Numerical investigation of entry length dependence on inlet turbulence intensity in pipe flow

Abstract

The hydrodynamic entry length is a critical parameter in internal pipe flows, directly influencing pressure drop, wall shear stress, and heat transfer characteristics. While classical correlations typically estimate the entry length as a function of Reynolds number, the role of inlet turbulence intensity has not been systematically quantified. In this study, a combined numerical and experimental investigation is conducted to quantify the dependence of hydrodynamic entry length on inlet turbulence intensity over a wide range of Reynolds numbers as a function of turbulence intensity. Numerical simulations are performed using ANSYS Fluent for Reynolds numbers spanning from 5000 to 150,000 and inlet turbulence intensities ranging from 2 % to 10 %. The entry length is quantified based on the axial development of mean velocity profiles, wall shear stress, and centerline velocity overshoot criteria. The results demonstrate that inlet turbulence intensity has a pronounced influence on the hydrodynamic entry length, especially at low turbulence levels. For inlet turbulence intensities of approximately 2–3 %, the entry length significantly exceeds conventional estimates, reaching values greater than 80–100D, whereas at higher turbulence intensities (8–10 %), the entry length reduces substantially to below 30–40D, depending on the Reynolds number. The influence of pipe diameter on entry length is also examined, revealing consistent scaling behavior with the numerical simulation. Experimental validation using hot-wire anemometry confirms an inlet turbulence intensity of approximately 4.5 %, which agrees well with numerical predictions. An alternative methodology for estimating entry length, independent of conventional velocity-profile-based criteria, is also proposed. The findings provide quantitative guidance for designing pipe flow systems, flow measurement installations, and jet-based thermal and fluid applications. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.