Jan Vermant: Katalogdaten im Frühjahrssemester 2023

NameHerr Prof. Dr. Jan Vermant
NamensvariantenJan Vermant
J. Vermant
J Vermant
LehrgebietWeiche Materialien
Adresse
Professur für Weiche Materialien
ETH Zürich, HCI H 503
Vladimir-Prelog-Weg 1-5/10
8093 Zürich
SWITZERLAND
Telefon+41 44 633 33 55
E-Mailjan.vermant@mat.ethz.ch
URLhttp://www.softmat.mat.ethz.ch
DepartementMaterialwissenschaft
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
327-2201-00LTransport Phenomena II5 KP4GJ. Vermant
KurzbeschreibungNumerical and analytical methods for real-world "Transport Phenomena"; atomistic understanding of transport properties based on kinetic theory and mesoscopic models; fundamentals, applications, and simulations
LernzielThe teaching goals of this course are on five different levels:
(1) Deep understanding of fundamentals: kinetic theory, mesoscopic models, ...
(2) Ability to use the fundamental concepts in applications
(3) Insight into the role of boundary conditions
(4) Knowledge of a number of applications
(5) Flavor of numerical techniques: finite elements, lattice Boltzmann, ...
InhaltThermodynamics of Interfaces
Interfacial Balance Equations
Interfacial Force-Flux Relations
Polymer Processing
Transport Around a Sphere
Refreshing Topics in Equilibrium Statistical Mechanics
Kinetic Theory of Gases
Kinetic Theory of Polymeric Liquids
Transport in Biological Systems
Dynamic Light Scattering
SkriptThe course is based on the book D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018)
Literatur1. D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018)
2. R. B. Bird, W. E. Stewart, and E. N. Lightfoot, Transport Phenomena, 2nd Ed. (Wiley, 2001)
3. Deen,W. Analysis of Transport Phenomena, Oxford University Press, 2012
4. R. B. Bird, Five Decades of Transport Phenomena (Review Article), AIChE J. 50 (2004) 273-287
Voraussetzungen / BesonderesComplex numbers. Vector analysis (integrability; Gauss' divergence theorem). Laplace and Fourier transforms. Ordinary differential equations (basic ideas). Linear algebra (matrices; functions of matrices; eigenvectors and eigenvalues; eigenfunctions). Probability theory (Gaussian distributions; Poisson distributions; averages; moments; variances; random variables). Numerical mathematics (integration). Statistical thermodynamics (Gibbs' fundamental equation; thermodynamic potentials; Legendre transforms; Gibbs' phase rule; ergodicity; partition functions; Einstein's fluctuation theory). Linear irreversible thermodynamics (forces and fluxes; Fourier's, Newton's and Fick's laws for fluxes). Hydrodynamics (local equilibrium; balance equations for mass, momentum, energy and entropy). Programming and simulation techniques (Matlab, Monte Carlo simulations).