A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants.
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| Title: | A Physics-Informed Manifold Neural Operator Framework for Multi-Parameter Prediction of Polymer Aging in HTPB Solid Propellants. |
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| Authors: | Liu, Shun1 (AUTHOR), Qiang, Hongfu1 (AUTHOR), Geng, Tingjing1 (AUTHOR), Wang, Xueren1 (AUTHOR) rocketwangxr@126.com, Pei, Shudi1 (AUTHOR), Ju, Xin1 (AUTHOR) |
| Source: | Polymers (20734360). Jun2026, Vol. 18 Issue 11, p1400. 26p. |
| Subjects: | Solid propellants, Crosslinking (Polymerization), Machine learning, Viscoelasticity, Prediction models, Polymer degradation |
| Abstract: | Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mechanisms. This study proposes a Physics-Informed Manifold Neural Operator (PIMANO) framework for multi-parameter prediction of polymer aging in HTPB solid propellants. Accelerated thermal aging, stress relaxation, and swelling experiments were used to obtain aging temperature, aging time, crosslinking density, and viscoelastic Prony-series parameters. A continuous aging-state field was first reconstructed over the temperature–time domain by radial basis function interpolation. Crosslinking density was then introduced as a physically interpretable bridge-state variable linking aging conditions with viscoelastic responses. Among three candidate kinetic models, the modified Arrhenius–Avrami model gave the best fitting performance for crosslinking-density evolution, with R2 = 0.988 and MRE = 0.0199. By combining local multi-scale neighborhood features, manifold latent representations, and DeepONet-based operator learning, PIMANO established a unified mapping from aging conditions to multi-parameter viscoelastic responses while incorporating bridge-state consistency, parameter non-negativity, and evolution-direction constraints. Under the RBF-augmented validation setting, PIMANO-ae achieved RMSE = 0.7847, MAE = 0.3366, R2 = 0.9995, and MRE = 0.0027. Compared with the traditional model, RMSE, MAE, and MRE were reduced by 94.93%, 96.47%, and 96.85%, respectively. Temperature leave-one-out validation further yielded average R2 values of 0.9469–0.9647 and MRE values of 4.98–6.21% at unseen aging temperatures. These results demonstrate that PIMANO provides an accurate, stable, and physically interpretable framework for multi-parameter aging prediction and life-assessment modeling of polymer-based energetic materials. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mechanisms. This study proposes a Physics-Informed Manifold Neural Operator (PIMANO) framework for multi-parameter prediction of polymer aging in HTPB solid propellants. Accelerated thermal aging, stress relaxation, and swelling experiments were used to obtain aging temperature, aging time, crosslinking density, and viscoelastic Prony-series parameters. A continuous aging-state field was first reconstructed over the temperature–time domain by radial basis function interpolation. Crosslinking density was then introduced as a physically interpretable bridge-state variable linking aging conditions with viscoelastic responses. Among three candidate kinetic models, the modified Arrhenius–Avrami model gave the best fitting performance for crosslinking-density evolution, with R2 = 0.988 and MRE = 0.0199. By combining local multi-scale neighborhood features, manifold latent representations, and DeepONet-based operator learning, PIMANO established a unified mapping from aging conditions to multi-parameter viscoelastic responses while incorporating bridge-state consistency, parameter non-negativity, and evolution-direction constraints. Under the RBF-augmented validation setting, PIMANO-ae achieved RMSE = 0.7847, MAE = 0.3366, R2 = 0.9995, and MRE = 0.0027. Compared with the traditional model, RMSE, MAE, and MRE were reduced by 94.93%, 96.47%, and 96.85%, respectively. Temperature leave-one-out validation further yielded average R2 values of 0.9469–0.9647 and MRE values of 4.98–6.21% at unseen aging temperatures. These results demonstrate that PIMANO provides an accurate, stable, and physically interpretable framework for multi-parameter aging prediction and life-assessment modeling of polymer-based energetic materials. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20734360 |
| DOI: | 10.3390/polym18111400 |
