The Application of Computational Thermo-Fluid-Dynamics to the Simulation of Hybrid Rocket Internal Ballistics with Classical or Liquefying Fuels: A Review
Giuseppe Daniele Di Martino, Carmine Carmicino, Stefano Mungiguerra and 1 more
Aerospace · 2019
doi:10.3390/aerospace6050056 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
The computational fluid dynamics of hybrid rocket internal ballistics is becoming a key tool for reducing the engine operation uncertainties and development cost as well as for improving experimental data analysis. Nevertheless, its application still presents numerous challenges for the complexity of modeling the phenomena involved in the fuel consumption mechanism and its coupling with the chemically reacting flowfield. This paper presents a review of the computational thermo-fluid-dynamic models developed for the internal ballistics of hybrid rockets burning gaseous oxygen with classical polymeric or paraffin-based fuels, with a special focus on the interaction between the fluid and the solid fuel surface. With the purpose of predicting the local fuel regression rate, which is the main parameter needed for the hybrid rocket design, the model is coupled with an improved gas/surface interface treatment based on local mass, energy and mean mixture-fraction balances, combined to either a pyrolysis-rate equation in the case of classical polymers, or to an additional equation for the liquid paraffin entrainment fraction of the total fuel consumption rate. A number of experimental test cases obtained from the static firing of two different laboratory-scale rockets are simulated to determine the models’ capabilities, showing very good agreement between the calculated and measured fuel regression rates with both standard pyrolyzing and liquefying fuels. The prediction of the chamber pressure measured with paraffin fuel resulted in it being more cumbersome for the single-phase flow assumption. The advantages and limitations of the models are discussed.
Abstract licence: CC BY
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.