Sunil Kumar

About

I am a Chemical Engineer with expertise in Particle Technology, Discrete Element Method (DEM), and data-driven modeling using Machine Learning. My research bridges computational simulations, experimental validation, and intelligent systems modeling to solve complex granular flow problems across terrestrial and extraterrestrial environments.

My Ph.D. focused on the segregation behavior of non-spherical particles in rotating drums, combining custom-built experimental rigs with large-scale DEM simulations using LIGGGHTS® and EDEM®. I’ve presented my work at premier conferences like PARTEC and DEM9, and published in high-impact journals.

Professionally, I have:

1. Simulated lunar regolith–tool interactions using DEM to evaluate excavation loads and cohesion effects under reduced gravity conditions, contributing to ongoing planetary science and ISRU tool design projects.

2. Designed and reviewed high-difficulty STEM evaluations used to benchmark large AI models, contributing structured, expert-level technical content in particle systems, transport processes, and process engineering.

I also taught undergraduate chemical engineering courses (reaction engineering, process design, energy systems), mentored award-winning student projects, and co-led the development of a university research laboratory dedicated to particle-scale experimentation and modeling.

My goal is to apply my multidisciplinary experience—spanning granular mechanics, HPC-enabled simulations, and ML-driven process analysis—to industrial R&D, advanced materials processing, and cross-domain innovation.

Employment

• Indian Institute of Technology Roorkee

Education

• Indian Institute of Technology Roorkee Ph.D
  2018 - 2023

Projects & Funding

Projects & funding information is unavailable.

Publications (11)

• Impact of particle shape on mixing dynamics in a vibrated packed bed: An analysis using orientational entropy
  Powder Technology 2025 DOI: 10.1016/j.powtec.2025.121431
  +Save
• Predicting Mixing: A Strategy for Integrating Machine Learning and Discrete Element Method
  Industrial & Engineering Chemistry Research 2024 DOI: 10.1021/acs.iecr.4c02147
  +Save
• Influence of aspect ratio of ellipsoidal particles on mixing in vibrated packed bed using discrete element method
  AIChE Journal 2024 DOI: 10.1002/aic.18405
  +Save
• Shape-dependent radial segregation in rotating drum: Insights from DEM simulations
  Powder Technology 2024 DOI: 10.1016/j.powtec.2023.119134
  +Save
• Shape-dependent size polydispersity: DEM investigation of mixing behavior in a vibrating packed bed system
  Powder Technology 2024 DOI: 10.1016/j.powtec.2024.119804
  +Save
• Cumulative effect of particle properties on mixing of multi-component mixture in a vibrated packed bed
  Powder Technology 2023 DOI: 10.1016/j.powtec.2022.118000
  +Save
• Effect of aspect ratio of ellipsoidal particles on segregation of a binary mixture in a rotating drum
  Powder Technology 2023 DOI: 10.1016/j.powtec.2023.118682
  +Save
• Effect of non-sphericity of a narrow-sized binary mixture on mixing in convective vibrated packed bed using Discrete Element Method
  Powder Technology 2023 DOI: 10.1016/j.powtec.2023.118600
  +Save
• Experimental Investigation of Segregation in the Dilute Phase Transport of Nonspherical Particles through a Pneumatic Conveying System
  Industrial and Engineering Chemistry Research 2022 DOI: 10.1021/acs.iecr.2c01191
  +Save
• Experimental investigation of segregation in a rotating drum with non-spherical particles
  Powder Technology 2022 DOI: 10.1016/j.powtec.2022.117918
  +Save