Effect of Particle Agglomeration on Thermal Conductivity of Solar Salt Nanofluids

doi:10.12096/j.2096-4528.pgt.23046

Abstract

Abstract:

Objectives Nanofluid technology is an important means to improve the thermal conductivity of energy storage materials in solar thermal power plants. However, the nanoparticles in actual molten salt based nanofluid usually aggregate and settle down, which significantly affects the thermal performance of the composites. To gain a deeper insight into the variation of thermophysical properties of molten salt based nanofluids, the silica nanoparticles with different aggregation morphologies were constructed in solar salt. Methods The effects of system temperature, mass fraction of nanoparticles, and micromorphology of aggregates were investigated based on the molecular dynamics simulation and lattice Boltzmann method. Results It is found that the inclusion of nanoparticles can effectively enhance the thermal conductivity of the base fluid. Compared with the uniformly dispersed nanofluid, the heat conduction performance of the aggregated nanofluid is better, and it decreases with the increase of fractal dimension of agglomerates. Moreover, the thermal conductivity of nanofluid is negatively correlated with temperature and aggregate size, and positively correlated with mass fraction of nanoparticles, backbone length of each aggregate and degree of agglomeration. Conclusions The research results reveal the mechanism of nanoparticle aggregation on the heat transport within phase change materials and provide critical reference on the design of thermophysical properties of molten salt based nanofluids.

Key words: solar thermal power generation, heat storage, nanofluids, molten salt, molecular dynamics, lattice Boltzmann method

CLC Number:

TK 11

Lixiang QIU, Chao HUANG, Gaosheng WEI, Liu CUI, Xiaoze DU. Effect of Particle Agglomeration on Thermal Conductivity of Solar Salt Nanofluids[J]. Power Generation Technology, 2024, 45(5): 878-887.

Figures/Tables 15

Fig. 1 Molecular dynamics model of nanofluids

Tab. 1 Related potential parameters of Buckingham potential function

原子	A_ii / (×4 184 J/mol)	ρ_ii /nm	C_ii /(×10^-60m×4 184 J/mol)	q_i /eV
Na	9 778.06	0.031 7	24.18	1
K	35 833.47	0.033 7	349.9	1
N	33 652.75	0.026 46	259.1	0.95
NO₃^-中O	62 142.9	0.023 92	259.4	-0.65
Si	72 460.64	0.035 1	14 415.29	1.910 241 8
SiO₂中O	15 170.70	0.038 6	617.24	-0.955 209

Tab. 2 Potential energy parameters of atomic interactions in NO3-

K_r /(eV⋅10^-20m)	r₀ /nm	K_θ /eV	θ /(°)	K_UB /eV	r_UB/nm
17.534 4	0.126	2.712 2	120	4.960 8	0.219 55

Fig. 2 Calculation domain and boundary conditions in LBM

Fig. 3 Temperature fluctuation in the relaxation process of pure molten salt

Fig. 4 Comparison of experimental and simulated values of pure molten salt density

Fig. 5 Series model for two-component system

Fig. 6 Comparison of simulated and analytical values of temperature distribution when λ1∶λ2=1∶5

Fig. 7 Thermal conductivity of different structural models

Fig. 8 Effect of mass fraction on thermal conductivity of nanofluid

Fig. 9 Effect of temperature on thermal conductivity of nanofluid

Fig. 10 Thermal conductivity at different volume fractions obtained by two simulation methods

Fig. 11 Thermal conductivity of nanofluid with different aggregation degrees

Fig. 12 Thermal conductivity of aggregated nanofluids with different backbone lengths

Fig. 13 Thermal conductivity of nanofluids with different aggregate sizes

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