The choice of time step size can have a strong impact on the behavior of FDTD algorithms. In this lecture, we provide a simple and intuitive argument on deriving an important condition on choosing time step size, known as the Courant–Friedrichs–Lewy (CFL) condition.
- Graphically illustrate how information propagates over 1D and 2D Yee grids, which aids an easy derivation of the speed of numerical dependency and the CFL condition.
- Introduce Courant number, and explain that CFL condition imposes an upper bound of time step size decided by the smallest grid size in the computational domain.
- Show that computational cost increases rapidly with spatial resolution under two considerations: spatially more grid points, and temporally finer time step size required by CFL condition.
We use necessary cookies to run this website. With your permission, we also use analytics cookies to understand site usage and marketing cookies for advertising, retargeting, and HubSpot tracking. Learn more about our cookie policy.
Choose which optional cookies Flexcompute may use. Necessary cookies are always on because they support core website behavior, security, and saving your consent record.
Your browser is sending a Global Privacy Control signal, so marketing cookies are disabled.
Thanks for subscribing
TERMS & CONDITIONS
EFFECTIVE DATE: January 1, 2025
Terms and Conditions for User-Submitted Content
By submitting content to Flexcompute, you agree to the following terms:
1. Ownership and Copyright
2. Responsibility for Content
3. Modification and Presentation
4. Liability Disclaimer
5. Content Use Rights
6. Privacy and Confidentiality
Enter your email address below to receive the presentation slides. In the future, we’ll share you very few emails when we have new tutorial release, development updates, valuable toolkits and technical guidance . You can unsubscribe at any time by clicking the link at the bottom of every email. We’ll never share your information.