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Hi, I’m Lu Zhu 👋

I am a Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. I have a broad research interests in computational fluid dynamics, machine learning, data mining in complex flow systems. I'm currently focused on exploring how polymers can influence drag enhancement and turbulent transitions in viscoelastic flow around cylinders. To tackle this, I use advanced numerical tools like direct numerical simulations (DNS), machine learning, and data mining. The goal is to better understand these fascinating flow dynamics and apply that knowledge to improve industrial designs. Beyond that, I've also worked on a variety of other interesting projects, like studying environmental stratified flows, crystallization reaction flows, and other complex fluid systems in both industry and nature.

My research interests include:

Computational fluid dynamics; Artificial Intelligent; Polymeric flow interactions; Stratified flows; Turbulence; Optimization; High performance computing.

Contact Me:

Email: lz447@cam.ac.uk

Address: Centre for Mathematical Sciences, Wilberforce Rd, Cambridge CB3 0WA

Employment & Experience
  • ...
    Research Associate@University of Cambridge

    Applied Mathematics and Theoretical Physics (Dec 2023-present)

    Collabrate with Prof. Rich R. Kerswell

    Focus: elastic-inertial ineractions of viscoelastic flows in complex geometry

  • ...
    Research Associate@University of Cambridge

    Applied Mathematics and Theoretical Physics (May 2021-Dec 2023)

    Collabrate with Prof. Paul F. Linden and Prof. Rich R. Kerswell

    Focus: mixing and turbulence in stratified inclined ducts

  • ...
    Postdoc Fellow@McMaster University

    Chemical Engineering (Mar 2020-May 2021)

    Collabrate with Prof. Prashant Mhaskar and Prof. Li Xi

    Focus: Kinetic modelling and CFD simulation of reactive crystallization processes for pharmaceutical manufacturing

Education & Certifications
  • ...
    Doctor of Philosophy@McMaster University

    Chemical Engineering (Sep 2015-Dec 2019)

    Supervised by Prof. Li Xi

    Thesis: Inertia- and elasticity-driven turbulence in viscoelastic fluids with high levels of drag reduction

  • ...
    Master of Science@East China University of Science and Technology

    Fluid Machinery and Engineering (Sep 2012-Jun 2015)

    Supervised by Prof. Huanxin Lai

    Thesis: Numerical study of thermal and flow fields in a galvanizing zinc plot

  • ...
    Bachelor of Engineering@East China University of Science and Technology

    Mechanical and Power Engineering (Sep 2008-Aug 2012)

  1. Long-wave instabilities of sloping stratified exchange flows
    Zhu, Lu, Atoufi, Amir, Lefauve, Adrien, Kerswell, Rich R., Linden, and Paul F.
    Journal of Fluid Mechanics 2024
  2. New insights into experimental stratified flows obtained through physics-informed neural networks
    Zhu, Lu, Jiang, Xianyang, Lefauve, Adrien, Kerswell, Rich R., and Linden, and Paul F.
    Journal of Fluid Mechanics 2024
  3. Geometry of stratified turbulent mixing: local alignment of the density gradient with rotation, shear and viscous dissipation
    Jiang, Xianyang, Atoufi, Amir, Zhu, Lu, Lefauve, Adrien, Taylor, John R., Dalziel, Stuart B., and Linden, and Paul F.
    Journal of Fluid Mechanics 2024
  4. Stratified inclined duct: two-layer hydraulics and instability
    Atoufi, Amir, Zhu, Lu, Lefauve, Adrien, Taylor, John R., Kerswell, Rich R., Dalziel, Stuart B., Lawrence, Gregory A., and Linden, and Paul F.
    Journal of Fluid Mechanics 2023
  5. Stratified inclined duct: direct numerical simulations
    Zhu, Lu, Atoufi, Amir, Lefauve, Adrien, Taylor, John R., Kerswell, Rich R., Dalziel, Stuart B., Lawrence, Gregory A., and Linden, and Paul F.
    Journal of Fluid Mechanics 2023
  6. Bagged stepwise cluster analysis for probabilistic river flow prediction
    Zhang, Qianqian, Zhang, Fei, Erfani, Tohid, and Zhu, Lu
    Journal of Hydrology 2023
  7. A novel linear hybrid model predictive control design: application to a fed batch crystallization process
    McKay, Alexander, Ghosh, Debanjan, Zhu, Lu, and Xi, Li, Mhaskar, Prashant,
    Digital Chemical Engineering 2021
  8. Nonasymptotic elastoinertial turbulence for asymptotic drag reduction
    Zhu, Lu, and Xi, Li
    Physical Review Fluids 2021
  9. Real-time prediction of river chloride concentration using ensemble learning
    Zhang, Qianqian, Li, Zhong, Zhu, Lu, Zhang, Fei, Sekerinski, Emil, Han, Jing-Cheng, and Zhou, Yang
    Environmental Pollution 2021
  10. Inertia-driven and elastoinertial viscoelastic turbulent channel flow simulated with a hybrid pseudo-spectral/finite-difference numerical scheme
    Zhu, Lu, and Xi, Li
    Journal of Non-Newtonian Fluid Mechanics 2020
  11. Vortex dynamics in low- and high-extent polymer drag reduction regimes revealed by vortex tracking and conformation analysis
    Zhu, Lu, and Xi, Li
    Physics of Fluids 2019
  12. Vortex axis tracking by iterative propagation (VATIP): a method for analyzing three-dimensional turbulent structures
    Zhu, Lu, and Xi, Li
    Journal of Fluid Mechanics 2019
  13. Transient dynamics of turbulence growth and bursting: effects of drag-reducing polymers
    Zhu, Lu, Bai, Xue, Krushelnycky, Evan, and Xi, Li
    Journal of Non-Newtonian Fluid Mechanics2019
  14. Distinct transition in flow statistics and vortex dynamics between low- and high-extent turbulent drag reduction in polymer fluids
    Zhu, Lu, Schrobsdorff, Hecke, Schneider, Tobias M., and Xi, Li
    Journal of Non-Newtonian Fluid Mechanics 2018
  15. Coherent structure dynamics and identification during the multistage transitions of polymeric turbulent channel flow
    Zhu, Lu, and Xi, Li
    Journal of Physics: Conference Series 2018
  16. Inertia- and elasticity-driven turbulence in viscoelastic fluids with high levels of drag reduction
    Zhu, Lu
    PhD thesis, 2019

CHEMENG 2O04 Fluid Mechanics

Jan 2016 - Apr 2019

Graduate Teaching Assistant responsible for leading tutorials and office hours.

Undergraduate summer internship

May 2016 - Aug 2020

Mentor of the summer internship of undergraduates.

  1. New insights into experimental stratified flows obtained through physics-informed neural networks
    Zhu, Lu, Jiang, Xianyang, Lefauve, Adrien, Kerswell, Rich R., and Linden, and Paul F.
    In Fluid Mechanics Seminars2024
  2. Numerical investigation of laminar-turbulence transition in stratified inclined ducts
    Zhu, Lu, Atoufi, Amir, Lefauve, Adrien, Taylor, John R., Kerswell, Rich R., Dalziel, Stuart B., Lawrence, Gregory A., and Linden, and Paul F.
    In IX International Symposium on Stratified Flows2022
  3. Vortex axis tracking by iterative propagation (VATIP): analyzing three-dimensional vortex structures in viscous and viscoelastic turbulent flows
    Zhu, Lu, and Xi, Li
    In APS March Meeting2019
  4. Vortex dynamics for high levels of polymer drag reduction: quantitative analysis enabled by a new vortex-tracking algorithm
    Zhu, Lu,and Xi, Li
    In 2018 AIChE Annual Meeting2018
  5. Vortex dynamics for high levels of polymer drag reduction: quantitative analysis enabled by a new vortex-tracking algorithm
    Zhu, Lu, and Xi, Li
    In The Society of Rheology 90th Annual Meeting 2018
  6. Polymer Effects on the Development and Bursting of Turbulent Vortices: Implication on High-Extent Drag Reduction
    Zhu, Lu, Bai, Xue, and Xi, Li
    In 2017 AIChE Annual Meeting 2017
  7. Polymer-turbulence interactions and vortex dynamics in polymer additives turbulent channel flow
    Zhu, Lu, and Xi, Li
    In 67th Canadian Chemical Engineering Conf. 2017
  8. Polymer-turbulence interactions in high-extent drag-reducing polymer flow
    Zhu, Lu, and Xi, Li
    In International Conference in Aerospace for Young Scientists 2017

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Exact Coherent Structures in Viscoelastic Turbulence
@DAMTP, University of Cambridge ( Dec 2023 - )

Dissolving polymers into fluids can fundamentally alter flow behavior, giving rise to intriguing phenomena such as drag enhancement and reduction, with various industrial applications. In collaboration with Prof. Rich Kerswell, we are examining drag enhancement and elastic instabilities in periodic flow past cylinders. By integrating direct numerical simulations, stability analysis, and machine learning, this project aims to provide a deeper and more comprehensive understanding of these unique flow dynamics.

Technologies
DNS Stability analysis machine learning python
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Stratified turbulence and mixing processes
@DAMTP, University of Cambridge ( May 2021 - Dec. 2023 )

Mixing efficiency in large stratified fluid bodies, such as the ocean, plays a critical role in predicting and controlling global climate patterns. In collaboration with Prof. Paul F. Linden and Prof. Rich Kerswell, our research aims to investigate mixing processes and turbulent transitions in sustained shear flows within a stratified inclined duct apparatus. This work integrates numerical simulations, experimental observations, and theoretical analysis to enhance our understanding of these complex dynamics.
1. Zhu, L., Atoufi, A, Lefauve, A., Kerswell, R. R., Linden, P. F. (2024). Long-wave instabilities of sloping stratified exchange flows. J. Fluid Mech., 983, A12.
2. Zhu, L., Jiang, X., Lefauve, A., Kerswell, R. R., Linden, P. F. (2024). New insights into experimental stratified flows obtained through physics-informed neural networks. J. Fluid Mech, 981, R1.
3. Atoufi, A., Zhu, L., Lefauve, A., Taylor, J. R., Kerswell, R. R., Dalziel, S. B., Linden, P. F. (2023), Stratified inclined duct: two-layer hydraulics and instability. J. Fluid Mech., 977, A25.
3. Zhu, L., Atoufi, A., Lefauve, A., Taylor, J. R., Kerswell, R. R., Dalziel, S. B., Linden, P. F. (2023), Stratified inclined duct: direct numerical simulations. J. Fluid Mech., 969, A20.

Technologies
DNS Hydralic instabilities machine learning c++
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Study on the dynamics of vortices with a vortex tracking algorithm - VATIP
@Dept. of Chemical Engineering, McMaster University ( Jun 2016 - )

The dynamics and statistics of turbulence are highly influenced by vortices. With the newly developed vortex tracking algorithm, we sort through all kinds of vortices and study the influece of polymers on the shape of vortices. The project is supervised by Dr. Li Xi.
1. Zhu, L. & Xi, L. (2019). Vortex dynamics in low- and high-extent polymer drag reduction regimes revealed by vortex tracking and conformation analysis. Phys. Fluids, 31(9), 095103.
2. Zhu, L. & Xi, L. (2019). Vortex axis tracking by iterative propagation (VATIP) A method for analysing three-dimensional turbulent structures. J. Fluid Mech., 866, 169-215.
3. Zhu, L. & Xi, L. (2018). Coherent structure dynamics and identification during the multistage transitions of polymeric turbulent channel flow. J. Phys. Conf. Ser. 1001.

Technologies
python DNS
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Understanding of polymer-turbulence interfactions in polymeric flows
@Dept. of Chemical Engineering, McMaster University ( Sep 2015 - )

Adding a small amount of polymers into the polymeric turbulence can drastically reduce the flow friction drag and enhance transportation efficiency. This project targets to understand the complex polymer-vortex-turbulence interactions using some numerical tools, e.g., DNS and mechine learning. The project is supervised by Dr. Li Xi.
1. Zhu, L. & Xi, L. (2021). Non-asymptotic elastoinertial turbulence for asymptotic drag reduction. Phys. Rev. Fluids, 014601.
2. Zhu, L. & Xi, L. (2020). Inertia-driven and elastoinertial viscoelastic turbulent channel flow simulated with a hybrid pseudo-spectral/finite-difference numerical scheme. J. Non-Newton. Fluid Mech, 286, 104410.
3. Zhu, L., Bai, X., Krushelnycky, E. & Xi, L. (2019). Transient dynamics of turbulence growth and bursting Effects of drag-reducing polymers. J. Non-Newton. Fluid Mech., 266, 127-142.
4. Zhu, L., Schrobsdorff, H., Schneider, T. M. & Xi, L. (2018). Distinct transition in flow statistics and vortex dynamics between low-and high-extent turbulent drag reduction in polymer fluids. J. Non-Newton. Fluid Mech., 262, 115-130.

Technologies
python c++ DNS machine learning
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Investigation of crystallization kinetics in industrial semi-batched reactors
@Dept. of Chemical Engineering, McMaster University ( May 2020 - May 2021 )

In this project, we worked with our industrial counterparts to investigate the crystalization process in lab- and industry-level semi-batched reactors. The mixing performance is investigated by CFD technology. The crystallization kinetics is understanded using the population balance models and data-driven models. The project is supervised by Dr. Li Xi and Prof. Prashant Mhaskar.

Technologies
python RANS Ansys crystallization kinetic model data-driven modeling
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Study on the thermal and flow dynamics in a galvanizing zinc pot
@Fluid Machinery and Engineering, East China University of Science and Technology ( Sep 2012 - Jun 2015 )

This project aims to analyze the thermal and flow behaviors in a galvanizing zinc pot, in order to achieve an optimal coating performance. A muti-physics system is built which couples the electromagnetic field with the flow and thermal fields to approach the realistic condition. The project is supervised by Prof. Huanxin Lai.

Technologies
python RANS Ansys crystallization kinetic model data-driven modeling

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