森林地表火蔓延的加速效应及其物理预测模型研究

发展森林地表火蔓延加速预测技术，是森林火灾预测预警技术所须突破的关键瓶颈问题，为此从基础层面须解决的重大科学问题是森林地表火蔓延的加速机制与规律。本项目紧密围绕该重大科学问题开展深入研究，包括：1)研究地表火前锋合并诱发火蔓延加速效应，揭示低强度火前锋相互作用产生火焰合并而诱发高强度火前锋的物理机理；2)研究复杂地形下的地表火蔓延加速效应，揭示高坡度地形下火焰附着和火旋风导致的对流传热强化机制，以及峡谷地形下复杂火线演变模式和对流强化传热机制；3)研究环境风作用下的地表火蔓延加速效应，揭示环境风作用下连续和离散燃料的火蔓延传热模式和加速机制，建立环境风作用下耦合飞火颗粒燃烧动力学的飞火输运模型。项目有望从物理层面系统揭示燃烧-流动-传热耦合作用下地表火蔓延的加速效应，形成的理论和模型可为森林火灾（尤其是高强度火灾）的防控与应急决策提供重要参考依据和工具，有力提升森林火灾预测预警能力。

The development of prediction technology for forest surface fire spread acceleration is the key bottleneck problem for forest fire prediction and warning technology. Correspondingly, the major scientific problem to be solved is the acceleration mechanism and effects of forest surface fire spread. This project, systematically probing into the acceleration effect of forest surface fire spread, intends to 1) study the acceleration effect induced by merging of fire fronts, and reveal the physical mechanism of high-intensity fire fronts induced by interaction and flame merging of low-intensity fire fronts; 2) investigate the acceleration effect of surface fire spread in complex terrains, and explore the strengthening mechanism of convective heat transfer caused by flame attachment and firewhirls in high-slope terrains, as well as the complex evolution mode of fire lines and the strengthening mechanism of convective heat transfer in canyon terrains; 3) analyze the acceleration effect of surface fire spread under the ambient wind, reveal the fire spread heat transfer mode and acceleration mechanism of continuous and discrete fuels under the ambient wind, and establish the fire spotting model coupled with the combustion dynamics of fuel particles under the ambient wind. The project aims to fully elucidate the physical acceleration mechanisms of surface fire under the coupled effects of combustion, flow and heat transfer. The theories and models to be established are expected to enrich important reference bases and tools for the prevention and control of forest fire (especially high-intensity forest fire), and thus substantially improve the accuracy of forest fire prediction and warning.