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The flow phenomena become more significant and varied when an extra heat source, a chemical reaction, and thermal and solutal buoyancy forces exist. Transport property optimization results from the current demand in several sectors dependent on the solutal reactant and the heat source given. Partial differential equations are transformed into ordinary equations using appropriate similarity transformations. Python was used as an efficient tool to solve equations in this research. According to the available facilities and libraries, Python can efficiently solve various physics and engineering problems. The homotopy perturbation method (HPM) and Akbari-Ganji Method (AGM) have been used to solve differential equations. The results calculated in Python indicate the high accuracy of calculations in Python. The effect of different parameters on the profile of velocity, temperature, and concentration has been investigated graphically. Also, the friction coefficient (), Nusselt number (), and Sherwood number () have also been investigated in this study. The results show that the velocity profile decreases with increased magnetic field intensity. Also, the porosity parameter has an inverse effect on the velocity and temperature profile, so the growth of the porosity parameter reduces the fluid velocity while improving the temperature profile.
Mahboobtosi et al. (Sun,) studied this question.
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