A dataset of energy-optimal driving waveforms in turbulent pipe flow - Felix Kranz (PANGAEA, 2025)
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.