Degree | Type | Year | Semester |
---|---|---|---|
2500097 Physics | OT | 4 | 1 |
There are no official prerequisites. However, it is assumed that students have knowledge in Thermodynamics and notions of Statistical Mechanics, especially the concepts and methods of ensemble theory, and basic knowledge of quantum mechanics and electromagnetism.
1.4. Fokker-Planck equation
1.5. Brownian motors.
2. Summary of statistical mechanics
3.Ideal gas of diatomic molecules
4. Magnetic systems
5. Biological systems
6. Interacting systems
7. Ideal quantum gas
8. Bosons and fermions ideal gases
9. Elementary kinetic theory of gases
9.1. Gas diluted in equilibrium
9.2. Transport coefficients
Thermal conductivity of the crystalline network and electrons
Master classes
The teacher will explain the content of the syllabus with the support of audiovisual material that will be available to students in the Virtual Campus web in advance, at the beginning of each course topic. It is recommended that students have this material at hand in order to follow the classes more easily. Classes combine the use of slides with developments on the board. Student participation in class will be promoted. The teacher will solve some practical examples to illustrate the theory.
Problem Classes
The teacher will solve selected problems from the list that they will find on the Virtual Campus. In previously established dates, students in groups of 3 students will deliver resolved problems (one delivery per group).
Some sessions will be devoted to the use of simulation tools. Students will make a simple code and analyze simulation results.
If a group believes that there is a participant who has not worked reasonably equitable, it can be expelled from the group.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Exercises classes | 16 | 0.64 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 15, 22, 21, 20, 17, 16, 18, 24, 30, 19, 25, 26, 27, 29, 28, 31, 32, 34, 33, 36, 35 |
Theory Classes | 33 | 1.32 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 29, 31, 32, 34, 33, 35 |
Type: Supervised | |||
Delivery activities | 10 | 0.4 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 28, 31, 32, 34, 33, 36, 37, 35 |
Type: Autonomous | |||
Group work | 25 | 1 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 15, 21, 20, 17, 16, 18, 24, 30, 19, 25, 26, 27, 31, 32, 34, 33, 37, 35 |
Personal work | 57 | 2.28 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 28, 31, 32, 34, 33, 36, 37, 35 |
1. Group work. It will consist of solving selected exercises (in groups of 3 students) and some numerical simulations (in groups of 2 students). The score in this evaluation group represents 25% of the final (individual) grade
2. Individual assesment: this part assess individually scientific and technical knowledge of the subject achieved by the student, as well as its capacity for analysis, synthesis and critical reasoning. It will consist of:
Partial exams: 75%.
Resit exam: 75%. It includes all the syllabus of the course (not each partial separately).
Important: In order to average the grade of the exam with the other 25%, the average score of the exams must be greater than or equal to 4 in a scale of 10.
Resit exam: in order to attend the retake exam the student must have attended the two partial exams.
Those students who pass the partial tests can attend the resit exam to improve the grade. If the score got in the resit exam is up to 1.5 points lower than the average partials score, it is kept the average partials score (unless it is less than 4). If you think you will not upgrade the score, you may not deliver the exam.
Not Assessable: The Not Assessable qualification will be obtained if the student does not attend any exam.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exercises and projects delivery | 25% | 0 | 0 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 29, 28, 31, 32, 34, 33, 36, 37, 35 |
Partial Exams | 75% | 6 | 0.24 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 31, 32, 34, 33, 36, 35 |
Resit Exam | 75% | 3 | 0.12 | 3, 4, 5, 1, 2, 13, 7, 9, 6, 8, 10, 11, 12, 14, 15, 22, 21, 20, 17, 16, 18, 24, 23, 30, 19, 25, 26, 27, 31, 32, 34, 33, 36, 35 |
Basic
- R.K. Pathria, Statistical Mechanics, (3rd Ed), Academic Press, 2011.
- K. Huang, Introduction to statistical physics,Boca Raton, CRC Press, 2001
- F. Reif, Física estadística. Barcelona, Reverté, 1969
- J. Ortín, J.M. Sancho, Curso de Física Estadística, Barcelona, Publicacions i Edicions de la Universitat de Barcelona, cop. 2006
Advanced
- D. A. McQuarrie, Statistical Mechanics. University Science Books, cop. 2000.
- D.J. Amit and Y. Verbin, Statistical Physics: An introductory course. Singapore, World Scientific, 1995.
- D. Chandler, Introduction to Modern Statistical mechanics. Oxford, New York, 1987
- C. Fernandez, J.M. Rodríguez Parrondo, 100 problemas de Física Estadística, Madrid, Alianza, 1996
- R. Kubo. Statistical Mechanics: an advanced course with problems and solutions. Amsterdam, North-Holland, 1990.
- K.A. Dill and S. Bromberg. Molecular driving forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience. Garland Science; 2nd edition, 2010.
Specialized articles and Web links