Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants

As a particle-reinforced polymer composite with high inclusion ratio, the macro-mechanical properties of solid propellants depend on their meso-structures. Especially, under external loads, the stress concentration usually happens besides the regions of the initial imperfections and the particle agglomerations, leading to the interfacial debonding between the particles and the polymeric binders, consequently deteriorating macroscopic mechanical properties. How to build a relationship between the microscopic damage states and the macroscopic mechanical performances is the key issue for both the rational usage of the microscopic experimental results of solid propellants and the accurate prediction of structural disasters in solid rocket motors. For this purpose, this article develops an artificial neural network (ANN) based on the framework of continuum mechanics, with the scalar invariant of the deformation gradient tensor as the input and the scalar free energy as the output. Existing free energy functions and damage growth functions are selected as the activation functions of the ANN, and therefore the ANN can naturally satisfy the requirements of the continuum mechanics, including the deformation continuity, the coordinate invariance, and the thermodynamic consistency. These merits can guarantee the rapid convergence of the ANN with sparse training data, and additionally can obtain a bottom-up mapping of the microscopic damage states towards the macroscopic mechanical performances. Finally, using the dataset obtained from finite element analysis, the predictive ability of the ANN on the mechanical properties of solid propellants with different pre-damage states under uniaxial tension, biaxial tension, and pure shear are validated.