Degradation-entropy Generation Theorem Research Articles

Gear wear prediction based on the theorem of degradation entropy generation

Tooth surface material loss caused by gear wear alters the surface morphology of gears, which impacts their vibration, noise, and remaining lifespan. Although gear wear modeling and prediction have been extensively studied, this paper proposes a novel approach based on the theorem of degradation entropy generation (DEG). A point-by-point calculation method is introduced to determine the degradation coefficient for each measurement point on the tooth profile, accounting for varying working conditions along the tooth profile during the actual meshing process of the gear pair. First, the FZG gear’s bearing capacity is tested. Next, a surface roughness profilometer is employed to in-situ measure the tooth profile after each load stage. The profile deviation curve and the amount of profile wear following each load stage are obtained by processing the measured profile morphology data. Then, the pitting safety factor for each point on the tooth surface is calculated according to the ISO 6336–22:2018 standard and used to correct the degradation coefficient for that point. Finally, the entropy generation of the system during each load stage of the FZG gear is calculated. The degradation coefficient suggested in the DEG theorem is employed to link gear wear with system entropy generation, realizing gear wear modeling and prediction. The results demonstrate that the gear wear calculation method based on the DEG theorem can accurately predict the evolution of tooth profile surface morphology during the experimental process. This research provides a unified calculation method for surface morphology evolution caused by gear wear during service.

Predicting fatigue crack growth rate under block spectrum loading based on temperature evolution using the degradation-entropy generation theorem

This study presents the temperature evolution of dual-phase steel in the prediction of the fatigue crack growth rate under block spectrum loading. The degradation coefficient that can be determined from the degradation-entropy generation theorem can be obtained based on the relationship between entropy generation and crack growth. The fatigue crack growth rate under block spectrum loading was then evaluated using the measured surface temperature evolution of the specimen during a fatigue crack growth test. The results of the present model were then compared with experimental fatigue data and the correlation coefficient $$(R^{\mathrm {2}})$$ in all conditions was observed to be more than 0.8. Therefore, the results show that entropy generation of the specimen is a reliable parameter in predicting fatigue crack growth rate under block spectrum loading using the intensity of the degradation coefficient.

A temperature-only system degradation analysis based on thermal entropy and the degradation-entropy generation methodology

System degradation and material transformation are detected, analyzed and predicted using only temperature measurements. The method combines fundamental thermal energy and entropy balances with the degradation-entropy generation theorem to characterize multi-physics system-process interactions. Various experimental measurements verify the analysis and results. A universally consistent system- and process-independent model is derived, that relates heat transfer and heat storage to the system's phenomenological transformation and renders new material and process parameters for system analysis. The recently proposed maximum work-based DEG methodology is reviewed and compared to the temperature-only approach of this article. Applications to general fatigue, grease aging and lithium-ion battery degradation cycling are presented. System- and process-characteristic degradation coefficients are obtained from measured data. Similar to the maximum work models, a near 100% correlation between the temperature-only model and the nonlinear data from uncontrolled experimental measurements is observed. Model-predicted trends accord with established system behavior. The approach can be easily adapted to all systems undergoing active thermal transformations for performance characterization and optimization.

Thermodynamics of Fatigue: Degradation-Entropy Generation Methodology for System and Process Characterization and Failure Analysis.

Formulated is a new instantaneous fatigue model and predictor based on ab initio irreversible thermodynamics. The method combines the first and second laws of thermodynamics with the Helmholtz free energy, then applies the result to the degradation-entropy generation theorem to relate a desired fatigue measure—stress, strain, cycles or time to failure—to the loads, materials and environmental conditions (including temperature and heat) via the irreversible entropies generated by the dissipative processes that degrade the fatigued material. The formulations are then verified with fatigue data from the literature, for a steel shaft under bending and torsion. A near 100% agreement between the fatigue model and measurements is achieved. The model also introduces new material and design parameters to characterize fatigue.

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

Presented is a lithium-ion battery degradation model, based on irreversible thermodynamics, which was experimentally verified, using commonly measured operational parameters. The methodology, applicable to all lithium-ion batteries of all chemistries and composition, combined fundamental thermodynamic principles, with the Degradation–Entropy Generation theorem, to relate instantaneous capacity fade (loss of useful charge-holding capacity) in the lithium-ion battery, to the irreversible entropy generated via the underlying dissipative physical processes responsible for battery degradation. Equations relating capacity fade—aging—to battery cycling were also formulated and verified. To show the robustness of the approach, nonlinear data from abusive and inconsistent battery cycling was measured and used to verify formulations. A near 100% agreement between the thermodynamic battery model and measurements was achieved. The model also gave rise to new material and design parameters to characterize all lithium-ion batteries.

Thermodynamics of Lead-Acid Battery Degradation: Application of the Degradation-Entropy Generation Methodology

This article details a lead-acid battery degradation model based on irreversible thermodynamics, which is then verified experimentally using commonly measured operational parameters. The model combines thermodynamic first principles with the Degradation-Entropy Generation theorem, to relate instantaneous and cyclic capacity fade (loss of useful charge-holding capacity) in the lead-acid battery to the entropy generated via the underlying dissipative physical processes responsible for battery degradation. Equations relating capacity fade to battery cycling are also formulated and verified. To show robustness of the approach, nonlinear data from uncontrolled and severely abusive battery cycling—including overdischarge—was measured and used to verify formulations. A near 100% agreement between the thermodynamic battery model and measurements is achieved. The model also gives rise to new material and design parameters to characterize all lead-acid batteries.

Entropy-Based Approach for Fatigue Crack Growth Rate of Dual-Phase Steel

This paper presents an entropy-based approach for the fatigue crack growth of dual-phase steel under a constant amplitude loading. According to the degradation-entropy generation theorem, the degradation coefficient can be derived from the correlations of entropy and crack propagation. The temperature evolutions induced for the duration of the fatigue crack growth tests on the as-received and dual-phase steel till it failed were measured to ensure their validity. The results of the present model and the calculated Paris-regime crack growth data were analysed to reach the conclusion that the temperature at the surface of a specimen during a fatigue crack growth test can be used for the assessment of fatigue crack growth by the intensity of the degradation coefficient. The predicted results showed that the present model could accurately predict the fatigue crack growth rate of dual-phase steel with a regression value (R2) of 0.9952.

Evaluation of fatigue crack growth rate for iron-carbon metals based on degradation-entropy generation theorem

No Abstract Keywords: degradation-entropy generation theorem; entropy generation; fatigue crack growth rate; tempered Fe-C steel

Modeling Degradation Using Thermodynamic Entropy


 
 
 Manufacture transforms raw materials into finished components. Ageing and degradation of components, driven by dissipative processes, irreversibly alter material structures. The second and third laws of thermodynamics assert that these dissipative processes must generate entropy. This entropy is a fundamental quantity to describe ageing and degradation.
 This recognition led to a Thermodynamic Degradation Paradigm encapsulated in a Degradation Entropy Generation (DEG) Theorem, wherein the rate of degradation was related to the irreversible entropies produced by the underlying dissipative physical processes that age and degrade components. This paradigm and theorem permit a structured approach to modeling degradation of any kind. If properly applied, the DEG Theorem leads to a differential equation in a variable that describes the degradation. The equation depends on the operational and environmental variables that characterize the system. Integration of the equation accumulates the degradation over time. This approach has led to accurate models for progression of and failure by wear, fatigue, and battery degradation that are consistent with prior models.
 This article will review the Thermodynamic Degradation Paradigm and Degradation Entropy Generation Theorem, and apply these to formulate predictive models of wear, fatigue, and battery degradation, i.e., differential equations that govern the degradation or ageing. The article will conclude with a discussion on how to use these governing degradation equations for machine prognosis.