A technique for characterizing spatial distributions of particles based on Nth-nearest neighbor statistics
- 83 Downloads
Spatial heterogeneity in secondary phase particle distributions can strongly influence failure processes. In developing models that capture the stochastic nature of failure, the fact that real particle distributions rarely exhibit the “true” randomness of an equilibrium ensemble (as may be generated computationally using a Metropolis algorithm ) presents a challenge. In modeling investigations, some form of “random” state has typically been assumed. Representative volume element models are often employed in which particles are added via Random Sequential Addition (RSA) [2, 3, 4, 5]. Microstructures that deviate from equilibrium have been modeled by distributing particles within randomly dispersed spherical clusters , or by adopting a cellular automata approach [7, 8, 9]; in each case, the model microstructures were arbitrarily constructed.
Enhancing the fidelity of multiphase material models requires the construction of models that recreate the true spatial statistics of real...
KeywordsPoint Process Particle Distribution Deviation Ratio Random Sequential Addition Particulate Reinforce Metal Matrix Composite
- 3.Shen H, Lissenden CJ (2002) Mater Sci Eng A338:2355Google Scholar
- 22.Cressie NAC (1993) Statistics for spatial data. Wiley Interscience, New YorkGoogle Scholar
- 26.Leggoe JW, and Riggs JB, (2005) Mater Sci Eng A submittedGoogle Scholar
- 27.Underwood EE (1970) Quantitative stereology. Addison-Wesley, ReadingGoogle Scholar