Position control of an acoustic cavitation bubble by reinforcement learning

Reinforcement Learning (RL) is employed to develop control techniques for manipulating acoustic cavitation bubbles. This paper presents a proof of concept in which an RL agent is trained to discover a policy that allows precise control of bubble positions within a dual-frequency standing acoustic wa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ultrasonics sonochemistry Jg. 115; S. 107290
Hauptverfasser: Klapcsik, Kálmán, Gyires-Tóth, Bálint, Rosselló, Juan Manuel, Hegedűs, Ferenc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.04.2025
Elsevier
Schlagworte:
Bubble dynamics
Bubble position control
GPU programming
Reinforcement learning
Selected Papers from EES 2024
Bubble dynamics
GPU programming
Bubble position control
Reinforcement learning
ISSN:1350-4177, 1873-2828, 1873-2828
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Reinforcement Learning (RL) is employed to develop control techniques for manipulating acoustic cavitation bubbles. This paper presents a proof of concept in which an RL agent is trained to discover a policy that allows precise control of bubble positions within a dual-frequency standing acoustic wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1350-4177
1873-2828
1873-2828
DOI:10.1016/j.ultsonch.2025.107290