Designing Robust Fuel Cells and Electrolyzers with Multiphysics Simulation - Archived

This is a recording of a webinar that originally aired on November 17, 2021

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If you are interested in learning how to efficiently develop fuel cell and electrolyzer systems, join us for this interactive webinar.

There is an increased focus on developing alternative green energy sources for use in transport and power generation. Fuel cells and electrolyzers have been steadily gaining popularity as a potential solution for reducing greenhouse gas emissions as well as the negative environmental impact of car pollutants. The high R&D cost, however, is a common roadblock for companies developing this new technology. Multiphysics simulation tools like COMSOL Multiphysics® help engineers accurately understand and design fuel cells and electrolyzer systems in a time- and cost-effective manner.

Key aspects we will cover:

  • Modeling fundamental components of electrochemical cells
  • Designing different types of fuel cell and electrolyzer systems, including proton exchange membrane fuel cells (PEMFCs), hydroxide exchange (alkaline) fuel cells (AFCs), molten carbonate fuel cells (MCFCs), and solid oxide fuel cells (SOFCs)
  • Analyzing the distribution of currents, potentials, temperature, and chemical species within a cell
  • Evaluating cell operating conditions by simulating electrode configurations and membranes
  • Modeling multiphase and single-phase flow in gas diffusion electrodes
  • Accounting for multiphysics phenomena in fuel cells involving heat transfer, fluid flow, and chemical reactions

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Archived Webinar Details

Speaker

Rustam Shekhar
Applications Engineer II

Rustam Singh Shekhar is an applications engineer at COMSOL, specializing in electrochemistry and battery simulation. He received his PhD in energy science and engineering from IIT Bombay, masters in chemical engineering from IIT Hyderabad, and bachelors in chemical engineering from MITS Gwalior. His expertise includes electrode microstructure and cell- and pack-level modeling.