The lungs are among the most deformable body organs, a mechanical feature that is key to the vital process of breathing. Current micromechanical constitutive models of the lung parenchyma construct the tissue response function either as strain-driven or pressure-driven. However, the lung parenchyma resembles an open-cell foam material consisting of a solid phase and a fluid phase that closely interact with each other. In this work, we introduce a novel finite-deformation micromechanical poroelastic model of the lung parenchyma. Using a two-scale homogenization framework for poroelasticity, we construct the effective coarse-scale response of the tissue by solving a poroelastic fine-scale problem. To this end, we develop a non-linear structural model based on a tetrakaidecahedron (TKD) unit cell that only depends on four microstructural parameters. We validate the TKD model showing that it predicts the effective response of representative volume elements (RVE) constructed from micro-computed-tomography images of the lung under several combinations of deformation and alveolar pressure. Further, we show that the estimation of the effective stress using the TKD model delivers a speed-up in computation time of more than 284,000 × when compared to RVE simulations, at the same time that it delivers higher numerical stability. In addition, we demonstrate through a sensitivity analysis that the model response predominantly depends on the alveolar-wall elasticity and initial tissue porosity, which are parameter values that are inherently connected to measurable microstructural features of the lung tissue. The present TKD model opens the door to large-scale poroelastic simulations of the lung by providing a predictive yet efficient constitutive model of the lung parenchyma. Codes are available for download at https://github.com/dehurtado/PoroelasticTKDModel.

肺部是最易变形的身体器官，这是至关重要的呼吸过程的关键机械特征。当前的肺实质的微机械本构模型构建了应变驱动或压力驱动的组织反应功能。然而，肺实质类似于开孔泡沫材料，其由彼此紧密相互作用的固相和液相组成。在这项工作中，我们介绍了一种新型的肺实质的有限变形微机械多孔弹性模型。使用多孔弹性的两尺度均质化框架，我们通过解决多孔弹性的细尺度问题，构造了组织的有效粗尺度响应。为此，我们开发了一个基于四十二面体（TKD）晶胞的非线性结构模型，该模型仅取决于四个微结构参数。我们验证了TKD模型，该模型表明它预测了在变形和肺泡压力的几种组合下，由肺部微计算机断层扫描图像构建的代表体积元素（RVE）的有效响应。此外，我们显示，与RVE仿真相比，使用TKD模型估算有效应力可以使计算时间加快284,000×以上，同时还可以提供更高的数值稳定性。此外，我们通过敏感性分析证明，模型响应主要取决于肺泡壁弹性和初始组织孔隙度，这些参数值固有地与肺组织的可测量微观结构特征相关。当前的TKD模型通过提供肺实质的预测性但有效的本构模型，为大规模的肺多孔弹性模拟打开了大门。可以从https://github.com/dehurtado/PoroelasticTKDModel下载代码。