Dr. Prabhakar Ranganathan
Dr. Prabhakar Ranganathan
Prabhakar Ranganathan leads Computational Rheology for Sustainable Industries (CReSI) in the Department of Mechanical and Aerospace Engineering at Monash University. His group is currently focused on understanding and predicting the flow of complex polymer solutions and particle suspensions, with an eye on applications in energy storage, energy efficiency and advanced manufacturing.
The core question is: how do microscopic structures and interactions in these materials – polymer chains, particle networks, soft aggregates – give rise to the strongly nonlinear, history-dependent behaviour that engineers measure and use? To answer this, his group combines constitutive modelling, numerical simulation and data-driven methods to build predictive links between microstructure, flow history and measurable rheology across shear, extensional and mixed flows.
A major theme is “full-stack rheology“: developing the theory, computation and AI tools needed to go from molecular or particulate models, through continuum constitutive equations, all the way to process-level predictions such as filament breakup dynamics, coating quality or drop-size distributions in spraying. Current projects involve polymer solutions and particle-polymer slurries relevant to battery electrodes, functional coatings and other thixo-elasto-visco-plastic materials, where reliable rheology can enable more energy-efficient and robust processes.
On the methods side, the group works on multiscale simulations, inverse problems and physics-informed machine learning for constitutive model discovery, as well as strategies for autonomous or semi-autonomous rheology labs that couple experiment design, high-performance computing and AI. Earlier work in the group has examined flagellar propulsion, ciliary flows and other active-matter systems, and this continues to inform the broader perspective on how local driving and interactions produce emergent macroscopic transport.
Prabhakar welcomes PhD students with strong interests in fluid mechanics, soft matter, applied mathematics, scientific computing or AI to be crazy about CReSI, and is keen to collaborate with experimentalists and industry partners working with complex fluids in energy and manufacturing applications.
Qualifications
- B Tech, Chemical Engineering, Indian Institute of Technology Madras.
- MS, Chemical Engineering, Indian Institute of Technology Madras.
- Ph.D, Chemical Engineering, Monash University.
Expertise
- Rheology
- Viscoelasticity
- Polymer solutions
- Particle suspensions
- Complex fluids
- Mesoscale simulation methods
- Computational Fluid Dynamics
- Artificial Intelligence & Machine Learning
PhD opportunities in CReSI
CReSI Lab is looking for PhD students who want to work at the interface of rheology, statistical mechanics, scientific computing and AI, with a strong focus on polymer solutions and particle suspensions in energy-related applications (battery slurries, coatings, complex fluids for energy efficiency).
We currently have projects at three interconnected levels:
1. Microstructure-resolved simulations of complex fluids
Example directions:
-
Build comprehensive simulation codes for Brownian particles with full hydrodynamic and other long-range interactions, in various periodic and confined geometries.
-
Use these codes to probe structure–property relations in homogeneous steady flows, time-dependent flows and stochastic driving.
-
Develop and benchmark efficient algorithms and data structures for large-scale HPC runs (e.g. fast summation methods, domain decomposition, GPU-friendly kernels).
2. Microstructure-based constitutive models
Example directions:
-
Construct microstructure-based constitutive models for polymer solutions and particle–polymer suspensions using tools from statistical mechanics and kinetic theory.
-
Calibrate and test these models against rheometric data from the literature and from collaborators (shear, extensional and mixed flows).
-
Extend models to capture thixo-elasto-visco-plastic behaviour in slurries and coatings relevant to energy storage and advanced manufacturing.
3. Continuum simulations and process-level predictions
Example directions:
-
Develop continuum CFD simulations for complex flows of complex fluids using finite-volume, finite-element, spectral or meshless methods.
-
Apply these tools to problems such as capillary thinning and breakup, coating flows, and air-assisted spraying and atomisation (e.g. drop-size distributions, film stability).
-
Use analytical techniques such as stability and bifurcation analysis, and advanced data visualisation, to interpret simulation and experimental results.
4. Physics-aware AI and autonomous rheology
Example directions:
-
Develop physics-aware machine-learning frameworks for constitutive model discovery and model reduction, informed by simulation and experimental data.
-
Explore “self-driving” rheology concepts: closed-loop workflows that use AI to design experiments, run simulations and update models under human supervision.
-
Build rheology-informed digital twins of key processes (for example, electrode slurry mixing and coating) that can be used for optimisation and control.
Research Interests
Complex flows of viscoelastic polymer solutions
Polymers are ubiquitous in nature (e.g. DNA, cellulose) or as plastics. When dissolved, flexible polymer molecules can behave as nanosprings and make the fluid viscoelastic. Even a small amount of polymeric additives can dramatically change the flow behaviour of their Newtonian solvents. These additives can also reduce turbulent friction by as much as 40%. They can suppress micro-droplet (or mist) formation in pesticide spraying. On the other hand, if they are not chosen correctly, they can cause toxic mist formation in roll-coating of paper and other products. The connections between polymer size, chemistry and concentration, and the behaviour of their solutions in complex flow situations such as turbulent pipe flows, jets and free surface flows, etc. are, however, not yet well understood. We develop advanced microstructure-based rheological models and computational fluid dynamics (CFD) simulations that connect the nanoscale fluid mechanics of polymer molecules with the macroscale flow behaviour of their solutions. The goal is design of polymer solutions for use in flow applications such as spraying, surface-coatings, ink-jet printing, turbulence control, etc.
Key outcomes
- Correct prediction of the non-trivial concentration dependence of hysteretic behaviour of polymer solutions using a new microstructure-based rheological model [Prabhakar et al., Physical Review Fluids, 2017].
- A new microstructure-based model that correctly predicts complex self-concentrating/self-diluting behaviour of unentangled polymer solutions in strongly stretching flows: This was a collaboration with Tam Sridhar and Ravi Prakash Jagadeeshan. Model predictions were successfully tested against experiments (Sridhar) and mesoscale Brownian Dynamics simulations (Jagadeeshan) [Prabhakar et al.,Journal of Rheology, 2016].
- Development of the Acoustically-Driven Microfluidic Extensional Rheometer (ADMiER): This device was developed in collaboration with Leslie Yeo and James Friend. It measures the extensional viscosities of microlitre-sized drops of complex fluids [Bhattacharjee et al., New Journal of Physics, 2011; McDonnell et al., Soft Matter, 2015].
Complex flows of Active Matter
Interesting things happen when highly-mobile individuals get together! Complex motion patterns emerge in bird flocks, wildebeest herds, human crowds, fish shoals, ants, etc. This is also true of suspensions of swimming cells or of synthetic nanomotors. The behaviour of such active matter is rich and diverse. Living matter — organelles, cells, tissues, organs, organisms, species — is active matter perfected for specific functions by billions of years of evolution. Our goal is to develop models and techniques to connect the properties and behaviour of active matter with the motility and other interactions of the mobile particles that make up such materials.
Key outcomes
- Correct prediction of intricate furrow networks created by motile cells moving through soft media: These networks were originally observed in experiments with Pseudomonas aeruginosa biofilms, performed by Cynthia Whitchurch and Michelle Gee, and quantified using measures developed in our group [Gloag et al., PNAS, 2013]. Simulations peformed in collaboration with Mandar Inamdar and Raghunath Chelakkot demonstrated that the network patterns are caused by the phenomenon of motility-induced clustering [Imaran et al., Soft Matter, 2021].
- Simulations of pattern-formation in epithelial monolayers consisting of a “foam” consisting of tightly-packed polygon-shaped motile cells: in collaboration with Mandar Inamdar and Raghunath Chelakkot [Bajpai et al., Journal of the Royal Society Interface, 2021].
- Correct prediction of the changes in suspension extensional viscosity induced by propulsive forces exerted by swimming bacteria, algae and sperm: This was a collaboration with Leslie Yeo and James Friend who performed the experiments. A new microstructure-based model for the extensional viscosity of suspensions of rod-shaped microswimmers captured the concentration-dependence observed in motility-induced changes in extensional viscosity [McDonnell et al., Soft Matter, 2015].
Flagellar propulsion & steering in sperm
Understanding how sperm swim to find an egg and fertilize it is crucial to designing artificial reproduction technologies that have a high chance of success and reliably select the best quality sperm to create offspring free of serious genetic defects. Sperm cells swim by “beating” flexible tails called flagella. These flexible propellers are driven by an incredibly complex nanoengine, called the axoneme. Thousands of protein nanomotors, called dyneins, act in a concerted manner within this engine to drive complex beating patterns in the flagellum. How do all these motors manage to coordinate themselves in a highly noisy nanoscale environment to produce beating patterns to propel and steer the swimming cell towards an egg? We are developing mathematical models that account for the interplay between the action of the motors, the elastic resistance of the flexible body, and the fluid forces inside and outside the cell. Using these simulations, we aim to connect the internal and external physical parameters that govern this system to the experimentally observable beating patterns that arise under different physical conditions. We hope to diagnose the internal health of a sperm cell from its beat signature. This will enable the design of microfluidic strategies to select cells of high genetic quality for in vitro fertilization.
Key outcomes
- Image analysis of long-duration high-speed, high-resolution videos of single tethered sperm to measure flagellar energetics: This was achieved through a three-year collaboration with an experimental group led by Moira O’Bryan, Reza Nosrati and Julio Soria [Nandagiri et al., eLife, 2021].
- Image-analysis of flagellar beating patterns in single sperm demonstrating that a set of proteins called CRISPs boost flagellar propulsion [Gaikwad et al., Frontiers in Cell and Developmental Biology, 2021].
- Measurements of 3D-ness of flagellar beating patterns of individual sperm cells close to a wall [Powar et al., Small Methods, 2022].
Research Projects
Current projects
Microstructure-resolved simulations of complex fluids
-
Build comprehensive simulation codes for Brownian particles with full hydrodynamic and other long-range interactions, in various periodic and confined geometries.
-
Use these codes to probe structure–property relations in homogeneous steady flows, time-dependent flows and stochastic driving.
-
Develop and benchmark efficient algorithms and data structures for large-scale HPC runs (e.g. fast summation methods, domain decomposition, GPU-friendly kernels).
Microstructure-based constitutive models
-
Construct microstructure-based constitutive models for polymer solutions and particle–polymer suspensions using tools from statistical mechanics and kinetic theory.
-
Calibrate and test these models against rheometric data from the literature and from collaborators (shear, extensional and mixed flows).
-
Extend models to capture thixo-elasto-visco-plastic behaviour in slurries and coatings relevant to energy storage and advanced manufacturing.
Physics-aware AI and autonomous rheology
-
Develop physics-aware machine-learning frameworks for constitutive model discovery and model reduction, informed by simulation and experimental data.
-
Explore “self-driving” rheology concepts: closed-loop workflows that use AI to design experiments, run simulations and update models under human supervision.
-
Build rheology-informed digital twins of key processes (for example, electrode slurry mixing and coating) that can be used for optimisation and control.
Continuum simulations and process-level predictions
-
Develop continuum CFD simulations for complex flows of complex fluids using finite-volume, finite-element, spectral or meshless methods.
-
Apply these tools to problems such as capillary thinning and breakup, coating flows, and air-assisted spraying and atomisation (e.g. drop-size distributions, film stability).
-
Use analytical techniques such as stability and bifurcation analysis, and advanced data visualisation, to interpret simulation and experimental results.
Interdisciplinary Seed Grant
Moira O’Bryan, RP, Julio Soria, David Potter
Monash University, 2016
Teaching Commitments
- MEC3451 - Fluid mechanics II
- MEC2405 - Thermodynamics
- ENG5105 - Integrated Design
PhD applications
Methods and skills PhD candidates will develop
Depending on your project, you can expect to develop a substantial toolkit, for example:
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Mathematical modelling of complex fluids (continuum and particle-based)
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Statistical mechanics and kinetic theory for polymers and suspensions
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Scientific computing and HPC: writing efficient code (e.g. C/C++/Fortran/Python/MATLAB/Julia), parallelisation, working on clusters
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CFD and numerics: finite-volume / finite-element / spectral / meshless methods, time integration, stability and bifurcation analysis
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Data analysis and visualisation for high-dimensional simulation and experimental data
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Machine learning for physics: from regression and surrogate modelling to physics-informed ML for constitutive modelling and experiment design
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Collaborative research skills: working with experimental groups, reading and critiquing the literature, communicating results clearly in talks and papers
This is good preparation for careers in academia, scientific computing, or high-end R&D roles in energy, materials and advanced manufacturing.
What I am looking for
I am keen to work with students who:
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have a strong background in at least one of: fluid mechanics, soft matter, applied mathematics, statistical mechanics, or computational physics
-
are comfortable with programming or willing to learn it seriously
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enjoy thinking carefully about both physics and numerics, not just pushing buttons
Contact
If you are interested in a PhD in this area, please email prabhakar.ranganathan@monash.edu with:
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a short statement of your interests and background
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your CV
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your academic transcripts (unofficial is fine at the initial stage)
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a brief description of any research or substantial coding projects you have done (links to code or reports are helpful)
If there is a good fit, we can then discuss possible projects and scholarship options. It is also really important that applicants familiarize themselves with the admission process at Monash: https://www.monash.edu/graduate-research/study/apply.