Experimental data of high pressure jets have to be limited in terms of size and complexity of the scenario due to the inherit dangers and expenses correlated to the experiment itself. Alternative methods to gather data, such as representative models, can be used in order to predict what the maximum extend of the gas cloud at its LFL concentration can reach. Simpler models like a gaussian gas dispersion model (e.g., SCREEN3 - a dispersion model) or integral model (e.g., PHAST- an integral model) can be useful to have a quick and qualitative overview on how the jet may extend. Unfortunately, their inability to properly simulate jet-obstacle interactions make them impossible to use beyond preliminary calculations. This is the reason why Computational Fluid Dynamic (CFD) simulations are generally preferred for more complex scenarios.
Although there exists several approaches for CFD simulations, a common approach is the use of a finite volume method that discretizes the volume into smaller cells of varying shapes. Every single cell will represent a fluid-filled volume where the scenarios parameters will be applied. Every cell that was modeled solves a set of conservation equations of mass, momentum and energy, along with the continuity equation. Fluid-obstacle interaction is then modeled with varying algorithms based on the closure turbulent model used.
Depending on the number of total cells within the volume, the better the quality of the simulation, the longer the simulation time. Convergence problems can arise within the simulation as large momentum, mass and energy gradients appear in the volume. The points where these problems are expected to appear (like in the nearfield zone of the jet) need to have a higher number of cells to achieve gradual changes between one cell and another. Ideally, through CFD simulations, a simpler model can be derived which, for a specific set of scenarios, allows to have results with an accuracy and precision level similar to the CFD simulation itself.