In view of the advantages of CFD technology in the simulation of small and medium-scale chemical hazard diffusion, one near-field prediction model of chemical hazard diffusion named CHDNFP is constructed based on CFD technology, and the accuracy and efficiency of the model are improved by improved differential evolution algorithm (DEA) and fireworks algorithm (FWA). Firstly, based on the component conservation equation, momentum conservation equation, and turbulence control equation, CHDNFP model is constructed, whose basic solution process is proposed from three aspects: non-uniform mesh refinement in diffusion space, model discretization, and iterative solution of control equation. Secondly, comprehensive considering the global search ability, local search ability, and convergence characteristics of integrated DEA and FWA, a hybrid optimization algorithm IDEFWA is designed, which is suitable for predictive model solving. Finally, the CHDNFP model and IDEFWA are verified by tracer experiments. The result shows that: IDEFWA can reduce the relative root mean square error between the predicted concentration field and the observed concentration field to about 25%, with the calculation accuracy of 10^–19 and the standard deviation accuracy of 10^–9; compared with ABCA and GA, IDEFWA can get more accurate solutions faster under the same algebraic and population conditions; the calculation accuracy of CHDNFP–IDEFWA and PISOFOAM is almost the same, where the relative difference is about 3%, and CHDNFP–IDEFWA has better calculation accuracy than PISOFOAM, which is improved by about 26.05%.

针对CFD技术在中小规模化学危害扩散模拟中的优势，基于CFD技术构建了一种化学危害扩散近场预测模型CHDNFP，提高了模型的精度和效率通过改进的差分进化算法（DEA）和烟花算法（FWA）。首先，基于分量守恒方程、动量守恒方程和湍流控制方程，构建了CHDNFP模型，从扩散空间非均匀网格细化、模型离散化、控制迭代求解三个方面提出了其基本求解过程。方程。其次，综合考虑集成DEA和FWA的全局搜索能力、局部搜索能力和收敛特性，设计了一种混合优化算法IDEFWA，适用于预测模型求解。最后，通过示踪实验验证了 CHDNFP 模型和 IDEFWA。结果表明：IDEFWA可以将预测浓度场与观测浓度场的相对均方根误差降低到25%左右，计算精度为10^–19和 10 ^–9的标准偏差精度；与ABCA和GA相比，IDEFWA在相同的代数和种群条件下可以更快地得到更准确的解；CHDNFP-IDEFWA 和 PISOFOAM 的计算精度几乎相同，相对差异在 3% 左右，CHDNFP-IDEFWA 的计算精度比 PISOFOAM 好，提高了约 26.05%。