To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based sim...To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.展开更多
为研究电子束退火对Li-N共掺杂Zn O薄膜性能的影响,首先利用溶胶-凝胶旋涂法在p型Si(111)衬底上制备Li-N共掺杂的Zn O前驱膜,然后用电子束对前驱膜进行退火。退火时,电子束加速电压10 k V,退火时间5 min,聚焦束流123 m A,束流为0.7~1.9 ...为研究电子束退火对Li-N共掺杂Zn O薄膜性能的影响,首先利用溶胶-凝胶旋涂法在p型Si(111)衬底上制备Li-N共掺杂的Zn O前驱膜,然后用电子束对前驱膜进行退火。退火时,电子束加速电压10 k V,退火时间5 min,聚焦束流123 m A,束流为0.7~1.9 m A,最后得到Li-N共掺杂的Zn O薄膜。XRD谱分析表明,当束流高于1.5 m A之后,薄膜为六方Zn O和立方Zn O的混合多晶薄膜,且有金属Zn生成,导致薄膜有较强的绿光发射。SEM图片分析显示,薄膜的晶粒尺寸随束流增加而增大,当束流高于1.5 m A后,晶粒尺寸变化不大,约为60 nm。光致发光(PL)谱和激光拉曼谱的分析结果证实Li、N元素已掺入Zn O晶格中,PL谱中观察到Li元素掺杂引起的紫光发射,拉曼散射光谱中观察到N替代O位的缺陷振动模式。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52271317 and 52071149)the Fundamental Research Funds for the Central Universities(HUST:2019kfy XJJS007)。
文摘To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.
文摘为研究电子束退火对Li-N共掺杂Zn O薄膜性能的影响,首先利用溶胶-凝胶旋涂法在p型Si(111)衬底上制备Li-N共掺杂的Zn O前驱膜,然后用电子束对前驱膜进行退火。退火时,电子束加速电压10 k V,退火时间5 min,聚焦束流123 m A,束流为0.7~1.9 m A,最后得到Li-N共掺杂的Zn O薄膜。XRD谱分析表明,当束流高于1.5 m A之后,薄膜为六方Zn O和立方Zn O的混合多晶薄膜,且有金属Zn生成,导致薄膜有较强的绿光发射。SEM图片分析显示,薄膜的晶粒尺寸随束流增加而增大,当束流高于1.5 m A后,晶粒尺寸变化不大,约为60 nm。光致发光(PL)谱和激光拉曼谱的分析结果证实Li、N元素已掺入Zn O晶格中,PL谱中观察到Li元素掺杂引起的紫光发射,拉曼散射光谱中观察到N替代O位的缺陷振动模式。
文摘目的通过监测沙滩椅位(BCP)肩关节镜手术患者的局部脑氧饱和度(rScO_(2))变化,探究允许性高碳酸血症(PHC)联合星状神经节阻滞(SGB)对患者术中脑氧合的影响。方法选择择期行肩关节镜手术患者120例,男58例,女62例,年龄18~64岁,BMI 18.5~27.0 kg/m^(2),ASAⅠ或Ⅱ级。采用随机数字表法将患者分为三组:对照组(C组)、PHC组(P组)和SGB联合PHC组(SP组)。麻醉诱导前,SP组在超声引导下行术侧SGB,颈长肌表面的星状神经节处注射0.25%罗哌卡因+1%利多卡因混合液5 ml,C组和P组于同一部位注射等容量生理盐水。10 min后麻醉诱导行气管插管机械通气辅助呼吸,改为BCP开始手术。术中调整V T及RR,P组和SP组控制P ET CO_(2)升至50 mmHg,C组控制P ET CO_(2)40 mmHg。记录入室时(T_(0))、SGB操作后10 min(T_(1))、BCP 5 min(T_(2))、手术开始后30 min(T_(3))、手术开始后1 h(T_(4))、手术结束时(T_(5))术侧和非术侧rScO_(2)、HR、MAP、SpO_(2)、PaCO_(2)、P ET CO_(2)。记录术前1 d、术后1、2 d静息时VAS疼痛评分。记录术中大脑去氧饱和度事件(CDE)的发生情况、术中血管活性药物使用情况和术后恶心、呕吐、头晕等不良反应的发生情况。结果与T_(2)时比较,P组T_(4)、T_(5)时术侧和非术侧rScO_(2)明显升高,SP组T_(3)—T_(5)时术侧rScO_(2)和T_(4)、T_(5)时非术侧rScO_(2)明显升高(P<0.05)。与C组比较,P组T_(5)时术侧rScO_(2)明显升高,SP组T_(3)—T_(5)时术侧rScO_(2)和T_(5)时非术侧rScO_(2)明显升高(P<0.05)。与P组比较,T_(3)—T_(5)时SP组术侧rScO_(2)明显升高(P<0.05)。与T_(0)时比较,T_(3)—T_(5)时P组和SP组PaCO_(2)、P ET CO_(2)明显升高(P<0.05)。与C组比较,T_(3)—T_(5)时P组和SP组PaCO_(2)、P ET CO_(2)明显升高(P<0.05)。三组不同时点HR、MAP、SpO_(2)、静息时VAS疼痛评分、术中CDE发生率、术中血管活性药使用率及术后不良反应发生率差异无统计学意义。结论PHC可改善肩关节镜手术患者双侧rScO_(2),PHC联合SGB较单独使用PHC能进一步改善患者术侧脑氧合,提高rScO_(2),降低脑血管事件的风险。
