The decentralized fuzzy inference method (DFIM) is employed as an optimization technique to reconstruct time- and space-dependent heat flux of two-dimensional (2D) participating medium. The forward coupled radiative and conductive heat transfer problem is solved by a combination of finite volume method and discrete ordinate method. The reconstruction task is formulated as an inverse problem, and the DFIM is used to reconstruct the unknown heat flux. No prior information on the heat flux distribution is required for the inverse analysis. All retrieval results illustrate that the time- and space-dependent heat flux of participating medium can be exactly recovered by the DFIM. The present method is proved to be more efficient and accurate than other optimization techniques. The effects of heat flux form, initial guess, medium property, and measurement error on reconstruction results are investigated. Simulated results indicate that the DFIM is robust to reconstruct different kinds of heat fluxes even with noisy data.

分散模糊推理方法 (DFIM) 被用作一种优化技术来重建二维 (2D) 参与介质的时间和空间相关热通量。前向耦合辐射和传导传热问题采用有限体积法和离散纵坐标法相结合的方法求解。重建任务被表述为一个逆问题，并且DFIM用于重建未知的热通量。逆分析不需要有关热通量分布的先验信息。所有的反演结果表明，DFIM可以准确地恢复参与介质的时空相关热通量。事实证明，本方法比其他优化技术更有效、更准确。热流形式、初始猜测、介质性质的影响，并对重建结果的测量误差进行了研究。模拟结果表明，即使有噪声数据，DFIM 也能很好地重建不同类型的热通量。