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Olle Penttinen

Forskare

Tel

+46 10 516 50 47

Email

olle.penttinen@ri.se

Location

Göteborg

ORCID

0000-0002-9445-9669

Researcher with a background of applied flow research. Skilled in mathematical modeling and programming applied within fluid mechanics. A licentiate degree focused on computational fluid dynamics from Chalmers University of Technology. Special interest in applied machine learning. Formerly a RISE representative in the BIPM CCM Working Group on Fluid Flow, WGFF. Current research http://www.e-magin.se/paper/mpjttbth/paper/1#/paper/mpjttbth/48

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No H2O shortage with IoT, future-proofing of small municipalities

Tjörn is a small island municipality with long drinking water pipeline network per connection and large leakage. Through a network of digital water meters connected to an IoT platform water leaks can be located, the need for actio…

Numerical Cleanness Analysis

What are the underlying causes of contamination in Clean environments? With modern tools and efficient computations, it is now possible to give a detailed answer.

MLUP

Machine learning (ML) is an efficient tool to mimic complex processes. Numerical simulations, such as computational fluid dynamics (CFD) is often extremely time demanding and may occupy considerable computer resources for an exten…

CFD Modeling and Fluid mechanics

The use of Computational Fluid Mechanics (CFD) models and fluid mechanics has greatly increased in recent years, both in fire research and among fire consultants. To meet the growing demand, RISE has employed new experts in CFD m…

Uncertainty Quantification - UQ

Uncertainty Quantification (UQ) supports the decision-making process by delivering reliable simulation and experimental data with estimated uncertainties due to the impact of imperfectly known information on the design.

Publications

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