Substituting of a Thermodynamic Simulation with a Metamodel in the Scope of Multiscale Modelling

Piotr Macioł; Danuta Szeliga; Łukasz Sztangret

doi:10.4028/www.scientific.net/MSF.879.1207

Paper Titles

Influence of Coiling on Microstructural Evolution and Mechanical Properties of Strip-Cast Low-Carbon Low-Niobium Steel
p.1182

Preparation and Squeeze Casting of Nano-SiC_P/A356 Composite Assisted with Ultrasonic Vibration Process
p.1188

Enabling Diamond Deposition with Cold Spray through the Coated Particle Method
p.1194

The Effect of Thermo-Mechanical Coupling on Microstructures and Tensile Properties of Al-Li 2198-T8 Alloy via Friction Spot Welding
p.1200

Substituting of a Thermodynamic Simulation with a Metamodel in the Scope of Multiscale Modelling
p.1207

A Novel Facility to Investigate Dust Mobilization in Confined Environments with Applications to the Security of the Pharmaceutical Industry
p.1213

Thermal Desorption Spectroscopy Study on the Hydrogen Behavior in a Plasma-Charged Aluminum
p.1220

Microstructure Evolution of a Directionally Solidified Ternary Eutectic Mo-Si-B Alloy
p.1226

Friction Stir Welding on Light-Weight Metal - Aluminum Alloy Al6061
p.1233

HomeMaterials Science ForumMaterials Science Forum Vol. 879Substituting of a Thermodynamic Simulation with a...

Substituting of a Thermodynamic Simulation with a Metamodel in the Scope of Multiscale Modelling

Abstract:

A typical multiscale simulation consists of numerous fine scale models, usually one for each computational point of a coarse scale model. One of possible ways of limiting computing power requirements is replacing fine scale models with some simplified and speeded up ersatz ones. In this paper, the authors attempt to develop a metamodel, replacing direct thermodynamic computations of precipitation kinetic with an advanced approximating model. MatCalc simulator has been used for thermodynamic modelling of precipitation kinetic. Typical heat treatment of P91 steel grade was examined. Selected variables were chosen to be modelled with approximating models. Several attempts with various approximation variants (interpolation algorithms and Artificial Neural Networks) have been investigated and its comparison is included in the paper.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Materials Science Forum (Volume 879)

Pages:

1207-1212

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.879.1207

Citation:

Cite this paper

Online since:

November 2016

Authors:

Piotr Macioł*, Danuta Szeliga, Łukasz Sztangret

Keywords:

Metamodeling, Multiscale Modeling, Sensitivity Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] W. E, Principles of Multiscale Modeling, Cambridge University Press, (2011).

Google Scholar

[2] M. Kopernik, A. Milenin, Two-scale finite element model of multilayer blood chamber of POLVAD_EXT, Arch. Civ. Mech. Eng. 12 (2012) 178–185.

DOI: 10.1016/j.acme.2012.04.003

Google Scholar

[3] P. Macioł, L. Gotfryd, A. Macioł, Knowledge based system for runtime controlling of multiscale model of ion-exchange solvent extraction, in: AIP Conf. Proceedings, ICNAAM 2012 Int. Conf. Numer. Anal. Appl. Math., American Institute of Physics, Kos, Greece, 2012 125–128.

DOI: 10.1063/1.4756078

Google Scholar

[4] P. Macioł, R. Bureau, C. Sommitsch, An Object-Oriented Analysis of Complex Numerical Models, Key Eng. Mater. 611-612 (2014) 1356–1363.

DOI: 10.4028/www.scientific.net/kem.611-612.1356

Google Scholar

[5] P. Macioł, R. Bureau, C. Poletti, C. Sommitsch, P. Warczok, E. Kozeschnik, Agile Multiscale Modelling of the Thermo-Mechanical Processing of an Aluminium Alloy, in: Key Eng. Mater., 2015 1319–1324.

DOI: 10.4028/www.scientific.net/kem.651-653.1319

Google Scholar

[6] P. Macioł, A. Krumphals, S. Jędrusik, A. Macioł, C. Sommitsch, Rule-based expert system application to optimizing of multiscale model of hot forging and heat treatment of Ti-6Al-4V, in: V Int. Conf. Comput. Methods Coupled Probl. Sci. Eng. COUPLED Probl. 2013, Ibiza, 2013 1237–1248.

Google Scholar

[7] E. Kozeschnik, C. Bataille, K. Janssens, Modeling Solid-State Precipitation, (2013).

Google Scholar

[8] http: /matcalc. tuwien. ac. at/index. php/documentation.

Google Scholar

[9] A. Saltelli, K. Chan, E.M. Scott, Sensitivity Analysis, (2009).

Google Scholar

[10] D. Szeliga, Identification problems in metal forming. A comprehensive study, AGH University of Science and Technology Press, (2013).

Google Scholar

[11] R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, (2009).

Google Scholar

[12] M.D. Morris, Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics. 33 (1991) 161–174.

DOI: 10.1080/00401706.1991.10484804

Google Scholar

[13] J. Kusiak, Ł. Sztangret, M. Pietrzyk, Effective strategies of metamodelling of industrial metallurgical processes, Adv. Eng. Softw. 89 (2015) 90–97.

DOI: 10.1016/j.advengsoft.2015.02.002

Google Scholar