In the braking system, the heat dissipation generated by the friction between the disc and pad should be evacuated as quickly as possible. In this work, five common different automotive disc brakes were studied through mathematical theories of heat transfer and numerical methods using the ANSYS software. In addition, a direct comparison between experimental, theoretical, and simulation values found in the open literature was performed to propose a disc brake with an improved geometry in terms of dissipation of heat transfer. The numerical results were considered to propose two possible solutions of disc brake geometries using N-38 ventilation blades used in aeronautic engineering. An improvement in temperature dissipation was achieved by approximately 23.8% compared to the five geometries analyzed with a simple type N-38 ventilation blade. The heat dissipation in the brakes strongly depends on the geometry of the disc, the geometry of the blades, the material from which it is manufactured, the material of the pad, the weight of the vehicle, and the operating conditions, as can be verified with mathematical calculations and experiments. The results obtained demonstrate that the discs can be used effectively in extreme working conditions (80 km/h and 33°C), without affecting the safety of the occupants and the braking system.

Accurately and instantly estimating the hydrodynamic characteristics in two-phase liquid–gas flow is crucial for industries like oil, gas, and other multiphase flow sectors to reduce costs and emissions, boost efficiency, and enhance operational safety. This type of flow involves constant slippage between gas and liquid phases caused by a deformable interface, resulting in changes in gas volumetric fraction and the creation of structures known as flow patterns. Empirical and numerical methods used for prediction often result in significant inaccuracies during scale-up processes. Different methodologies based on artificial intelligence (AI) are currently being applied to predict hydrodynamic characteristics in two-phase liquid–gas flow, which was corroborated with the bibliometric analysis where AI techniques were found to have been applied in flow pattern recognition, volumetric fraction determination for each fluid, and pressure gradient estimation. The results revealed that a total of 178 keywords in 70 articles, 29 of which reached the threshold (machine learning, flow pattern, two-phase flow, artificial intelligence, and neural networks as the high predominance), were published mainly in Flow Measurement and Instrumentation. This journal has the highest number of published articles related to the studied topic, with nine articles. The most relevant author is Efteknari-Zadeh, E, from the Institute of Optics and Quantum Electronics.

In this work, an application of artificial intelligence in the oils & gas industry is developed to identify flow patterns in horizontal and vertical pipes of two-phase flow of oil and water, normalizing the word information and converting it to numerical values through the development of an artificial neural network, whose input layer is composed of the surface velocities of each fluid, the velocity of the mixture, the volumetric fraction of the substances, diameter and the inclination of pipelines and the oil viscosity. The Artificial Neural Networks (ANN) has two hidden layers composed of 45 neurons. The database with which the model was trained, validated, and tested has 6993 rows of information corresponding to the inputs of the intelligent system and particular-ized for annular flow in horizontal pipes and DO/W in vertical pipelines. Notice that the information was obtained after re-engineering the information presented by 12 and 18 authors for horizontal and vertical piping, respectively. Finally, the mean square error obtained by the model was around 1.38%, with a maximum coefficient of determination of 0.79.

La detección adecuada de plagas y enfermedades en la producción de cultivos es fundamental para aumentar la producción agrícola de forma sostenible. Es por esta razón, que se incorpora el término Agricultura 4.0, la cual integra un conjunto de tecnologías, dispositivos, protocolos y paradigmas computacionales para mejorar los procesos agrícolas. La información sobre las condiciones climáticas, suelos, enfermedades, insectos, semillas, fertilizantes, constituye una importante contribución al desarrollo económico y sostenible de este sector. Las técnicas de procesamiento digital de imágenes son una herramienta que permite la identificación temprana de las plagas o enfermedades en los cultivos como: cereales, frutales, raíces, hojas y tubérculos; y de esta forma, mitigar pérdidas económicas en el sector agrícola. A nivel mundial, alrededor del 40% de los cultivos son desechados por diversas enfermedades y plagas. En la mayoría de los casos, las enfermedades de los cultivos producen síntomas y características visibles durante el crecimiento de las plantas. Debido a la escasez de tecnologías utilizadas en los cultivos, el diagnóstico de las enfermedades y plagas se soporta en gran parte en la inspección humana, generando errores ocasionados por la subjetividad de los individuos. La presente revisión de literatura se llevó a cabo con la finalidad de identificar diferentes técnicas de procesamiento digital de imágenes para la prevención de plagas y enfermedades en cultivos de los diferentes sectores agrícolas. Los resultados demostraron que el sistema de diagnóstico está compuesto de la adquisición de imágenes, preprocesamiento de imágenes, la segmentación, la extracción de características, la selección de características y la posterior clasificación de las plagas o enfermedades; asimismo, se presentan las tendencias y desafíos actuales en la temática.

This document presents an experimental study that supports probabilistic decisions based on neural networks to detect the presence of leaks in pipeline transport systems, since such leaks can cause serious consequences. In addition to the economic losses presented by the lost product, process stoppage and repair of the damage, there can be insurmountable environmental and social losses such as the death of human beings. The probabilistic model correlates measurements of inlet and outlet pressures and flow to the state of leakage. The study and experimentation presented in this work are based on information acquired by simulating the behavior of the fluid in a pilot tube installed in the Fluid Mechanics laboratory of the Universidad Francisco de Paula Santander, Seccional Ocaña. Finally, experimental tests were carried out to obtain the data of the physical variables of the flow sensors at the entrance and exit, with these data a multilayer neural network of perception was trained. The results obtained from the test equipment, with intentionally caused leaks, showed that the structure of the multilayer Perceptron neural network was capable of detecting leaks in the pilot tubes. With the accomplishment of this work it was found that the neural network presents favorable results at the time of detecting the leak compared with other methods of detection since this one presents immunity to the noise, a parallel structure and, consequently, capacities of fast processing and classification.