New Entrainment Model for Modelling the Regression Rate in Hybrid Rocket Engines

Gallo, Giuseppe; Mungiguerra, Stefano; Savino, Raffaele

doi:10.2514/1.b38333

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…Moreover, the retrieved profile was more severe as the port diameter enlarged, deplacing downstream the position of maximum fuel regression. A concave v r profile having a near-leading edge region with an accentuated fuel consumption associated to an existing recirculation flow has been found in other authors' HRE simulations [3,4,75]. However, the 1-D nature of our model does not allow us to simulate this 2-D effect related to the type of injection.…”

Section: Validation Of the Pseudo 2-d Model By Comparison With The Ex...mentioning

confidence: 72%

“…In order to close the system of equations resulting from the integration of the BL equations, it is required to know the boundary conditions at the fuel surface, which are formulated through the Gas-Surface Interaction model (GSI). This one has been used by several researchers [2][3][4]35] to model fuel consumption inside a HRE combustion chamber by CFD codes and describe the physics in the vicinity of the wall. In this approach, mass and energy balances are applied at the solid / gaseous flow interface at each axial station x i .…”

Section: Boundary Conditions: Gas-surface Interaction Modelmentioning

confidence: 99%

“…Nevertheless, the high costs implied have boosted the development of numerical models. Indeed, two-dimensional / three-dimensional (2-D / 3-D) Computational Fluid Dynamics (CFD) codes can allow the obtention of quite accurate results (averaged fuel regression rate errors inferior to 10%) in the simulation of the combustion process of hybrid rockets [2][3][4], being able to simulate combustion kinetics, turbulence transport and parietal injection precisely. Nevertheless, the large computing time thereof represents a substantial hindrance for their applicability during the engine predesign phases, in which the thrust and the specific impulse are the results of interest.…”

mentioning

confidence: 99%

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Pseudo-Two-Dimensional Modeling and Validation of a Hybrid Rocket Combustion Chamber

Granado

Hijlkema

Lestrade

et al. 2022

Journal of Propulsion and Power

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A pseudo 2-D model for the cylindrical combustion chamber of a hybrid rocket is developed in this article with the purpose of its integration in a system design tool used for engine predesign phases. A compromise of simplicity and accuracy is thus required. Integral transient boundary layer equations are included and coupled with the mass and energy balances at the fuel surface, where fuel pyrolysis is described by an Arrhenius law. Studies assessing the model in the non-reactive case provide satisfactory results regarding the description of the boundary layer. Concerning the hybrid case, the computed steady-state fuel regression rates are found to be in the low region of the reference values coming from semiempirical laws for several oxidizer/fuel couples and oxidizer mass fluxes, producing errors up to 50% for certain sources. The effect of mass flux and motor size is correctly simulated. Eventually, the whole engine operation is assessed through the comparison of the computed results with three experimental tests realized at our laboratory and using a 87.5% Hydrogen Peroxide/High-Density Polyethylene couple. Corresponding results lead to regression rate errors varying in a wide range (from 10

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Section: Validation Of the Pseudo 2-d Model By Comparison With The Ex...mentioning

confidence: 72%

Section: Boundary Conditions: Gas-surface Interaction Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Pseudo-Two-Dimensional Modeling and Validation of a Hybrid Rocket Combustion Chamber

Granado

Hijlkema

Lestrade

et al. 2022

Journal of Propulsion and Power

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“…This is likely due to the fact that, when the residence time is too low, the fuel released from the grain does not have enough time to reach the axis and therefore mixing efficiency is lower, as highlighted by Fig. 2, showing the results of CFD simulations carried out, by means of the model presented in [6], for test S at 𝑚𝑚̇𝑜𝑜 𝑜𝑜 ≈ 40 𝑔𝑔/𝑠𝑠 with the corresponding average grain port diameter.…”

Section: Characteristic Velocity Analysismentioning

confidence: 99%

Tests and simulations on 200N paraffin-oxygen hybrid rocket engines with different fuel grain lengths

Mungiguerra

2023

Materials Research Proceedings

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Abstract. An experimental campaign, in the framework of the HYPROB-NEW hybrid rocket studies, was carried out on a 200N-thrust class hybrid rocket engine, using gaseous oxygen as the oxidizer and paraffin wax-based fuel, to investigate the effect of fuel grain length on motor performance and internal ballistics. Numerical analysis have been also performed to support the experimental findings. It was observed that, for given oxidizer flow rate, fuel grain length directly affects the characteristic velocity, because of its influence on residence time and mixing efficiency, so that the shortest grain configuration displayed the lowest performance. Moreover, CFD simulations provided an estimation of the regression rate profile along the grain length, providing a possible interpretation for the measured space-time-averaged fuel regression rate. Finally, a method for the rebuilding of the convective heat-transfer coefficient in the nozzle was used, based on a combination of numerical simulations and experimental acquisitions.

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“…Hybrid rockets are recently gaining attention and becoming a competitive propulsion system because they have the benefits of solid and liquid rockets in one platform [17,24]. The fuel and oxidizer are in two different states: typically the fuel is a solid and the oxidizer is a gas/liquid, providing a highly dense fuel source similar to a bi-propellant solid motor but with the throttle control of a liquid motor [4,10,28].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning

Surina,

Georgalis,

Aphale

et al. 2021

Preprint

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This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates.A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. A Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

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New Entrainment Model for Modelling the Regression Rate in Hybrid Rocket Engines

Cited by 14 publications

References 34 publications

Pseudo-Two-Dimensional Modeling and Validation of a Hybrid Rocket Combustion Chamber

Pseudo-Two-Dimensional Modeling and Validation of a Hybrid Rocket Combustion Chamber

Tests and simulations on 200N paraffin-oxygen hybrid rocket engines with different fuel grain lengths

Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning

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