 Nano Express
 Open Access
 Published:
Constructal blade shape in nanofluids
Nanoscale Research Letters volume 6, Article number: 240 (2011)
Abstract
Blade configuration of nanofluids has been proven to perform much better than dispersed configuration for some heat conduction systems. The analytical analysis and numerical calculation are made for the cylindershaped and regularrectangularprismshaped building blocks of the bladeconfigured heat conduction systems (using nanofluids as the heat conduction media) to find the optimal crosssectional shape for the nanoparticle blade under the same composing materials, composition ratio, volumetric heat generation rate, and total building block volume. The regulartriangularprismshaped blade has been proven to perform better than all the other three kinds of blades, namely, the regularrectangularprismshaped blade, the regularhexagonalprismshaped blade, and the cylindershaped blade. Thus, the regulartriangularprismshaped blade is selected as the optimally shaped blade for the two kinds of building blocks that are considered in this study. It is also proven that the constructal cylinderregulartriangularprism building block performs better than the constructal regularrectangularprismregulartriangularprism building block.
Introduction
Nanofluids are mixtures of nanoparticles and base fluids, which have different thermal conductivities [1–5]. They were identified and proposed as a result of people's persistent pursuit for more and more efficient heattransfer media. It should be noted that conventional heat transfer fluids have normally very low thermal conductivity, thus destroying much exergy during heat transport. At present, a great amount of attention is paid to studies on nanofluids, with the aim of addressing many unsolved issues [6–10].
Constructal theory is a novel thought for nature and society [11–17]. It tries to explain phenomena based on optimization, or natural selection in biological terms. One of its viewpoints is that two flow mechanisms are better than one [18]. This is what one sees naturally in river basins, lung structure, and the percolation threshold effect [19]. Our previous studies have proved that the blade configuration of nanofluids is much better than the dispersed configuration for the two kinds of diskshaped heat conduction systems with different boundary conditions [20, 21]. It is believed that the continuous nanoparticle blades with higher thermal conductivity serve as the second conduction mechanism, and its optimized cooperation with the base fluid of low thermal conductivity leads to the much better performance. In this study, the blade configuration of nanofluids is considered in detail by studying the influence of the shapes of highconductivity blades in two kinds of building blocks of the total bladeconfigured heat conduction systems. This study is inspired by the need for the optimization of the cross section of duct for minimum flow resistance [11]. By treating heat as a flow medium flowing in blades, one can also find the optimal shape for the blades, which offers minimum thermal resistance.
Optimal blade shape for two kinds of building blocks of bladeconfigured heat conduction systems
The cylindershaped and regularrectangularprismshaped building blocks are studied in this article. The nanoparticle blade has four different shapes: regular triangular prism, regular rectangular prism, regular hexagonal prism, and cylinder. For conciseness, the word "regular" is omitted hereafter. A format of "buildingblockshapebladeshape" is used to indicate the eight kinds of building blocks. Figure 1 shows the two kinds of building blocks with cylinder blades. For all the eight kinds of building blocks, uniform heat generation rate occurs in the base fluid region. All the external surfaces, except the crosssectional plane x = 0 of the blade, are adiabatic. The crosssectional plane x = 0 of the blade serves as the heat sink with constant temperature. The composition of these twomaterial building blocks is fixed by volume fraction
It is assumed that ϕ ≪ 1, and the thermal conductivity ratio of nanoparticle material and base fluid material is fixed and large. The thermal contact resistance is not considered.
In order to study the influence of the blade shape, the materials of the base fluid and nanoparticle, volumetric heat generation rate, and volumes of the eight kinds of building blocks are also fixed, besides the volume fraction and thermal conductivity ratio; however, the slenderness is free to vary to achieve the constructal system (building block) overall temperature difference. Here, the slenderness refers to the ratio of the radius to length for the cylinder building block, and the ratio of the circumscribing cylinder radius to length for the rectangular prism building block. For the simplest cylindercylinder building block, analytical analysis can be made; the system overall temperature difference, the constructal system overall temperature difference, and the constructal slenderness can be obtained analytically. Based on a slenderness range predicted by the analytic result, the numerical calculation is then conducted for all the eight kinds of systems to obtain, as accurately as possible, the results for comparison among different blade shapes and different building block shapes.
Analytical analysis for cylindercylinder building block
Owing to the much higher thermal conductivity of nanoparticle material, heat conduction inside this kind of building block can be considered to consist of two onedimensional routesradial conduction inside crosssectional planes of the base fluid region and axial conduction along the blade.
For the radial heat conduction inside base fluid, the governing equation and boundary conditions are
and
respectively, where, k_{f} is the thermal conductivity of the base fluid; q''' is the volumetric heat generation rate; T_{c} is the temperature at the interface of the blade and base fluid at x = L_{0} (L_{0} is the length of the building block); r_{0} is the radius of the inner blade; and R_{0} is the outer radius of the building block. By solving Equations (2) and (3), the radial temperature distribution of the crosssectional plane x = L_{0} of the base fluid region is obtained:
For the axial conduction inside the blade, the governing equation and boundary conditions, respectively, are
and
where k_{p} is the thermal conductivity of the nanoparticle material, and T_{0} is the heatsink temperature at the crosssectional plane x = 0 of the blade. By solving Equations (5) and (6), the axial temperature distribution along the blade is obtained as
Thus, the overall temperature difference of the building block is
Nondimensionalizing this overall temperature difference with (constant), one has
Where is the nondimensional system (building block) overall temperature difference, and is the ratio of the thermal conductivities of the nanoparticle material and the base fluid material. By substituting Equation (1) in Equation (9), becomes
which indicates that the building block's overall temperature difference (or thermal resistance) depends on its slenderness under the same composing materials, composition ratio, volumetric heat generation rate, and total volume. Figure 2 typifies this dependence at ϕ = 0.05 and = 641.6667 (thermal conductivity ratio of copper and water). By minimizing this nondimensional system overall temperature difference with respect to , the nondimensional constructal system overall temperature difference can be obtained:
and
At , the bestperforming cylindercylinder building block can be obtained. If one specifies ϕ = 0.05 and = 641.6667, then the optimal slenderness will be 0.240618, and the nondimensional constructal system overall temperature difference will be 0.296778, which is the lowest point in Figure 2. Similarly, for the other kinds of building blocks considered here, there also exists such a bestperforming slenderness .
Numerical calculation for all the eight kinds of building blocks
The actual heat conduction in the cylindercylinder building block is of course not a simple combination of two onedimensional conductions. For the other kinds of building blocks, the flow of heat is even more complex. In order to have as accurately as possible results for the comparison, a finite volume computational fluid dynamics (CFD) code [22] is used for obtaining numerical results for all the eight kinds of building blocks.
The conservation of energy equations are
and
for the base fluid and the blade, respectively. Here,
For rectangularprismseries building blocks, R_{0} stands for the radius of the circumscribing cylinder, for which one has (where a is the side length of the rectangular cross section). Constant, , is introduced to use fewer grids to achieve accurate enough results. The "+1" in the nondimensional temperature expression is introduced to ensure that the computing process does not touch the limit of 0 K. At the interface between the k_{f} region and k_{p} blade, the continuity of heat flux requires that
where ñ is the nondimensional normal vector; . As all the external surfaces, except the plane x = 0 of the k_{p} blade, are adiabatic,
and for the plane x = 0 of the k_{p} blade, one has
Note that is exactly the nondimensional system overall temperature difference, , as shown in Equation (9), where is the maximal nondimensional temperature in the heat conduction building blocks.
It is specified that ϕ = 0.05 and = 641.6667 for the numerical calculation, the value used in the analytical analysis. The finite volume CFD code is chosen because of its efficiency and flexibility to generate a large number of results for various geometries which differ slightly from each other. Hexahedron grids are used to mesh the cylinderhexagonalprism and cylindercylinder building blocks, whereas all the other building blocks are meshed with wedge grids. Appropriate grid number is determined by doubling the interval number in , , and directions each time, until the change of temperature becomes less than 0.05% (the maximal temperature, , is used specifically for this criterion). Table 1 shows an example of how this grid independence is reached.
The nondimensional system overall temperature difference is shown in Figures 3 and 4 for the eight kinds of building blocks. The optimal slenderness and nondimensional constructal system overall temperature difference for the cylindercylinder building block are 0.25 and 0.289577, respectively. Comparing with the approximate analytical results, the differences are only 3.75% and 2.49%, which confirms the accuracy of the finite volume CFD code.
All the eight kinds of heat conduction building blocks show strong performance dependence on the slenderness of the building blocks. Under the specific values for ϕ and , the optimal slendernesses for the cylinderseries and rectangularprismseries building blocks are 0.25 and 0.3, respectively, within a resolution of 0.05. Below the optimal slenderness, the performance dependence on increases in the order from cylindercylinder building block to cylinderhexagonalprism building block, cylinderrectangularprism building block, and then cylindertriangularprism building block. Above the optimal slenderness, this trend changes its direction. A similar situation happens for the rectangularprismseries building blocks. When the value is small enough ( for both the cylinderseries and rectangularprismseries building blocks), the blade shape begins to have very weak effect on the system's performance, due to the diminishing role played by the building block's crosssectional area when compared to its length. It can be seen from both Figures 3 and 4 that the four nondimensional system overall temperature difference curves almost collapse into one curve. For the cylinderseries building blocks, the collapsing curves approach the analytical result of the cylindercylinder building block as expected since the analytical result becomes more and more accurate as the value decreases. The analytical result shown in Figure 3 has the same ϕ and values as those for the numerical calculation (the same curve as Figure 2). Thus, the accuracy of the finite volume CFD code is verified again. It should be noted that both the cylinderseries and rectangularprismseries building blocks have fixed optimal slenderness. Therefore, the triangularprismshaped blade always performs the best among the four kinds of blades considered. Besides, the performance difference between successively shaped blades decreases from triangularprism blade to cylinder blade, which is consistent with their surface area changing under the same volume, as shown in Table 2. Here, the surface area of the blade is exactly the interfacial area between the base fluid and the blade. Thus, it is very likely that the performance difference can be attributed to the interfacial area difference: larger interfacial area will facilitate more lowtemperature surfaces for the heat generation region, thereby lowering the maximal temperature in that region.
Therefore, under the same composing materials ( = 641.6667), composition ratio (ϕ = 0.05), volumetric heat generation rate and total volume, the cylindertriangularprism and rectangularprismtriangularprism building blocks with slenderness values of 0.25 and 0.3, respectively, should be used for achieving the lowest system overall temperature difference (or, system thermal resistance) in practical applications. Both the pursuits of energy and material savings make this aim very significant. Furthermore, if one can also set the outer shape of the heat conduction building block free (often constrained by efficient packing and manufacturing), a comparison can be made between the constructal (both the blade shape and slenderness having been optimized) cylindertriangularprism building block and constructal rectangularprismtriangularprism building block. Since the total volumes for the cylinderseries and rectangularprismseries building blocks are and , respectively, to ensure that the comparison is based on the same total buildingblock volume, the nondimensional constructal system overall temperature difference of the cylindertriangularprism building block is divided by π^{2/3}
and the nondimensional constructal system overall temperature difference of the rectangularprismtriangularprism building block is divided by 2^{2/3}
Thus, the constructal cylindershaped heat conduction building block performs better than the rectangularprismshaped building block.
Conclusions
Inspired by the duct cross section optimization for minimum flow resistance, the shape of the nanoparticle blade is optimized for the cylindershaped and rectangularprismshaped building blocks of the bladeconfigured heat conduction systems (blade configuration of nanofluids) based on the same composing materials, composition ratio, volumetric heat generation rate, and total building block volume. The four kinds of blade shapes are triangular prism, rectangular prism, hexagonal prism, and cylinder. For the cylindercylinder building block, analytical analysis can be conducted. Explicit expressions for the system overall temperature difference, constructal system overall temperature difference, and constructal slenderness can be obtained. Then, based on the slenderness range predicted by the analytical result, numerical calculations are performed for the eight kinds of building blocks to obtain as accurately as possible results for comparison. One specifies that ϕ = 0.05 and = 641.6667 for the numerical calculation.
The performances of the eight kinds of building blocks depend strongly on the buildingblock slenderness. The constructal slendernesses leading to minimum system overall temperature differences (system thermal resistances) are 0.25 and 0.3, respectively, for the cylinderseries and rectangularprismseries building blocks. For both the cylinderseries and rectangularprismseries building blocks, the triangularprismshaped blade performs the best among all the four kinds of blades considered. This is explained by the size of interfacial area sustained by the four kinds of blades with a fixed volume. Also, the constructal cylindertriangularprism building block is proved to perform better than the constructal rectangularprismtriangularprism building block at the same composing materials, composition ratio, volumetric heat generation rate and total buildingblock volume.
Abbreviations
 CFD:

computational fluid dynamics.
References
 1.
Choi SUS: Nanofluids: from vision to reality through research. J Heat Transfer 2009, 131: 033106. 10.1115/1.3056479
 2.
Wang LQ, Wei XH: Nanofluids: synthesis, heat conduction, and extension. J Heat Transfer 2009, 131: 033102. 10.1115/1.3056597
 3.
Beck MP, Yuan YH, Warrier P, Teja AS: The thermal conductivity of aqueous nanofluids containing ceria nanoparticles. J Appl Phys 2010, 107: 066101. 10.1063/1.3330506
 4.
Lee J, Gharagozloo PE, Kolade B, Eaton JK, Goodson KE: Nanofluid convection in microtubes. J Heat Transfer 2010, 132: 092401. 10.1115/1.4001637
 5.
Sinha K, Kavlicoglu B, Liu YM, Gordaninejad F, Graeve A: A comparative study of thermal behavior of iron and copper nanofluids. J Appl Phys 2009, 106: 064307. 10.1063/1.3225574
 6.
Evans W, Prasher R, Fish J, Meakin P, Phelan P, Keblinski P: Effect of aggregation and interfacial thermal resistance on thermal conductivity of nanocomposites and colloidal nanofluids. Int J Heat Mass Transfer 2008, 51: 1431–1438. 10.1016/j.ijheatmasstransfer.2007.10.017
 7.
Jang SP, Choi SUS: Role of Brownian motion in the enhanced thermal conductivity of nanofluids. Appl Phys Lett 2004, 84: 4316–4318. 10.1063/1.1756684
 8.
Özerinç S, Kakaç S, Yazıcıoğlu AG: Enhanced thermal conductivity of nanofluids: a stateoftheart review. Microfluid Nanofluid 2010, 8: 145–170.
 9.
Buongiorno J, Venerus DC, Prabhat N, McKrell T, Townsend J, Christianson R, Tolmachev YV, Keblinski P, Hu LW, Alvarado JL, Bang IC, Bishnoi SW, Bonetti M, Botz F, Cecere A, Chang Y, Chen G, Chen HS, Chung SJ, Chyu MK, Das SK, Di Paola R, Ding YL, Dubois F, Dzido G, Eapen J, Escher W, Funfschilling D, Galand Q, Gao JW, Gharagozloo PE, Goodson KE, Gutierrez JG, Hong HP, Horton M, Hwang KS, Iorio CS, Jang SP, Jarzebski AB, Jiang YR, Jin LW, Kabelac S, Kamath A, Kedzierski MA, Kieng LG, Kim C, Kim JH, Kim S, Lee SH, Leong KC, Manna I, Michel B, Ni R, Patel HE, Philip J, Poulikakos D, Reynaud C, Savino R, Singh PK, Song PX, Sundararajan T, Timofeeva E, Tritcak T, Turanov AN, Van Vaerenbergh S, Wen DS, Witharana S, Yang C, Yeh WH, Zhao WH, Zhou SQ: A benchmark study on the thermal conductivity of nanofluids. J Appl Phys 2009, 106: 094312. 10.1063/1.3245330
 10.
Teja AS, Beck MP, Yuan YH, Warrier P: The limiting behavior of the thermal conductivity of nanoparticles and nanofluids. J Appl Phys 2010, 107: 114319. 10.1063/1.3354094
 11.
Bejan A: Shape and Structure, From Engineering to Nature. Cambridge: Cambridge University Press; 2000.
 12.
Bejan A, Lorente S: Design with Constructal Theory. Hoboken: Wiley; 2008.
 13.
Bejan A, Lorente S, Lee J: Unifying constructal theory of tree roots, canopies and forests. J Theor Biol 2008, 254: 529–540. 10.1016/j.jtbi.2008.06.026
 14.
Bejan A, Lorente S: The constructal law and the thermodynamics of flow systems with configuration. Int J Heat Mass Transfer 2004, 47: 3203–3214. 10.1016/j.ijheatmasstransfer.2004.02.007
 15.
Bejan A, Lorente S: Constructal theory of generation of configuration in nature and engineering. J Appl Phys 2006, 100: 041301. 10.1063/1.2221896
 16.
Reis AH: Constructal theory: from engineering to physics, and how flow systems develop shape and structure. Appl Mech Rev 2006, 59: 269–282. 10.1115/1.2204075
 17.
Rocha LAO, Lorente S, Bejan A: Constructal design for cooling a discshaped area by conduction. Int J Heat Mass Transfer 2002, 45: 1643–1652. 10.1016/S00179310(01)002691
 18.
Bejan A, Lorente S: The constructal law of design and evolution in nature. Philos Trans R Soc B 2010, 365: 1335–1347. 10.1098/rstb.2009.0302
 19.
Chodak I, Krupa I: "Percolation effect" and mechanical behavior of carbon black filled polyethylene. J Mater Sci Lett 1999, 18: 1457–1459. 10.1023/A:1006665527806
 20.
Bai C, Wang LQ: Constructal allocation of nanoparticles in nanofluids. J Heat Transfer 2010, 132: 052404. 10.1115/1.4000473
 21.
Bai C, Wang LQ: Constructal structure of nanofluids. J Appl Phys 2010, 108: 074317. 10.1063/1.3486501
 22.
FLUENT 6.3 Documentation, Fluent Inc
Acknowledgements
The financial support from the Research Grants Council of Hong Kong (GRF718009 and GRF717508) and the CRCG of the University of Hong Kong (Project 10400920) is gratefully acknowledged.
Author information
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Both authors contributed equally.
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Bai, C., Wang, L. Constructal blade shape in nanofluids. Nanoscale Res Lett 6, 240 (2011) doi:10.1186/1556276X6240
Received
Accepted
Published
DOI
Keywords
 Building Block
 Base Fluid
 Thermal Conductivity Ratio
 Shaped Blade
 Nanoparticle Material