Abstract
Trials had been performed for heated square workpiece in a rectangular channel to observe heat exchange in pulsating flow. A square workpiece consisting of aluminum is utilized amid the examinations. The impact of oscillating frequency and Reynolds number of the stream on heat exchange enhancement and Nusselt number is investigated. The trials are done in the range of 0–60 Hz signal frequency. The support vector machine algorithm based on distinct kernels is used to evaluate the workpiece temperature. The support vector machine algorithm using PUK kernel presents the best results for workpiece temperature. Different diagrams are plotted to demonstrate the impact of RE number and recurrence frequency on the heat exchange enhancement. With an increment in the value of RE number, the increase in heat exchange takes place at all recurrence frequencies. Up to some point, heat exchange additionally improved with an increment in recurrence frequency and after that starts to decline.
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Nain, S.S., Rathi, R., Srinivasa Varma, B., Panthangi, R.K., Kumar, A. (2020). Machine Learning Application for Pulsating Flow Through Aluminum Block. In: Krolczyk, G., Prakash, C., Singh, S., Davim, J. (eds) Advances in Intelligent Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4565-8_17
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