Coolant, Heat Exchanger
Effectiveness of magnetic nanofluid as coolant was studied in double-pipe temperature exchanger.•
Effectation of applying quadrupole magnetic area with various magnitudes ended up being reviewed.•
Magnetic force helps make the concentration distribution much more uniform in pipe side.•
Applying magnetic industry enhances both pressure drop and heat transfer.•
Optimization ended up being carried out to achieve optimum temperature transfer and minimum force fall.
The present research attempts to investigate the overall performance of liquid based Mn–Zn ferrite magnetized nanofluid in a counter-flow double-pipe temperature exchanger under quadrupole magnetic field using the two-phase Euler–Lagrange strategy. The nanofluid moves inside tube part as coolant, while the hot-water moves inside annulus side. The consequences of different variables including focus, measurements of the particles, magnitude associated with the magnetic area and Reynolds quantity tend to be examined. Circulation associated with the particles is non-uniform within cross-section associated with tube such that the concentration is greater at central elements of the tube. Application regarding the magnetic area makes the distribution of particles much more uniform and this uniformity increases by increasing the distance from pipe inlet. Increasing each one of the variables of concentration, particle size and magnitude for the magnetized area will trigger a larger pressure drop also greater temperature transfer improvement. At greater Reynolds figures, the consequence of magnetized power is reduced. Optimization was done utilizing hereditary algorithm along with compromise development technique to attain the utmost general temperature transfer coefficient along with the minimum pressure fall. For this purpose, the models of unbiased functions of general temperature transfer coefficient and stress fall regarding the nanofluid were very first removed with regards to the effective variables using neural network. The neural system model predicts the result factors with a very good reliability. The suitable values had been obtained thinking about different circumstances for relative need for the aim features.
- Magnetic nanofluid;
- Heat exchanger;
- Neural system;
- Two-phase approach