Configurational Mechanics of Soft Materials: Revolutionising Geometrically Nonlinear Fracture
SoftFrac aims to advance soft fracture mechanics through innovative modeling and algorithms, enhancing the resilience of soft devices in robotics, electronics, and tissue engineering.
Projectdetails
Introduction
SoftFrac will revolutionise geometrically nonlinear fracture mechanics of soft materials (in short soft fracture) by capitalising on configurational mechanics, an unconventional continuum formulation that I helped shape over the past decades.
Impact on Soft Devices
Mastering soft fracture will result in disruptive progress in designing the failure resilience of soft devices, i.e. soft robotics, stretchable electronics, and tissue engineering applications.
Challenges of Soft Materials
Soft materials are challenging since they can display moduli as low as only a few kPa, thus allowing for extremely large deformations. Geometrically linear fracture mechanics is well established; nevertheless, it is not applicable for soft fracture given the over-restrictive assumptions of infinitesimal deformations.
Need for Nonlinear Approaches
The appropriate geometrically nonlinear, finite deformation counterpart is, however, still in its infancy. By combining innovative data-driven/data-adaptive constitutive modelling with novel configurational-force-driven fracture onset and crack propagation, I will overcome the fundamental obstacles to date preventing significant progress in soft fracture.
Research Threads
I propose three interwoven research threads jointly addressing challenging theoretical, computational, and experimental problems in soft fracture:
- Theoretical Thread: Establishes a new constitutive modelling ansatz for soft in/elastic materials and develops the transformational configurational fracture approach.
- Computational Thread: Provides the associated novel algorithmic setting and delivers high-fidelity discretisation schemes to numerically follow crack propagation driven by accurately determined configurational forces.
- Experimental Thread: Generates and analyses comprehensive experimental data of soft materials and their geometrically nonlinear fracture for properly calibrating and validating the theoretical and computational developments.
Conclusion
Ultimately, SoftFrac, for the first time, opens up new horizons for holistically exploring the nascent field of soft fracture.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.494.538 |
Totale projectbegroting | € 2.494.538 |
Tijdlijn
Startdatum | 1-1-2023 |
Einddatum | 31-12-2027 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERGpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
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Dynamic control of Gaussian morphing structures via embedded fluidic networksThe project aims to create fully controllable shape-morphing materials using hybrid elastic plates with fluid-filled cavities, enabling precise programming of shape, mechanics, and deformation dynamics for biomedical applications. | ERC Starting... | € 1.499.601 | 2025 | Details |
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Automated Model Discovery for Soft Matter Systems
The project aims to democratize constitutive modeling of soft materials through automated neural network discovery, enhancing accessibility and innovation in scientific research and training.
Solving the multi-scale problem in materials mechanics: a pathway to chemical design
Develop a groundbreaking computational framework to predict the viscoelastic and plastic behavior of complex materials across various deformation rates, overcoming current simulation limitations.
Engineering soft microdevices for the mechanical characterization and stimulation of microtissues
This project aims to advance mechanobiology by developing soft robotic micro-devices to study and manipulate 3D tissue responses, enhancing understanding of cell behavior and potential cancer treatments.
Dynamic control of Gaussian morphing structures via embedded fluidic networks
The project aims to create fully controllable shape-morphing materials using hybrid elastic plates with fluid-filled cavities, enabling precise programming of shape, mechanics, and deformation dynamics for biomedical applications.
Wide-ranging Probabilistic Physics-guided Machine Learning Approach to Break Down the Limits of Current Fatigue Predictive Tools for Metals
BREAKDOWN aims to revolutionize engineering design by integrating micro-scale material inhomogeneities into a probabilistic framework to enhance fatigue understanding and sustainability in structural applications.