The use of risk information in establishing code and standard requirements enables: (1) An adequate and appropriate level of safety; and (2) Deployment of hydrogen facilities are as safe as gasoline facilities. This effort provides a template for clear and defensible regulations, codes, and standards that can enable international market transformation.
A series of experiments consisting of vessel-to-vessel transfers of pressurized gas using Transient PVT methodology have been conducted to provide a data set for optimizing heat transfer correlations in high pressure flow systems. In rapid expansions such as these, the heat transfer conditions are neither adiabatic nor isothermal. Compressible flow tools exist, such as NETFLOW that can accurately calculate the pressure and other dynamical mechanical properties of such a system as a function of time. However to properly evaluate the mass that has transferred as a function of time these computational tools rely on heat transfer correlations that must be confirmed experimentally. In this work new data sets using helium gas are used to evaluate the accuracy of these correlations for receiver vessel sizes ranging from 0.090 L to 13 L and initial supply pressures ranging from 2 MPa to 40 MPa. The comparisons show that the correlations developed in the 1980s from sparse data sets perform well for the supply vessels but are not accurate for the receivers, particularly at early time during the transfers. This report focuses on the experiments used to obtain high quality data sets that can be used to validate computational models. Part II of this report discusses how these data were used to gain insight into the physics of gas transfer and to improve vessel heat transfer correlations. Network flow modeling and CFD modeling is also discussed.
Part I of this report focused on the acquisition and presentation of transient PVT data sets that can be used to validate gas transfer models. Here in Part II we focus primarily on describing models and validating these models using the data sets. Our models are intended to describe the high speed transport of compressible gases in arbitrary arrangements of vessels, tubing, valving and flow branches. Our models fall into three categories: (1) network flow models in which flow paths are modeled as one-dimensional flow and vessels are modeled as single control volumes, (2) CFD (Computational Fluid Dynamics) models in which flow in and between vessels is modeled in three dimensions and (3) coupled network/CFD models in which vessels are modeled using CFD and flows between vessels are modeled using a network flow code. In our work we utilized NETFLOW as our network flow code and FUEGO for our CFD code. Since network flow models lack three-dimensional resolution, correlations for heat transfer and tube frictional pressure drop are required to resolve important physics not being captured by the model. Here we describe how vessel heat transfer correlations were improved using the data and present direct model-data comparisons for all tests documented in Part I. Our results show that our network flow models have been substantially improved. The CFD modeling presented here describes the complex nature of vessel heat transfer and for the first time demonstrates that flow and heat transfer in vessels can be modeled directly without the need for correlations.
Detailed herein are the results of a validation comparison. The experiment involved a 2 meter diameter liquid pool of Jet-A fuel in a 13 m/s crosswind. The scenario included a large cylindrical blocking object just down-stream of the fire. It also included seven smaller calorimeters and extensive instrumentation. The experiments were simulated with Fuego. The model included several conduction regions to model the response of the calorimeters, the floor, and the large cylindrical blocking object. A blind comparison was used to compare the simulation predictions with the experimental data. The more upstream data compared very well with the simulation predictions. The more downstream data did not compare very well with the simulation predictions. Further investigation suggests that features omitted from the original model contributed to the discrepancies. Observations are made with respect to the scenario that are aimed at helping an analyst approach a comparable problem in a way that may help improve the potential for quantitative accuracy.