-This review reports research on liquid impingement jets and the abilities, limitations and features of this method of heat transfer. Some available and important correlations for Nusselt number are collected here. Also we demonstrate the capability of nanofluids to be applied in heat transfer processes involved liquid impingement jets.
A new impingement oven was designed and tested for its thermal and quality performance for baking of thin flat bread. The test facility was instrumented to monitor and record during the baking process. Bread temperature and weight loss were recorded on-line during baking at oven temperatures of 150, 175, 200, 225, and 250 C and 1, 2.5, 5, 7.5, and 10 m/s jet velocities, respectively. Image processing was used for monitoring the bread volume and surface color changes during baking. Better baking conditions were obtained with the impingement oven in comparison with the conventional direct fired ovens. Experiments showed that a higher jet velocity can be used for flat bread baking at lower oven temperatures and yield shorter baking times for the same quality product. For very thin breads (such as the Iranian breads), results show that conduction heat transfer from the bottom surface of the tray must also be considered along with the convective heat transfer from the jets in selection of optimal operating parameters. It is noted that desirable baking conditions, e.g., good browning, uniform color, high volume increase, etc., can be achieved in a well-designed and operated impingement oven in shorter baking times compared to the conventional ovens.
An artificial neural network (ANN) was developed to model the effect of baking parameters on the quality attributes of flat bread; i.e., crumb temperature, moisture content, surface color change and bread volume increase during baking process. As the hot air impinging jets were employed for baking, the baking control parameters were the jet temperature, the jet velocity, and the time elapsed from the beginning of the baking. The data used in the training of the network were acquired experimentally. In addition, using the data provided by ANN, a multi-objective optimization algorithm was employed to achieve the baking condition that provides the quality of the bread in all aspects simultaneously.
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