The case of a gas explosion occurring in a geometrically simple enclosure, equipped with a vent is considered. It is well known in the gas explosion scientific community that the calculation of the reduced explosion overpressure, determinant in safety studies, is not trivial. Not only there is a strong dependency on the chemical kinetics of the combustible but also on the enclosure geometry, the fluid flow, the vent mechanical behaviour, shape, etc … As a result, the modelling of the physics at stake is challenging, a wide range of models are proposed in the scientific literature and this reference situation is still the object of extensive research. A new simulation approach ignoring a large part of the underlying physics is investigated. It is based on the use of an artificial neural network (ANN). The focus is given on the method of use and results obtained with the ANN rather than on the neural network itself. Our observations are discussed within the scope of industrial safety problems. Calculations performed with the relatively simple ANN proposed in the official TensorFlow tutorial, on a vented explosion database containing 268 tests, led to surprisingly good results considering the ANN implementation efforts. The tool might look promising but is also far from being as trivial as it seems at a first glance: not only the results of simulations obtained with this type of model must be examined with the greatest care but also the initial data base must be very well controlled. Routes are proposed to enhance the initial database and perform relevant analyses of the neural network predictions.

考虑了在几何简单的外壳中发生气体爆炸的情况，该外壳配备有通风口。气体爆炸科学界众所周知，计算减少的爆炸超压（安全研究中的决定因素）并非易事。不仅强烈依赖于可燃物的化学动力学，而且还依赖于外壳几何形状、流体流动、通风口机械行为、形状等......因此，物理建模具有挑战性，范围广泛科学文献中提出了一系列模型，这种参考情况仍然是广泛研究的对象。研究了一种忽略大部分基础物理的新模拟方法。它基于人工神经网络（ANN）的使用。重点是使用 ANN 的方法和获得的结果，而不是神经网络本身。我们的观察是在工业安全问题的范围内讨论的。使用官方 TensorFlow 教程中提出的相对简单的 ANN 在包含 268 个测试的通风爆炸数据库上执行的计算，考虑到 ANN 的实施工作，得出了令人惊讶的好结果。该工具可能看起来很有前途，但也远非乍一看那样微不足道：不仅必须非常小心地检查使用此类模型获得的模拟结果，而且初始数据库也必须非常好受控。提出了一些路线来增强初始数据库并执行神经网络预测的相关分析。