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Lecture

Kinetic Theory of Gases: Statistical Distribution and Perfect Gas

In course

PHYS-105: Advanced physics II (thermodynamics)

Ce cours présente la thermodynamique en tant que théorie permettant une description d'un grand nombre de phénomènes importants en physique, chimie et ingéniere, et d'effets de transport. Une introduction à la physique statistique renforce les notions acqu ...

Description

This lecture covers the statistical distribution of a gas, the equipartition principle, and the virial theorem. It explains the concept of a perfect gas, the energy of particles free from external forces, and the statistical mean of a gas. The lecture also delves into the generalized coordinates, degrees of freedom, and the density of a gas in relation to collisions with walls.

Instructors (2)

Cécile Hébert

Cécile Hébert was born in France in 1970. She obtained her Ingeneer degree (physics) and her PhD degree (design of a new energy filter for transmission electron microscopy, under the direction of Prof. B. Jouffrey) at the Ecole Centrale in Paris. As a post doc with Prof. P. Schattschneider, she was working on the calculation of the fine structure of ionisation edges in EELS. As a research assistant at the Vienna University of Technology, she was one of the main participant to the CHIRALTEM project dealing with the measurement of magnetic circular dichroism in the electron microscope.

Jean-Philippe Ansermet

Jean-Philippe Ansermet was born March 1, 1957 in Lausanne (legal origin Vaumarcus, NE). He obtained a diploma as physics engineer of EPFL in 1980. He went on to get a PhD from the University of Illinois at Urbana-Champaign where, from 1985 to 1987, he persued as post-doc with Prof. Slichter his research on catalysis by solid state NMR studies of molecules bound to the surface of catalysts. From 1987 to 1992 he worked at the materials research center of Ciba-Geigy, on polymers for microelectronics, composites, dielectrics and organic charge transfer complexes. In March 1992, as professor of experimental physics, he developed a laboratory on the theme of nanostructured materials and turned full professor in 1995. Since 1992, he teaches classical mechanics, first to future engineering students, since 2004 to physics majors. Since 2000, he teaches thermodynamics also, to the same group of students. He offers a graduate course in spintronics, and another on spin dynamics. His research activities concern the fabrication and properties of magnetic nanostructures produced by electrodeposition. His involvement since the early days of spintronics have allowed him to gain recognition for his work on giant magnetoresistance (CPP-GMR), magnetic relaxation of single nanostructures, and was among the leading groups demonstrating magnetization reversal by spin-polarized currents. Furthermore, his group uses nuclear magnetic resonance , on the one hand as means of investigation of surfaces and electrodes, on the other hand, as a local probe of the electronic properties of complex ferromagnetic oxides.

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Related concepts (32)

Gas

Gas is one of the four fundamental states of matter. The others are solid, liquid, and plasma. A pure gas may be made up of individual atoms (e.g. a noble gas like neon), elemental molecules made from one type of atom (e.g. oxygen), or compound molecules made from a variety of atoms (e.g. carbon dioxide). A gas mixture, such as air, contains a variety of pure gases. What distinguishes a gas from liquids and solids is the vast separation of the individual gas particles.

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.

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.

Statistical inference

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.

Equipartition theorem

In classical statistical mechanics, the equipartition theorem relates the temperature of a system to its average energies. The equipartition theorem is also known as the law of equipartition, equipartition of energy, or simply equipartition. The original idea of equipartition was that, in thermal equilibrium, energy is shared equally among all of its various forms; for example, the average kinetic energy per degree of freedom in translational motion of a molecule should equal that in rotational motion.

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Related concepts (32)

Gas

Statistical model

Statistical assumption

Statistical inference

Equipartition theorem