Module also offered within study programmes:
General information:
Annual:
2017/2018
Code:
MIM-2-301-AM-s
Name:
Multiscale modelling
Faculty of:
Metals Engineering and Industrial Computer Science
Study level:
Second-cycle studies
Specialty:
Advanced Materials - Processing and Characterization
Field of study:
Materials Science
Semester:
3
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
Madej Łukasz (lmadej@agh.edu.pl)
Academic teachers:
Madej Łukasz (lmadej@agh.edu.pl)
Perzyński Konrad (kperzyns@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Can appreciate the advantages of the use of multiscale modeling techniques to develop new technologies that can be useful for the society. IM2A_K03 Participation in a discussion
Skills
M_U001 Has the ability to use a model that is based on knowledge about analyzed physical phenomenon. IM2A_U08 Execution of a project
M_U002 Has the ability to identify parameters of the developed model. IM2A_U08 Report
Knowledge
M_W001 Has general knowledge about the advantages and possibilities of application of multiscale modeling techniques in engineering. IM2A_W12 Examination,
Test
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Social competence
M_K001 Can appreciate the advantages of the use of multiscale modeling techniques to develop new technologies that can be useful for the society. + - + - - - - - - - -
Skills
M_U001 Has the ability to use a model that is based on knowledge about analyzed physical phenomenon. - - + - - - - - - - -
M_U002 Has the ability to identify parameters of the developed model. + - + - - - - - - - -
Knowledge
M_W001 Has general knowledge about the advantages and possibilities of application of multiscale modeling techniques in engineering. + - - - - - - - - - -
Module content
Lectures:
  1. Computational material science – introduction
  2. Macro scale analysis techniques
  3. Mezo scale analysis techniques
  4. Micro scale analysis techniques
  5. Multi scale analysis techniques – classification
  6. Space and time scales bridging methods
  7. Concurrent uncoupled multi scale approaches
  8. Concurrent coupled multi scale approaches
  9. Hierarchical uncoupled multi scale approaches
  10. Hierarchical coupled multi scale approaches
  11. Hierarchical-concurrent multi scale approaches
  12. Computational efficiency issues
  13. Industrial applications of the multi scale approaches
  14. Lecture overview
Laboratory classes:
  1. Development of the macro scale numerical model – part 1
  2. Development of the macro scale numerical model – part 2
  3. Development of the macro scale numerical model – part 3
  4. Identification of model parameters
  5. Numerical simulation on the basis of macro scale model
  6. Development of the micro scale numerical model – part 1
  7. Development of the micro scale numerical model – part 2
  8. Development of the micro scale numerical model – part 3
  9. Development of the micro scale numerical model – part 4
  10. Development of the micro scale numerical model – part 5
  11. Development of the micro scale numerical model – part 6
  12. Development of the micro scale numerical model – part 7
  13. Development of the micro scale numerical model – part 8
  14. Development of the micro scale numerical model – part 9
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 148 h
Module ECTS credits 5 ECTS
Participation in lectures 28 h
Participation in laboratory classes 28 h
Preparation for classes 60 h
Realization of independently performed tasks 20 h
Examination or Final test 2 h
Contact hours 10 h
Additional information
Method of calculating the final grade:

Weighted average: 0.5 * grade from classes + 0.5 * grade from exam

Prerequisites and additional requirements:

Zgodnie z Regulaminem Studiów AGH podstawowym terminem uzyskania zaliczenia jest ostatni dzień zajęć w danym semestrze. Termin zaliczenia poprawkowego (tryb i warunki ustala prowadzący moduł na zajęciach początkowych) nie może być późniejszy niż ostatni termin egzaminu w sesji poprawkowej (dla przedmiotów kończących się egzaminem) lub ostatni dzień trwania semestru (dla przedmiotów niekończących się egzaminem).

Recommended literature and teaching resources:

1. O. C. Zienkiewicz, R. L. Taylor, The Finite Element Method Set, Butterworth-heinemann, 2005.
2. Fries T.P., Matthies H.G., Classification and overview of meshfree methods, Scientific Computing, Informatikbericht, 2003-3, Brunswick, 2004.
3. R. Wit, ,,Metody Monte Carlo – wykłady”, Wydawnictwo Politechniki Częstochowskiej, 2004.
4. Tao Pang, Metody obliczeniowe w fizyce, PWN, 2001.
5. S. Wolfram, A New kind of science, Wolfram Media, 2002.
6. K. Kułakowski, Automaty komórkowe, Ośrodek Edukacji Niestacjonarnej, Kraków, 2000.
7. Pietrzyk M., Madej L., Rauch L., Szeliga D., Computational Materials Engineering: achieving high accuracy and efficiency in metals processing simulations, Butterworth-Heinemann Elsevier, 2015.

Scientific publications of module course instructors related to the topic of the module:

1. Madej L., Wang J., Perzynski K., Hodgson P.D., Numerical modelling of dual phase microstructure behavior under deformation conditions on the basis of digital material representation, Computational Material Science, 95, 2014, 651–662.
2. Madej L., Sieradzki L., Sitko M.,Perzynski K., Radwański K., Kuziak R., Multi scale cellular automata and finite element based model for cold deformation and annealing of a ferritic-pearlitic microstructure, Computational Materials Science, 77, 2013, 172–181.
3. Halder C., Madej L., Pietrzyk M., Chakraborti N., Optimization of cellular automata model for the heating of Dual Phase steel by Genetic Algorithm and Genetic Programming, Materials and Manufacturing Processes, 30:4, 2015, 552-562.
4. Szyndler J., Madej L., Numerical analysis of the influence of number of grains, FE mesh density and friction coefficient on representativeness aspects of the polycrystalline Digital Material Representation – plane strain deformation case study, Computational Material Science, 96, 2015, 200–213.
5. Perzyński K., Madej L., Wang J., Kuziak R., Hodgson P.D., Numerical investigation of influence of the martensite volume fraction on DP steels fracture behavior on the basis of digital material representation model, Metallurgical and Materials Transactions A, 45, 2014, 5852-5865.

Additional information:

This lecture is devoted to multi scale numerical models available for solving various problems in research on materials processing and in industrial applications. The first part of the lecture is devoted to macro, micro and mezo scale modeling techniques. The basis of the commonly used methods are presented. Their advantages as well as disadvantages are discussed.
The second part of the lecture is focused on the multi scale modeling techniques. Problems of classification (concurrent, upscaling, coupled, uncoupled), scale bridging (space, time) and computational efficiency will be discussed. Examples of applications and possibilities provided by these multi scale method in industrial applications will be presented as well.