
Heat exchangers play a vital role in a wide spectrum of industrial applications ranging from power generation, petrochemical processing, refrigeration, and air conditioning to food processing and renewable energy systems. Their efficiency directly influences energy consumption, system performance, and overall operational cost. Computational Fluid Dynamics (CFD) has emerged as a powerful numerical approach to analyze fluid flow and heat transfer phenomena in complex geometries, providing engineers with predictive insights that cannot be obtained easily through traditional experimental methods alone. In this work, CFD simulations were conducted to study the thermal-hydraulic behavior of a shell-and-tube heat exchanger under varying flow conditions, geometrical configurations, and thermal loads. The simulations aimed to assess parameters such as velocity distribution, temperature contours, pressure drop, and overall heat transfer coefficient.The study integrates turbulence modeling using the k-? and k-? SST models, meshing strategies with refinement near boundary layers, and steady-state solutions for various Reynolds number regimes. The results highlight the importance of optimizing baffle spacing, tube pitch, and flow arrangement (counter-flow vs. parallel flow) to achieve maximum heat transfer efficiency with minimum pumping power. Furthermore, the comparison of CFD outcomes with available experimental correlations demonstrates the high fidelity and reliability of CFD-based approaches. This research not only reinforces CFD as a cost-effective tool for design and performance evaluation of heat exchangers but also offers practical design recommendations for industrial engineers aiming at energy-efficient systems
Computational Fluid Dynamics, Heat Exchanger, Thermal-Hydraulic Analysis, Turbulence Modeling, Energy-Efficient Design
Computational Fluid Dynamics, Heat Exchanger, Thermal-Hydraulic Analysis, Turbulence Modeling, Energy-Efficient Design
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
