Assimilating rough features: A data-driven framework to infer rough wall properties from sparse experimental data
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914272986005504 |
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| author | Formichetti, Martina Padmanaban, Uttam Cadambi He, Ping Symon, Sean Ganapathisubramani, Bharathram |
| author_facet | Formichetti, Martina Padmanaban, Uttam Cadambi He, Ping Symon, Sean Ganapathisubramani, Bharathram |
| contents | Surface roughness influences turbulent boundary layers (TBLs) primarily through the roughness function $ΔU^+$ and the equivalent sand-grain roughness height \(k_s\). Direct determination of \(k_s\) typically requires detailed velocity and wall-shear stress measurements, which are often impractical. As an alternative, this study presents a data assimilation framework that modifies a smooth-wall Reynolds-Averaged Navier-Stokes (RANS) baseline to match sparse rough-wall particle image velocimetry (PIV) data in the fully rough regime. Through this approach, secondary variables such as the friction velocity, \(u_τ\), and \(k_s\) can be inferred from the assimilated flow fields. The assimilated TBL reproduces experimental velocity profiles within 1\% and predicts friction velocity within 1-6\% of the experimental measurements. Furthermore, the \(k_s\) values inferred from the assimilation also match the experimental data up to 1\%. These results demonstrate the potential of data assimilation as a cost-effective alternative to high-fidelity methods and support the generalisation of the framework to model streamwise-varying roughness by treating \(k_s\) as a function of fetch length. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_15980 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Assimilating rough features: A data-driven framework to infer rough wall properties from sparse experimental data Formichetti, Martina Padmanaban, Uttam Cadambi He, Ping Symon, Sean Ganapathisubramani, Bharathram Fluid Dynamics Surface roughness influences turbulent boundary layers (TBLs) primarily through the roughness function $ΔU^+$ and the equivalent sand-grain roughness height \(k_s\). Direct determination of \(k_s\) typically requires detailed velocity and wall-shear stress measurements, which are often impractical. As an alternative, this study presents a data assimilation framework that modifies a smooth-wall Reynolds-Averaged Navier-Stokes (RANS) baseline to match sparse rough-wall particle image velocimetry (PIV) data in the fully rough regime. Through this approach, secondary variables such as the friction velocity, \(u_τ\), and \(k_s\) can be inferred from the assimilated flow fields. The assimilated TBL reproduces experimental velocity profiles within 1\% and predicts friction velocity within 1-6\% of the experimental measurements. Furthermore, the \(k_s\) values inferred from the assimilation also match the experimental data up to 1\%. These results demonstrate the potential of data assimilation as a cost-effective alternative to high-fidelity methods and support the generalisation of the framework to model streamwise-varying roughness by treating \(k_s\) as a function of fetch length. |
| title | Assimilating rough features: A data-driven framework to infer rough wall properties from sparse experimental data |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2601.15980 |