Position PhD-student
Irène Curie Fellowship No
Department(s) Mechanical Engineering
FTE 1,0
Date off 03/07/2024
Reference number V35.7571
Computational fluid dynamics is a vital tool for scientific research and addressing significant societal issues. It’s indispensable for advanced and emerging technologies that need precise control of heat and mass transfer in flows, ranging from continuum to highly rarefied conditions. These flows often involve electromagnetic fields, chemical reactions, and complex boundary interactions. The non-equilibrium nature of rarefied flows and the significant impact of molecular effects make these transport processes highly intricate and non-standard. Our current understanding is insufficient to support the growth of emerging technologies. Computational modeling is extremely demanding and, in most situations, well beyond foreseeable computing capabilities.
We aim to revolutionize the modeling of rarefied flows for emerging technologies by developing novel approaches that merge data-driven (machine learning) and model-driven (physics-based) methodologies. Our goal is to integrate the precision of computationally intensive atomistic models into macroscopic approaches, while drastically reducing the computational cost.
This PhD project aims to develop a fast, accurate multiscale modeling paradigm that bridges continuum and molecular transport regimes. The goal is to create a Neuro Generalized Method of Moments (NGMoM) model that efficiently models gas-gas collision and gas-solid scattering kernels. This model will incorporate detailed molecular interactions using machine learning algorithms to infer collision kernels from molecular dynamics experiments. The derived kernels will be used to formulate boundary conditions and collision operators for the MoM, ensuring the preservation of fundamental thermodynamic properties. This approach will enable efficient prediction of rarefied transport phenomena in complex engineering domains.
This PhD position is embedded in a consortium with 2 other PhD students and a Postdoc. The consortium consists of university groups with complementary skills in fluid dynamics, statistical physics, and machine-learning techniques, and commercial partners with a strong need for these new methodologies.
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Do you recognize yourself in this profile and would you like to know more?
Please contact dr. Arjan Frijns, a.j.h.frijns@tue.nl.
Visit our website for more information about the application process or the conditions of employment. You can also contact HRadviceME@tue.nl.
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Application
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We are an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude.
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