Intermetallic titanium aluminides, leveraging the ordered γ-TiAl phase, attract increasing attention in aerospace and automotive engineering due to their favorable mechanical properties at high temperatures. Of particular interest are γ-TiAl-based alloys with a Niobium (Nb) concentration of 5–10 at.%. It is a key question how to model such ternary alloys at the atomic scale with molecular dynamics (MD) simulations to better understand (and subsequently optimize) the alloys. Here, we present a comparative analysis of ternary TiAlNb interatomic potentials developed by the moment tensor potential (MTP) and deep potential molecular dynamics (DeePMD) methods specifically for the above mentioned critical Nb concentration range. We introduce a novel dataset (TiAlNb dataset) for potential training that establishes a benchmark for the assessment of TiAlNb potentials. The potentials were evaluated through rigorous error analysis and performance metrics, alongside calculations of material properties such as elastic constants, equilibrium volume, and lattice constant. Additionally, we explore finite temperature properties including specific heat and thermal expansion with both potentials. Mechanical behaviors, such as uniaxial tension and the calculation of generalized stacking fault energy, are analyzed to determine the impact of Nb alloying in TiAl-based alloys. Our results indicate that Nb alloying generally enhances the ductility of TiAl-based alloys at the expense of reduced strength, with the notable exception of simulations using DeePMD for the γ-TiAl phase, where this trend does not apply.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Comparative analysis of ternary TiAlNb interatomic potentials: moment tensor vs. deep learning approaches


    Beteiligte:
    Chandran, Anju (Autor:in) / Santhosh, Archa (Autor:in) / Pistidda, Claudio (Autor:in) / Jerabek, Paul (Autor:in) / Aydin, Roland (Autor:in) / Cyron, Christian J. (Autor:in) / TUHH Universitätsbibliothek (Gastgebende Institution)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629 / 530 / 620




    Next generation interatomic potentials for condensed systems

    Handley, Christopher Michael / Behler, Jörg | Tema Archiv | 2014


    The effect of interatomic potentials on the molecular dynamics simulation of nanometric machining

    Oluwajobi, A. / Chen, X. | British Library Online Contents | 2011



    A tensor-based deep learning framework

    Charalampous, K. / Gasteratos, A. | British Library Online Contents | 2014


    Potentials of modern active suspension control strategies – from model predictive control to deep learning approaches

    Khandavalli, Guru Bhargava / Kalabis, Marcus / Wegener, Daniel et al. | Springer Verlag | 2019