高翔,王子强,闫利青,等. 大型空心梁框架式模具热分布仿真与模型优化[J]. 南昌航空大学学报(自然科学版),2026,40(3):81-89,104. doi: 10.3969/j.issn.2096-8566.2026.03.008
引用本文: 高翔,王子强,闫利青,等. 大型空心梁框架式模具热分布仿真与模型优化[J]. 南昌航空大学学报(自然科学版),2026,40(3):81-89,104. doi: 10.3969/j.issn.2096-8566.2026.03.008
GAO Xiang,WANG Ziqiang,YAN Liqing,et al. Simulation of thermal distribution of large hollow beams frame mold and model optimization[J]. Journal of Nanchang Hangkong University (Natural Sciences),2026,40(3):81-89,104. doi: 10.3969/j.issn.2096-8566.2026.03.008
Citation: GAO Xiang,WANG Ziqiang,YAN Liqing,et al. Simulation of thermal distribution of large hollow beams frame mold and model optimization[J]. Journal of Nanchang Hangkong University (Natural Sciences),2026,40(3):81-89,104. doi: 10.3969/j.issn.2096-8566.2026.03.008

大型空心梁框架式模具热分布仿真与模型优化

Simulation of Thermal Distribution of Large Hollow Beams Frame Mold and Model Optimization

  • 摘要: 空心梁框架式模具具有吸热小、传热快、通风好的特点,相较于传统筋板框架式模具更适用于大型飞机复合材料壁板热压罐成型。本文通过建立流固耦合模型对大型空心梁框架式模具热分布进行仿真分析,提出热物理参数等效的方法以解决空心梁框架网格量巨大、难以计算的问题,并通过实验验证等效模型计算温度的可靠性。结果表明,减小比热容可以明显降低模具型面的滞后温度,提高热导率可以减小模具型面温差。对等效热物理参数进行优化,当比热容为128.75 J•(kg•K)−1,热导率为26.64 W•(m•K)−1时,等效模型预测误差仅为0.24 ℃,且等效系数不受升降温速率和模型尺寸的影响。通过对模型逐级放大,最终实现8 m复杂结构空心梁框架式模具的热分布预测,使计算效率提升17倍,预测误差小于5%。

     

    Abstract: Compared to traditional ribbed frame-type molds, the hollow beam frame-type mold features the advantages of low heat absorption, rapid heat transfer, and excellent ventilation, making it more suitable for autoclave curing and forming of large aircraft composite panels. This study analyzed the thermal distribution of large hollow beam frame-type molds based on fluid-solid coupling model, and proposed the method of equivalent thermal physical parameters. This method effectively addresses the computational difficulties caused by mold massive meshing, and the reliability of temperature calculation obtained by equivalent model was verified. The results indicate that reducing specific heat capacity can significantly minimize the temperature lag on the mold surface, and increasing thermal conductivity can alleviate temperature differences of the mold surface. After optimizing equivalent thermal physical parameters, the equivalent model prediction error is only 0.24 ℃ when specific heat capacity is 128.75 J∙(kg∙K)−1 and thermal conductivity is 26.64 W∙(m∙K)−1. Furthermore, the equivalent coefficient is independent of heating/cooling rates or model dimensions. Through progressive model scaling, the study achieves thermal distribution prediction for an 8 m complex hollow beam frame-type mold. The computational efficiency increases by over 17 times and the prediction error is below 5%.

     

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