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A physically-based elasto-viscoplastic constitutive model is proposed to examine the size effects of the precipitate and blocks on the creep for martensitic heat-resistant steels with both the dislocation creep and diffusional creep mechanisms considered.

The model relies upon the initial dislocation density and the sizes of M₂₃C₆ carbide and MX carbonitride, through the use of internal variable based governing equations to address the dislocation density evolution and precipitate coarsening processes. Most parameters of the model can be obtained from existing literature, while a small subset requires calibration. Based on the least-squares fitting method, the calibration is successfully done by comparing the modeling and experimental results of the steady state creep rate at 600° C across a wide range of applied stresses.

The model predictions of the creep responses at various stresses and temperatures, the carbide coarsening and the dislocation density evolution are consistent with the experimental data in literature. The modeling results indicate that considerable effect of the sizes of precipitates occurs only during the creep at relatively high stress levels where dislocation creep dominates, while the martensite block size effect happens during creep at relatively low stress levels where diffusion creep dominates. The size effect of M₂₃C₆ carbide on the steady creep rate is more significant than that of MX precipitate.

The present study also reveals that the two creep mechanisms compete such that at a given temperature the contribution of the diffusion creep mechanism decreases with increasing stress, while the contribution of the dislocation creep mechanism increases.

The purpose of this paper is to investigate the diffusion patterns of knowledge in inspiring technological innovations and to enable monitoring development trends of technological innovations based on patent data analysis, thus, to manage knowledge wisely to innovate.

The notion of knowledge innovation potential (KIP) is proposed to measure the innovativeness of knowledge by the cumulative number of patents originated from its inspiration. KIP calculating formula is regressed in forms of two specific diffusion models by conducting a series of empirical studies with the patent-based indicators involving forward and backward citation numbers to reveal knowledge managing strategies regarding innovative activities.

Two specific diffusion models for regressing KIP formula are compared by empirical studies with the result indicating the Gompertz model has higher accuracy than the Logistic model to describe the developing curve of technological innovations. Moreover, the analysis of patent-based indicators over diffusion stages also revealed that patents applied at earlier diffusion stages normally has higher forward citation numbers indicating higher innovativeness meanwhile the patents applied at the latter stages usually requiring more knowledge inflows observed by their larger non-patent citation and backward citation amounts.

Although there is a large body of literature concerning knowledge-based technological innovation, there still room for discussing the mechanism of how knowledge diffuses and inspired knowledge. To the best of authors' knowledge, this study is the first attempt to quantitate the innovativeness of knowledge in technological innovation from the knowledge diffusion perspective with findings to support rational knowledge management related to innovation activities.