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First results from the Warm Object Rossiter-McLaughlin Survey (WORMS)
No full textThe origins of warm Jupiters (WJs) are unclear. If they formed beyond the snow line, then they must have migrated, but we don’t know which migration mechanism(s) are the most important. Stellar obliquity is a key tracer of migration history. Dynamically violent, high eccentricity migration leads to planets in significantly misaligned orbits with large obliquities, whereas disc-driven migration should result in orbits coplanar with the stellar equator. In contrast to the hot Jupiters, the imprint of dynamical migration in WJs should not be erased through tidal interactions with the convective zone of their stars, because they are tidally detached. Our VLT/ESPRESSO programme to measure the obliquities of an unbiased sample of eleven WJs, alongside other recent results, will greatly increase the size of the measured sample. The first observations were made last year, and here we present the data gathered so far, and our preliminary interpretation. As already seen, there are hints of an unexpected correlation with orbital eccentricity, as well as tentative evidence for the orbits of lower-mass planets to be preferentially misaligned w.r.t. the stellar spin axis
Characteristics of GPU-Accelerated Multi-Component Lattice-Boltzmann Simulations for Electrolyte Flow in Electrochemical Energy Systems
No full textAn Approach to Generate Training Data for Question-to-AQL Querying Models
Get PDFGraph databases are powerful tools for representing and querying complex knowledge structures. Query languages such as ArangoDB's AQL are challenging for non-expert users. Leveraging large language models (LLMs) for natural-language interfaces is an obvious step, but their preparation depends on suitable training corpora that map user questions to executable queries. For AQL, such corpora do not yet exist. This thesis introduces an approach to automatically generate such training data. The method combines schema-guided path sampling with LLM verbalization, ensuring that queries remain executable while questions are expressed in natural language. Fine-tuning an instruction-tuned model on the resulting corpus yields robust, well-formed AQL queries with high execution accuracy. Most remaining discrepancies concern semantic aspects such as collection choice, traversal direction, or operator selection, whereas syntax remains largely stable.
Overall, the results demonstrate that schema-guided generation with LLM support can provide a faithful and sufficiently broad dataset, enabling the training of functional question-to-AQL models and offering a reproducible foundation for future NL2AQL research and system development
Quantum Software and Its Engineering
Get PDFQuantum computing (QC) is gaining considerable attention from industry and academia alike. It has been making great progress in recent years, and quantum computers are increasingly available to a larger community. It is foreseeable that the technology will become established for specific use cases in optimization, machine learning (ML), or material simulation
VERIFICATION AND EFFICIENCY STUDY USING THE TIME-SPECTRAL METHOD (TSM) FOR FAST PREDICTIONS OF GUST ENCOUNTER IN VISCOUS FLOWS
No full textParameterizing physics-based degradation models in Li-ion batteries with Bayesian methods
Get PDFModeling physical processes inside a battery is an inevitable step in understanding and improving the lifetime of lithium-ion batteries (LIBs). To assess the validity of a model, it has to be correctly parameterized by comparing it to experimental data. However, modeling the observed degradation is a persistent challenge due to the complex coupling of many different processes [1], leaving the dominant degradation mechanism yet unclear.
To fully understand the measured degradation in LIBs, one has to model several degradation mechanisms and their coupling all-encompassing. As the information about the degradation occurring in the battery is mainly encoded in the measured capacity loss only, disentangling the various mechanisms at once is insurmountable. To still obtain a valid degradation modeling, one must first analyze isolated effects. In a first study, we investigate the responsible growth mechanism of the Solid-Electrolyte Interphase (SEI), as this effect can be isolated for the most part by looking at storage experiments. The ongoing growth of the SEI is considered the primary degradation mechanism during battery storage, but it also makes a significant contribution during battery operation [2]. We inversely model degradation data with an automated parameterization routine based on Bayesian methods [3] to distinguish the proposed theoretical growth mechanisms, i.e., solvent diffusion, electron diffusion, and electron conduction. With a valid SEI growth model, we can analyze the impact and behavior of additional degradation mechanisms in a follow-up study.
We show that sample-efficient Bayesian methods [3,4] are outstanding tools to parametrize physics-based models within reasonable sample numbers, as they successfully tackle obstacles like consistent model selection, reliable uncertainties, and correlations in the parametrization [5]. Suitable feature selection can further improve the algorithmic performance and ensure the correct identification of the physical features. As a result, we identify electron diffusion [6] as the dominant growth mechanism of the SEI during battery storage. Then, we can investigate more complex degradation data and model further degradation mechanisms, such as loss of active material and particle or SEI cracking. In conclusion, our inverse model routine helps to identify and parametrize degradation mechanisms of LIBs and is generalizable to include more mechanisms. This automatable method applies to analyzing battery data, model development, and validation and can, therefore, accelerate battery research.
1. S. OKane et al., Phys. Chem. Chem. Phys, 2022, DOI: 10.1039/d2cp00417h
2. B. Horstmann et al., Current Opinion in Electrochemistry, 2019, DOI 10.1016/j.coelec.2018.10.013
3. Y. Kuhn, H. Wolf, A. Latz, B. Horstmann, Batteries & Supercaps. 2023, DOI: 10.1002/batt.202200374.
4. M. Adachi et al., IFAC-PapersOnLine, 2023, DOI: 10.1016/j.ifacol.2023.10.1073.
5. M. Philipp, Y. Kuhn, A. Latz, B. Horstmann, arXiv:2410.19478
6. L. Köbbing, A. Latz, B. Horstmann, J. Power Sources 2023, DOI: 10.1016/j.jpowsour.2023.232651
High-precision chemical quantification of contaminated or geometrically unsuitable samples using in situ FIB-SEM preparation with coupled EDS and WDS
No full textIn quantitative measurements with EDS and WDS in a SEM, factors like conductivity, contamination, homogeneity, and sample geometry
significantly affect results. WDS requires a high-quality sample surface to achieve accuracy below 1%, which is why it was used here to verify
new procedures. Oxidation, carbon contamination, and non-planar samples (e.g., particles) reduce measurement quality. FIB enables
preparation of smooth, planar cross-sections. Using a coupled FIB-SEM allows contamination-free, in-situ preparation and analysis in
vacuum, improving measurement quality. Since FIB cross-sections are usually not perpendicular to the electron beam, a new procedure was
developed: samples are prepared at -10° tilt and then tilted to +28° for analysis, aligning the cross-section perpendicular to the beam. This
method yielded reliable quantification for oxidized samples (100.23 wt.% total), while unprepared showed large deviations. FIB-prepared
particles gave 100.99 wt.% without oxygen contamination. This procedure significantly enhances the quality of EDS and WDS quantification
for samples initially unsuitable for analysi
Post-Earthquake Damage Mapping via Remote Sensing: Lessons from the 2023 Türkiye Disaster
Get PDFThis review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studies published in the IEEE JSTARS Special Issue on post-earthquake damage and risk assessment. These studies cover areas such as damage classification with deep learning, fusion of multisource remote sensing data, creation of benchmark datasets, detailed damage mapping, and analysis of geophysical signals using outgoing longwave radiation. The article summarizes the methodological approaches and the practical relevance of the reviewed studies for detecting, evaluating, and quantifying damage, and outlines key challenges, including model generalization, class ambiguity, and data integration. It also discusses emerging trends, including explainable artificial intelligence, multimodal data fusion, and open-data platforms. This synthesis provides a foundation for building robust, interpretable, and real-time disaster response systems and aims to guide future research in earthquake-related Earth observation and rapid damage assessment
Towards electrical control of graphene nonlinearity
Get PDFThis conference contribution explores the potential of graphene as a platform for THz nonlinear photonic applications. By applying DC electric fields or currents to break inversion symmetry, we aim to induce an effective second-order nonlinearity in graphene, enabling applications such as difference frequency generation and heterodyne detection. This study investigates the general feasibility and the achievable magnitude of electric-field induced nonlinearities in graphene