Modern ultra-large wind turbines can expose their outboard blade sections to Reynolds numbers above 1 × 107 and Mach numbers above 0.3. In conventional fixed-geometry tests, both parameters vary with inflow velocity. Their individual aerodynamic effects are, therefore, difficult to distinguish. This study presents a variable-chord sectional framework to examine the Reynolds- and Mach-number effects separately. Transitional unsteady Reynolds-averaged Navier–Stokes simulations are conducted for the FFA-W3-211 airfoil. The results reveal a regime-dependent competition between viscous scaling and compressibility. Increasing the isolated Reynolds number promotes earlier transition, strengthens boundary-layer momentum exchange, and accelerates flow reattachment. The lift-hysteresis intensity decreases by 67.2%. Increasing the isolated Mach number enhances lift in attached flow. During deep stall, however, local supersonic flow and shock–boundary-layer interaction promote earlier separation and delay pressure recovery. The downstroke aerodynamic efficiency decreases by 68.1% at the selected post-stall state. These findings indicate that extrapolating traditional uncorrected dynamic stall models to modern large-scale blades may substantially mispredict stall margins. The results suggest that incorporating distinct, decoupled time constants for viscous scaling and compressibility-induced structural persistence may improve predictions of unsteady sectional loads. Their quantitative implications for complete rotors remain to be established through three-dimensional rotating aeroelastic simulations.
Chengyong Zhu, Xiufeng Huang, Zeling Zhu et al.· The Physics of Fluids· 0 citations
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. However, existing general-purpose model development largely overlook this adversarial nature, and often remain insufficient for realistic safety scenarios involving planning, tool use, and multi-step reasoning, causing measured safety performance to overestimate real deployment robustness. To address this gap, we present Yuvion LLM, a large language model built for adversarially robust content safety and broader AI safety. Yuvion LLM treats adversarial robustness and agentic capability as first-class objectives. Its pipeline combines adversarially aware data construction, knowledge-enhanced continued pretraining, and policy-grounded multi-task safety post-training, including risk-aware supervised fine-tuning and reinforcement learning-based policy optimization, together with safety-aware agentic reinforcement learning for tool use and multi-step reasoning in complex safety scenarios. We further introduce the Yuvion LLM RiskEval (YLRE), a collection of 93 benchmarks across four evaluation categories, covering diverse open and internal evaluations with a focus on safety, adversarial robustness, and real-world capability requirements. Across these evaluations, Yuvion LLM demonstrates clear advantages on safety-focused benchmarks and particularly strong robustness under adversarial conditions, while maintaining solid overall capability. Notably, Yuvion-8B outperforms most state-of-the-art baselines, including substantially larger models such as GPT-5.4 and Qwen3-MAX, on several safety tasks.
Ting Ma, Xiufeng Huang, Benlei Cui et al.· 0 citations