04 Fakultät Energie-, Verfahrens- und Biotechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/5

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    Assessing fatigue life cycles of material X10CrMoVNb9-1 through a combination of experimental and finite element analysis
    (2023) Rahim, Mohammad Ridzwan Bin Abd; Schmauder, Siegfried; Manurung, Yupiter H. P.; Binkele, Peter; Dusza, Ján; Csanádi, Tamás; Ahmad, Meor Iqram Meor; Mat, Muhd Faiz; Dogahe, Kiarash Jamali
    This paper uses a two-scale material modeling approach to investigate fatigue crack initiation and propagation of the material X10CrMoVNb9-1 (P91) under cyclic loading at room temperature. The Voronoi tessellation method was implemented to generate an artificial microstructure model at the microstructure level, and then, the finite element (FE) method was applied to identify different stress distributions. The stress distributions for multiple artificial microstructures was analyzed by using the physically based Tanaka-Mura model to estimate the number of cycles for crack initiation. Considering the prediction of macro-scale and long-term crack formation, the Paris law was utilized in this research. Experimental work on fatigue life with this material was performed, and good agreement was found with the results obtained in FE modeling. The number of cycles for fatigue crack propagation attains up to a maximum of 40% of the final fatigue lifetime with a typical value of 15% in many cases. This physically based two-scale technique significantly advances fatigue research, particularly in power plants, and paves the way for rapid and low-cost virtual material analysis and fatigue resistance analysis in the context of environmental fatigue applications.
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    Physics-driven machine learning : from biomolecules to crystals
    (2024) Díaz Carral, Ángel; Schmauder, Siegfried (Prof. Dr. rer. nat. Dr. h. c.)
    Physical systems and their interactions exhibit inherent equivariance. In machine learning (ML), predicting quantities derived from these interactions follows two main approaches: constructing invariant scalar features as inputs to invariant models or employing equivariant models directly. This thesis focuses on the former, investigating feature extraction and data representation in the context of physics-driven machine learning (PDML). PDML leverages prior physical knowledge to construct descriptors that encode symmetries inherent in the data, thereby reducing dimensionality, enhancing interpretability, and improving generalization performance. The research addresses critical questions such as the limitations of physics-informed descriptors, the feasibility of dimensionality reduction without compromising prediction accuracy, the comparative performance of PDML against traditional ML methods, and the scalability of PDML in atomistic systems. Key investigations include: 1. Copper-based alloys: Combining molecular simulations and active learning (AL) to discover stable precipitate phases and assess mechanical properties. This involves density functional theory (DFT) simulations and the development of machine learning interatomic potentials (MLIPs) using moment tensor potentials (MTPs), leveraging invariant polynomials to model multi-component alloys. 2. Nanopore translocations: Improving DNA sequencing accuracy by training ML models on experimental ionic blockade data from DNA translocation through nanopores. The approach employs dimensionality reduction through a set of physical descriptors to efficiently classify nucleotide identities, with an emphasis on increasing readout accuracy and reducing model complexity. 3. High-Tc superconductivity: Proposing an effective PDML model to predict critical temperatures of superconductors by extracting key electronic and atomic features. Despite the reduced feature space, the model achieves high accuracy, offering a streamlined approach to predicting superconductor properties with minimal computational overhead. This work bridges the gap between machine learning and physics by embedding physical principles into ML feature representations, enhancing the ability to model, predict, and control complex physical systems with greater precision and efficiency. These advancements aim to unlock transformative applications and discoveries across a range of scientific and technological domains.