Seminar & Colloquium
[세미나: 12월 15일(금), 오후 2시] Prof. Junichiro Shiomi, Institute of Engineering Innovation, School of Engineering, the University of Tokyo
Title
Materials informatics for heat transfer
Speaker
Prof. Junichiro Shiomi, Institute of Engineering Innovation, School of Engineering, the University of Tokyo
Bio
Junichiro Shiomi is Professor in Institute of Engineering Innovation, School of Engineering, the University of Tokyo (UTokyo). He received B.E. (1999) from Tohoku University, and Ph. D. (2004) from Royal Institute of Technology (KTH), Sweden. Leading the Thermal Energy Engineering Lab, he has been pursuing research to advance thermal management, waste heat recovery, and energy harvesting technologies based on nano-to-macro innovation in materials, structures, and systems. Dr. Shiomi has been leading several projects including Grant-in-Aid for Scientific Research (S) (JSPS), Core Research for Evolutional Science and Technology (JST-CREST), Precursory Research for Embryonic Science and Technology (JST-PRESTO), and New Energy and Industrial Technology Development Organization (NEDO) projects. He is Fellow of Japan Society of Mechanical Engineers and Member of Science Council of Japan. He serves as an associate editor of Nanoscale and Microscale Thermophysical Engineering. He is a recipient of the Zeldovich Medal from the Committee on Space Research, the Commendation for Science and Technology by the Minister of Educational, Culture, Sports, Science and Technology, the Academic award of Heat Transfer Society of Japan, the Academic Award of Thermoelectric Society of Japan, the JSPS Award, and the Nukiyama Memorial Award.
| Date | Friday, December 15th , 2023
| Time | 14:00 ~
| Venue | 온라인(https://snu-ac-kr.zoom.us/j/96112982141?pwd=WDZ1dEIrQWVSK255bXcvbjdkOXR0QT09)
ID: 961 1298 2141
PW: 1010
[Abstract]
Over the last decades, a great advance has been made in the development of thermal function material through nanostructuring. For lattice heat conduction, nanostructures with length scale comparative to or smaller than the characteristic length of phonon transport can greatly reduce thermal conductivity, which is useful for thermoelectrics and thermal insulators. When considering phonon as a particle, the characteristic length is the intrinsic mean free path of incoherent particles collision, and extrinsic collision with surfaces and interfaces of nanostructure shortens the effective mean free path. On the other hand, when considering phonon as a wave, the characteristic length is the coherence length of phonon wave, and interfaces of nanostructure, when their roughness is smaller than the phonon wavelength, can give rise to interference and impede propagation of phonon. These effects of nanostructure on phonon transport clearly depend on the frequency, wavevector, and polarization of phonon, leading to spectral controllability of heat conduction. For thermal radiation, the picture is less complicated because its wavelength and coherence length are much larger, and coherent and spectral control of photon transport can be realized by larger structures. Whether it is lattice heat conduction or thermal radiation, nanostructuring massively broadens the structure degrees-of-freedom, and the challenge is to identify the optimal nanostructure to maximize the figure of merit (FoM) of interest. This is where materials informatics (MI), which is to develop or study materials with an aid of informatics or machine learning, becomes useful. Use of MI for heat transfer and thermal functional materials started later than other fields but now there are growing number of reports showing good compatibility. A typical approach is to train a black box model that relates basic descriptors (structure, composition, etc) and FoM (target properties) and predict or design a material with largest FoM. At Thermal Energy Engineering Lab at University of Tokyo, together with the collaborators, we have been working on MI for heat transfer since 2015. One of the initial works was to design binary multilayered nanostructure to minimize or maximize thermal conductance by coupling thermal transport calculation and Bayesian optimization, which showed excellent efficiency. Later, the search space has been greatly expanded by utilizing quantum annealing. We have applied the methodology to computationally design and experimentally realize aperiodic superlattice that optimally impedes coherent thermal transport and multilayer metamaterial with wavelength-selective thermal radiation. Recently this has evolved to autonomous MI experiments. In the talk, I will introduce these progresses and discuss the capability of MI for heat transfer and remaining challenges for further development.
| Host | 장혜진 교수(02-880-7096)