probabilistically accept worse solutions, enhancing global search capability and avoiding local optima. Secondly, the inner layer calculates the fitness value of each solution from the outer layer, considering both the shortest path and minimum risk objectives. The path optimization is carried out by A∗ algorithm on the basis of constructing the safety matrix by using breadth-first search algorithm, and the distance matrix by using Dijkstra’s algorithm. Finally, this method can search for the shortest safe path, effectively guiding people safely from the starting point to the exit. The experiments show that compared to traditional evacuation path planning methods, the proposed method significantly improves path optimization capabilities, quickly planning the shortest and safest evacuation route. It can provide guidance for fire safety and emergency plans for evacuations.

Abstract

This paper presents a computer simulation model for predicting solar irradiance in a three-dimensional (3D) environment. Solar irradiance prediction is critical for solar energy systems and related fields. Machine learning techniques, such as recurrent neural networks (RNNs), are employed for more accurate predictions. Integrating a 3D environmental simulation with the RNN models achieves accurate predictions with reasonable resolutions. The training model uses selected astronomical and atmospheric factors to train the RNN models. The proposed method allows the user to obtain the corresponding solar irradiance prediction values for arbitrary periods. Astronomical and atmospheric factors affect solar irradiance; hence, data from the Korea Meteorological Administration are used for training. The RNNs, including the long short-term memory (LSTM) and gated recurrent unit methods, are employed for the prediction. The LSTM layers outperformed other configurations, accurately predicting zero irradiation values. A set of solar irradiance models is presented using RNNs by configuring their layers, and the layout consisting of four LSTM layers performed best. This layout achieved reasonable error bounds, with relatively good root mean squared error and mean absolute error values. A computer graphics-based solar irradiance prediction model is proposed based on this prediction model, incorporating simulations of the surrounding environment. A case study is presented with surrounding buildings to analyze the solar irradiance over the year with a one-hour forecasting horizon to demonstrate its feasibility. Moreover, we plan to improve the results with other neural network models, such as the fuzzyembedded RNN.OPEN ACCESS Received: 19/09/2024 Accepted: 13/12/2024

Abstract

Mammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification of cancer is very complicated and represents a considerable workload for radiologists. Machine learning (ML) can help address these challenges by providing accurate, automated diagnosis, but traditional ML methods are complex and resourceintensive. Google AutoML Vision offers a simplified approach, enabling healthcare professionals with minimal programming skills to develop effective diagnostic models. The aim of this study was to evaluate the ability of automated deep learning using mammography images using Google AutoML with different augmentation cases. In this work, two models were created: one for binary classification and another for multiclassification. The binary classification model includes two scenarios: noncancerous and malignant, while the multi-classification approach includes three scenarios: normal, benign and malignant. The average accuracy of the two classifications was evaluated and compared. The average accuracy of the binary and multi-classification models was 77.98% and 79.29%, respectively. These results suggest that Google AutoML can simplify the use of ML models in the clinical setting and provide a reliable diagnostic tool that can reduce the workload of radiologists. This study shows that AutoML has the potential to streamline diagnostic workflows in healthcare and make machine learning more accessible and effective in medical practise.OPEN ACCESS Received: 01/08/2024 Accepted: 29/11/2024

Abstract

This paper presents a computer simulation model for predicting solar irradiance in a three-dimensional (3D) environment. Solar irradiance prediction is critical for solar energy systems and related fields. Machine learning techniques, such as recurrent neural networks (RNNs), are employed for more accurate predictions. Integrating a 3D environmental simulation with the RNN models achieves accurate predictions with reasonable resolutions. The training model uses selected astronomical and atmospheric factors to train the RNN models. The proposed method allows the user to obtain the corresponding solar irradiance prediction values for arbitrary periods. Astronomical and atmospheric factors affect solar irradiance; hence, data from the Korea Meteorological Administration are used for training. The RNNs, including the long short-term memory (LSTM) and gated recurrent unit methods, are employed for the prediction. The LSTM layers outperformed other configurations, accurately predicting zero irradiation values. A set of solar irradiance models is presented using RNNs by configuring their layers, and the layout consisting of four LSTM layers performed best. This layout achieved reasonable error bounds, with relatively good root mean squared error and mean absolute error values. A computer graphics-based solar irradiance prediction model is proposed based on this prediction model, incorporating simulations of the surrounding environment. A case study is presented with surrounding buildings to analyze the solar irradiance over the year with a one-hour forecasting horizon to demonstrate its feasibility. Moreover, we plan to improve the results with other neural network models, such as the fuzzyembedded RNN.OPEN ACCESS Received: 19/09/2024 Accepted: 13/12/2024