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

Document Type: Research Paper

Authors

1 Post graduate student of pediatric Dentistry, Department of pediatric dentistry, Faculty of dentistry, Shahid Beheshti University of medical sciences, Tehran, Iran

2 Department of Biomedical Engineering, Tehran Medical Branch, Islamic Azad University, Terhan, Iran

Abstract

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.

Keywords


1. Ahmadi MH, Nazari MA, Ghasempour R, Madah H, Shafii MB, Ahmadi MA. Thermal Conductivity Ratio Prediction of A l 2 O 3 /water Nanofluid by Applying Connectionist Methods. Colloids Surfaces A Physicochem Eng Asp. 2018; 541: 154-164 .
2. Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim. 2018; 1–11.
3. Ahmadi MH, Mirlohi A, Nazari MA, Ghasempour R. A review of thermal conductivity of various nanofluids. J Mol Liq. 2018; 265: 181-188.
4. Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int Commun Heat Mass Transf. 2018; 91: 90–94.
5. Alhuyi Nazari M, Ahmadi MH, Ghasempour R, Shafii MB. How to improve the thermal performance of pulsating heat pipes: A review on working fluid. Renew Sustain Energy Rev. 2018; 91: 630–638.
6. Akbarianrad N, Mohammadian F, Alhuyi Nazari M, Rahbani Nobar B. Applications of nanotechnology in endodontic: A Review. Nanomed J. 2018; 5(3): 121–126.
7. Atai M, Pahlavan A, Moin N. Nano-porous thermally sintered nano silica as novel fillers for dental composites. Dent Mater. 2012; 28(2): 133–145.
8. Cremar L, Gutierrez J, Martinez J, Materon LA, Gilkerson R, Xu F, Lozano K. Development of antimicrobial chitosan based nanofiber dressings for wound healing applications. Nanomed J. 2018; 5(1): 6–14.
9. Ghasemi SM, Dormanesh B, Hosseini Abari A, Aliasghari A, Farahnejad Z. Comparative characterization of silver nanoparticles synthesized by spore extract of Bacillus subtilis and Geobacillus stearothermophilus. Nanomed J. 2018; 5(1): 46–51.
10. Pirtarighat S, Ghannadnia M, Baghshahi S. Antimicrobial effects of green synthesized silver nanoparticles using Melissa officinalis grown under in vitro condition. Nanomed J. 2017; 4(3): 184–190.
11. Alabdulmohsen Z, Saad A. Antibacterial effect of silver nanoparticles against Enterococcus faecalis. Saudi Endod J. 2017; 7(1): 29.
12. Rodrigues CT, de Andrade FB, de Vasconcelos LRSM, Midena RZ, Pereira TC, Kuga MC, Duarte MAH, Bernardineli N. Antibacterial properties of silver nanoparticles as a root canal irrigant against Enterococcus faecalis biofilm and infected dentinal tubules. Int Endod J 2018.
13. Mollick MMR, Bhowmick B, Maity D, Mondal D, Roy I, Sarkar J, Rana D, Acharya K, Chattopadhyay S, Chattopadhyay D. Green synthesis of silver nanoparticles-based nanofluids and investigation of their antimicrobial activities. Microfluid Nanofluidics. 2014; 16(3): 541–551.
14. Ahmadi MH, Tatar A, Alhuyi Nazari M, Ghasempour R, Chamkha AJ, Yan W-M. Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks. Int J Heat Mass Transf. 2018; 126: 1079–1086.
15. Ahmadi MH, Ahmadi MA, Mehrpooya M, Rosen MA. Using GMDH neural networks to model the power and torque of a stirling engine. Sustain. 2015; 7(2): 2243–2255.
16. Kasaeian A, Ghalamchi M, Ahmadi MH, Ghalamchi M. GMDH algorithm for modeling the outlet temperatures of a solar chimney based on the ambient temperature. Mech Ind. 2017; 18(2): 216.
17. Pourkiaei SM, Ahmadi MH, Hasheminejad SM. Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network. Mech Ind. 2016; 17(1): 105.
18. Soltani O, Akbari M. Effects of temperature and particles concentration on the dynamic viscosity of MgO-MWCNT/ethylene glycol hybrid nanofluid: Experimental study. Phys E Low-dimensional Syst Nanostructures. 2016; 84: 564–570.
19. Ghasemi S, Karimipour A. Experimental investigation of the effects of temperature and mass fraction on the dynamic viscosity of CuO-paraffin nanofluid. Appl Therm Eng. 2018; 128: 189–197.
20. Godson L, Lal DM, Wongwises S. Measurement of Thermo Physical Properties of Metallic Nanofluids for High Temperature Applications. Nanoscale Microscale Thermophys Eng. 2010; 14(3): 152–173.
21. Koca HD, Doganay S, Turgut A, Tavman IH, Saidur R, Mohammed I. Effect of particle size on the viscosity of nano fl uids : A review. Renew Sustain Energy Rev. 2018; 82: 1664–1674.
22. Alade IO, Oyehan TA, Popoola IK, Olatunji SO, Bagudu A. Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression. Adv Powder Technol. 2018; 29(1): 157–167.
23. Hemmat Esfe M, Saedodin S, Biglari M, Rostamian H. An experimental study on thermophysical properties and heat transfer characteristics of low volume concentrations of Ag-water nanofluid. Int Commun Heat Mass Transf. 2016; 74: 91–97.
24. Nikkam N, Toprak MS. Fabrication and thermo-physical characterization of silver nanofluids: An experimental investigation on the effect of base liquid. Int Commun Heat Mass Transf. 2018; 91: 196–200.