Rule-based controlling of a multiscale model of precipitation kinetics

by

Rule-based controlling of a multiscale model of precipitation kinetics

Piotr Macioł

Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland.

DOI:

https://doi.org/10.7494/cmms.2018.2.0615

Abstract:

One of the most important obstacles of widening of multiscale modelling is its high computational demand. It is caused by the fact, that each of numerous fine scale models has comparable computational requirements to a coarse scale one. There are several ways of decreasing of computational time of multiscale models. Optimization of a structure of a model is one of the most promising. In this paper the Adaptive Multiscale Modelling Methodology is described, including Knowledge-Based adaptation of the multiscale model of precipitation kinetics during heat treatment. Core features of the methodology are introduced. The numerical model of heat treatment of an aluminium alloy based on the methodology and the dedicated framework is presented. Besides modelling of macroscopic heat transfer, models of precipitation kinetics based on thermodynamic calculations are included. To decrease computational requirements arising from coupling of the macroscale model and the thermodynamic models, metamodeling and similarity approaches are applied. Computations with several configuration of rules are described, as well as their results. Reliability and time consumption of computations are discussed. Future perspectives of combining of modelling and metamodeling in one, integrated model are discussed.

Cite as:

Macioł, P. (2018). Rule-based controlling of a multiscale model of precipitation kinetics. Computer Methods in Materials Science, 18(2), 64 – 78. https://doi.org/10.7494/cmms.2018.2.0615

Article (PDF):

Keywords:

Multiscale modelling, Precipitation kinetic, Knowledge-based systems, Knowledge-based optimization, Aluminium alloys, Metamodeling

References:

Biyikli, E., To, A.C., 2016, Multiresolution molecular mechanics:Adaptive analysis, Comput. Methods Appl. Mech.Eng., 305, 682-702.

Chopard, B., Borgdorff, J., Hoekstra, A.G., 2014, A frameworkfor multi-scale modelling, Philos. Trans. A. Math. Phys.Eng. Sci,. 372.

da Silveira, E.S.S., Lages, E.N., Ferreira, F.M.G., 2012,DOOLINES : an object-oriented framework for non-linearstatic and dynamic analyses of offshore lines., Eng. Comput.28, 149-159.

Delalondre, F., Smith, C., Shephard, M.S., 2010, Collaborativesoftware infrastructure for adaptive multiple model simulation,Comput. Methods Appl. Mech. Eng., 199, 1352-1370.

Ghedini, E., Hashibon, A., Friis, J., Goldbeck, G., Schmitz, G.,2018, EMMO the European Materials Modelling Ontology,Cambridge.Kozeschnik, E., 2012, Modeling solid-state precipitation, MomentumPress, New York.

Kozeschnik, E., Svoboda, J., Fratzl, P., Fischer, F.D., Fratzl, P.,Kozeschnik, E., 2004, Modelling of kinetics in multicomponentmulti-phase systems with spherical precipitates,Mater. Sci. Eng. A, 385, 166-174.

Kusiak, J., Sztangret, Ł., Pietrzyk, M., 2015, Effective strategiesof metamodelling of industrial metallurgical processes,Adv. Eng. Softw., 89, 90-97.

Macioł, P., Bureau, R., Poletti, C., Sommitsch, C., Warczok, P.,Kozeschnik, E., 2015, Agile multiscale modelling of thethermo-mechanical processing of an aluminium alloy,ESAFORM Conf on Material Forming, Graz, Key Eng.Mater., 651-653, 1319-1324.

Macioł, P., Bureau, R., Sommitsch, C., 2014, An object-orientedanalysis of complex numerical models, Key Eng. Mater.,611-612, 1356-1363.

Macioł, P., Gotfryd, L., Macioł, A., 2012a, Knowledge basedsystem for runtime controlling of multiscale model of ionexchangesolvent extraction, ICNAAM, Int. Conf. on NumericalAnalysis and Applied Mathematics, Kos, AIP Conf.Proc., 125-128.

Macioł, P., Jedrusik, S., Macioł, A., Jędrusik, S., Macioł, A.,2012b, The concept of a rule-based expert system applicationin multiscale modelling. Proc.14th Int. Conf. MetalForming, Wiley-VCH Verlag GmbH & Co., Kraków,1327-1330.

Macioł, P., Krumphals, A., Jędrusik, S., Macioł, A., Sommitsch,C., 2013, Rule-based expert system application to optimizingof multiscale model of hot forging and heat treatmentof Ti-6Al-4V, Proc. V Int. Conf. on Coupled Problems,eds, Idlesohn, S., Papadrakakis, E., Schrefler, B., Ibiza,1237-1248.

Macioł, P., Macioł, A., Rauch, Ł., 2017a, Ontology dedicated toknowledge-driven optimization for ICME approach, Proc.4th World Congr. Integr. Comput. Mater. Eng. ICME2017, 113-121.

Macioł, P., Michalik, K., 2016, Parallelization of fine-scalecomputation in agile multiscale modelling methodology,19th. ESAFORM Conference on Material Forming,Nantes, AIP Conf. Proc., e-book.

Macioł, P., Michalik, K., 2018, Application of metaprogrammingand generic programming in multiscale modelling,Comput. Sci. Eng., 20(6), 81-94.

Macioł, P., Regulski, K., 2016, Development of semantic descriptionfor multiscale models of thermo-mechanicaltreatment of metal alloys, JOM, J. Miner. Met. Mater.Soc., 68(8), 2082-2088.

Macioł, P., Szeliga, D., Sztangret, Ł., 2017b, Substituting of athermodynamic simulation with a metamodel in the scopeof multiscale modeling, Materials Science Forum, Proc. ofTHERMEC 2016, Graz, 1207-1212.

Macioł, P., Szeliga, D., Sztangret, Ł., 2018, Methodology formetamodelling of microstructure evolution: precipitationkinetic case study, Int. J. Mater. Form., 11, 867-878.

Panchal, J.H., Kalidindi, S.R., McDowell, D.L., 2013, Keycomputational modeling issues in Integrated ComputationalMaterials Engineering, Comput. Des., 45, 4-25.

Schmitz, G.J., 2015, ICME: Bridging interfaces, JOM, J. Miner.Met. Mater. Soc., 68(1), 25-26.

Shephard, M.S., Smith, C., Kolb, J.E., 2013, Bringing HPC toengineering innovation, Comput. Sci. Eng., 15(1), 16-25.