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Heat Transfer Simulation
Optimizing shell-and-tube heat exchangers with GPU-accelerated CFD
Heat exchangers are a cornerstone of industrial thermal management , especially in the chemical and energy sectors . Among these , shell-and-tube heat exchangers ( SHTX ) are a preferred solution due to their mechanical robustness , ease of maintenance , and high heat transfer efficiency . Traditional design relies heavily on empirical correlations , but these approaches fall short when faced with unconventional geometries or operating conditions .
By Harlley Parno & Benjamin Turner , LATTICEPT
To address the aforementioned limitations , advanced computational tools like M-Star CFD are paving the new way engineers analyze and optimize heat exchanger performance . By leveraging the lattice Boltzmann method ( LBM ), M-Star CFD enables rapid and accurate simulations of complex fluid dynamics and heat transfer , all powered by modern GPUs . Recently , Thomas et al . ( 2024 ) conducted extensive validation of a generalized approach for the calculation of convective heat transfer coefficients across multiple industrially applicable geometries : agitated tanks , pipe flow systems , cylinders in crossflow , and tube bundles . The authors validated the method implemented against expectations from experimentally derived empirical design correlations for SHTX .
Why use M-Star CFD for heat exchangers M-Star CFD is purpose-built to take advantage of the GPU ’ s parallel processing capabilities , offering dramatic reductions in computational time compared to traditional finite-volume methods ( FVM ). Unlike FVM , which relies on solving differential equations , the LBM simulates fluid behavior as interactions between particle-like elements . This method simplifies grid generation and allows for more natural modeling of turbulence , a critical factor in heat exchanger performance . Recent studies have shown that M-Star CFD can deliver high-fidelity results for heat transfer and pressure drop in SHTX designs while significantly reducing computational overhead . A key innovation is its use of a generalized convective heat transfer coefficient approach , which eliminates the need for near-wall turbulence models . Instead , local energy dissipation rates in the bulk flow are used to compute heat transfer coefficients , streamlining the simulation process .
Shell-and-tube heat exchanger geometry .
Mesh |
Lattice Size ( m ) |
Fluid Cells ( millions ) |
Computational Time ( min ) |
Coarse |
0.001590 |
3.42 |
42 |
Intermediate |
0.001136 |
9.39 |
128 |
Finest |
0.000935 |
15.59 |
257 |
Table 1 – Mesh parameters for sensitivity study |
28 Heat Exchanger World March 2025 www . heat-exchanger-world . com