@misc{ZanounBauerWagneretal., author = {Zanoun, El-Sayed and Bauer, Christian and Wagner, Claus and Durst, Franz and Egbers, Christoph and Bellani, Gabriele and Talamelli, Alessandro}, title = {Cross-validation of numerical and experimental data in turbulent pipe flow with new scaling correlations}, series = {Journal of turbulence}, journal = {Journal of turbulence}, publisher = {Taylor \& Francis}, address = {London}, doi = {10.1080/14685248.2025.2560314}, pages = {1 -- 22}, abstract = {The dependence of turbulence statistics and wall friction on Reynolds number in fully developed turbulent pipe flow remains a fundamental subject in fluid mechanics. This paper cross-validates experimental and numerical results, focusing on the scaling of turbulence statistics at the pipe centerline and across the inner-outer flow region. Pipe flow experiments were reviewed for friction Reynolds numbers 810≀Reπœβ‰€55Γ—103, where Re𝜏=π‘’πœβ’π‘…/𝜈, π‘’πœ is the wall friction velocity, 𝑅 the pipe radius, and 𝜈 the kinematic viscosity. Complementary DNS data for 180≀Reπœβ‰€2880 provide detailed insight into near-wall turbulence. A novel friction correlation, Re𝜏=0.048Re0.923 c is introduced, predicting pipe-wall friction across a wide range of Re𝑐 with accuracy better than Β±2.06\%, where Re𝑐 is the Reynolds number based on the centerline streamwise mean velocity component π‘ˆzc. This correlation enables reliable friction estimates from centerline single-point measurements or DNS data without requiring near-wall or streamwise pressure-gradient information and is validated by consistent agreement with both experiments and DNS. The monotonic decrease in centerline turbulence intensity βŸ¨π‘’β€²π‘§2⟩1/2/π‘ˆzc with increasing Re𝑐 is explained using the streamwise mean momentum equation. Finally, azimuthal spatial filtering of DNS data highlights the limitations of hot-wire resolution near the wall. For Re𝜏β‰₯2880, higher-order experimental statistics agree well with DNS for 𝑦+β‰₯30 and into the logarithmic region, with both datasets equally well described by logarithmic or power-law correlations, while near-wall discrepancies remain due to resolution limits.}, language = {en} }