CFD simulation for evaluation of optimum heat transfer rate in a heat exchanger of an internal combustion engine

. Heat exchanger is an essential component of an engine cooling system. Radiators are compact heat exchangers used to transfer the heat absorbed from engine to the cooling media. The jacket cooling water gets cooled and re-circulated into system after exchanging the heat with cooling water in a heat exchanger. Conventional ﬂ uids like water, oil, ethylene glycol, etc. possess less heat transfer performance; therefore, it is essential to have a compact and effective heat transfer system to obtain the required heat transfer. A reduction in energy consumption is possible by improving the performance of heat exchanging systems and incorporating various heat transfer enhancement techniques. In this paper, the heat transfer rate using nano-sized ferro ﬂ uid with and without magnetization is analysed using CFD simulation and compared with the experimental values obtained from a heat exchanger using water as base ﬂ uid. The heat transfer rate is measured using different combinations by varying the percentage of nano particles and by introduction of different magnetic intensity (gauss) on to the ferro ﬂ uid. The optimum heat transfer rate and ef ﬁ ciency of heat exchanger is calculated with the different combinations and the values are compared with the values of CFD simulation. CFD simulation was undertaken for water alone as cooling media and for water with ferro particle addition from 2% to 5%. The difference in temperature observed to be similar with experimental values. The deviation is within the acceptable limit and therefore the experimental ﬁ ndings are validated. The experiment was conducted on a parallel ﬂ ow heat exchanger with water alone as cooling media, water with varying percentage of ferro ﬂ uid and water with varying magnetic intensity on ferro ﬂ uid. Percentage of ferro particles added up to where the optimum temperature difference could be obtained and the magnetic intensity also varied up to the optimum value.


Introduction
By introduction of magnetic field into the ferrofluid, they transform into pseudo solids, which takes away more heat than liquids. The spike formed by magnetizing the ferrofluid increases the surface area and carries more heat. The temperature difference across the heat exchanger was increased by 11°C when 4% ferro particles were added to the cooling water. When the ferro particles were magnetized, the temperature difference increased to 15°C. The optimum was at 4% addition of ferro particles with a magnetic intensity of 300 gauss [3]. Ferrofluids posse more heat carrying capacity than conventional fluids used in heat exchangers. Heat carrying property of ferrofluid increases by magnetization up to a particular magnetic intensity. Beyond that bifurcation takes place and obstruction in flow is observed [5]. Nanofluid refers to fluids by suspending nano particles in the base fluid [1,2,4].
Heat carrying capacity of liquid is less compared to solids. Ferrofluids have superior properties which includes variable viscosity, improved thermal conductivity and formation of spike like structure in the presence of an external magnetic field [6,8]. Addition of ferro particles or nano particles into cooling water of IC Engines increases the heat transfer which results in the size reduction of heat exchanger [9]. This will facilitate reduction of size of overall weight of the engine, which will reduce the fuel consumption. The world is facing scarcity of drinking water, marine engines and huge diesel power plants use huge quantity of water for cooling the jackets. By using nano-sized ferro particles along with cooling water, the quantity of water carried for cooling the jackets can be reduced thereby increasing the heat transfer, reducing the fuel consumption and usage of fresh water.
Computational Fluid Dynamics (CFD) is the art of replacing the PDEs used in Fluid flow and heat transfer systems, by a set of algebraic equations which can be solved using digital computers. Computational fluid dynamics (CFD) is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by solving the mathematical equations which govern these processes using a numerical process. The heat transfer rate using nano-sized ferrofluid with and without magnetization is analysed using CFD simulation and compared with the experimental values obtained from a heat exchanger using water as base fluid. The heat transfer rate is measured using different combinations by varying the percentage of nano particles and by introduction of different magnetic intensity (gauss) on to the ferrofluid. The optimum heat transfer rate and efficiency of heat exchanger is calculated with the different combinations and the values are compared with the values of CFD simulation. CFD simulation consisted of pre processing stage, processing stage and post processing stage. Pre processing stage consists of preparing the geometry, meshing the geometry, specifying boundaries and continuum types. Processing stage follows setting the numerical method, specifying the boundary conditions, initialization and solving and post processing stage analyses the results.

Discretization
Domain is discretized into a finite set of control volumes or cells. The discredited domain is called the "grid" or the "mesh."

Computing the solution
-The discretized conservation equations are solved iteratively. A number of iterations are usually required to reach a converged solution. -Convergence is reached when: • changes in solution variables from one iteration to the next are negligible, • residuals provide a mechanism to help monitor this trend, • overall property conservation is achieved.
-The accuracy of a converged solution is dependent upon: • appropriateness and accuracy of the physical models, • grid resolution and independence, • problem setup.

CFD analysis
Simulation consists of the following steps: Pre-processing -Preparing the geometry -Meshing the geometry -Specifying boundaries and continuum types Processing -Setting the numerical method -Specifying the boundary conditions -Initialization -Solving Post processing -Analyzing the results

Computational domain
Below shown are both front and side views of the computational domain used for the CFD analysis. Here all dimensions are in cm.

Pre processing 3.1.1.1 Preparing the geometry
The geometry of the Radiator is generated in Ansys Design Modeler. Figure 1 shows the front view of geometry created and Figure 2 shows isometric zoomed view of geometry created.<H4>Meshing the geometryTetrahedral mesh was selected for meshing the domain, because it produces quick mesh and easily solves this mesh type [7]. The mesh details are: Mesh Interval size = 4 Â 10 À4 m No of nodes = 1 188 746 Type of cells = Tetrahedral Figure 3 shows the meshed model and Figure 4 shows the zoomed isometric meshed model.

Boundary condition and continuum types
The boundary name and type provided to the radiator is shown in Table 1.  Hot water outlet -P out = p atm = Zero (gauge) -dT/ds = 0.          R. Kocheril and J. Elias: Int. J. Simul. Multidisci. Des. Optim. 11, 6 (2020) 7 Fig. 10. Temperature contours for water with 2% ferro particle (hot water inlet temperature 343K (70°C). Fig. 9. Temperature contours for water with 2% ferro particle (hot water inlet temperature 333 K (60°C). From CFD analysis, at hot water inlet temperature 333 K (60°C) the resultant hot water outlet temperature is 320.41 K (using area weighted average at the fluid outlet). Figure 5 shows temperature contour on the surface of fin, Figure 6 shows temperature contour (planar sectional view) and Figure 7 shows temperature contour (planar sectional view (zoomed view)). Figure 8 shows the temperature contour (planar sectional view (zoomed view)) with hot water inlet temperature 343 K (70°C) resulted in hot water outlet temperature 330.79 K (using area weighted average at the fluid outlet) using CFD.

Processing
Temperature contour with 2% ferro particle addition at hot water inlet temperature 333 K (60°C) shows hot water outlet temperature 318.47 K (using area weighted average at the fluid outlet) using CFD simulation shown in Figure 9. The total heat transfer rate is 479.67 W as per CFD analysis.
Temperature contour with 2% ferro particle addition at hot water inlet temperature 343 K (70°C) shows hot water outlet temperature 332.34 K (using area weighted average at the fluid outlet) using CFD simulation is shown in Figure 10. The total heat transfer rate is 499.72 W as per CFD analysis.
Temperature contour with 2% ferro particle addition at hot water inlet temperature 353 K (80°C) shows hot water outlet temperature 339.87 K (using area weighted average at the fluid outlet) using CFD simulation is shown in Figure 10. The total heat transfer rate is 515.93 W as per CFD analysis.

Comparison of CFD and experimental results
-Water alone as cooling fluid (experimental result).
-Water alone as cooling fluid (comparison with CFD simulation).
-Water with addition of ferro particles (experimental result).

Conclusion
Heat transfer rate using nano-sized ferrofluid with and without magnetization is analysed using CFD simulation and compared with the experimental values obtained from a heat exchanger using water as base fluid. The heat transfer rate is measured using different combinations by varying the percentage of nano particles and by introduction of different magnetic intensity (gauss) on to the ferrofluid. The optimum heat transfer rate and efficiency of heat exchanger is calculated with the different combinations and the values are compared with the values of CFD simulation CFD simulation was undertaken for water alone as cooling media and for water with ferro particle addition from 2% to 5%. The result of the analysis shows that the values are comparable with experimental values. The temperature difference across the heat exchanger was 6°C when water alone was used as cooling media. At 4% addition of ferro particles into the cooling water the temperature difference across the heat exchanger increased to 11°C. When the ferro particles were magnetized, the temperature difference (DT) increased to 15°C. The result of CFD simulation using FLUENT software shows that the values obtained from experiment is similar.