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Lecture

Energy Transfer by Waves

In course

ME-469: Nano-scale heat transfer

In this course we study heat transfer (and energy conversion) from a microscopic perspective. First we focus on understanding why classical laws (i.e. Fourier Law) are what they are and what are their limits of validity. Next we discuss emerging opportunit ...

Description

This lecture covers the energy transfer by waves, including photons, electrons, and phonons, discussing waves at interfaces, evanescent waves, and tunneling. It explores topics such as nanoscale heat transfer, quantum mechanics, statistical thermodynamics, and energy conversion mechanisms.

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Instructor

Giulia Tagliabue

Dr. Giulia Tagliabue is an Assistant Professor at the Institute of Mechanical Engineering (IGM). She obtained her B.S. and M.S degrees cum laude in Mechanical Engineering from the University of Udine in Italy. Concurrently she also obtained the diploma from the Scuola Normale Superiore of Udine. In 2015 she obtained her PhD in Mechanical Engineering from ETH Zurich with a thesis on nanophotonic design for light-to-heat and light-to-charge conversion devices. In 2015 Dr. Tagliabue was awarded the Early Mobility Fellowship from the Swiss National Science Foundation and moved to Caltech as a PostDoctoral fellow. In 2017, thanks to the award of an Advanced Mobility Fellowship, she prolonged her stay at Caltech. Here, in collaboration with the Joint Center for Artificial Photosynthesis (JCAP) she investigated coupled light, heat and charge transfer processes in nanophotonic structures and low-dimensional materials for photoelectrochemical and photovoltaic sustainable energy conversion.

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Related concepts (32)

Wave

In physics, mathematics, engineering, and related fields, a wave is a propagating dynamic disturbance (change from equilibrium) of one or more quantities. Waves can be periodic, in which case those quantities oscillate repeatedly about an equilibrium (resting) value at some frequency. When the entire waveform moves in one direction, it is said to be a traveling wave; by contrast, a pair of superimposed periodic waves traveling in opposite directions makes a standing wave.

Statistical mechanics

In physics, statistical mechanics is a mathematical framework that applies statistical methods and probability theory to large assemblies of microscopic entities. It does not assume or postulate any natural laws, but explains the macroscopic behavior of nature from the behavior of such ensembles. Sometimes called statistical physics or statistical thermodynamics, its applications include many problems in the fields of physics, biology, chemistry, and neuroscience.

Thermodynamics

Thermodynamics is a branch of physics that deals with heat, work, and temperature, and their relation to energy, entropy, and the physical properties of matter and radiation. The behavior of these quantities is governed by the four laws of thermodynamics which convey a quantitative description using measurable macroscopic physical quantities, but may be explained in terms of microscopic constituents by statistical mechanics.

Second law of thermodynamics

The second law of thermodynamics is a physical law based on universal experience concerning heat and energy interconversions. One simple statement of the law is that heat always moves from hotter objects to colder objects (or "downhill"), unless energy in some form is supplied to reverse the direction of heat flow. Another definition is: "Not all heat energy can be converted into work in a cyclic process." The second law of thermodynamics in other versions establishes the concept of entropy as a physical property of a thermodynamic system.

Maximum entropy thermodynamics

In physics, maximum entropy thermodynamics (colloquially, MaxEnt thermodynamics) views equilibrium thermodynamics and statistical mechanics as inference processes. More specifically, MaxEnt applies inference techniques rooted in Shannon information theory, Bayesian probability, and the principle of maximum entropy. These techniques are relevant to any situation requiring prediction from incomplete or insufficient data (e.g., , signal processing, spectral analysis, and inverse problems).

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