Towards a PIRL framework for efficient airflow diffuser design
- Alfredo Lopez (Software Competence Center Hagenberg)
- Florian Sobieczky (oftware Competence Center Hagenberg)
- Christopher Lackner (CERBSim)
- Matthias Hochsteger (CERBSim)
- Bernhard Scheichl (Institute of Fluid Mechanics and Heat Transfer)
- Helmuth Sobieczky (Institute of Fluid Mechanics and Heat Transfer)
- Christoph Feichtinger (Windpuls)
Abstract
This extended abstract presents a physics-informed reinforcement learning framework for optimal diffuser design to improve airflow homogeneity upstream of a heat exchanger. This approach addresses key challenges in simulation-based optimization, including high-dimensional design spaces, expensive CFD evaluations, and the lack of gradient information. Physics-based flow features related to early pressure loss occurrence and eddy formation were employed as low-cost proxies for the target homogeneity objective. The problem is formulated as a partially observable Markov decision process in which the agent sequentially selects the geometries to be evaluated. Using an expected improvement reward function, the method adaptively balances exploration and exploitation. The approach is demonstrated on a synthetic one-dimensional example, and a two-dimensional diffuser optimization problem is presented.
How to Cite:
Lopez, A., Sobieczky, F., Lackner, C., Hochsteger, M., Scheichl, B., Sobieczky, H. & Feichtinger, C., (2026) “Towards a PIRL framework for efficient airflow diffuser design”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 146-151.
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