为研究旋流器结构对航空发动机燃烧室点火性能的影响,使用大涡模拟方法结合wall-adapting local eddy-viscosity(WALE)亚格子模型、动态增厚火焰模型,并设置单个脉冲火花,模拟了轴径向和双轴向旋流器燃烧室的点火过程。结果表明:相同结构和工况下,点火位置的流场因湍流脉动随时间变化,因而点火时刻会影响点火模拟结果。对于实验中能成功点火的结构和工况,为避免模拟时使用单脉冲火花影响点火结果,应选择在速度方向指向回流区,速度幅值小于平均值的时刻点火。对比轴径向和双轴向旋流器燃烧室的动态流场演变过程,发现双轴向旋流器燃烧室的火花正对旋转射流,点火位置瞬时速度指向回流区的概率更低,火焰更易向下游移动而非进入回流区。因此其点火性能劣于轴径向旋流器燃烧室。
Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’experience.This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients.The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care(MIMIC)-IV.The time point of serum sodium detection was selected from the ICU clinical records,and the ICU records of 25risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis.A prediction model of serum sodium value within 48 h was established using a feedforward neural network,and compared with previous methods.Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h,and has better prediction effect than the serum sodium formula and other machine learning models.
