Peng Zhang

Weiming Xu, Tao Yang, Peng Zhang

Binary droplet collisions are ubiquitous in dense sprays. Traditional deterministic models cannot adequately represent transitional and stochastic behaviors of binary droplet collision. To bridge this gap, we developed a probabilistic model by using a machine learning approach, the Light Gradient-Boosting Machine (LightGBM). The model was trained on a comprehensive dataset of 33,540 experimental cases covering eight collision regimes across broad ranges of Weber number, Ohnesorge number, impact parameter, size ratio, and ambient pressure. The resulting machine learning classifier captures highly nonlinear regime boundaries with 99.2% accuracy and retains sensitivity in transitional regions. To facilitate its implementation in spray simulation, the model was translated into a probabilistic form, a multinomial logistic regression, which preserves 93.2% accuracy and maps continuous inter-regime transitions. A biased-dice sampling mechanism then converts these probabilities into definite yet stochastic outcomes. This work presents the first probabilistic, high-dimensional droplet collision model derived from experimental data, offering a physically consistent, comprehensive, and user-friendly solution for spray simulation.

Yazhou Zhang, Qimeng Liu, Qiuchi Li et al.

Evaluating the value alignment of large language models (LLMs) has traditionally relied on single-sentence adversarial prompts, which directly probe models with ethically sensitive or controversial questions. However, with the rapid advancements in AI safety techniques, models have become increasingly adept at circumventing these straightforward tests, limiting their effectiveness in revealing underlying biases and ethical stances. To address this limitation, we propose an upgraded value alignment benchmark that moves beyond single-sentence prompts by incorporating multi-turn dialogues and narrative-based scenarios. This approach enhances the stealth and adversarial nature of the evaluation, making it more robust against superficial safeguards implemented in modern LLMs. We design and implement a dataset that includes conversational traps and ethically ambiguous storytelling, systematically assessing LLMs' responses in more nuanced and context-rich settings. Experimental results demonstrate that this enhanced methodology can effectively expose latent biases that remain undetected in traditional single-shot evaluations. Our findings highlight the necessity of contextual and dynamic testing for value alignment in LLMs, paving the way for more sophisticated and realistic assessments of AI ethics and safety.