| _version_ | 1867169170900123648 |
|---|---|
| author | Kranz, Felix |
| author_facet | Kranz, Felix |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | We compute drag- and energy-optimal driving waveforms in turbulent pipe flow using direct numerical simulations combined with a gradient-free, black-box optimisation framework. Our results demonstrate that Bayesian optimisation significantly outperforms conventional gradient-based methods in terms of efficiency and robustness, owing to its ability to handle noisy objective functions that arise from the finite-time averaging of turbulent flows. Optimal waveforms are identified for three Reynolds numbers and two Womersley numbers. At a Reynolds number of 8600 and a Womersley number of 10, the optimal waveforms reduce total energy consumption by up to 22% and drag by up to 37%. This dataset includes the optimal waveforms, instantaneous and time-averaged velocity fields, as well as post-processing scripts. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_986097 |
| institution | PANGAEA |
| language | en |
| publishDate | 2025 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | A dataset of energy-optimal driving waveforms in turbulent pipe flow Kranz, Felix Binary Object; Binary Object (File Size); drag reduction; Figure; File name; Numerical simulated; Title; turbulence control; turbulent pipe flow; Variable We compute drag- and energy-optimal driving waveforms in turbulent pipe flow using direct numerical simulations combined with a gradient-free, black-box optimisation framework. Our results demonstrate that Bayesian optimisation significantly outperforms conventional gradient-based methods in terms of efficiency and robustness, owing to its ability to handle noisy objective functions that arise from the finite-time averaging of turbulent flows. Optimal waveforms are identified for three Reynolds numbers and two Womersley numbers. At a Reynolds number of 8600 and a Womersley number of 10, the optimal waveforms reduce total energy consumption by up to 22% and drag by up to 37%. This dataset includes the optimal waveforms, instantaneous and time-averaged velocity fields, as well as post-processing scripts. |
| title | A dataset of energy-optimal driving waveforms in turbulent pipe flow |
| topic | Binary Object; Binary Object (File Size); drag reduction; Figure; File name; Numerical simulated; Title; turbulence control; turbulent pipe flow; Variable |
| url | https://doi.org/10.1594/PANGAEA.986097 |