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

摘要

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 interferom-eter. The method used a combination of a digital high speed camera and an interferom-eter 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 ×10~3, 5 × 10~4, 1 × 10~5, and 2.7 ×10~5, respectively, and the enclosure aspect ratio (HAV) ranged from A = 18 to 76. The test fluid was air and the temperature differential was about 15 Kfor all measurements, alyuda neuromtelligence (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.