A Modified Directional Flame Thermometer: Development, Calibration, and Uncertainty Quantification

A Modified Directional Flame Thermometer: Development, Calibration, and Uncertainty Quantification
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改进的定向火焰温度计:开发、校准和不确定性量化

DOI:
10.1115/1.4046657
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发表时间:
2020
期刊:
Validation and Uncertainty Quantification
影响因子:
--
通讯作者:
Ezekoye, O. A.
Ezekoye, O. A.
中科院分区:
--
文献类型:
--
作者:
Cabrera, J. M.;Moser, R. D.;Ezekoye, O. A.

文献摘要

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定向火焰温度计(DFT)是一种用于测量火灾场景等恶劣环境中的热通量的坚固设备,但与其他标准热通量测量设备相比,它的体积较大。为了更好地了解在这些环境中与热通量测量相关的不确定性,贝叶斯框架被用来传播已知和未知参数的不确定性,描述了一个修改后的,更小的DFT的热模型。构造的修改DFT描述沿着与用于预测其感测表面的入射热通量的热模型的推导。模型的参数被校准到使用施密特-伯尔特(SB)计收集的数据,在入射热通量为5 kW/m2,10 kW/m2,和15 kW/m2的精度为±3%。马尔可夫链蒙特卡罗模拟被用来获得热模型的自由参数以及建模不确定性的后验分布。参数校准过程产生的自由参数值与文献中的值相似,在5 kW/m2、10 kW/m2和15 kW/m2下的相对不确定度分别为17%、9%和7%。导出的模型产生的预测和SB计输出之间的均方根误差为0.37,0.77,和1.13千瓦/平方米的5,10,和15千瓦/平方米的情况下,分别为0.53,1.12,和1.66千瓦/平方米的能量存储方法(ESM)在ASTM E3057中描述。
The directional flame thermometer (DFT) is a robust device used to measure heat fluxes in harsh environments such as fire scenarios but is large when compared to other standard heat flux measurement devices. To better understand the uncertainties associated with heat flux measurements in these environments, a Bayesian framework is utilized to propagate uncertainties of both known and unknown parameters describing the thermal model of a modified, smaller DFT. Construction of the modified DFT is described along with a derivation of the thermal model used to predict the incident heat flux to its sensing surface. Parameters of the model are calibrated to data collected using a Schmidt–Boelter (SB) gauge with an accuracy of ±3% at incident heat fluxes of 5 kW/m2, 10 kW/m2, and 15 kW/m2. Markov Chain Monte Carlo simulations were used to obtain posterior distributions for the free parameters of the thermal model as well as the modeling uncertainty. The parameter calibration process produced values for the free parameters that were similar to those presented in the literature with relative uncertainties at 5 kW/m2, 10 kW/m2, and 15 kW/m2of 17%, 9%, and 7%, respectively. The derived model produced root-mean-squared errors between the prediction and SB gauge output of 0.37, 0.77, and 1.13 kW/m2for the 5, 10, and 15 kW/m2cases, respectively, compared to 0.53, 1.12, and 1.66 kW/m2for the energy storage method (ESM) described in ASTM E3057.
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