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Aug 2026

Blockage-driven turbulent vortex dynamics and modal evolution in high-Reynolds-number cavity flows

This study presents two-dimensional particle image velocimetry measurements acquired at the mid-plane of the cavity, together with higher-order dynamic mode decomposition (HODMD) analysis, for lid-driven square-cavity flows containing cubical obstacles at high Reynolds numbers (Re = 1.161 × 105–3.483 × 105). Obstacle sizes of 3, 6, and 9 cm, corresponding to h/D = 0.10, 0.20, and 0.30, were systematically examined to clarify blockage-driven variations in vortex dynamics, velocity distribution, planar turbulent kinetic energy (TKE2D), and modal characteristics on the cavity middle plane. The results show that increasing Reynolds number drives the primary vortex toward the cavity center and promotes a transition from relatively fragmented, high-rotation structures to smoother large-scale recirculation. Meanwhile, increasing obstacle size strengthens geometric confinement, shifts the primary vortex toward the left wall, and induces vortex splitting at the largest blockage ratio. The low-velocity region expands markedly, reaching 43.29% for h/D = 0.30, while the high-velocity region is reduced to 0.8% at the highest Reynolds number. At Re = 2.322 × 105, increasing h/D from 0.10 to 0.30 increases the area proportion of the high normalized-TKE region from 0.83% to 4.74%, while decreasing that of the low normalized-TKE region from 50.72% to 36.52%, indicating a blockage-induced redistribution of the resolved in-plane fluctuation energy. HODMD further identifies a dominant zero-frequency mean-flow mode and higher-order modes with clear harmonic relations, corresponding to the multi-scale evolution of the cavity flow. The reconstructed flow fields show a root mean square error as low as 2.08%, substantially lower than that of standard DMD in strongly nonlinear conditions. These results provide experimental evidence for blockage-controlled turbulent vortex dynamics in high-Reynolds-number cavity flows and offer reference data for the validation of numerical simulations in confined flow systems.

Ping Wang, Hui-Song Bai, Yong Peng et al. · 1 citation
#artificial intelligence Preprint Sep 2026

xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems

Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally make in practice. Real-world requests are often open-ended, casually specified, and context-dependent, requiring models not only to follow explicit instructions but also to infer unstated needs from user background and situational context. We introduce xDailyBench, a benchmark of 248 carefully curated tasks spanning 51 scenarios across personal life, white-collar work, learning and research, and cross-domain activities. The tasks are grounded in requests that users have actually completed or genuinely intended to accomplish with AI, and are evaluated with fine-grained binary rubrics covering both explicit and implicit requirements. We evaluate 11 frontier models under standardized agentic settings. The best models achieve a task-level score of 75.6\%, while all models perform substantially worse on implicit than explicit requirements, with gaps no less than 9 percentage points. These results reveal implicit requirement inference as a persistent bottleneck for reliably satisfying real-world everyday user needs.

Yong Peng, Qing-Shui Gu, Li-Ya Zhu et al. · 0 citations

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