The failures, unexpected and catastrophic, are presented in large mechanical systems, specifically, in the electric power generation. The development of new methodologies of analysis and research to evaluate and calculate useful life must be proposed. In this work, an analytical-experimental novel methodology was developed using laws of similarity and models at the scale of prototypes, parts or mechanical components of any system. The methodology developed in the dynamic study of steam turbine blades was applied to calculate their stresses, deformations and the prediction of their useful life in mechanical fatigue conditions. The analytic-experimental and numerical results showed a relative error of less than 3,16% for both models. The new methodology was validated with these results. The use of scale models with the application of similarity laws will be of help in the analysis of failures where due to the size or high costs, real systems cannot be analyzed.
Keywords: scale models, experimental analysis, deformations, similarity laws, useful life, mechanical fatigue
In this article, particle image velocimetry studies were conducted in a low-speed wind tunnel to investigate the effects of blowing ratio and blade span in terms of the characteristics of the flow field around a film-cooled blade leading edge. The measurements were performed at 20%, 40%, 60%, and 80% of blade span and blowing ratios of M = 0.5, M = 0.75, M = 1, M = 1.5, and M = 2. Velocity, turbulence intensity, and structure of vortices during the interaction between cooling flow and mainstream were analyzed in detail. The analysis shows a significant increase in mainstream velocity at low blowing ratios, M \ 1. Peaks of turbulence were observed at low-and high-span locations. Aerodynamical losses are expected at higher blowing ratios due to the formation of secondary vortices near the outgoing jet. These vortices were a consequence of velocity gradients at this zone.
En este artículo se discute el uso de sistemas de decisión de soporte basados en minería de datos (para la evaluación histórica) y máquinas de vectores de soporte, con la finalidad de obtener los valores óptimos relacionados con la eficiencia de enfriamiento en un álabe de turbina de gas para determinar la adecuada selección de componentes y construir escenarios bajo incertidumbre. Esta investigación permite seleccionar un número específico de componentes, los cuales son evaluados a partir de un depósito de información con datos de otro sistema de energía. La intención de la presente investigación es aplicar propiedades computacionales, en este caso un modelo de optimización inteligente. El caso de estudio permite analizar las características individuales de cada componente con la emulación de una serie de características correspondientes (valores óptimos alcanzados por el algoritmo híbrido). De esta manera es posible predecir una mejor funcionalidad en un sistema de este tipo.
Overall performance of hydraulic submersible pump is strongly linked to its geometry, impeller speed and physical properties of the fluid to be pumped. During the design stage, given a fluid and an impeller speed, the pump blades profiles and the diffuser shape has to be determined in order to achieve maximum power and efficiency. Using Computational Fluid Dynamics (CFD) to calculate pressure and velocity fields, inside the diffuser and impeller of pump, represents a great advantage to find regions where the behavior of fluid dynamics could be adverse to the pump performance. Several trials can be run using CFD with different blade profiles and different shapes and dimensions of diffuser to calculate the effect of them over the pump performance, trying to find an optimum value. However the optimum impeller and diffuser would never be obtained using lonely CFD computations, by this means are necessary the application of Artificial Neural Networks, which was used to find a mathematical relation between these components (diffusers and blades) and the hydraulic head obtained by CFD calculations. In the present chapter artificial neural network algorithms are used in combinations with CFD computations to reach an optimum in the pumps performance.
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