PD Dr. Yan Jin

Eissendorfer Str. 38, bulding O, Room 3.019

Telephone +49 40 42878-4644

E-Mail: PD Dr. Jan Yin.

Research Interests

Turbulence modelling, simulation, and control

A turbulence model with high accuracy and low computational cost, see Jin (2019), has been developed through the DFG-Heisenberg program (299562371). The developed turbulence model has higher accuracy than classic LES and RANS models when the same mesh resolution is used. It is particularly suitable for simulating complex turbulent flows in industry, e.g., flows in turbomachinery (Jin 2020), see Fig. 1. We are also interested in the techniques of controlling turbulence and reducing the corresponding irreversible losses, see Jin & Herwig (2014) and Li, et al. (2021) as examples.

Convection in porous media

Porous media are an important material in nature and industry. Convection in porous media receives a lot of attentions in recent years with the emergence of some new engineering applications, e.g., long term storage of CO2 in deep saline aquifers, thermal energy storage systems using stones/bricks as storage materials, etc. Based on deep investigation of physics, we try to develop efficient and accurate macroscopic models for predicting losses and heat/mass transfer rate in porous media (Fig. 2), see details in Jin, et al. (2015; 2017), Uth, et al. (2016), Kranzien & Jin (2018), Rao, et al. (2020) and Gasow, et al. (2020) for the details of this research. This research is funded by the DFG (408356608). 

Flows in biological and physiological processes

Bio-fluid mechanics is an interdisciplinary study which is located at the interface of fluid mechanics and biology. This is a new and promising research field. We are studying the digestion process in human-stomach using a CFD method, see Li & Jin (2021). We have also investigated the “Magenstrasse” based on the numerical results (Fig. 3), see Li, et al. (2021). This research is funded by the Chinese Scholar Council (CSC). In another research topic, we are investigating the flow and particle transportation in a human’s respiratory system (Fig. 4).



Title: A macroscopic two-length-scale model for natural convection in porous media driven by a species-concentration gradient.
Written by: Gasow S.; Kuznetsov, A.V.; Avila, M.; Y. Jin
in: <em>Journal of Fluid Mechanics</em>. (2021).
Volume: <strong>926</strong>. Number: (A8),
on pages:
Publisher: Cambridge University Press:
how published:
DOI: https://doi.org/10.1017/jfm.2021.691


Abstract: The modelling of natural convection in porous media is receiving increased interest due to its significance in environmental and engineering problems. State-of-the-art simulations are based on the classic macroscopic Darcy–Oberbeck–Boussinesq (DOB) equations, which are widely accepted to capture the underlying physics of convection in porous media provided the Darcy number, Da, is small. In this paper we analyse and extend the recent pore-resolved direct numerical simulations (DNS) of Gasow et al. (J. Fluid Mech, vol. 891, 2020, p. A25) and show that the macroscopic diffusion, which is neglected in DOB, is of the same order (with respect to Da) as the buoyancy force and the Darcy drag. Consequently, the macroscopic diffusion must be modelled even if the value of Da is small. We propose a ‘two-length-scale diffusion’ model, in which the effect of the pore scale on the momentum transport is approximated with a macroscopic diffusion term. This term is determined by both the macroscopic length scale and the pore scale. It includes a transport coefficient that solely depends on the pore-scale geometry. Simulations of our model render a more accurate Sherwood number, root mean square (r.m.s.) of the mass concentration and r.m.s. of the velocity than simulations that employ the DOB equations. In particular, we find that the Sherwood number Sh increases with decreasing porosity and with increasing Schmidt number (Sc). In addition, for high values of Ra and high porosities, Sh scales nonlinearly. These trends agree with the DNS, but are not captured in the DOB simulations.