Author Contributions
Conceptualization, R.Z. and M.F.A.; Methodology, M.F.A. and A.S.N.; Software, N.H.S., M.F.A. and A.S.N.; Validation, M.F.A. and A.S.N.; Formal Analysis, M.F.A.; Investigation, R.Z., Z.H., A.S.N. and N.H.S.; Data Curation, M.F.A., A.S.N. and N.H.S.; Writing-Original Draft Preparation, M.F.A.; Writing-Review & Editing, R.Z., H.M. and M.F.A.; Visualization, S.A. and W.A.W.G.; Supervision, R.Z., S.A., Z.H., and W.A.W.G.; Project Administration, R.Z.; Funding Acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.
Figure 1.
Schematic of twin impingement jets tests setup.
Figure 1.
Schematic of twin impingement jets tests setup.
Figure 2.
A typical procedure of the method in the preparation of Nano solution and experimental procedures.
Figure 2.
A typical procedure of the method in the preparation of Nano solution and experimental procedures.
Figure 3.
Twin jet impingement mechanism and heat flux microsensor on the aluminum impingement surface.
Figure 3.
Twin jet impingement mechanism and heat flux microsensor on the aluminum impingement surface.
Figure 4.
Thermographic distribution of aluminum flat plate model (1) to (9) at Re = 17,000.
Figure 4.
Thermographic distribution of aluminum flat plate model (1) to (9) at Re = 17,000.
Figure 5.
X-ray diffraction (XRD) analysis.
Figure 5.
X-ray diffraction (XRD) analysis.
Figure 6.
FESEM micrographs of the surface coated aluminum plate (a) uncoated; coated with TiO2 at (b) 0.1 M, (c) 0.5 M, and (d) 1.0 M.
Figure 6.
FESEM micrographs of the surface coated aluminum plate (a) uncoated; coated with TiO2 at (b) 0.1 M, (c) 0.5 M, and (d) 1.0 M.
Figure 7.
Model adequacy checking by examining (a) the normal probability of residuals, (b) the predicted vs. actual, (c) the independence residuals on run, and (d) the independence residuals on predicted response.
Figure 7.
Model adequacy checking by examining (a) the normal probability of residuals, (b) the predicted vs. actual, (c) the independence residuals on run, and (d) the independence residuals on predicted response.
Figure 8.
Nusselt number contours and 3D surface formed by the effect of Reynolds number and frequency at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1% M; (b) 0.5% M; (c) 1% M.
Figure 8.
Nusselt number contours and 3D surface formed by the effect of Reynolds number and frequency at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1% M; (b) 0.5% M; (c) 1% M.
Figure 9.
Nusselt number contours and 3D surface formed by the effect of Reynolds number nozzle-plate distance at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 9.
Nusselt number contours and 3D surface formed by the effect of Reynolds number nozzle-plate distance at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 10.
Nusselt number contours and 3D surface formed by the effect of Reynolds number and phase angle at the optimum conditions for the optimization target of maximum Nusselt number and Nano concentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 10.
Nusselt number contours and 3D surface formed by the effect of Reynolds number and phase angle at the optimum conditions for the optimization target of maximum Nusselt number and Nano concentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 11.
Nusselt contours and 3D surface formed by the impact of the nozzle-nozzle spacing and nozzle-plate distance at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 11.
Nusselt contours and 3D surface formed by the impact of the nozzle-nozzle spacing and nozzle-plate distance at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 12.
Nusselt number contours and 3D surface formed by the effect of the nozzle-nozzle spacing and phase angle at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 12.
Nusselt number contours and 3D surface formed by the effect of the nozzle-nozzle spacing and phase angle at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 13.
Nusselt number contours and 3D surface formed by the effect of the nozzle-plate distance and Nano concentration at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 13.
Nusselt number contours and 3D surface formed by the effect of the nozzle-plate distance and Nano concentration at the optimum conditions for the optimization target of maximum Nusselt number and nanoconcentration equal to (a) 0.1%; (b) 0.5%; (c) 1%.
Figure 14.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 1 cm.
Figure 14.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 1 cm.
Figure 15.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 2 cm.
Figure 15.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 2 cm.
Figure 16.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 3 cm.
Figure 16.
Interaction of Reynolds number and nozzle-plate distance levels at optimum levels of other factors at nozzle-nozzle spacing = 3 cm.
Figure 17.
Interaction of nozzle-plate distance and phase angle levels at optimum levels of other factors at nozzle-nozzle spacing = 1 cm.
Figure 17.
Interaction of nozzle-plate distance and phase angle levels at optimum levels of other factors at nozzle-nozzle spacing = 1 cm.
Figure 18.
Interaction of nozzle-nozzle spacing and nozzle-plate distance levels at optimum levels of other factors.
Figure 18.
Interaction of nozzle-nozzle spacing and nozzle-plate distance levels at optimum levels of other factors.
Figure 19.
Interaction of nozzle-plate distance and nozzle-nozzle spacing levels at optimum levels of other factors.
Figure 19.
Interaction of nozzle-plate distance and nozzle-nozzle spacing levels at optimum levels of other factors.
Figure 20.
Interaction of Nusselt number and Reynolds number.
Figure 20.
Interaction of Nusselt number and Reynolds number.
Figure 21.
Interaction of the thermal conductivity with Nanofluid concentration.
Figure 21.
Interaction of the thermal conductivity with Nanofluid concentration.
Figure 22.
Interaction of Nusselt number values with different concentrations of Nanofluid.
Figure 22.
Interaction of Nusselt number values with different concentrations of Nanofluid.
Figure 23.
Interaction of nozzle-plate distance levels with Nusselt number values.
Figure 23.
Interaction of nozzle-plate distance levels with Nusselt number values.
Table 1.
The experimental ranges and levels of the considered parameters.
Table 1.
The experimental ranges and levels of the considered parameters.
Parameter | Ranges and Levels | Units |
---|
−1 | 0 | +1 |
---|
Reynolds number (Re) | 9000 | 13,000 | 17,000 | - |
Nozzle to nozzle spacing (S) | 1 | 2 | 3 | cm |
Nozzle to plate distance (H) | 1 | 6 | 11 | cm |
Nano particle concentration (Ø) | 0.1% | 0.5% | 1% | M |
Table 2.
The DOE plan of twin jets impingement heat transfer.
Table 2.
The DOE plan of twin jets impingement heat transfer.
Standard Order | Run Order | Re | S | H | Ø | Nu |
---|
14 | 1 | 17,000 | 3 | 6 | 0.5 | 88.19 |
5 | 2 | 17,000 | 1 | 11 | 1 | 81.10 |
9 | 3 | 13,000 | 3 | 11 | 1 | 65.03 |
15 | 4 | 13,000 | 2 | 1 | 0.5 | 60.86 |
23 | 5 | 9000 | 1 | 1 | 0.1 | 66.07 |
8 | 6 | 9000 | 1 | 1 | 0.5 | 73.08 |
16 | 7 | 17,000 | 1 | 1 | 1 | 90.80 |
21 | 8 | 9000 | 2 | 6 | 0.5 | 60.31 |
2 | 9 | 9000 | 1 | 1 | 1 | 69.98 |
10 | 10 | 17,000 | 2 | 1 | 1 | 70.10 |
20 | 11 | 13,000 | 1 | 6 | 0.5 | 75.01 |
25 | 12 | 17,000 | 2 | 11 | 0.5 | 81.90 |
1 | 13 | 13,000 | 1 | 6 | 0.1 | 71.3 |
18 | 14 | 13,000 | 3 | 6 | 0.1 | 69.6 |
12 | 15 | 17,000 | 3 | 1 | 0.1 | 66.24 |
7 | 16 | 17,000 | 2 | 11 | 0.1 | 76.3 |
13 | 17 | 9000 | 3 | 1 | 1 | 50.24 |
4 | 18 | 9000 | 3 | 11 | 0.1 | 53.07 |
3 | 19 | 9000 | 1 | 11 | 0.5 | 52.87 |
22 | 20 | 13,000 | 3 | 11 | 0.5 | 67.84 |
17 | 21 | 9000 | 3 | 1 | 0.1 | 48.02 |
19 | 22 | 13,000 | 2 | 11 | 1 | 69.91 |
11 | 23 | 9000 | 2 | 6 | 1 | 54.18 |
24 | 24 | 17,000 | 1 | 1 | 0.5 | 94.7 |
6 | 25 | 17,000 | 1 | 1 | 0.1 | 85.2 |
Table 3.
ANOVA for Response Surface Quadratic Model (Analysis of variance table).
Table 3.
ANOVA for Response Surface Quadratic Model (Analysis of variance table).
Source | Sum of Squares | df | Mean Square | F Value | p-Value Prob > F | |
---|
Model | 3717.54 | 14 | 265.54 | 16.02 | <0.0001 | significant |
A-Re | 2247.14 | 1 | 2247.14 | 135.58 | <0.0001 | |
B-S | 204.52 | 1 | 204.52 | 12.34 | 0.0056 | |
C-H | 0.33 | 1 | 0.33 | 0.020 | 0.8914 | |
D-Ø | 27.26 | 1 | 27.26 | 1.64 | 0.2286 | |
AB | 1.00 | 1 | 1.00 | 0.060 | 0.8111 | |
AC | 116.61 | 1 | 116.61 | 7.04 | 0.0242 | |
AD | 1.89 | 1 | 1.89 | 0.11 | 0.7423 | |
BC | 440.51 | 1 | 440.51 | 26.58 | 0.0004 | |
BD | 17.17 | 1 | 17.17 | 1.04 | 0.3328 | |
CD | 6.57 | 1 | 6.57 | 0.40 | 0.5431 | |
A^2 | 32.48 | 1 | 32.48 | 1.96 | 0.1918 | |
B^2 | 96.64 | 1 | 96.64 | 5.83 | 0.0364 | |
C^2 | 123.59 | 1 | 123.59 | 7.46 | 0.0212 | |
Table 4.
Optimised factors at maximum response.
Table 4.
Optimised factors at maximum response.
ϕ | Re | S | H | Nu |
---|
0.5 | 17,000 | 1 | 1 | 94.7 |
1 | 16,999 | 1 | 1 | 89.28 |
0.1 | 17,000 | 1 | 1 | 86.119 |
Table 5.
Improvement ratio of TiO2 nanocoating surface plate.
Table 5.
Improvement ratio of TiO2 nanocoating surface plate.
Model. | Concentration of TiO2 by Molarity | Nu Values (with Coating) | Nu Values (without Coating) | Improvement Ratio (%) |
---|
M1 | 0.1% | 86.2 | 84.7 | 10.6% |
0.5% | 93.7 |
1% | 90.8 |
M2 | 0.1% | 80.14 | 77.4 | 9.7% |
0.5% | 84.92 |
21% | 83.01 |
M3 | 0.1% | 78.63 | 75.94 | 9.2% |
0.5% | 83 |
1% | 82.1 |
Table 6.
Comparison of convective heat transfer in Nano particle applications.
Table 6.
Comparison of convective heat transfer in Nano particle applications.
Researchers and Year | Research Technique | Geometry | Base Fluid | Main Outcomes |
---|
Reddy and Rao (2014) [35] | Experimental | Tube | EG/W (4:6) | The maximum improvement of the h and friction factor was achieved by 10.73% and 8.73% at 0.02 vol.% loading. |
Megatif et al. (2015) [36] | Experimental | Shell and tube heat exchange | Water | At 0.2 wt.% loading and 388℃, the maximum rise in heat transfer coefficient was 38%. |
Ali et al. (2015) [37] | Experimental | Vertical helical coiled | Water | Heat transfer coefficient improved with increased loading of nanoparticles and Re. |
Abbassi et al. (2014) [38] | Experimental | Vertical annulus | Water | The coefficient of heat transfer of nanofluids was higher than water and improved with increased particulate load. |
Bhanvase et al. (2014) [39] | Experimental | Tube | EG/W (4:6) | The maximum improvement of (h) was 105% at 0.5 vol.% particle loading |
Perarasu et al. (2012) [40] | Experimental | Coiled agitated vessel | Water | The maximum improvement of h was 17.59% at 0.3 vol.% particle loading. |
Barzegarian et al. (2016) [41] | Experimental | Brazed plate heat exchanger | Water | The maximum improvement of local h at 0.3%, 0.8% and 1.5% particle weight loading were 6.6%, 13.5% and 23.7% respectively. |
Arulprakasajothi et al. (2015) [42] | Experimental | Horizontal tube | Water | Nu increased by 10%, 11.2%, 12% and 16.3% at particle volume loading of 0.1%, 0.25%, 0.5% and 0.75% respectively. |
Chen and Cheng. (2016) [43] | Numerical | Heat exchanger | Water | Nu improved with the rise in particle loading and flow rate but reduced with increase of particle aggregations owing to the high viscosity. |
Abdolbaq et al. (2015) [44] | Numerical | Straight channel | Water | Nu and friction factor improved with particle loading. |
Vakili et al. (2013) [45] | Experimental | Vertical pipe | EG/W (6:4), water | (a) The improvement of h improved with an increased in particle loading. (b) The influence of nanoparticles on h was greater in higher Re or heat flux. (c) The enhancement of h was greater for EG/W (6:4) base fluid than pure water. |
Ebrahimnia-Bajestan et al. (2016) [46] | Experimental and numerical | Horizontal tube | Water | (a) A maximum enhancement of h was 21%. (b) h increased as the increase in particle loading and the decrease in particle size. |
Chen et al. (2008) [47] | Experimental | Vertical tube | Water | The enhancements of heat transfer coefficient at 0.5, 1.0 and 2.5 wt.% loading was respectively 11.8%, 23.5% and 24.9%. |
Goutam and Manosh. (2014) [48] | Numerical | Horizontal pipe | Water | (a) Size of particle plays greater roles in the enhancement of convection heat transfer than that in thermal conductivity. (b) Particle migrations on the boundary layer can improve h of TiO2 nanofluids. |
Salman et al. (2015) [49] | Experimental | Tube inserted with conic cut twist tape | Water | Heat transfer coefficient increased with rise in nanoparticle loading and Re. |
Singh et al. (2016) [50] | Experimental | Steel plate | Water | CHF increased with the increase of loading of TiO2 nanoparticle in nanofluid (impingement jet). |
Table 7.
Comparison of nanocoating application enhancements.
Table 7.
Comparison of nanocoating application enhancements.
Authors (Year) [Reference] | Research Method | Geometry | Nanoparticle | Main Findings |
---|
Senthilkumar et al. (2013) [12] | Experimental | Brass Surface | CNT | The enhancement ratio was around 12% |
Mayilsamy (2014) [13] | Experimental | Finns | CNT | Finns effectiveness increased by 21.8%. Heat transfer rate increased by 7%. Finns efficiency increased 6.2% |
Ray (2017) [14] | Experimental | Cooper surface | TiO2 | Surface Coated by TiO2 and R134 in 2 thickness 100 and 200 nm. Coated surface is better than uncoated surface due to augment the roughness |
Li et al. (2018) [51] | Experimental | Hot surface | Cuo + Al2O3 | Significantly increase the heat transfer rate |
Mitrovic and Hartman (2004) [52] | Experimental | Finns Electrocoating | Micro-fin structured R-141 | Significantly Improve the heat transfer rate by using electrocoating for Finns |