Submitted:
26 January 2026
Posted:
28 January 2026
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Abstract
Keywords:
1. Introduction
2. Background and Relation to Existing Models
2.1. Classical Viscoelastic and Internal-Variable Formulations
2.2. Hereditary iNtegrals, Fractional Models, and Memory Effects
2.3. Aging, Effective Time, and Internal-Clock Approaches
2.4. Thermodynamic Perspectives on Reversibility and Irreversibility
2.5. Positioning and Novelty of the Temporal Duality Framework
3. Temporal Duality Framework
3.1. Reversible and Irreversible Time Regimes
3.2. Dual-Time Description of Material State Variables
3.3. Coupling Between Time Regimes
3.4. Reduction to Single-Time Descriptions
4. Constitutive Modeling Under Temporal Duality
4.1. General Constitutive Relations
4.2. Thermodynamic Admissibility and Dissipation
4.3. Recovery of Classical Single-Time Models
4.4. Interpretation and Modeling Advantages
- Memory effects and rate dependence arise naturally from the dual-time structure, rather than requiring empirical convolution kernels.
- Aging is modeled as evolution along , enabling clear interpretation of time-dependent changes under quiescent conditions.
- Coupling mechanisms, such as damage-induced softening or healing, can be systematically introduced through dependencies between t and .
- The framework is compatible with existing constitutive laws and can be embedded in finite element or continuum simulation schemes without requiring wholesale reformulation.
5. Memory Effects and Aging
5.1. Memory Effects and Non-Markovian Behavior
5.2. Aging as Irreversible Time Evolution
5.3. Coupling Between Memory and Aging
- Rejuvenation, where applied mechanical loading alters or resets the aging trajectory.
- Over-aging, in which prolonged relaxation results in increased stiffness or brittleness.
5.4. Implications for Modeling and Experiments
6. Illustrative Applications
6.1. Viscoelastic Materials
6.2. Creep and Stress Redistribution
6.3. Aging in Disordered Materials
6.4. Qualitative Comparison with Experiments
7. One-Dimensional Worked Example: Linear Stress Relaxation
7.1. Model Setup
(i) Reversible (elastic) stress
(ii) Viscous (irreversible) evolution along
7.2. Governing Evolution Along and Solution (No Aging)
7.3. Relation to Laboratory (Single) Time and Classical Maxwell Model
7.4. Multi-Mode Extension (Prony-Series Analogue in )
7.5. Remarks and Modeling Implications
- Structural derivation of memory effects: The exponential relaxation in Equation (10) arises directly from irreversible evolution along , rather than from imposed hereditary kernels or integral formulations.
- Modeling flexibility for aging: Aging can be introduced structurally by allowing to evolve slowly, capturing stiffening, softening, or non-monotonic trends depending on the material.
- Experimental adaptability: The mapping connects dual-time predictions to laboratory observations. It can be generalized or fitted from experimental data to reflect complex thermomechanical histories or evolving internal clocks.
8. Numerical Simulation: Application to Creep and Stress Relaxation
8.1. Numerical Framework
8.2. Stress Relaxation Simulation
8.3. Creep Simulation
8.4. Numerical Implementation Details
8.5. Discussion of Numerical Results
- Reproduces classical viscoelastic responses as limiting cases,
- Captures non-exponential relaxation through nonlinear irreversible time evolution,
- Naturally incorporates aging effects without empirical memory kernels.
9. Empirical Illustration via Reanalysis of Stress Relaxation Data
10. Discussion
- Extensions to inelastic phenomena: Damage, healing, and phase transformation processes may be included by associating additional irreversible mechanisms with , enabling unified treatment of mechanical and structural evolution.
- Coupling with multiscale models: Incorporating the dual-time structure into multiscale frameworks could enable simultaneous resolution of microscale reversible fluctuations and macroscale irreversible evolution [21].
- Experimentally guided models: Designing experiments that probe short-time and long-time responses separately may provide data to calibrate the reversible and irreversible time contributions directly.
- Integration with data-driven approaches: The structural clarity of the dual-time framework may also benefit physics-informed machine learning or hybrid modeling schemes that combine analytical structure with experimental data [25].
Integration with Numerical Modeling Tools
- Storing dual-time-dependent internal variables (e.g., ) at each integration point.
- Modifying evolution equations to account for the nonlinear mapping .
- Updating material stiffness and damping terms according to -dependent moduli to model aging or damage.
11. Conclusions
- Dual-time formulation: Material state variables are described as functions of both reversible time t and irreversible time , enabling simultaneous representation of fast elastic response and slow structural evolution.
- Unified interpretation of classical behavior: Creep, stress relaxation, rate dependence, and aging emerge naturally from coupled evolution in the space. Classical viscoelastic and aging models are recovered as limiting cases.
- Intrinsic modeling of memory: Non-Markovian behavior arises structurally from the dual-time dependence, eliminating the need for empirical memory kernels or heuristic internal variables.
- Compatibility with existing methods: The framework complements and extends conventional constitutive models, allowing integration into standard simulation tools and theoretical treatments.
- Incorporating additional irreversible mechanisms (e.g., damage, healing, plasticity, or phase transitions).
- Calibrating dual-time models against experimental data to determine material-specific evolution laws and coupling structures.
- Integrating with multiscale or data-driven modeling approaches to bridge atomistic and continuum descriptions.
- Designing experiments that separately probe reversible and irreversible time dynamics, validating the distinct contributions of t and .
Acknowledgments
Conflicts of Interest
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| Model | Parameter | Value |
|---|---|---|
| Maxwell | ||
| AIC | ||
| BIC | ||
| Dual-time (Linear ) | ||
| 1 | ||
| AIC | ||
| BIC | ||
| Dual-time (Nonlinear ) | ||
| AIC | ||
| BIC | ||
| Best Fit? | Yes |
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