(65e) Estimating the Consequences of Deflagration to Detonation Transition (Ddt) in Hydrogen Explosions

Due to the development in computational resources, Computational Fluid Dynamics (CFD) has assumed increasing importance in recent years as a tool for predicting the consequences of accidents in petrochemical and process industries. CFD has also been used more and more for explosion predictions for input to risk assessments and design load specifications. The CFD software FLACS has been developed and experimentally validated continuously for more than 25 years. As a result, it is established as a tool for simulating hydrocarbon gas deflagrations with reasonable precision and is widely used in petrochemical industry and elsewhere. In recent years the focus on predicting hydrogen explosions have increased, and with the latest release the validation status for hydrogen deflagrations is considered good. However, in many of these scenarios, especially involving reactive gases such as hydrogen, deflagration to detonation transition (DDT) may be a significant threat. In previous work that was presented in the 40th Annual Loss Prevention Symposium in Orlando, FLACS was extended to identify whether DDT is likely in a given scenario and indicate the regions where it might occur [1]. The likelihood of DDT has been expressed in terms of spatial pressure gradients across the flame front (see Fig. 1). This parameter is able to visualize when the flame front captures the pressure front, which is the case in situations when fast deflagrations transition to detonation [2]. Reasonable agreement was obtained with experimental observations in terms of explosion pressures, transition times, and flame speeds. The DDT model has now been extended to develop a more meaningful criterion for estimating the likelihood of DDT by comparison of the geometric dimensions with the detonation cell size. A shock ignition model has also been proposed in order to simulate a ?detonation front? which is expected to occur after the transition. This article will discuss the new models to predict DDT, and compare predictions with relevant experiments.