# 327-2201-00L Transport Phenomena II

Semester | Spring Semester 2020 |

Lecturers | J. Vermant |

Periodicity | yearly recurring course |

Language of instruction | English |

Abstract | Numerical and analytical methods for real-world "Transport Phenomena"; atomistic understanding of transport properties based on kinetic theory and mesoscopic models; fundamentals, applications, and simulations |

Objective | The 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, ... |

Content | Thermodynamics 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 |

Lecture notes | The course is based on the book D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018) |

Literature | 1. 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 |

Prerequisites / Notice | Complex 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). |