Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

A new treatment of transient grain growth

MPG-Autoren
/persons/resource/persons121298

Fratzl,  P.
Peter Fratzl, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Svoboda, J., Fratzl, P., Zickler, G. A., & Fischer, F. (2016). A new treatment of transient grain growth. Acta Materialia, 115, 442-447. doi:10.1016/j.actamat.2016.05.020.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002A-F4F4-B
Zusammenfassung
The grain radius R distribution function f ( R , t ) with R c ( t ) as critical grain radius is formulated, inspired by the Hillert self-similar solution concept, as product of 1 / R c 4 and of a shape function g ( ρ , t ) as function of the dimension-free radius ρ = R / R c and time t , contrarily to the Hillert self-similar solution concept with time-independent g ( ρ ) . The evolution equations for R c ( t ) as well as for g ( ρ , t ) are derived, guaranteeing that the total volume of grains remains constant. The solution of the resulting integro-differential equations for R c ( t ) and g ( ρ , t ) is performed by standard numerical tools. Remarkable advantages of this semi-analytical concept are: (i) the concept is a deterministic one, (ii) its computational treatment is very efficient and (iii) the shape function g ( ρ , t ) remains localized in a fixed interval of ρ . The shape function g ( ρ , t ) evolves towards the well-known Hillert self-similar distribution, which is demonstrated for two initial shape functions (one of them is triangular). Also a study on “nearly” self-similar distribution functions proposed as useful approximations of experimental data is presented.