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Lecture

Boltzmann Equation: Scattering Term

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 Boltzmann equation, focusing on the scattering term and carrier scattering, including phonons, electrons, photons, and molecules. It delves into the phase-space, Liouville equation, and relaxation time approximation.

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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)

Scattering

Scattering is a term used in physics to describe a wide range of physical processes where moving particles or radiation of some form, such as light or sound, are forced to deviate from a straight trajectory by localized non-uniformities (including particles and radiation) in the medium through which they pass. In conventional use, this also includes deviation of reflected radiation from the angle predicted by the law of reflection.

Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.

Empirical distribution function

In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified value of the measured variable is the fraction of observations of the measured variable that are less than or equal to the specified value.

Statistical assumption

Statistics, like all mathematical disciplines, does not infer valid conclusions from nothing. Inferring interesting conclusions about real statistical populations almost always requires some background assumptions. Those assumptions must be made carefully, because incorrect assumptions can generate wildly inaccurate conclusions. Here are some examples of statistical assumptions: Independence of observations from each other (this assumption is an especially common error). Independence of observational error from potential confounding effects.

File system

In computing, a file system or filesystem (often abbreviated to fs) is a method and data structure that the operating system uses to control how data is stored and retrieved. Without a file system, data placed in a storage medium would be one large body of data with no way to tell where one piece of data stopped and the next began, or where any piece of data was located when it was time to retrieve it. By separating the data into pieces and giving each piece a name, the data are easily isolated and identified.

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