Thermal metamaterials represent an innovative class of materials characterized by unique thermal properties rarely found in nature, owing to their novel structures. These materials offer the capability to manipulate heat flow, presenting applications that contribute to enhancing the efficiency of thermal appliances, advancing heat transfer, facilitating heat energy harvesting, constructing thermal circuits, and exploring novel applications like detection-antidetection and thermal computing.
Traditional approaches to thermal metamaterial design rely on analytical methods, such as transformational thermotics and scattering cancellation methods, that struggle with accommodating complex geometries, diverse boundary conditions, and various design constraints or regularizations. In response to these limitations, we advocate for the adoption of a more flexible and versatile numerical tool based on structural optimization for the design of thermal metamaterials.
Structural optimization not only surmounts the limitations inherent in conventional design methods but also introduces flexibility and robustness often absent in analytical approaches. Unlike transformation thermotics, it does not require a pre-established form-invariance of governing equations or intuition-based coordinate transformations, providing a more adaptable and efficient avenue for creating advanced thermal metamaterials.
References:
Jansari, Chintan, Stéphane PA Bordas, and Elena Atroshchenko. "Design of metamaterial-based heat manipulators by isogeometric shape optimization." International Journal of Heat and Mass Transfer 196 (2022): 123201.
Jansari, Chintan, Stéphane PA Bordas, and Elena Atroshchenko. "Design of metamaterial-based heat manipulators by isogeometric level-set topology optimization." Structural and Multidisciplinary Optimization (2023).
The general interest of the project is on a shape-shifting robotic material able to transform into any shape or machine. In the majority of examples, the common superior capabilities of the shape-shifters are their adaptability to external environments or tasks and their tolerance to damage. The foreseeable application domains of shape-shifters seem as futuristic as the technology itself. Try to imagine a future computer game in which you can physically interact with avatars of other online players. In medicine, a shape-shifting material could be injected into the bloodstream, enter the desired areas in organs and treat them. Such adaptable, multi-functional, shape-shifting devices are indeed exciting prospects which could change the way we interact with the world around us. Yet they are still in their infancy, and we remain unable to predict which of the exciting potential applications are actually achievable.
In this project, the shape-shifters, also known as programmable matter, are viewed as structures composed of interconnected, microscopic, active robotic modules, which are able to process and exchange information, reconnect and move with respect to their neighbours. As such, they compose a computing network of continuously changing connection topology, which must collectively decide how to physically reorganise (similarly to fire-ants, which can form engineering structures from their bodies). One of the obstacles for them to freely reorganise is that, when shape-shifting proceeds, these physical computing collectives may experience a mechanical failure. Just like any other structure, a modular robot may break or turn over if it is not properly balanced. The goal of this project is to appropriately design and programme the collective to achieve both: structurally-safe and efficient transformations of its shape.
References:
CORDIS project report: https://cordis.europa.eu/project/id/800150/reporting
Piranda B., Chodkiewicz P., Hołobut P., Bordas S.P.A., Bourgeois J., Lengiewicz J. (2021) Distributed prediction of unsafe reconfiguration scenarios of modular robotic programmable matter, IEEE Transactions on Robotics, vol. 37, no. 6, pp. 2226-2233 .
Hołobut P., Bordas S.P.A., Lengiewicz J. (2020) Autonomous model-based assessment of mechanical failures of reconfigurable modular robots with a conjugate gradient solver, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.11696-11702 .