Mechanical Engineer · Pune, India · Pharma · Motorsport · CFD
Mechanical engineer with a strong background in structural and thermal simulation and end-to-end product development — taking complex assemblies and novel joining systems from initial concept all the way to manufacturing release. Based in Pune, India.
At Glatt Systems, I took complete design ownership of a first-of-its-kind containment bin-washing solution for highly potent APIs — a structure over 9 metres tall, partially embedded in the ground, with specialised containment valves, washing bins from 600 to 2500 litres — and of the Glatt iEP lab-scale extruder spheronizer, guided through its full development cycle from mechanical CAD to manufacturing, functional testing, and exhibition. I delivered the full design packages and BOMs from scratch while coordinating procurement and manufacturing.
Alongside product work, I bring a digital mindset to engineering: from-scratch CFD solvers in Python. Engineering by instinct, verified by numbers.
Complete design ownership of a first-of-its-kind containment bin-washing solution for highly potent APIs at Glatt Systems. The structure stands over 9 metres tall, is partially embedded in the ground with specialized containment valves, and washes bins from 600 to 2500 litres. Delivered the full design package and BOMs from scratch while coordinating both procurement and manufacturing.
Design ownership of the Glatt iEP, a first-of-its-kind vertical integrated lab-scale extruder spheronizer, guided through its complete development cycle — mechanical CAD, manufacturing, functional testing, and presentation at industry exhibitions. The tool-less, all-in-one design allows rapid changeover between pharmaceutical formulations without disassembly.
Designed a twist-lock joining mechanism for large-diameter process ducting at Glatt Systems, replacing legacy flanged plant joints. The design cuts assembly steps and field installation complexity versus bolted flanges. Prototype qualified at 2 bar in a single-connection acceptance test.
Contributed structural design to a 3-story all-steel vertical granulation line for a global pharmaceutical OEM, built for pre-production validation. Part of a 4-member core team coordinating across mechanical, process, and controls sub-teams.
Engineered a jet-impingement cooling enclosure for the motor controller of VJTI Racing's e-BAJA entry using SLA and FDM additive manufacturing. Designed low-loss manifold geometry to minimise static pressure drop across the forced-air distribution network, reducing auxiliary battery load.
Led low- and high-voltage electrical system design for VJTI Racing across the SAE e-BAJA/ATVC and ISIE IKR 2023 EV entries. Implemented a multi-layer startup interlock sequence for HV safety. Recognised with Best Accelerating Vehicle, Best Business Proposal, and AIR 5 EV category.
Designed a converging-diverging nozzle for 300 N sea-level thrust using isopropyl alcohol-air propellant. Validated the internal flow geometry with a clear-resin printed prototype before machining the final nozzle in mild steel on a Jyoti VMC850, handling the fixturing design and toolpath programming.
Surveyed the water-cooling circuit of a 35T Electric Arc Furnace and continuous casting line at Saarloha Advanced Materials, using plant instrumentation data to identify thermal loss pathways caused by slag deposition on the cooling panels. Delivered a structured improvement report proposing design modifications to reduce parasitic heat losses, aligning with the site's green manufacturing targets.
Numerical solution of the 1D Euler equations for the classical Sod shock tube problem. Implements Lax and MacCormack finite-difference schemes and benchmarks their accuracy and stability across varying grid subdivisions and CFL numbers. Captures the three characteristic wave structures: rarefaction fan, contact discontinuity, and shock front.
# Predictor: forward difference
U_pred[:, 1:-1] = U[:, 1:-1] - dt/dx * (F[:, 2:] - F[:, 1:-1])
# Corrector: backward difference on predicted fluxes
F_pred = compute_flux(U_pred)
U[:, 1:-1] = 0.5*(U[:, 1:-1] + U_pred[:, 1:-1]
- dt/dx*(F_pred[:, 1:-1] - F_pred[:, :-2]))A collection of from-scratch numerical implementations spanning classical CFD problems: finite-difference schemes for the convection-diffusion equation, heat conduction, lid-driven cavity flow, and boundary layer analysis. Focuses on scheme accuracy, numerical diffusion, and grid convergence behaviour rather than commercial solvers.
# CFL = u·dt/dx, Pe = u·dx/ν (cell Peclet number)
for n in range(nt):
u_new[1:-1] = (u[1:-1]
- CFL*(u[1:-1] - u[:-2]) # upwind convection
+ (dt*nu/dx**2)*(u[2:] - 2*u[1:-1] + u[:-2])) # diffusion
u = u_new.copy()Developed and meshed lattice unit-cell geometries in ANSYS Fluent, conducting mesh convergence studies to understand convective heat transfer enhancements and pressure-drop trade-offs across various candidate topologies.
Modeled graded-density lattice structures under uniaxial compression using ANSYS Mechanical, extracting load-displacement curves via structural FEA to quantify their energy absorption.