Group leader at KIT working on multiphase flows, turbulence and heat transfer
Joined November 2015
- Tweets 24
- Following 301
- Followers 73
- Likes 540
10 Photos and videos
Alexander Stroh retweeted
7 Apr 2023
Closing ceremony of #ICMF2023
Arigato #Kobe 💙
Rendez-vous à #Toulouse 😊
@MatarLab @PREMIERE_UKRI #ICMF2025 #ICMFToulouse
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14 Sep 2022
Our website with the ML-model for prediction of ks for rough surfaces went online: roughness.org - you can try it out uploading your rough surfaces.
#EFMC14 #ISTM #KIT
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12 Sep 2022
Please join our #EFMC14 talk "Predicting roughness-induced drag based on
active learning" by
Jiasheng Yang during the mini-symposium on shear flows
over complex surfaces on coming Wednesday, 14th at 18.45. We will present our newly released online ML-tool for prediction of ks!
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12 Sep 2022
So with our tool you can simply upload your 2D height distribution of the rough surface and get an estimation of ks from our ML-model. #EFMC14 #roughness #MachineLearning #KIT #istm
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4 May 2022
Our new paper on generation and simulation of roughness in minimal channels went online today!
cambridge.org/core/journals/…
We demonstrate a set of tools for a cheap DNS of a turbulent flow over roughness. Next step: creation of a large dataset and doing some machine learning! 😉
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22 Jan 2022
It's great to see that we can print riblets for our wind-tunnel facility on an in-house SLA 3D printer with this precision! #formlabs #istm #kit #riblets #dragreduction
formlabs.com/de/blog/model-u…
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30 Dec 2021
Our new article on secondary motions of Prandtl's second kind went online:
link.springer.com/article/10…
Interestingly the motions are present not only in generic turbulent flow configurations, but also in non-equlibrium technical flows e.g. in combustion engines.
#kit #tudarmstadt
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23 Dec 2021
Check our new publication on applying transfer learning for prediction of roughness effects on turbulent flow:
cambridge.org/core/journals/…
#jfm #roughness #turbulence #MachineLearning
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27 Nov 2021
Take a look at our new publication where parametric forcing is used for roughness stripes modelling. This method allows the use of usual DNS resolution (instead of high-res IBM) and still properly reproduces
secondary motions formation and topology change. doi.org/10.1017/jfm.2021.850
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Alexander Stroh retweeted
21 Jan 2021
Machine Learning has great potential for use in Chemical Engineering - here is our first preprint from work at @KITKarlsruhe with @MattSchniewind @blitz737 and @P_Friederich #AI #ML #KI arxiv.org/abs/2101.08130
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Alexander Stroh retweeted
7 Oct 2021
Reminder of two excellent researcher positions available in our Chemical Engineering/Machine Learning DFG project with Pascal Friederich (@P_Friederich) and Alexander Stroh (@blitz737)
aimat.iti.kit.edu/75_158.php
pse.kit.edu/karriere/joboffe…
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Alexander Stroh retweeted
20 May 2021
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Alexander Stroh retweeted
23 Feb 2021
Machine Learning for Chemical Engineering - watch this video for a summary of our recent work at @KITKarlsruhe with @blitz737 @P_Friederich & @MattSchniewind youtu.be/5054M6JRIyc
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9 Jan 2020
Our paper on secondary motions generated over rough stripes in a turbulent channel flow went online! #JFM #JFMrapid #ISTM #KIT #fluidmechanics #turbulence #secondaryflows
cambridge.org/core/journals/…
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