Project

Machine learning for the design of chemical engineering unit operations (https://chemengml.org/research-projects)

Research Interest

Flow control
Heat and mass transfer
Flow through porous media
Adjoint optimization
Immersed boundary method

Journal Publications

2024

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Koide, Y.; Kaithakkal, A. J.; Schniewind, M.; Ladewig, B. P.; Stroh, A.; Friederich, P.
2024. APL Machine Learning, 2 (1), 016108. doi:10.1063/5.0187783

Conference Contributions

2024

Active learning based surrogate model for drag and heat transfer prediction in internal flows with eddy-promoters
Kaithakkal, A. J.; Koide, Y.; Müller, D.; Friederich, P.; Stroh, A.
2024, September 19. 1st European Fluid Dynamics Conference (EFDC1 2024), Aachen, Deutschland, 16.–20. September 2024

Understanding CNN-Based Shape Optimization for Thermo-Hydraulic Efficiency Using Explainable Deep Learning
Kaithakkal, A. J.; Koide, Y.; Friederich, P.; Stroh, A.
2024, September 9. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024), Vilnius, Litauen, 9.–13. September 2024

2023

Heat transfer enhancement in laminar channel flow by machine learning guided shape optimization of wall geometry
Kaithakkal, A. J.; Koide, Y.; Schniewind, M.; Friederich, P.; Stroh, A.
2023. 17th International Heat Transfer Conference (IHTC 2023), Kapstadt, Südafrika, 14.–18. August 2023

Betreute studentische Arbeiten

Masterarbeit von Daniel De la Torre (2024): "Development of an experimental setup to study the thermo-hydraulic performance of structured channel flows"
Bachelorarbeit von Ngoc Hoang Ta (2024) : "A comparative study of shape optimization methods"
Masterarbeit von Lunes Hamadi (2025): "Prediction of flow field inside a structured laminar channel using physics-informed neural network"