Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Real Fluids, Phenomenology

In course

PHYS-106(a): General physics : thermodynamics

Le but du cours de Physique générale est de fournir les notions de base nécessaires à la compréhension des phénomènes physiques. L'objectif est atteint lorsqu'il est possible de prévoir quantitativement les conséquences de ces phénomènes avec des outils th ...

Description

This lecture covers the phenomenology of real gases and fluids, focusing on the example of the Van der Waals fluid equation of state. It discusses the behavior of real fluids compared to ideal gases, including compression and liquefaction processes.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

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.

Romuald Houdré

Curriculum Vitae CV 2011 Appointed as Adjunct Professor 2006 Appointed as Maitre d'Enseignement et de Recherche 2004 Joins the "Laboratory of Quantum Electronics" led by Prof. B. Deveaud-Plédran 2001-2004 Appointed as "Adjoint Scientifique" at the Institute for Quantum Photonics and Electronics (previously Institute for Micro and Optoelectronics led by Prof. M. Ilegems) 1998 Habilitation, University Pierre et Marie Curie, Paris 6 (France) 1997 Invited researcher at NTT, Optoelectronics Department (Atsugi, Japan) 1988-2000 "Collaborateur scientifique" at the Institut for Micro and Optoelectronics with Prof. M.Ilegems at the Swiss Federal Institut of Technology in Lausanne (Switzerland). In charge of the Molecular Beam Epitaxy (1988-1996) and the research on optical microcavities (1996-2000) 1987-1988 Laboratoire de Physique de la Matière Condensée at Ecole Polytechnique (France). 1986-1987 Postdoctoral fellow at the University of Illinois at Urbana-Champaign (U.S.A.) with Prof. H.Morkoç in the molecular beam epitaxy group 1983-1985 Ph.D. thesis on the photoemission from quantum wells and superlattices under negative electron affinity at Laboratoire de Physique de la Matière Condensée, Ecole Polytechnique (France), G.Lampel and C.Hermann as advisors

Official source

Related concepts (13)

Data compression

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.

Lossless compression

Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).

Lossy compression

In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. The different versions of the photo of the cat on this page show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression (reversible data compression) which does not degrade the data.

Image compression

Image compression is a type of data compression applied to s, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics.

Compression artifact

A compression artifact (or artefact) is a noticeable distortion of media (including , audio, and video) caused by the application of lossy compression. Lossy data compression involves discarding some of the media's data so that it becomes small enough to be stored within the desired or transmitted (streamed) within the available bandwidth (known as the data rate or bit rate). If the compressor cannot store enough data in the compressed version, the result is a loss of quality, or introduction of artifacts.

Related lectures (35)

Thermodynamics: Exams and Transformations

Covers thermodynamic concepts and exam questions from 2012, 2013, and 2015.

Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Neural Signal Compression

Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Data Compression: Shannon-Fano Algorithm