Many gas turbine combustors use bluff-body flameholders to enhance mixing and maintain flame stabilization inside the combustor. Computational Fluid Dynamics (CFD) can greatly help in the design and development of gas turbine combustors. In this regard, CFD analyses using k-ε and Reynold Stress Model (RSM) approaches are being evaluated through simulating the combustion processes inside a bluff body stabilized gas turbine combustor where a mixture of lean premixed methane-air are burnt. The numerical study is performed under a steady state condition utilizing the commercial software ANSYS-FLUENT. The simulated results are compared with available experimental data as well as published simulation results found in the literature. The results are presented and compared in terms of velocity fields, temperature profiles and species distributions. The results show that both adopted turbulence models k-ε and RSM reasonably made a well predictions of the combustion process with such kind of combustor, especially k-ε turbulence model.
The effect of blade number on small Horizontal Axis Wind Turbine (HAWT) has been studied experimentally and numerically in this research. The turbine blade is made of a flat metal sheet and the tip was formed to shape a winglet. The 5-blades turbine was tested inside a wind tunnel for performance investigation at different wind speeds. The experiment was conducted under various wind speed, i.e. 3.5 m/s, 3.9 m/s, 4.3 m/s, 4.6 m/s dan 5 m/s. Furthermore, three wind turbines geometry with different blade number (3, 4, and 5 blades) were built for numerical study purpose by using Ansys Fluent and the results were compared to the experimental one. The results show that the blade number does increase the wind turbine torque and there is also more power generated from the turbine with more blade numbers since torque is related to pressure. Moreover, the winglet helps the blade to retain the flow and increases the pressure on the blade surface. However, the experimental measurements obtained were smaller than the numerical predictions about 50% on the average since more unidentified losses existed and not accounted for the calculation.
It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data.
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