压降是纤维滤棒性能质量的重要指标，其与材料和几何结构密切相关，为建立用于纤维滤棒设计与生产验证的新方法，通过结合有监督学习方法，以丝束单旦／总旦、长度和圆周为自变量，引入多元回归方法建立滤棒压降的预测模型；对滤棒进行多区域协同建模，并基于多孔介质模型模拟气体滤棒内流动行为，获得适用于表征丝束规格的压降渗透系数。结果表明：压降与滤棒长度具有正相关的线性关系，且改变滤棒丝束类型对压降的影响大于改变圆周，证明滤棒压降受到多因素共同影响；基于多元线性回归模型，可以设计压降值为目标快速反推出滤棒长度、圆周及丝束类型的组合方式；基于数值模型对所预测滤棒参数组合进行模拟以验证生产参数的合理性，确定结合多元线性回归模型和数值模型的方法能够有效指导滤棒的设计和生产。

Pressure drop plays an important effect on the performance of fiber filter-stick, and is determined by the materials and geometry structure. In order to develop a method to design and guide the production of the fiber filter, the multiple regression method uses the density of fiber tow, length, and circumference of the sticker as the argument to train the pressure drop model based on the production data. The fiber filter is modeled as multizone represented as the fiber tow and the forming paper. The flow dynamics in these zones are simulated based on the porous media model. The osmotic coefficients represent the pressure drop in the zone packed with the fiber tow. The simulation results show that the pressure drop is positively correlated with the length of the fiber rob, and the type of filter tow has a greater influence on the pressure drop than the circumference. For the design of the fiber filter, the regression model is first used to obtain the consumption of the fiber tow based on the design pressure drop. Then the simulation based on the porous media model is carried out to validate the prediction. If the error between the two methods is within 10%, this predicted fiber stick can be produced. By analyzing the production data and the prediction from models, it is concluded that the method proposed in this work is sufficient to direct the design and production.