Type A
|
Code |
Competences Specific |
|
Professional |
|
AP1 |
A1.1 Effectively apply knowledge of basic, scientific and technological materials pertaining to engineering. |
|
AP2 |
A1.2 Design, execute and analyze experiments related to engineering. |
|
AP4 |
A1.4 Know how to establish and develop mathematical models by using the appropriate software in order to provide the scientific and technological basis for the design of new products, processes, systems and services and for the optimization of existing ones. (G5) |
|
AP9 |
A3.2 Design and optimize products, processes, systems and services for the chemical industry on the basis of various areas of chemical engineering, including processes, transport, separation operations, and chemical, nuclear, elctrochemical and biochemical reactions engineering (I2). |
|
AP10 |
A3.3 Conceptualize engineering models and apply innovative problems solving methods and appropriate IT applications to the design, simulation, optimization and control of processes and systems (I3). |
|
AP11 |
A3.4 Be able to solve unfamiliar and ill-defined problems by taking into account all possible solutions and selecting the most innovative. (I4) |
Type B
|
Code |
Competences Transversal |
|
Professional |
|
BP1 |
B1.1 Communicate and discuss proposals and conclusions in a clear and unambiguous manner in specialized and non-specialized multilingual forums (G9). |
Type C
|
Code |
Competences Nuclear |
|
Common |
Objectives |
Competences |
Be familiar with tools for modelling the macroscopic behaviour of systems of interest in Chemical Engineering starting from a microscopic viewpoint |
AP1 AP2 AP4 AP9 AP10 AP11
|
BP1
|
|
Be knowledgeable of the Monte Carlo technique |
AP1 AP2 AP4 AP9 AP10 AP11
|
BP1
|
|
Be knowledgeable of molecular dynamics |
AP1 AP2 AP4 AP9 AP10 AP11
|
BP1
|
|
Topic |
Sub-topic |
1. Thermodynamic Postulates |
|
2. Classical mechanics and quantum mechanics. Statistical Mechanics |
|
The Monte Carlo technique. |
Importance of sampling
Metropolis algorithm
Basic Monte Carlo algorithm
Trial moves |
Molecular dynamics |
Intergration of the equations of motion
Estimation of statistical information |
Monte Carlo simulation in different ensembles |
Microcanconical
Isothermal-isobaric
Grand canonical |
Molecular dynamics in different ensembles |
Canonical ensemble
Car-Parinello method |
Free energy calculations |
Thermodynamic Integration
Chemical Potentials |
Methodologies :: Tests |
|
Competences |
(*) Class hours |
Hours outside the classroom |
(**) Total hours |
Introductory activities |
|
0.5 |
0 |
0.5 |
|
Lecture |
|
10 |
15 |
25 |
Seminars |
|
12 |
20 |
32 |
Practicals using information and communication technologies (ICTs) in computer rooms |
|
5 |
10 |
15 |
Presentations / expositions |
|
1 |
0 |
1 |
|
Personal tuition |
|
0.5 |
0 |
0.5 |
|
Mixed tests |
|
1 |
0 |
1 |
|
(*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
|
Description |
Introductory activities |
an overview of the course |
Lecture |
lectures on the course material |
Seminars |
exercises carried out in class |
Practicals using information and communication technologies (ICTs) in computer rooms |
molecular simulation exercises using computers |
Presentations / expositions |
presentations of assigned projects |
|
Description |
Individual Tutorials: Tuesday and Thursday from 10:00 to 13:00 |
|
|
Description |
Weight |
Seminars |
exercises carried out in the classroom |
25 |
Practicals using information and communication technologies (ICTs) in computer rooms |
practical simulation exercises |
25 |
Presentations / expositions |
group presentation of assigned project |
25 |
Mixed tests |
individual exam |
25 |
|
Other comments and second exam session |
|
Basic |
D. Frenkel and B. Smit, Understanding Molecular Simulation, , Academic Press
|
|
Complementary |
B Widom, • Statistical Mechanics: A Concise Introduction for Chemists, , Cambridge University Press
D. A. McQuarrie, • Statistical Thermodynamics, , University Science Books
J-P. Hansen and I.R. McDonald, • Theory of Simple Liquids, , Academia Press
D. Chandler, • Introduction to Modern Statistical Mechanics, , Oxford University Press
P. Ungerer, B.Tavitian and A. Boutin , Applications of Molecular Simulation in the Oil and Gas Industry. Monte Carlo Methods , , Editions Technip
M. P. Allen and D.J. Tildesley, Computer Simulation of Liquids, , Oxford Science Publications
|
|
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. |
|