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Prediction of TTR diagrams via physically based creep simulations of martensitic 9-12% Cr-steels

Research output: Contribution to journalArticlepeer-review

Abstract

This work deals with the prediction of time-to-rupture (TTR) diagrams of martensitic 9- 12% Cr steels. Martensitic 9-12% Cr steels are state of the art materials for powerplants due to their high creep strength and oxidation resistance. Since the experimental determination of TTR diagrams is costly and time-expensive (minimum 10 years), it is of particular interest to be able to model TTR diagrams and gradually replace experiments. Here, we approach the question to what extent we can generate a TTR diagram of a material out of a fraction of experimental results plus detailed understanding of the underlying microstructural/physical phenomena during creep. Our model is based on dislocation creep and includes multiple interactions between the microstructural constituents. We show the applicability of our approach by reproducing a TTR diagram of the well-known material P92. Input parameters are basic material data from literature, the starting microstructure before creep, chemical composition, some model parameters determined on the similar material P91, and one single creep curve of P92. The precipitate evolution is simulated by the software MatCalc, the other microstructural constituents (dislocation densities, subgrain boundaries etc.) by our creep model. By varying the stress between individual creep simulations whilst keeping all input parameters (starting microstructure, temperature and material parameters) constant, we produce multiple creep curves and thus generate the complete dataset for a TTR diagram. The model is of particular interest when it comes to the development of new materials, as the application range of these materials can be estimated quickly and with good reproducibility.
Original languageEnglish
Pages (from-to)159-165
Number of pages7
JournalMaterials Science Forum
Volume1105
DOIs
Publication statusPublished - 2023

Fields of science

  • 203 Mechanical Engineering
  • 203007 Strength of materials
  • 203024 Thermodynamics
  • 203034 Continuum mechanics
  • 211103 Physical metallurgy
  • 211105 Nonferrous metallurgy
  • 101014 Numerical mathematics
  • 101028 Mathematical modelling
  • 102001 Artificial intelligence
  • 102022 Software development
  • 103006 Chemical physics
  • 103018 Materials physics
  • 103042 Electron microscopy
  • 105113 Crystallography
  • 203002 Endurance strength
  • 203013 Mechanical engineering
  • 203037 Computational engineering
  • 205019 Material sciences
  • 211101 Iron and steel metallurgy
  • 103009 Solid state physics
  • 103043 Computational physics

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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