General information:
Name:
Advanced simulation methods for modeling of novel manufacturing technologies
Code:
int.courses-201
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Spring
Responsible teacher:
prof. dr hab. inż. Svyetlichnyy Dmytro (svetlich@metal.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)
Skills: he can
M_U001 Development and preparation of the model for a given problem Report,
Execution of laboratory classes,
Activity during classes
M_U002 Student will be able to carry out model simulations Report,
Execution of laboratory classes,
Activity during classes
Knowledge: he knows and understands
M_W001 Basic knowledge about advanced methods of simulation of novel technologies Activity during classes,
Examination
M_W002 Knowledge in thermodynamic and kinetics modeling Activity during classes,
Examination
M_W003 Extended knowledge of modern methods for modeling: Cellular Automata and Lattice Boltzmann Execution of exercises,
Activity during classes,
Examination
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
56 28 0 28 0 0 0 0 0 0 0 0
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
Prace kontr. przejść.
Lektorat
Skills
M_U001 Development and preparation of the model for a given problem + - + - - - - - - - -
M_U002 Student will be able to carry out model simulations - - + - - - - - - - -
Knowledge
M_W001 Basic knowledge about advanced methods of simulation of novel technologies + - - - - - - - - - -
M_W002 Knowledge in thermodynamic and kinetics modeling + - - - - - - - - - -
M_W003 Extended knowledge of modern methods for modeling: Cellular Automata and Lattice Boltzmann + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 151 h
Module ECTS credits 6 ECTS
Udział w zajęciach dydaktycznych/praktyka 56 h
Preparation for classes 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 30 h
Realization of independently performed tasks 30 h
Examination or Final test 5 h
Module content
Lectures (28h):

1. Forming processes and additive manufacturing.
2. Cellular Automata (CA) as a tool for microstructure evolution modeling.
3. CA fundamentals.
4. CA-based models of crystallization, phase transformation, recrystallization, grain refinement.
5. Applications for technological processes modeling.
6. Lattice Boltzman Method (LBM) for fluid flow modeling.
7. LBM fundamentals: streaming, equillibrium, collision.
8. LBM applications for diffusion, thermal and advection-diffusion problems.
9. Isothermal incompressible fluid flow.
10. Free surface flow.
11. Applications for additive manufacturing.

Laboratory classes (28h):

1. Cellular automata. one-dimensional CA. Rules.
2. Synchronous and asynchronous 1D CA.
3. 2D CA.
4. CA neighborhood.
5. Growth rate control. CA space isotropy.
6. Frontal CA.
7. Grain shape.
8. Initial microstructure.
9. Grain boundary.
10 Boundary condition. Time step.
11. Microstructure evolution. Phase transformation.
12. LBM model for thermal-diffusion problems.
13. LBM model of flow of incompressible fluid.
14. Creation of free surface Lattice Boltzmann model

Additional information
Teaching methods and techniques:
  • Lectures: Treści prezentowane na wykładzie są przekazywane w formie prezentacji multimedialnej w połączeniu z klasycznym wykładem tablicowym wzbogaconymi o pokazy odnoszące się do prezentowanych zagadnień.
  • Laboratory classes: W trakcie zajęć laboratoryjnych studenci samodzielnie rozwiązują zadany problem praktyczny, dobierając odpowiednie narzędzia. Prowadzący stymuluje grupę do refleksji nad problemem, tak by otrzymane wyniki miały wysoką wartość merytoryczną.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
  • Laboratory classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Studenci wykonują ćwiczenia laboratoryjne zgodnie z materiałami udostępnionymi przez prowadzącego. Student jest zobowiązany do przygotowania się w przedmiocie wykonywanego ćwiczenia, co może zostać zweryfikowane kolokwium w formie ustnej lub pisemnej. Zaliczenie zajęć odbywa się na podstawie zaprezentowania rozwiązania postawionego problemu. Zaliczenie modułu jest możliwe po zaliczeniu wszystkich zajęć laboratoryjnych.
Method of calculating the final grade:

Weighted average: 0.7 * grade from classes + 0.3 * grade from exam

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Prerequisites and additional requirements:

Basic knowledge of materials science
Basic skill in programming.

Recommended literature and teaching resources:

1. Svyetlichnyy D.S. Frontalne automaty komórkowe, Wydawnictwa AGH, Kraków, 2013. (in Polish)
2. Wolfram S. A new kind of science, Champaign: Wolfram Media, Cambridge: Cambridge University Press, 1999.
3. Schiff J.L. Cellular automata: a discrete view of the world, Hoboken: John Wiley & Sons Inc., cop. 2008.
4. Mohamad A.A. Lattice Boltzmann Methods. Fundamentals and Engineering Applications with Computer Codes. Springer, 2011.
5. Kruger T., Kusumaatmaja H., Kuzmin A., SHardt O., Silva G., Viggen E.M. The Lattice Boltzmann Method. Principles and Practice. Springer, 2017.

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

1. Svyetlichnyy D.S. Frontalne automaty komórkowe, Wydawnictwa AGH, Kraków, 2013.
2. Svyetlichnyy, D.S., Modelling of the microstructure: From classical cellular automata approach to the frontal one, Computational Materials Science, 2010, 50 (1), pp. 92-97.
3. Svyetlichnyy, D.S. Modeling of microstructure evolution in process with severe plastic deformation by cellular automata, Materials Science Forum, 2010, 638-642, pp. 2772-2777.
4. Svyetlichnyy, D.S., Simulation of microstructure evolution during shape rolling with the use of frontal cellular automata, ISIJ International, 2012, 52 (4), pp. 559-568.
5. Svyetlichnyy, D.S., Reorganization of cellular space during the modeling of the microstructure evolution by frontal cellular automata, Computational Materials Science, 2012, 60, pp. 153-162.
6. Svyetlichnyy, D.S., Modeling of grain refinement by cellular automata, Computational Materials Science, 2013, 77, pp. 408-416.
7. Łach, Ł., Svyetlichnyy, D., Multiscale model of shape rolling taking into account the microstructure evolution – Frontal cellular automata, Advanced Materials Research, 2014, 998-999, pp. 545-548.
8. Łach, L., Svyetlichnyy, D.S., Frontal cellular automata simulations of microstructure evolution during shape rolling, Materials Research Innovations, 2014, 18, pp. S6-295-S6-302.
9. Svyetlichnyy, D.S., Mikhalyov, A.I., Three-dimensional frontal cellular automata model of microstructure evolution – Phase transformation module, ISIJ International, 2014, 54 (6), pp. 1386-1395.
10. Svyetlichnyy, D.S., A three-dimensional frontal cellular automaton model for simulation of microstructure evolution – Initial microstructure module, Modelling and Simulation in Materials Science and Engineering, 2014, 22 (8), 085001.
11. Svyetlichnyy, D.S., Muszka, K., Majta, J., Three-dimensional frontal cellular automata modeling of the grain refinement during severe plastic deformation of microalloyed steel, Computational Materials Science, 2015, 102, 6397, pp. 159-166.

Additional information:

None