Understanding the fire behaviour in buildings is fundamental and crucial to the practice of structural fire safety design. Traditionally, time-temperature curves associated with a burning rate developed from the “compartment fire framework” are most widely used by structural engineers and applied as a load to the structure. However, the adequacy of homogenous temperature distribution in fully developed fires was questioned by researchers after reviewing the existing fire test data, which suggested a localised burning nature of the fires in relatively large compartments. A groundbreaking travelling fire concept and travelling fire models were then proposed intending to provide an engineering description to this type of natural fire behaviour. The work in this paper was driven by such a trend and first summarises the modelling infrastructure in OpenSees to estimate the thermal response of structural members subjected to various scenario fires, followed by providing a smart application interface to capture the appropriate form of natural fire model through Python-OpenSees framework. The developed modelling infrastructure is validated against uniform and localised fire tests, which are also discussed regarding the smoke effect afterwards. Using the Python-OpenSees infrastructure, a real-scale localised fire test and the Malveira travelling fire test are modelled to demonstrate the modelling strategy. The work as preliminary attempts has shown the necessity of introducing additional variables when describing the natural fire impact, and this framework can be further improved in future by including more fire dynamics research and full-scale fire test input.

中文翻译：

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OpenSees for Fire 中的热分析基础设施及其面向自然火灾建模的智能应用界面

了解建筑物的火灾行为是结构消防安全设计实践的基础和关键。传统上，与从“隔间火灾框架”开发的燃烧速率相关的时间-温度曲线被结构工程师最广泛地使用，并作为负载施加到结构上。然而，研究人员在审查了现有的火灾测试数据后，对充分发展的火灾中均匀温度分布的充分性提出了质疑，这表明火灾在相对较大的隔间中具有局部燃烧性质。然后提出了开创性的移动火灾概念和移动火灾模型，旨在为这种类型的自然火灾行为提供工程描述。本文的工作就是受这样一种趋势的推动，首先总结了 OpenSees 中的建模基础设施，以估计结构构件在各种情景火灾下的热响应，然后提供一个智能应用程序界面，通过以下方式捕获适当形式的自然火灾模型Python-OpenSees 框架。开发的建模基础设施针对统一和局部火灾测试进行了验证，之后还会讨论烟雾效应。使用 Python-OpenSees 基础设施，对真实规模的局部火灾测试和 Malveira 移动火灾测试进行建模，以演示建模策略。作为初步尝试的工作表明在描述自然火灾影响时引入额外变量的必要性，