Heat Transfer Enhancement in Turbulent Flow Systems

Turbulent flow systems are harnessed in a vast array of engineering applications—from compact heat exchangers to power-plant condensers—owing to their inherently high convective heat transfer coefficient. However, the efficient management of thermal energy demands strategies that further elevate heat flux without incurring prohibitive pressure losses. Passive enhancement techniques, such as micro-fins, ribs and vortex generators, introduce surface protrusions to disturb thermal boundary layers and augment mixing. Active approaches apply external forces—pulsatile flow, electric fields or acoustic waves—to invigorate turbulence. Hybrid strategies combine engineered surfaces with functionalised fluids, notably nanofluids, to exploit synergistic effects. Recent advances in additive manufacturing enable the fabrication of complex microtextured surfaces, while data-driven design methods employ machine-learning algorithms to navigate vast design spaces. The central challenge remains the balance between improved thermal performance and hydraulic penalty, a trade-off quantified by composite indices that guide the development of energy-efficient and sustainable thermal systems.

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Heat Transfer Enhancement in Turbulent Flow Systems publication trend

The graph below shows the total number of articles in heat transfer enhancement in turbulent flow systems across all publications each year (not limited to Nature Index journals).

Technical terms

Nusselt number: Dimensionless ratio of convective to conductive heat transfer across a fluid–solid interface.

Reynolds number: Dimensionless parameter denoting the relative significance of inertial to viscous forces, indicating flow regime.

Performance Evaluation Criterion (PEC): Composite index balancing heat transfer enhancement against the penalty of increased pressure drop.

Fanning friction factor: Dimensionless measure of shear stress on a surface, used to quantify hydraulic losses in pipes and channels.

Large-eddy simulation (LES): Computational technique that resolves the large, energy-carrying turbulent eddies to predict flow and heat transfer behaviour.

References

1. Robust optimization of heat-transfer-enhancing microtextured surfaces based on machine learning surrogate models. International Communications in Heat and Mass Transfer (2024).
2. Multi-objective optimization of 3D micro-fins using NSGA-II. International Journal of Heat and Mass Transfer (2022).
3. Investigation of heat transfer and pressure drop for a multiple‐started ribbed pipe using large‐eddy simulation. AIChE Journal (2022).