Kim Jong-Hyun, Lee Jung
College of Software and Convergence (Department of Design Technology), Inha University, Incheon, Michuhol-gu, Republic of South Korea.
Department of Computer Engineering, Hanbat National University, Yuseong-gu, Daejeon, Republic of South Korea.
PeerJ Comput Sci. 2025 Apr 22;11:e2836. doi: 10.7717/peerj-cs.2836. eCollection 2025.
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 particles using two approaches: 1) chaotic advection to simulate the flow and 2) controlled buoyant flow, which varies based on the temperature and lifespan of the fire-flake particles. Additionally, we collect a fire-flake dataset through this simulation and extends the solver to learn the representation of fire-flake motion using neural networks. During the advection process of fire-flake particles, a new stochastic solver is used to calculate the subgrid interactions between them. In this article, not only we propose algorithms that can express these techniques through numerical simulation, but we also extend this solver using artificial intelligence techniques to enable learning representation. By using the proposed technique, it is possible to efficiently simulate fire-flake particles with various movements in chaotic regions, and it allows for more detailed representation of fire-flake particles compared to existing methods. Unlike the typical random walk approach that adds noise randomly to the movement, our method considers the size and direction of the flame. This allows us to express fire-flake particles stably in most scenes without the need for parameter adjustments.
在本文中，我们提出了一些方法，用于利用混沌平流和各种浮力流技术来模拟分散的火片颗粒响应火焰运动的详细流动。此外，我们利用这些技术收集详细火片颗粒的合成数据集，并扩展求解器以基于基于学习的方法来表示火片颗粒的运动。火片颗粒不仅自身呈现独特而复杂的运动，而且还受到火焰运动和周围气流的显著影响。由于其混沌且不断变化的性质，逼真地模拟火片颗粒的流动具有挑战性。本文没有基于流体力学显式地对火焰中的复杂火片颗粒进行建模，而是使用两种方法有效地近似火片颗粒的混沌运动：1）混沌平流来模拟流动；2）受控浮力流，其根据火片颗粒的温度和寿命而变化。此外，我们通过此模拟收集了一个火片数据集，并扩展求解器以使用神经网络学习火片运动的表示。在火片颗粒的平流过程中，使用一种新的随机求解器来计算它们之间的亚网格相互作用。在本文中，我们不仅提出了可以通过数值模拟来表达这些技术的算法，而且还使用人工智能技术扩展了此求解器以实现学习表示。通过使用所提出的技术，可以有效地模拟混沌区域中具有各种运动的火片颗粒，并且与现有方法相比，可以更详细地表示火片颗粒。与典型的随机游走方法不同，后者在运动中随机添加噪声，我们的方法考虑了火焰的大小和方向。这使我们能够在大多数场景中稳定地表达火片颗粒，而无需进行参数调整。
