RSM and ANN Studies for the Intensification of Nanofluid Application

In this chapter, sophisticated modeling and optimization methods that aim at boosting the performance of nanofluids in thermal and energy applications are examined. As the demand for efficient heat transfer fluids grows in automotive cooling, electronic, and renewable energy, nanofluids are highly beneficial because of their augmented thermal and flow characteristics. Nevertheless, their mode of behavior in the real world is controlled by nonlinear relationships of various variables, which are too complicated to handle using the conventional modeling strategies. In this chapter, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) are established as two conflicting and complementary tools in modeling, prediction, and process optimization. RSM can find practical empirical models by statistically designed experiments, and ANN uses data to generate models by making use of data-driven learning. The three types of comparative studies, or hybrid RSM-ANN methods, confirm advantages of both methods in the ability to discover system behavior, in minimizing experimentation demands, and in the capacity to use process intensification. One important step forward in the direction of intelligent design of thermal systems is the combination of these techniques, providing real-time applications with high accuracy.