About

Dr. Krunal R. Panchal.

Kinetics, optimization, and uncertainty — treated as one connected problem.

PhD, Mechanical Engineering / IIT Madras / 2026

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Now
Project Associate, Dept. of Mechanical Engineering, IIT Madras (through 30 Nov 2026)
Based
Dept. of Mechanical Engineering, IIT Madras, Chennai 600036, India
Experimental & Numerical CombustionCombustion Kinetics & Mechanism ReductionFair Multi-Objective Optimization & UQReduced-Order Modelling & Scientific MLGasificationCFD

Who I am

I’m Dr. Krunal Panchal. I hold a PhD in Mechanical Engineering from IIT Madras (thesis submitted Oct 2025, degree conferred Jul 2026, advised by Prof. Krithika Narayanaswamy) and am currently a Project Associate in the same department through 30 November 2026. My work builds computational methods that connect detailed chemical models with practical combustion applications. My PhD work is a fast, robust, kinetics-based fair multi-objective optimization framework for combustion kinetic models — including mechanism reduction and reduced-order surrogate modelling — built into the open-source MOSIAC workbench. Alongside the computational work, I’ve done hands-on experimental and numerical combustion research: pool-flame characterization of n-heptane, designed and commissioned jointly with Gagan Verma, with an eye toward extending it to Methanol-to-Gasoline surrogate fuels, quantified with flamelab, the computer-vision measurement instrument I built for it; and, earlier, an M.Tech project on steam/catalyst-assisted downdraft gasification, paired with CFD and chemical-kinetics simulation of the same systems. I’m also building experience in quantum-chemical calculations (Gaussian) for reaction-rate estimation — an area I’m actively working to strengthen further.

Research philosophy

I try to understand the structure of a problem before optimizing it. A rate constant is a range, not a value — uncertainty is a first-class citizen in everything I build, not an afterthought bolted on at the end. I value methods that are accurate and interpretable, reproducible, and computationally honest about their own cost. When something doesn’t work, I’d rather document why than quietly move on — the clearest example is a 115-run statistical audit of my own scientific-ML results that found a supposed “10× improvement” was mostly seed noise. That finding is now part of the record, not something I buried.

Background

  1. 2012–2016

    B.E., Mechanical Engineering

    Government Engineering College, Modasa

  2. 2016–2018

    M.Tech, Thermal Engineering

    Nirma University, Institute of Technology · ISTE National Award for Best M.Tech Thesis, 2018

    Experimental steam/catalyst-assisted downdraft gasification research — first hands-on rig work.

  3. 2018–2026

    PhD, Mechanical Engineering

    Indian Institute of Technology Madras · advised by Prof. Krithika Narayanaswamy

    MUQ-SAC joint-Arrhenius uncertainty quantification (published, Combustion Theory and Modelling, 2024); a multi-stage optimization strategy (ASPACC 2023); the open-source MOSIAC workbench; n-heptane pool-flame characterization.

  4. 2026–present

    Project Associate

    Dept. of Mechanical Engineering, IIT Madras · through 30 Nov 2026

    A Shell-funded combustion project, while preparing the MOSIAC software paper and extending the multi-dataset objective-function work to journal form.

Questions I'm exploring

  • How can uncertainty be represented honestly when model parameters are correlated, rather than treated independently for convenience?
  • Can optimization algorithms learn useful structure about a physical system, rather than merely finding parameter values that fit?
  • How can scientific ML remain trustworthy — and its claimed gains distinguishable from noise — when the underlying dynamics are stiff?
  • Where does a fast neural surrogate actually replace an expensive physical simulation without losing the structure that makes it scientifically meaningful?

Contact

  1. 2026-06 – 2026-11

    Project Associate, Department of Mechanical Engineering

    Indian Institute of Technology Madras · Chennai, India

    Shell-funded combustion project (PIs: Prof. V. Raghavan and Prof. Krithika Narayanaswamy), through 30 November 2026.

  2. 2025-07 – 2026-04

    Project Associate — AI/ML for chess game analysis

    Indian Institute of Technology Madras · Chennai, India

  3. 2024-08 – 2025-06

    Project Assistant

    Indian Institute of Technology Madras · Chennai, India

  4. 2018-08 – 2026-07

    Research Scholar (PhD)

    Indian Institute of Technology Madras · Chennai, India

    Developed MOSIAC (formerly RMIP), an open-source multi-objective optimization workbench for combustion kinetic-mechanism development, integrating uncertainty-quantified rate-constant calibration, stratified sampling and response-surface surrogates, alongside multi-stage kinetics-informed optimization and a Hölder/power-mean objective function for multi-dataset calibration.

    • Led design of the MUQ-SAC joint-Arrhenius uncertainty-quantification methodology (Combustion Theory and Modelling, 2024)
    • Joint pool-flame characterization work with Gagan Verma (rig: design → outsourced fabrication → commissioning → operation)
    • Decarbonisation Technology course project (Aug–Dec 2022, team of six): ANSYS Fluent study of H2/CH4 co-injection with pulverised coal in blast-furnace tuyeres — CO2 intensity fell from 0.121 to 0.067 g/kJ at 80% H2 substitution
    • Built flamelab, a computer-vision instrument measuring flame length, width and flicker from pool-flame video in physical units with propagated uncertainty — eight-run ensemble campaign, Hopf-sequence regime classification, thirteen analysis methods (~300 tests passing)

  5. 2016 – 2018

    Graduate Research Assistant (M.Tech)

    Nirma University · Ahmedabad, India

  1. 2018-08 – 2026-07

    PhD, Mechanical Engineering

    Indian Institute of Technology Madras · Chennai, India · 8.39 / 10 CGPA

    Thesis: Development of a Fast, Robust, and Kinetics-Based Optimization Framework for Combustion Kinetic Models

    • Advisor: Prof. Krithika Narayanaswamy
    • Thesis submitted Oct 2025 · viva 12 May 2026 · degree conferred 16 Jul 2026

  2. 2016 – 2018

    M.Tech, Mechanical Engineering (Thermal Engineering)

    Nirma University, Institute of Technology · Ahmedabad, India · 9.01 / 10 CGPA

    Thesis: Effect of Steam and Catalyst with Lignite Feedstock on the Performance of Downdraft Gasifier

    • Awarded the ISTE National Award for Best M.Tech Thesis 2018 (First Prize)

  3. 2012 – 2016

    B.E., Mechanical Engineering

    Government Engineering College, Modasa · Gujarat, India · 8.36 / 10 CGPA

Awards

  • 2018 ISTE National Award for Best M.Tech Thesis 2018 — First Prize · Indian Society for Technical Education
  • 2018 MHRD (Government of India) PhD Scholarship · Government of India

Teaching

  • 2022-07 Lab Demonstrator, Combustion Technology Lab (ME6510) · Indian Institute of Technology Madras
  • 2021-06 Teaching Assistant, Engineering Drawing (ME1100) · Indian Institute of Technology Madras
  • 2020-01 Teaching Assistant, Mechanical Lab I (ME2020) · Indian Institute of Technology Madras