Fast active thermal cloaking through PDE-constrained optimization and reduced-order modeling
arXiv:2110.10845 · doi:10.1098/rspa.2021.0813
Abstract
In this paper we show how to efficiently achieve thermal cloaking from a computational standpoint in several virtual scenarios by controlling a distribution of active heat sources. We frame this problem in the setting of PDE-constrained optimization, where the reference field is the solution of the time-dependent heat equation in the absence of the object to cloak. The optimal control problem then aims at actuating the space-time control field so that the thermal field outside the obstacle is indistinguishable from the reference field. In particular, we consider multiple scenarios where material's thermal diffusivity, source intensity and obstacle's temperature are allowed to vary within a user-defined range. To tackle the thermal cloaking problem in a rapid and reliable way, we rely on a parametrized reduced order model built through the reduced basis method, thus entailing huge computational speedups compared to high-fidelity, full-order model exploiting the finite element method while dealing both with complex target shapes and disconnected control domains.
Accepted for publication in the Proceedings of the Royal Society A
References in corpus (6)
- Acoustic cloaking theory
- Cloaking of Matter Waves
- Approximate quantum cloaking and almost trapped states
- Hyperelastic cloaking theory: Transformation elasticity with pre-stressed solids
- Design of arbitrarily shaped acoustic cloaks through PDE-constrained optimization satisfying sonic-metamaterial design requirements
- Fick's Second Law Transformed: One Path to Cloaking in Mass Diffusion
Cited by in corpus (4)
- Design of arbitrarily shaped acoustic cloaks through PDE-constrained optimization satisfying sonic-metamaterial design requirements
- Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
- Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
- Optimal Strategies to Steer and Control Water Waves