Numerical dispersed flow simulation of fire-flake particle dynamics and its learning representation
Jong-Hyun Kim, Jung Lee
TL;DR
This paper introduces new simulation techniques for fire-flake particles influenced by flame movement, using chaotic advection and learning-based approaches for realistic and efficient representation.
Contribution
The novel contribution is combining chaotic advection and learning-based solvers to simulate fire-flake dynamics influenced by flame and airflow.
Findings
Chaotic advection and controlled buoyant flow techniques effectively simulate fire-flake particle motion.
A synthetic fire-flake dataset was generated using the proposed simulation methods.
A learning-based solver improves the representation of fire-flake dynamics compared to traditional methods.
Abstract
In this article, we propose methods for simulating the detailed flow of dispersed fire-flake particles in response to the movement of a flame, using chaotic advection and various buoyant flow techniques. Furthermore, we utilize these techniques to gather a synthetic dataset of detailed fire-flake particles and extend the solver to represent the movement of fire-flake particles based on learning-based approaches. Fire-flake particles not only exhibit unique and complex movements on their own, but they are also significantly influenced by the movement of the flame and the surrounding airflow. Modeling the flow of fire-flake particles realistically is challenging due to their chaotic and constantly changing nature. Instead of explicitly modeling the complex fire-flake particles in the flame based on fluid mechanics, this article efficiently approximates the chaotic motion of fire-flake…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Evacuation and Crowd Dynamics · Data Visualization and Analytics