Author
S. Sathyanarayanan
Bio: S. Sathyanarayanan is an academic researcher from National Institute of Technology, Tiruchirappalli. The author has contributed to research in topics: Catalysis & Spark-ignition engine. The author has co-authored 1 publications.
Papers
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11 citations
10 citations
TL;DR: In this paper , the performance and emission characteristics of diisopropyl ether-gasoline blends at various engine speeds and compression ratios were studied, and the engine parameters were optimized using the response surface methodology.
6 citations
TL;DR: In this paper , the performance of eight different solar flat plate collector systems under the latitude and longitude of Tiruchirappalli, India, was evaluated under different power requirements.
Abstract: Among the available alternative energy source, the human community depends on solar power dominantly to fulfil their energy needs. The objective of this study is to suggest the right solar energy conversion system for the users based on their power requirements. This study observed the performance delivered by eight different solar flat plate collector systems under the latitude and longitude of Tiruchirappalli, India. Among the observed cases (A–G), D and G suited the user with electrical power output requirements. E and H are the choices for the user with thermal power output requirements.
2 citations
TL;DR: In this paper , a generalized regression neural network (GRNN) model was used to predict the rate of heat transfer between two fluids and the exit temperature using 15 real-time experimental datasets among which the GRNN-1 model was effectively designed to predict both the hot and the cold fluids flowing through the heat exchanger with an accuracy of 97.48%.
Abstract: A heat transfer device in which heat exchange takes place between two fluids at different temperatures. The performance of any heat exchanger is evaluated by its heat transfer rate. Such heat transfer rate directly depends on the difference in temperature between two fluids and the mass flow rate. In this study, an attempt has been made to predict the rate of heat transfer between two fluids and the exit temperature using the generalized regression neural network (GRNN) model. For this, two GRNN models are designed using 15 real-time experimental datasets among which the GRNN-1 model is effectively designed to predict the exit temperature of both the hot and the cold fluids flowing through the heat exchanger with an accuracy of 97.48%. Similarly, the GRNN-2 model is designed to predict the rate of heat transfer between the fluids with an accuracy of 94.04%.
1 citations