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    Impingement Heat Transfer Due to Circular Air Jet Over Rough Flat Surfaces
    (Bangladesh Institute of Technology (BIT), Khulna, 2000-08) Islam, Md. Nurul; Ahmed, Prof. Dr. Naseem
    An experimental investigation was carried out to investigate the pressure distribution and the local and average Nusselt number due to impinging of a circular air jet over uniformly heated rough flat surfaces. The present investigation shows the dependence of the pressure on jet exit Reynolds number, relative roughness of the surfaces and nozzle-to-surface spacings. It was observed that the overall pressure coefficient, cp increases with the increase of jet exit Reynolds number and decreases with the increase of surface roughness and nozzle-to-surface spacings. It also observed that the coefficient pressure at the stagnation point remains constant for the lower values of surface roughness but increases beyond a specific value of surface roughness. In this investigation the nature of dependence of heat transfer on various parameters namely, jet exit Reynolds number, relative roughness of the surface and nozzle-to-surface spacings are identified Jet exit Reynolds numbers of 6000. 8700. 16520 and 23400 , relative surface roughness of smooth. 0.01306, 0.01338, 0.01806. and 0.01952 and dimensionless nozzle-to-surface spacings of 1.61. 2.41, 3.22. and 4.03 are considered for the investigation. It was observed that the local Nusselt number increases with the increase of jet exit Reynolds number and surface roughness, but decreases with the increase of nozzle-to-surface spacings. It also observed that the stagnation point Nusselt number remains constant for the lower values of surface roughness but increases beyond a specific value of surface roughness, which predicts that there is a critical value of surface roughness. The average Nusselt number was calculated and a correlation developed in terms of jet Reynolds number, relative roughness of the surface and nozzle-to-surface spacings. The correlation yields ±10% accurately in context with experimental findings, however shows singularity for smooth surface. Experimental results provided useflul intbrmatiofl which have significant of potential industrial applications regarding the radius of the heat transfer area, nozzle-to-surface spacing atid surface roughness for maximizing the average Nusselt number.
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    Predicting the Effects of Process Parameters on Weldment Characteristics in MIG Welding using Artificial Neural Networks
    (Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2009-12) Saha, Subrata; Ahmed, Prof. Dr. Naseem
    In this project work an attempt has been taken to predicting the effects of process parameters on weldment characteristics in MIG welding with the help of artificial neural network technique. Electrode wire diameter, Electrode wire feed rate, Welding speed, Welding current and Arc length have been chosen as influential process parameters. More or less, these are the influential factors in deciding the weldment characteristics. Weldment characteristics like Bead Geometry, Depth of Penetration, Depth of Heat Affected Zone (HAZ) and Hardness of weld metal are important characteristics on the basis of structure and these have been considered in this project work. Metal Inert Gas (MIG) Welding process with automatic or robotic system in various industries is a demanding welding process now-a-days and this process is being used with increasing rate of applications. Due to these reasons, MIG welding process has been chosen for this project work and a semi-automatic MIG welding machine have been used. Single straight beads have been welded on the surface of the specimens of the medium carbon steel plate. Artificial Neural Network (ANN) refers to computing systems whose central theme is borrowed from the analogy of biological neural networks and the basic unit of such networks is a simple mathematical model. In this research work the Real-Time Recurrent Learning algorithm of ANN have been used with actual inputs and outputs of experimental values as inputs to the algorithm to complete the computational tasks. It has been observed that the computational values of weldment characteristics obtained by ANN are very close to the experimental values of those. So the ANN based approach can be used effectively for predicting the weldment characteristics in MIG welding.