Advancements in artificial intelligence (AI), particularly in generative AI and agentic AI, have intensified challenges related to transparency and explainability. While explainable AI (XAI) research has evolved to facilitate human interaction with such complex, black-box systems, research and practice lack clarity on the causal chain linking explanations, user perceptions, and real-world outcomes, a relationship that remains conceptually fragmented. To address this gap, we conducted a systematic literature review of 107 experimental user studies on XAI. We developed a conceptual framework guided by the stimulus-organism-response-consequences (S-O-R-C) model to systematize current human-XAI research and examine how users respond to explanations. Our study contributes to the literature by clarifying how explanations shape user interactions and downstream effects in real-world settings. We propose five research directions to help navigate the challenges of emerging AI systems (e.g., LLMs, AI agents) and evolving human-AI delegation.
The Taylor-Green vortex (TGV) serves as a canonical benchmark for studying the transition from laminar to turbulent flow in the absence of solid boundaries. Despite its widespread use in turbulence model validation, the degree to which the TGV exhibits true isotropy and homogeneity, particularly at late stages of decay, remains insufficiently examined. This study employs high-order numerical simulations to investigate these properties for both the standard and isotropic variants of the TGV. Statistical measures, including Reynolds stress anisotropy, coherent structure functions, homogeneity indices and integral length scales, are used to assess flow behaviour over time. Results show that the standard TGV remains anisotropic and inhomogeneous even during late decay stages, with unequal longitudinal length scales and directionally dependent homogeneity indices. The isotropic TGV maintains isotropy by design but still deviates from the characteristics of ideal homogeneous isotropic turbulence, exhibiting larger transverse than longitudinal length scales. Both configurations reveal persistent spatial inhomogeneities manifested as fixed peaks in turbulent kinetic energy and the coherent structure function of plane-averaged statistics. The findings highlight that while the isotropic TGV provides a more balanced and symmetric configuration, neither flow achieves fully homogeneous isotropic turbulence.
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, which represent a new generation of pre-trained, general-purpose AI models, developers can process heterogeneous inputs (e.g., natural language, sensor data, HD maps, and control actions), enabling the synthesis and interpretation of complex driving scenarios. In this paper, we conduct a survey about the application of foundation models for scenario generation and scenario analysis in autonomous driving (as of May 2025). Our survey presents a unified taxonomy that includes large language models, vision-language models, multimodal large language models, diffusion models, and world models for the generation and analysis of autonomous driving scenarios. In addition, we review the methodologies, open-source datasets, simulation platforms, and benchmark challenges, and we examine the evaluation metrics tailored explicitly to scenario generation and analysis. Finally, the survey concludes by highlighting the open challenges and research questions, and outlining promising future research directions. All reviewed papers are listed in a continuously maintained repository, which contains supplementary materials and is available at https://github.com/TUM-AVS/FM-for-Scenario-Generation-Analysis.
Additive manufacturing by laser beam powder bed fusion (PBF-LB) enables the integration of particle dampers into components through enclosed, powder-filled cavities. However, the filling ratio of these dampers cannot be specifically adjusted during the manufacturing process, even though it is known to have a significant influence on the damping performance in the case of conventional particle dampers. In this study, a heat treatment is employed to systematically reduce the filling ratio of PBF-LB/AlSi10Mg particle dampers. The heat-induced expansion of the cavities reliably decreases their filling ratio without leading to irreversible sintering of the enclosed powder. Experimental and numerical modal analyses are used to investigate the effects of the reduced filling ratio on natural frequencies, mode shapes, and damping behaviour. It is shown that changes in natural frequencies and mode shapes are attributed to macro- and microscopic effects of the heat treatment on the specimens rather than to the altered filling ratio. In contrast, the damping behaviour is strongly affected by the reduced filling ratio: For the lowest investigated bending mode, the damping initially increases as the filling ratio decreases, reaching a maximum at filling ratios between 35
The interaction of liquid ammonia with premixed hydrogen-air flames is critical for carbon-free combustion but remains insufficiently understood. This study examines laminar, spherically expanding lean hydrogen-air flames with monodisperse ammonia droplets using two-way coupled DNS with detailed chemistry and multicomponent transport. A hybrid Eulerian-Lagrangian framework captures gas-phase reactions and droplet dynamics for varying ammonia loadings (5-10%) and droplet sizes (10-20 mu m). Ammonia droplets modify the flame via evaporative cooling and local enrichment. Small droplets evaporate rapidly, increasing equivalence ratio and promoting early instabilities despite flame thickening. Larger droplets penetrate the flame and evaporate downstream, enhancing wrinkling and flame surface area. High ammonia loading with small droplets can cause extinction. Fuel NO dominates NO formation, while N2O forms in cooler regions linked to delayed evaporation.
