The proposed research is on modeling and control of non-equilibrium thermodynamic systems. These are physical systems that operate far away from thermodynamic equilibrium. Examples include miniaturized engines and biological processes. The classical theory of thermodynamics requires slow (quasi-static) operation and is therefore unsuitable for many applications, as in nano-technology, where time constraints are stringent. The research project seeks to close the gap between theory and applications by establishing a principled framework for modeling and control of non-equilibrium thermodynamic systems. The tools resulting from this project promise new possibilities for future nano-devices as well as a deeper understanding of mechanisms behind the apparent efficiency of biological processes at micro scales. The interdisciplinary nature of the research, bridging control engineering with thermal physics, will impact and cross-fertilize science and education in both. New course materials and experimental modules resulting from the research will attract and train talented physics and engineering students with an interdisciplinary perspective.
Non-equilibrium thermodynamics aims at physical systems that operate far from thermodynamic equilibrium. In contrast to classical irreversible thermodynamics where slow (quasi-static) operation and linearization of the underlying nonlinear dynamics provide a reasonable approximation of the physical response, non-equilibrium theory that explains the realm of fast transitions has not been achieved at present. Thus, while heat-transfer problems in mild temperature gradients can be handled within the context of the classical theory, most thermodynamic systems, especially those of minuscule size such as molecular and biological machines, operate far from equilibrium and, in addition, often experience high levels of thermal noise. General principles and reliable modeling and control that is suitable in such conditions, is crucial for the next generation of technologies of miniaturized devices. Thus, the aim of this research is to elucidate far-from-equilibrium thermodynamic transitions as well as help interface with control methods for devising future engineered thermodynamic systems. Specifically, the goal of this proposal is to lay down theoretical foundations for modeling and control of non-equilibrium thermodynamic systems. The proposal builds on these two complementary threads, modeling and control, focusing on control synthesis that ensures optimality or near-optimality when operating far from equilibrium. The proposed research is expected to advance the understanding of non-equilibrium dynamics, provide design tools and quantify attainable performance, and thereby enable future technological developments. The impact of the research extends to dynamical systems where uncertainty and noise are dominant, such as in social network games involving human interactions and in deep learning, drawing on analogies to non-equilibrium thermodynamics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
