arXiv subjects

Jan Frenzel

Publications and source records attributed to Jan Frenzel.

6 recordsLinked to original sources

arXiv2026

BiJuTy: An Interactive HPC-Aware Big Data Cluster Lifecycle Manager and Performance Assessment Utility for JupyterHub

The increasing demand for data processing has created a pressing need for access to high-performance computing (HPC) systems. Nevertheless, leveraging these systems to execute complex big data processing workflows remains a significant challenge, especially for beginners. This work presents BiJuTy, a solution designed to bridge the accessibility gap for big data workflows on HPC systems within the Jupyter ecosystem. By providing an interactive and user-friendly interface, BiJuTy simplifies cluster lifecycle management and performance assessment, making it more accessible on HPC systems to beginners and experienced users alike. The solution is presented as an interactive interface that guides the user through the entire process, from setting up the cluster configuration to carrying out initial performance assessments. Additionally, the framework enables seamless management of multiple clusters directly within the Jupyter Notebook interface, eliminating the need to switch outside of working environment. The collection of performance metrics from various sources further simplifies the optimization workflow. Furthermore, an illustrative example is provided to demonstrate how BiJuTy can be deployed to optimize the performance of a big data processing application. This example showcases how the entire big data processing lifecycle can be iteratively executed and optimized in just a few clicks, helping to reach the goal of optimization easily and interactively. By facilitating such workflows, this work contributes in bringing the field of big data computing and high-performance computing one step closer to the goal of seamless interaction and usability.

cs.DC↗

arXiv2026

NiTi Single Crystal Growth by Micro-Pulling-Down Method: Experimental Setup and Material Characterization

Nickel-titanium that has an austenite to martensite phase transition has been studied extensively in the past as a shape memory alloy, but a lot remains to be learned from such phase transitions. However, single crystals are needed for a detailed characterization of the emerging phase transition. In order to produce NiTi single crystals for research purposes, we have set up a micro-pulling-down ($\mu$PD) apparatus. The $\mu$PD process is a fast and flexible method for the fabrication of small single crystals. The apparatus is operated in vacuum. By pulling the crystal down through a hole in the crucible bottom, it is possible to reduce oxygen contamination, since oxides float on top of the melt due to their low density. Here we present a detailed characterization of as-grown NiTi crystals by electron backscatter diffraction (EBSD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), hot gas extraction method and differential scanning calorimetry (DSC). The characteristics of the phase transition in NiTi are very sensitive to dopants and alloying. The $\mu$PD method facilitates the introduction of different doping elements into the crystal.

cond-mat.mtrl-sci↗

arXiv2025

Size-dependent transformation patterns in NiTi tubes under tension and bending: Stereo digital image correlation experiments and modeling

The dependence of transformation pattern in superelastic NiTi tubes on tube outer diameter D and wall-thickness t is investigated through quasi-static uniaxial tension and large-rotation bending experiments. The evolution of outer-surface strain fields is synchronized with global stress-strain and moment-curvature responses using a multi-magnification, high-resolution stereo digital image correlation system at 0.5-2x magnifications. The transformation patterns exhibit systematic size-dependent behaviors. Under tension and for a specific D, as the diameter-to-thickness ratio D/t decreases, a decreasing number of fat/diffuse helical bands emerge, in contrast to sharp/slim bands in thin tubes. Consequently, the austenite-martensite front morphology transitions from finely-fingered to coarsely-fingered with decreasing D/t. Below a characteristic D/t, front morphology no longer exhibits patterning and phase transformation proceeds via propagation of a finger-less front. Moreover, the transformation pattern exhibits an interrelation between D and D/t, where a front possessing diffuse fingers is observed in a thin but small tube. Under bending, both the global moment-curvature response and transformation pattern exhibit D- and D/t-dependence. While wedge-like martensite domains consistently form across all tube sizes, their growth is noticeably limited in smaller and thicker tubes due to geometrical constraints. A gradient-enhanced model of superelasticity is employed to analyze the distinct transformation patterns observed in tubes of various dimensions. The size-dependent behavior is explained based on the competition between bulk and interfacial energies, and the energetic cost of accommodating martensite fingers. By leveraging an axisymmetric tube configuration as a reference energy state, the extra energy associated with the formation of fingers is quantified.

cond-mat.mtrl-sci↗

arXiv2020

Advanced Python Performance Monitoring with Score-P

Within the last years, Python became more prominent in the scientific community and is now used for simulations, machine learning, and data analysis. All these tasks profit from additional compute power offered by parallelism and offloading. In the domain of High Performance Computing (HPC), we can look back to decades of experience exploiting different levels of parallelism on the core, node or inter-node level, as well as utilising accelerators. By using performance analysis tools to investigate all these levels of parallelism, we can tune applications for unprecedented performance. Unfortunately, standard Python performance analysis tools cannot cope with highly parallel programs. Since the development of such software is complex and error-prone, we demonstrate an easy-to-use solution based on an existing tool infrastructure for performance analysis. In this paper, we describe how to apply the established instrumentation framework \scorep to trace Python applications. We finish with a study of the overhead that users can expect for instrumenting their applications.

cs.DC↗

arXiv2019

Discovery of $\omega$-free high-temperature Ti-Ta-X shape memory alloys from first principles calculations

The rapid degradation of the functional properties of many Ti-based alloys is due to the precipitation of the $\omega$ phase. In the conventional high-temperature shape memory alloy Ti-Ta the formation of this phase compromises completely the shape memory effect and high (>100{\deg}C) transformation temperatures cannot be mantained during cycling. A solution to this problem is the addition of other elements to form Ti-Ta-X alloys, which often modifies the transformation temperatures; due to the largely unexplored space of possible compositions, very few elements are known to stabilize the shape memory effect without decreasing the transformation temperatures below 100{\deg}C. In this study we use transparent descriptors derived from first principles calculations to search for new ternary Ti-Ta-X alloys that combine stability and high temperatures. We suggest four new alloys with these properties, namely Ti-Ta-Sb, Ti-Ta-Bi, Ti-Ta-In, and Ti-Ta-Sc. Our predictions for the most promising of these alloys, Ti-Ta-Sc, are subsequently fully validated by experimental investigations, the new alloy Ti-Ta-Sc showing no traces of $\omega$ phase after cycling. Our computational strategy is immediately transferable to other materials and may contribute to suppress $\omega$ phase formation in a large class of alloys.

cond-mat.mtrl-sci↗

arXiv2018

Unusual composition dependence of transformation temperatures in Ti-Ta-X shape memory alloys

Ti-Ta-X (X = Al, Sn, Zr) compounds are emerging candidates as high-temperature shape memory alloys (HTSMAs). The stability of the one-way shape memory effect (1WE), the exploitable pseudoelastic (PE) strain intervals as well as the transformation temperature in these alloys depend strongly on composition, resulting in a trade-off between a stable shape memory effect and a high transformation temperature. In this work, experimental measurements and first-principles calculations are combined to rationalize the effect of alloying a third component to Ti-Ta based HTSMAs. Most notably, an $\textit{increase}$ in the transformation temperature with increasing Al content is detected experimentally in Ti-Ta-Al for low Ta concentrations, in contrast to the generally observed dependence of the transformation temperature on composition in Ti-Ta-X. This inversion of trend is confirmed by the $\textit{ab-initio}$ calculations. Furthermore, a simple analytical model based on the $\textit{ab-initio}$ data is derived. The model can not only explain the unusual composition dependence of the transformation temperature in Ti-Ta-Al, but also provide a fast and elegant tool for a qualitative evaluation of other ternary systems. This is exemplified by predicting the trend of the transformation temperature of Ti-Ta-Sn and Ti-Ta-Zr alloys, yielding a remarkable agreement with available experimental data.

cond-mat.mtrl-sci↗