Laser interferometry is combined with high-speed digital cinematography to measure time-averaged transient and turbulent convective heat transfer rates. The method is applied to study free convection in a tall vertical air-filled enclosure. Measurements are made at three wall spacings in the turbulent flow regime (5.2×104≤RaW≤2.8×105). An automated image processing algorithm is used to calculate the instantaneous local heat flux from a sequence of interferograms that is captured by a high-speed camera. The local Nusselt number distributions on the hot and cold walls are obtained by time-averaging the fluctuations in local heat flux. The effects of key experimental parameters, such as the camera frame rate and the total image capture time, are investigated. For the current problem, it is shown that a total capture interval of about 10 s is required to accurately measure the time-average local Nusselt number. Within the measurement uncertainty, the average Nusselt number results are in agreement with a widely used empirical correlation from the literature.

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.

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