PD Dr. Yan Jin


Eißendorfer Str. 40

21073 Hamburg

Building N, Room 1.083

Phone +49 40 42878 - 4644

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.

Fig 1.: Turbulent flows in a compressor cascade

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). 

Fig. 2: Natural convection in porous media

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).

Fig. 3: Flows in human-stomach
Fig. 4: O2 - concentration and distribution of aerosol particles in a respiratory system

Publications

[123765]
Title: Direct numerical simulation of the interfacial mass transfer of a bubble in self-induced turburlent flows.
Written by: Jin, Y.; Schlüter, M.:
in: <em>International Journal of Heat and Mass Transfer</em>. June (2019).
Volume: <strong>135</strong>. Number:
on pages: 1248-1259
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DOI: 10.1016/j.ijheatmasstransfer.2019.02.067
URL: https://www.sciencedirect.com/science/article/pii/S0017931018353444?via%3Dihub
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Abstract: Bubbly flows and the interfacial mass transfer of bubbles are important processes in chemical engineering. The mass transfer rate of bubbles is usually approximated by using a correlation of the Sherwood number since the direct simulation or measurement of the behavior for each bubble is too expensive for industrial applications. However, the effect of turbulence induced by the bubble swarm on bubble’s mass transfer is very complicated and its impact on the Sherwood number is still not clear. As the first step of understanding the effect of swarm turbulence, we have simulated the interfacial mass transfer of a bubble in self-induced turbulence using a direct numerical simulation (DNS) method. The gas phase is accounted for using a volume of fluid method. The effects of bubble Reynolds number, Schmidt number, bubble’s volume fraction, and Eötvös number on the flow and species concentration fields have been studied. The Reynolds numbers in the range 100–400, the Schmidt numbers in the range 1–4 are under consideration. Based on our DNS results, a correlation for the Sherwood number has been proposed. The effect of the bubble’s volume fraction and the Eötvös number can be accounted for in the proposed correlation. We expect that the general form of the correlation can be used for real bubble swarms, however, the model coefficients should be determined from the experimental/DNS data obtained under the real conditions.