Advanced multiscale computational mechanics for physiopathological behavior analysis of tissues and organs

Leitung: | P. Wriggers, M. Marino |
E-Mail: | marino@ikm.uni-hannover.de |
Jahr: | 2015 |
Förderung: | Alexander von Humboldt-Stiftung |
Ist abgeschlossen: | ja |
The physiological functionalities of large biological structures are highly affected by the mechanics of living tissues which is, in turn, related to microstructural arrangement of histological constituents and biochemical environment at nanoscale. In this framework, homogenization methods were recently conceived for describing tissue elastic behavior and coupling tissue microstructure with molecular nanomechanics. Such an approach, namely the structural multiscale one, allows to avoid the employment of phenomenological laws for addressing biochemical informations in the constitutive modeling.
Present research activity aims to develop a novel tissue multiscale description, including also inelastic mechanisms, coupled with an advanced computational formulation under finite strain and large-displacement assumptions. Polyconvex anisotropic strain energies, finite plasticity theory, and the theory of generalized standard materials will be employed for obtaining analytical and numerical homogenization schemes suitable for predicting the in-vivo behavior of human organs. Models will be developed at nano-/micro-/macroscale and coupled in a unique computational framework through multiscale compatibility relationships. Numerical campaigns of validation will be conducted together with sensitivity analyses that will allow to identify the dominant mechanisms to be upscaled at tissue level.
As a result, an innovative in-silico tool for simulation of organs and large biological structures will be developed. Since all model parameters will be related to in-vivo measurable properties, patient-specific approaches will be employed for model calibration as well as for incorporating histological/biochemical (micro/nanoscale) alterations. Accordingly, the developed virtual environment will allow to predict pathology-related damage or pharmacological-related healing, providing novel diagnostic and clinical indications for highly patient-specific medical treatments.