采用Φ100 mm SHPB试验装置对纳米碳纤维(CNFs)体积掺量为0、0.1%、0.2%、0.3%、0.5%的纳米碳纤维增强混凝土(CNFRC)进行了动态劈拉试验,分析了CNFRC动态劈拉破坏的能耗规律,并与碳纤维(CFs)体积掺量为0.3%的碳纤维增强混凝土(CFRC)进...采用Φ100 mm SHPB试验装置对纳米碳纤维(CNFs)体积掺量为0、0.1%、0.2%、0.3%、0.5%的纳米碳纤维增强混凝土(CNFRC)进行了动态劈拉试验,分析了CNFRC动态劈拉破坏的能耗规律,并与碳纤维(CFs)体积掺量为0.3%的碳纤维增强混凝土(CFRC)进行了对比分析。结果表明:在动态劈拉破坏过程中,随着入射能平均变化率的增大,混凝土的应变率不断增大。采用二次多项式能较好地拟合应变率随入射能平均变化率的变化规律。CNFs可“加固”混凝土内部结构,从而使得CNFRC的应变率较普通混凝土小。CNFRC的吸收能具有明显的应变率效应和入射能平均变化率效应。在分析混凝土内部能量耗散时,建议采用入射能平均变化率作为自变量。CNFs可以提高混凝土的吸能特性和强度。入射能平均变化率相同时,随着CNFs掺量的增大,CNFRC的吸收能和动态劈拉强度均先增大后减小。CNFs掺量为0.3%时,CNFRC的吸收能和动态劈拉强度均最大。入射能平均变化率相同时,CNFs对混凝土强度的提高效果较CFs差,对混凝土吸能特性的提高效果接近CFs。展开更多
There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model f or ...There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model f or fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.展开更多
文摘采用Φ100 mm SHPB试验装置对纳米碳纤维(CNFs)体积掺量为0、0.1%、0.2%、0.3%、0.5%的纳米碳纤维增强混凝土(CNFRC)进行了动态劈拉试验,分析了CNFRC动态劈拉破坏的能耗规律,并与碳纤维(CFs)体积掺量为0.3%的碳纤维增强混凝土(CFRC)进行了对比分析。结果表明:在动态劈拉破坏过程中,随着入射能平均变化率的增大,混凝土的应变率不断增大。采用二次多项式能较好地拟合应变率随入射能平均变化率的变化规律。CNFs可“加固”混凝土内部结构,从而使得CNFRC的应变率较普通混凝土小。CNFRC的吸收能具有明显的应变率效应和入射能平均变化率效应。在分析混凝土内部能量耗散时,建议采用入射能平均变化率作为自变量。CNFs可以提高混凝土的吸能特性和强度。入射能平均变化率相同时,随着CNFs掺量的增大,CNFRC的吸收能和动态劈拉强度均先增大后减小。CNFs掺量为0.3%时,CNFRC的吸收能和动态劈拉强度均最大。入射能平均变化率相同时,CNFs对混凝土强度的提高效果较CFs差,对混凝土吸能特性的提高效果接近CFs。
基金This paper is supported by National Natural Science Foundation of China under Grant No.10372084
文摘There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model f or fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.
