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A microfluidic platform permits multiple drug testing of uniformly-sized microscale “cuboids” of live tissue with well-preserved microenvironments.

Desirable effective viscosity behaviour is an essential transport property required for the effective utilisation of nanofluids in industrial systems as well as other applications. Viscosity influences significantly the pumping power and heat transfer effectiveness in a thermal system since Reynolds and Prandtl numbers are functions of viscosity. In this study, the optimum energy required for the preparation of MgO-ethylene glycol (MgO-EG) nanofluids was determined by varying the ultrasonication energy input into the preparation process. The uniformly dispersed nanofluids were characterised and the viscosity measurements were carried out as a function of temperature (20 to Therefore, new correlation is proposed using the method of dimensional analysis and considering essential factors, including nanoparticle size, volume fraction temperature, capping layer thickness, viscosity of the base fluid, the density of base fluid and the density of nanofluid as input parameters.Furthermore, genetic algorithm-polynomial neural network (GA-PNN) and fuzzy C-means clusteringbased adaptive neuro-fuzzy inference system (FCM-ANFIS) was used to model the effective viscosity of the MgO-EG nanofluids considering the parameters mentioned above. The results of all the modelling techniques showed good agreement with the experimental data.

By using an FCM-based neuro-fuzzy inference

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