Efficient prediction method for 3D mesoscopic stress fields in composite solid propellants based on multi-slice input
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Energetic Materials and Combustion · Rocket and propulsion systems research · Spacecraft and Cryogenic Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
Show source-provided references
1. Multiscale modeling of solid propellants: from particle packing to failure10.1016/j.compscitech.2006.06.017 ↗
2. Finite element formulation for modeling particle debonding in reinforced elastomers subjected to finite deformations10.1016/j.cma.2006.06.008 ↗
3. Comparison of the finite strain macroscopic behavior and local damage of a soft matrix highly reinforced by spherical or polyhedral particles
4. Relationship between local damage and macroscopic response of soft materials highly reinforced by monodispersed particles10.1016/j.mechmat.2020.103408 ↗
5. Finite element analysis of adhesive failure of solid composite propellant multi-material structure for underwater intelligent equipment10.1007/s11668-020-01056-9 ↗
6. Difference-based deep learning framework for stress predictions in heterogeneous media10.1016/j.compstruct.2021.113957 ↗
7. Stress field prediction in fiber-reinforced composite materials using a deep learning approach10.1016/j.compositesb.2022.109879 ↗
8. Prediction of stress-strain behavior of carbon fabric woven composites by deep neural network10.1016/j.compstruct.2023.117073 ↗
9. Load history effects in fiber–polymer composites: a CRNN-based hybrid deep learning approach for fatigue life prediction and structural health monitoring via infrared thermography10.1016/j.compositesa.2025.109263 ↗
10. Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites10.1016/j.dt.2022.09.008 ↗
11. Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network10.1016/j.cma.2024.116816 ↗
12. Mesh/membrane composite with superior mechanical performance: a deep learning-based design10.1016/j.compscitech.2022.109735 ↗
13. A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties10.1016/j.engfracmech.2024.110675 ↗
14. A neural network transformer model for composite microstructure homogenization10.1016/j.engappai.2024.108622 ↗
15. Prediction of composite microstructure stress-strain curves using convolutional neural networks10.1016/j.matdes.2020.108509 ↗
16. A deep learning method for fast predicting curing process-induced deformation of aeronautical composite structures10.1016/j.compscitech.2022.109844 ↗
17. Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization10.1016/j.commatsci.2020.109850 ↗
18. Deep learning approach for predicting multi-component stress fields in fiber-reinforced composites under different load paths10.1016/j.compscitech.2025.111198 ↗
19. A two-stage deep learning framework for predicting crack patterns and mechanical properties of unidirectional composites with void defects10.1016/j.compscitech.2025.111357 ↗
20. Bayesian neural networks for predicting uncertainty in full-field material response10.1016/j.cma.2024.117486 ↗
21. A mechanics-informed machine learning approach for modeling the elastoplastic behavior of fiber-reinforced composites10.1016/j.compstruct.2023.117473 ↗
22. Microstructural characterisation of fibre-hybrid polymer composites using U-Net on optical images10.1016/j.compositesa.2024.108569 ↗
23. Integrated convolutional and graph neural networks for predicting mechanical fields in composite microstructures10.1016/j.compositesa.2024.108618 ↗
24. A triplet attention-enhanced deep learning approach to predict full-field stress of unidirectional CFRP composites with microvoids10.1016/j.compscitech.2025.111361 ↗
25. Predicting stress and damage in carbon fiber-reinforced composites deformation process using composite U-Net surrogate model
26. Model-data-driven constitutive responses: application to a multiscale computational framework
27. Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework10.1016/j.mattod.2023.09.007 ↗
28. Solid propellant formulations: a review of recent progress and utilized components10.3390/ma14216657 ↗
29. Damage analysis of solid propellants with default defects based on macro-microscopic approach10.1016/j.matdes.2024.113261 ↗
30. Viscoelastic debonding criterion-based interface for modeling the mechanical behavior of solid propellants subjected to large deformation
31. The mesoscopic numerical simulation of GAP/CL20/AP composite solid propellant based on MPM and FEM10.3390/app13074552 ↗
32. A two-dimensional automatic mesh generator for finite element analysis for random composites10.1016/0045-7949(91)90428-O ↗
33. Random sequential adsorption: an efficient tool for investigating the deposition of macromolecules and colloidal particles10.1016/j.cis.2022.102692 ↗
34. Modeling the microstructure of a heterogeneous particle-based material with high-volume fraction10.1007/s00466-025-02636-y ↗
35. X-ray tomography study on internal damage evolution of solid propellant for carrier rockets10.1016/j.msea.2023.145451 ↗
36. Finite-deformation irreversible cohesive elements for three-dimensional crack-propagation analysis10.1002/(SICI)1097-0207(19990330)44:9<1267::AID-NME486>3.0.CO;2-7 ↗
37. Biaxial tensile test and meso damage numerical simulation of HTPB propellant
38. Williams
39. A brain tumor segmentation method based on attention mechanism
40. Wei
41. 2024 · 1