Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel the future of Mechanics of Materials. The qualification of critical components requires a comprehensive assessment of the mechanical response under multiple loading conditions. Certification procedures requires the survival of components under realistic in-service conditions that can couple various degradation mechanisms such as fracture, fatigue, corrosion, wear, creep, etc. However, most damage prognosis methodologies lack strategies for the integration of damage mechanisms and rely on assessments for each mechanisms independently. Certainly, the decoupling of damage mechanisms carries a risk and conveys large uncertainty. An accurate prediction of the mechanical response of metallic materials should take into account the dominant degradation mechanisms at multiple length scales. However, most modelling approaches rely on correlations between phenomenological formulations and macroscopic experiments that are inaccurate at micro- and meso-scales. To mitigate modelling uncertainty, current leading research efforts employ multiscale and multiphysics approaches informed at many length scales. This PhD position will investigate the mechanical response of engineering alloys under various loading conditions (e.g., strain rate, temperature, and loading history) by informing physics-based attributes across scales. The work will integrate experimental information and multiscale computational approaches to validate a computational predictive tool amenable to engineering applications. The candidate will cooperate with leading industrial partners to link fundamental materials research with manufacturing routes to develop novel technologies and improve the science base of manufacturing research.

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Multiscale Predictive Approaches Applied to Mechanics of Materials PhD,,

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