Masters Thesis Work

In this work, one of the major objectives is to create a novel ANN model using

the simulation data gathered with numerical simulation using FEM for prediction of

different performance parameters in present framework. The objectives of this

investigation can be summarized as follows –

• Implementing machine learning techniques along with numerical simulation to

investigate mixed convection phenomena in lid-driven trapezoidal enclosure

filled with different nanofluids and hybrid nanofluids with double rotating

cylinder inside.

• Using simulation data to develop data-based Artificial Neural Network (ANN)

Architecture to predict results for this problem and a comparative study between

FEM and ANN technique. 

• Finding the required parameter values of volume fractions, dimensionless

rotating speed (|Ω|), Richardson Number (Ri), Reynolds Number (Re),

Hartmann Number (Ha), Lewis Number (Le) as well as the inclination angle (λ)

of the magnetic field with magnetic flux density Bo and buoyancy ratio (BR) to

get maximum heat and mass transfer.

• Observing dimensionless temperature (Θ), velocity (U, V) and mass

concentration (C) across the computational domain.

• Developing various novel Artificial Neural Network (ANN) architectures and

identifying the best architecture among those for the present investigation to

predict and compare results obtained by FEM and ANN.