Numerical simulation and machine learning study on heat transfer enhancement of nanofluids in Taylor-Couette flow with an elliptical slit surface
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- @Article{Sun:2025:icheatmasstransfer,
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author = "Si-Liang Sun and Dong Liu and Can Kang and
Hyoung-Bum Kim and Ya-Zhou Song and Peng-Gang Zhang",
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title = "Numerical simulation and machine learning study on
heat transfer enhancement of nanofluids in
Taylor-Couette flow with an elliptical slit surface",
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journal = "International Communications in Heat and Mass
Transfer",
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year = "2025",
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volume = "163",
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pages = "108788",
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keywords = "genetic algorithms, genetic programming,
Eulerian-Lagrangian method, Heat transfer enhancement,
Machine learning, Nanoparticles, Taylor-Couette flow",
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ISSN = "0735-1933",
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URL = "
https://www.sciencedirect.com/science/article/pii/S0735193325002131",
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DOI = "
doi:10.1016/j.icheatmasstransfer.2025.108788",
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abstract = "Energy-efficient and high-performance rotating
machinery is essential to address the pressing global
need for energy consumption saving and emission
reduction. One critical design challenge for their
thermal performance is managing the maximum hotspot
temperature in annular gaps. To tackle this issue,
nanofluids is used to enhance the heat transfer
efficiency of Taylor-Couette flows. The flow and heat
transfer characteristics of Al2O3/water nanofluid
within annular gap is evaluated in present study. The
Eulerian-Lagrangian method is employed to track the
trajectories of the particles. In addition, machine
learning is considered to predict the flow and heat
transfer behaviour of nanofluid. The findings indicate
that the heat transfer performance of Taylor-Couette
flow is positively correlated with volume fraction and
negatively correlated with particle size, while the
friction factor follows a similar trend. The maximum
thermal performance factor is 1.064. The enhanced heat
transfer performance of nanofluid is attributed to the
migratory motion of particles from the inner to the
outer cylinder and the microturbulence of particles
within the boundary layer. Adaptive neuro-fuzzy
inference system (ANFIS) serves as the most effective
model in predicting Nu, while the Multigene genetic
programming (MGGP) presents good results in estimating
f. The high-precision predictive model for the
convective heat transfer of nanofluid in annular gap is
established with the assistance of machine learning",
- }
Genetic Programming entries for
Si-Liang Sun
Dong Liu
Can Kang
Hyoung-Bum Kim
Ya-Zhou Song
Peng-Gang Zhang
Citations