Prediction of Local Heat Transfer in a Vertical Cavity Using Artificial Neutral Networks

Abstract

A time-averaging technique was developed to measure the unsteady and turbulent free convection heat transfer in a tall vertical enclosure using a Mach–Zehnder interferometer. The method used a combination of a digital high speed camera and an interferometer to obtain the local time-averaged heat flux in the cavity. The measured values were used to train an artificial neural network (ANN) algorithm to predict the local heat transfer. The time-averaged local Nusselt number is needed to study local phenomena, e.g., condensation in windows. Optical heat transfer measurements were made in a differentially heated vertical cavity with isothermal walls. The cavity widths were W=12.7 mm, 32.3 mm, 40 mm, and 56.2 mm. The corresponding Rayleigh numbers were about 3×103, 5×104, 1×105, and 2.7×105, respectively, and the enclosure aspect ratio (H/W) ranged from A=18 to 76. The test fluid was air and the temperature differential was about 15 K for all measurements. ALYUDA NEUROINTELLIGENCE (version 2.2) was used to generate solutions for the time-averaged local Nusselt number in the cavity based on the experimental data. Feed-forward architecture and training by the Levenberg–Marquardt algorithm were adopted. The ANN was designed to suit the present system, which had 4–13 inputs and one output. The network predictions were found to be in a good agreement with the experimental local Nusselt number values.