Rajneesh Anand

PhD Student, Chemical & Biomolecular Engineering Lehigh University, Bethlehem, PA Advisor: Prof. Mayuresh V. Kothare Research: Physics-based optimal control & reinforcement learning for droplet microfluidics Bachelors of Technology (B.Tech), Chemical Engineering National Institute of Technology (NIT) Durgapur, West Bengal, India Advisor: Dr. Abhiram Hens and Dr. Mrinal K. Mandal Research: Phase change of heat transfer & computational modeling (CFD) for turbulent fluid flow

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About Rajneesh

Rajneesh's research lies at the intersection of reinforcement learning (RL), automation & control systems, and fluid dynamics. He is currently developing feedback control policies for droplet-based systems operating at low Reynolds numbers. On the control-theoretic side, he works with both reduced-order ODE models and PDE-constrained optimal control formulations. On the data-driven side, he is exploring model-based RL ability to robustly automate droplet manipulation and verifies/guarantees closed-loop performance.

More broadly, Rajneesh is interested in bridging physics-based modeling with machine learning for dynamical systems and developing scalable control strategies for lab-on-a-chip applications.

HPCL NGIC (National Contest) 2022 Winner

Research Projects

Optimal Control and Closed-Loop Stability of Droplet Transport in a Microchannel

Rajneesh Anand, Mayuresh V. Kothare

Optimal control of droplet transport in a microchannel via reduced-order ODE (Pontryagin) and full PDE (CMA-ES) formulations, revealing distinct transport regimes. Cross-validation of ODE-derived controllers on the PDE identifies fidelity limits, and CLF analysis establishes exponential stability of the ODE system.

AIChE Annual Meeting 2026 Coming soon: Manuscript under preparation

Lubrication Theory FEniCS Optimal Control PMP CMA-ES Stability

Choreography of a Droplet: passive guiding, holding, and surfactant-free splitting

Rajneesh Anand, Mayuresh V. Kothare

A fully passive microfluidic Y-junction platform that enables droplet guidance, reversible trapping, and surfactant-free binary splitting of pure water droplets. Combined CFD and experiments reveal viscous–capillary breakup dynamics governing the splitting threshold.

Analyst (2026) APS Global Physics Summit 2026

Experiment Y-Junction CFD

Novel design for solar desalination unit for improved efficiency

Rajneesh Anand, Mrinal K Mandal, Rajiv G Chaudhuri, Abhiram Hens

A new basin-type solar desalination geometry designed to improve freshwater productivity and condensation efficiency. The patterned condensation surface increases the overall efficiency and water yield of the system compared to conventional solar still.

Applied Thermal Engineering (2024) Int. J. of Ambient Energy (2023)

CFD ANSYS Fluent VOF Phase Change (Heat Transfer)