Abstract: This work presents a Computational Fluid Dynamics
(CFD) simulation of a butterfly valve used to control the flow of
combustible gas mixture in an industrial process setting.The work
uses CFD simulation to analyze the flow characteristics in the
vicinity of the valve, including the pressure distributions and
Frequency spectrum of the pressure pulsations downstream the valves
and the vortex shedding allow predicting the torque fluctuations
acting on the valve shaft and the possibility of generating mechanical
vibration and resonance.These fluctuations are due to aerodynamic
torque resulting from fluid turbulence and vortex shedding in the
valve vicinity.
The valve analyzed is located in a pipeline between two opposing
90o elbows, which exposes the valve and the surrounding structure to
the turbulence generated upstream and downstream the elbows at
either end of the pipe.CFD simulations show that the best location for
the valve from a vibration point of view is in the middle of the pipe
joining the elbows.
Abstract: This paper reports the optimal process conditions for machining of three different types of MMC’s 65vol%SiC/A356.2; 10vol%SiC-5vol%quartz/Al and 30vol%SiC/A359 using PMEDM process. MRR, TWR, SR and surface integrity were evaluated after each trial and contributing process parameters were identified. The four responses were then collectively optimized using TOPSIS and optimal process conditions were identified for each type of MMC. The density of reinforced particles shields the matrix material from spark energy hence the high MRR and SR was observed with lowest reinforced particle. TWR was highest with Cu-Gr electrode due to disintegration of the weakly bonded particles in the composite electrode. Each workpiece was examined for surface integrity and ranked as per severity of surface defects observed and their rankings were used for arriving at the most optimal process settings for each workpiece.
Abstract: Response surface methodology (RSM) is a very
efficient tool to provide a good practical insight into developing new
process and optimizing them. This methodology could help
engineers to raise a mathematical model to represent the behavior of
system as a convincing function of process parameters.
Through this paper the sequential nature of the RSM surveyed for process
engineers and its relationship to design of experiments (DOE), regression
analysis and robust design reviewed. The proposed four-step procedure in
two different phases could help system analyst to resolve the parameter
design problem involving responses. In order to check accuracy of the
designed model, residual analysis and prediction error sum of squares
(PRESS) described.
It is believed that the proposed procedure in this study can resolve a
complex parameter design problem with one or more responses. It can be
applied to those areas where there are large data sets and a number of
responses are to be optimized simultaneously. In addition, the proposed
procedure is relatively simple and can be implemented easily by using
ready-made standard statistical packages.