Applying GMDH artificial neural network to predict dynamic viscosity of an antimicrobial nanofluid

Objective (s): Artificial Neural Networks (ANN) are widely used for predicting systems' behavior. GMDH is a type of ANNs which has remarkable ability in pattern recognition. The aim the current study is proposing a model to predict dynamic viscosity of silver/water nanofluid which can be used as antimicrobial fluid in several medical purposes. Materials and Methods: In order to have precise model, it is necessary to consider all influential factors. Temperature, concentration and size of nano particles are used as input variables of the model. In addition, GMDH artificial neural network is applied to design a proper model. Data for modeling are extracted from conducted experimental studies published in valuable journals. Results: The dynamic viscosity of Ag/water nanofluid is precisely modeled by using GMDH. The obtained values for R-squared is equal to 0.9996 which indicates perfect precision of the proposed model. In addition, the highest relative deviation for the model is 2.2%. Based on the values of these statistical criteria, the model is acceptable and very accurate. Conclusion: GMDH artificial neural network is reliable approach to predict dynamic viscosity of Ag/water nanofluid by using temperature, concentration and size of particles as input data.