SumiRiko will show how they transformed a 90 hour CFD workflow for activated carbon characterization into a process requiring less than one hour. Through a combination of DOE, CFD simulations and ODYSSEE CAE reduced order models, they achieved instant pressure drop prediction with high accuracy.

This session offers a complete look at DOE planning, dataset creation, ML model building and how ODYSSEE optimization tools were used to identify performance critical parameters.

What you will learn:

Key activated carbon properties that affect canister pressure drop performance
DOE strategies used for activated carbon CFD characterization
Processes for building reduced order models that give instant predictions
ML based techniques for exploring the full design space quickly
Fast sensitivity and optimization approaches supported by ODYSSEE
Validation results comparing ROM predictions to full CFD simulations

Register for individual sessions or attend the full series for a complete overview of how AI and machine learning enabled engineering is reshaping the industry.

Speakers

Speaker details

Kindly complete the form to secure your registration for the webinar.

Select webinar sessions General Motors - May 7, 2026TE Connectivity - May 12, 2026SumiRiko - May 13, 2026Honda - May 19, 2026Avient - June 09, 2026

First Name

Last Name

Company

I am a student

Phone

Country

Job Title

Industry

Please provide details for your request.

I consent to receive electronic marketing communications on products, services, publications, and events from MSC Software

Comments

Our Privacy Policy describes how we process Personal Data about you. It also provides information on how you may exercise any of your rights as a Data Subject. You have the right to withdraw your consent at any time by clicking on the "Unsubscribe" link within the electronic marketing communications sent to you. By submitting this form, you agree to our Terms of Use.

SumiRiko: Machine Learning Driven Activated Carbon Characterization with ODYSSEE CAE