Course - Thermodynamic Methods - TKP4175
Thermodynamic Methods
Choose study yearThis course is no longer taught and is only available for examination.
About
About the course
Course content
The theory of partial and total differentials, and the chain rule of differentiation. Energy functions, fundamental relations and canonical variables. Equations of state for the fluid state. Unit operations in chemical engineering. Control volume theory applied to kinetic, potential and chemical energies. Thermodynamic equilibrium. Vapor-liquid equilibrium. Multicomponent phase equilibrium. Chemical equilibria in ideal gases. Adiabatic combustion temperature. Sources of thermodynamic data with emphasis on the standard state. Heat engines using ideal gas as work fluid. Entropy production. Exergy analysis of stationary processes.
Learning outcome
After completing the course, the student will be able to perform: Thermodynamic analysis of physico-chemical equilibrium problems related to the mathematical modeling of chemical multicomponent systems, computation of chemical and phase equilibria in such systems, and, finally, implementation of thermodynamic equations of state and activity models in canonical form. The implementation must be verified (tested) and must contain partial derivatives of first and second order in the natural (canonical) variables of the model. The model will later be used to calculate a phase diagram problem given by the lecturer. The student will understand the meaning of: Canonical variables and thermodynamic potentials, the thermodynamic equation of state, the thermodynamic equilibrium principle applied to multicomponent mixtures, necessary and sufficient conditions for thermodynamic equilibrium, the Gibbs-Duhem's equation and how to apply this equation for consistency checking of thermodynamic computer code. The student will have skills in: partial derivation of functions with many variables, differential calculus applied to state functions of this type, numerical solution of systems of non-linear equations, and unit testing of own program code and verification of own calculation results.
Learning methods and activities
Classroom lectures (3 hours per week), tutored programming (2 hours per week), Individual programming (4-6 hours per week) and report writing (4 hours per week). The students can collaborate in groups of 2-3 people if they so wish. Lectures and exercises are mandatory and can be used to bring forward information that is not necessarily written anywhere. Experience based knowledge for example.
Compulsory assignments
- Partial submission
Further on evaluation
The student is asked to explain the theory for disseminating and solving the problem assigned by the teacher, explain details concerning the implementation, perform unit testing of the computer code, and summarise all of it including relevant calculation results in an essay of length and depth equivalent to 2-3 chapters in a MSc thesis. The nominal length of the report is 28 A4 pages printet in 11 point font and with 25 mm margins. This means approximately two pages of written text per week during the semester. The report shall as a minimum contain at least one page of figures of good quality and a maximum of three pages of such illustrations. The figure captions and the axes labels shall use 10 pt font. The figures should keep a high standard and must be positioned adequately in the report. The captions are made self-explanatory without undue references to the main text. The report should preferably be given an individual touch and feel. The same applies to the equations and to the program code. Copying from external sources is not allowed. The academic, linguistic and aesthetic profiles of the report are weighted 50%, 25% and 25%, respectively. The best of the reports will graded on the scale A-F. The other reports will be given a grade relative to this. The best grade will not necessarily be an A and the grades will not necessarily follow a Gaussian distribution. If the student fails to deliver a satisfactory report, or want to improve her grade, the entire course must be re-taken.
Recommended previous knowledge
Prior experience with programming in Python, Ruby, or Matlab is very useful. Physical chemistry and elementary thermodynamics, chemical process technology, kinetic gas theory, differential calculus, and abstract linear algebra at the undergraduate university level.
Required previous knowledge
TKP4107 Chemical Engineering Thermodynamics, TMA4100 Mathematics 1 (differential calculus, Newton's iteration method), TMA4105 Mathematics 2 (multivariable function analysis), TMA4115 Mathematics 3 (linear algebra), TDT4110 Information technology, or courses equivalent to these.
Course materials
T. Haug-Warberg, Den termodynamiske arbeidsboken, Kolofon forlag (2005), English version is available. A detailed curriculum list will be given at the startup.
Subject areas
- Technological subjects