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Evolution Strategy achieves 26% drag reduction in turbulent flow control

By PulseAugur Editorial · [3 sources] ·

Researchers have utilized an Evolution Strategy (ES) to develop a closed-loop wall controller for turbulent drag reduction, marking the first application of ES in turbulent flow control. This gradient-free method achieved a 26% reduction in skin friction, outperforming previous gradient-based multi-agent reinforcement learning controllers and classic opposition control. The ES controller's effectiveness stems from its correlation with streamwise velocity fluctuations, differing from opposition control's focus on wall-normal velocity. AI

IMPACT This research demonstrates a novel gradient-free approach for complex control problems, potentially applicable to other engineering domains.

RANK_REASON The cluster describes a novel application of an evolution strategy to a fluid dynamics problem, presented in an arXiv paper.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Evolution Strategy achieves 26% drag reduction in turbulent flow control

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The cluster describes a novel application of an evolution strategy to a fluid dynamics problem, presented in an arXiv paper.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Giorgio Maria Cavallazzi, Miguel P\'erez Cuadrado, Alfredo Pinelli ·

    Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

    arXiv:2607.12626v1 Announce Type: cross Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when c…

  2. arXiv cs.LG TIER_1 English(EN) · Alfredo Pinelli ·

    Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

    Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

    Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed…

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