Evaluación de resultados de fatiga con alto y ultra alto número de ciclos mediante un modelo de regresión de Weibull

El modelo de fatiga de regresión de Weibull, propuesto por Castillo-Canteli, representa una alternativa posible y adecuada para la evaluación y predicción de vidas de fatiga ultra altas (VHCF). Este modelo proporciona una definición probabilística del campo S-N para los posibles mecanismos de rotura determinantes, basada en distribuciones de Weibull de mínimos, así como la existencia de un límite de fatiga asintótico y la propiedad de reducir todo el campo SN a una única función de distribución mediante la variable normalizada V=(log N-B)(log Δσ-C). De este modo, el doble mecanismo de rotura, interno y superficial, que caracteriza las vidas altas (HCF) y ultra altas (VHCF) en fatiga puede ser satisfactoriamente tratado como distribuciones independientes en este particular y complejo caso de muestras con resultados concurrentes, conocido como problema de datos confundidos. Una vez estimados los parámetros de ambas funciones de distribución se procede a la combinación y reconversión de ambas como campo S-N conjunto. El modelo permite establecer una estrategia para optimizar la programación de los ensayos. Por último se presentan dos ejemplos de aplicación, uno de un programa externo experimental y otro de resultados simulados, ambos con ultra alto número de ciclos y dos posible mecanismos de rotura.The fatigue Weibull regression model proposed by Castillo-Canteli represents a possible and adequate alternative for the assessment and prediction of very high cycle fatigue (VHCF) lifetimes. This model provides a probabilistic definition of the S-N field for the two determining failure mechanisms based Weibull distributions for minima, as well as the existence of an asymptotic fatigue limit and the capability to reduce the S-N field to a single cumulative distribution function by considering the normalized variable V=(log N-B) (log Δσ). In this way, both dual fracture mechanisms, i.e. the internal and the surface ones, characterizing the HCF and VHCF data can be adequately interpreted and handled as independent distributions in such a particular and complex case of concurrent populations, known as a confounded data problem. Once the model parameters of both normalized cumulative distribution functions are independently estimated for both failure mechanisms and subsequently combined and reconverted to a joint S-N field whereby. The model allows a test strategy to be established for optimizing the the fatigue program planning. Finally, two examples of application are presented, the first related to an external experimental program, and the second to simulated data both for VCCF with twofold failure mechanisms

Impacts of climate change on the establishment, distribution, growth and mortality of Swiss stone pine (Pinus cembra L.)

Anticipated future climate changes are expected to significantly influence forest ecosystems, particularly in treeline ecotones. Climate change will have both direct and indirect effects on the future distribution of alpine tree species, some of which will be positive and others negative. Although increased temperatures are on the whole likely to have a positive impact on growth and distribution of Swiss stone pine (Pinus cembra L.), indirect effects that influence seed dispersal may threaten the population viability of species. The complexity of the interrelations between climatic and non-climatic factors demands further research, which should include long-term monitoring