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首页> 外文期刊>Journal of Physics: Conference Series >Application of the Kragten method in order to evaluate the uncertainty of the heat release rate determination using of the cone calorimeter
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Application of the Kragten method in order to evaluate the uncertainty of the heat release rate determination using of the cone calorimeter

机译:Kragten方法的应用,以评估使用锥形量热仪测定放热率的不确定性

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摘要

Assigning uncertainties to measurement results is primordial in a certification framework using fire benches as the cone calorimeter [1] developed by NIST [2]. Indeed, this process allows in the first place to validate the measurement method accuracy and in second place to provide proper results to compare obtained between different labs (reproducibility). However, even if numerous methods enable uncertainties determination, some can become complex when the data of interest is mathematical formulation powered by many measured parameters aimed to use in a model such as the Heat Release Rate (HRR). This article aims to propose an intermediate uncertainty calculation method whose difficulty of application is sized between the GUM method [3] and the Monte Carlo method [4]: the Kragten one [5]. In order to validate the opportunity of using such a method, the three approaches were used to assess the measurement uncertainty on the HRR parameter in the specific case of small scale fire tests using cone calorimeter. The results obtained show that the Kragten method allows to evaluate more easily and faster the HRR uncertainties measured at the Cone Calorimeter than the Monte Carlo method. Furthermore, the Kragten method is more reliable and reduces the risk of miscalculations than the GUM approach.
机译:在使用火凳作为NIST [2]开发的锥形量热仪[1]的认证框架中,为测量结果分配不确定性是首要的。确实,此过程首先允许验证测量方法的准确性,其次可以提供适当的结果以比较不同实验室之间获得的结果(可重复性)。但是,即使有许多方法可以确定不确定性,但是当目标数据是由许多旨在在模型中使用的测量参数(例如放热率(HRR))支持的数学公式表示时,某些方法可能会变得复杂。本文旨在提出一种中间不确定性计算方法,其计算难度在GUM方法[3]和蒙特卡洛方法[4]之间:Kragten方法[5]。为了验证使用这种方法的机会,在使用锥形量热仪进行小规模火灾测试的特定情况下,使用了三种方法来评估HRR参数的测量不确定性。获得的结果表明,与Monte Carlo方法相比,Kragten方法可以更轻松,更快地评估在圆锥量热仪上测量的HRR不确定度。此外,与GUM方法相比,Kragten方法更可靠,并减少了错误计算的风险。

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