Model for Prediction of Negative and Positive Erosive Burning

Greatrix, David R.

doi:10.5589/q07-002

Cited by 33 publications

(6 citation statements)

References 9 publications

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“…The good correlation between experiment and predictions of the originating SRM erosive burning model [6,7], a model closely analogous to the present HRE regression rate model, suggests that such a superposition may be applicable, at least in some cases. The addition of the two components is not entirely independent of one another as might first be suggested, with the mass-flux-dependent component partially a function of the overall regression rate (for the influence of transpiration/blowing on h), and thus, the base regression rate.…”

Section: Modelmentioning

confidence: 87%

“…Earlier relative success using a thin-layer, energy-film [15,16] approach for predicting the aforementioned erosive burning in solid-propellant rocket motors (SRMs) [6,7], suggested a comparable effort here for mass-flux-dependent fuel regression in HREs might prove worthwhile. By way of review, one first recalls that a convective heat feedback relationship, presented originally by Lenoir and Robillard [13] …”

Section: Modelmentioning

confidence: 99%

“…1), a similar approach is utilized. Based on past experience in modelling mass-flux-dependent erosive burning in solid-propellant rocket motors (SRMs) [6,7], with numerous comparisons to experimental SRM data, a number of factors are incorporated into the present HRE phenomenological regression rate (r b ) model, including transpiration (a.k.a., blowing), hydraulic port diameter (d), and effective fuel surface roughness height (ε). A number of comparisons to HRE experimental data are * Tel.…”

Section: Introductionmentioning

confidence: 99%

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Regression rate estimation for standard-flow hybrid rocket engines

Greatrix¹

2009

Aerospace Science and Technology

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Section: Modelmentioning

confidence: 87%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression rate estimation for standard-flow hybrid rocket engines

Greatrix¹

2009

Aerospace Science and Technology

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“…The flow-based erosive burning component (negative and positive) is established through the following expression [27]: For the above case, where the base burning rate r o is a function of the other mechanisms (pressure and acceleration), one finds the velocity-based component of burn rate from Eq. (34) via r u = r b -r o .…”

Section: Equations For Propellant Burning Ratementioning

confidence: 99%

“…lower flow speeds, the negative component resulting from a stretched combustion zone thickness (* r > * o ) may cause an appreciable drop in the base burn rate r o , while at higher flow speeds, the positive erosive burning component r e , established from a convective heat feedback premise[27], should dominate:…”

mentioning

confidence: 99%

Effect of Diminishing Particle Size on Solid Rocket Combustion Instability Symptom Suppression

Greatrix¹

2012

48th AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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Research towards predicting and quantifying undesirable transient axial combustion instability symptoms in solid-propellant rocket motors necessitates a comprehensive numerical model for internal ballistic simulation under dynamic flow and combustion conditions. In the present investigation, important elements of the framework for numerically evaluating the usage of reactive aluminum particles for the suppression of axial shock wave development are brought forward. A primary focus is placed on evaluating the qualitative trends associated with the time-dependent reduction in size of the aluminum particles as they move downstream in the central internal flow. In order to simplify the scope of this preliminary study, the reactive particle size regression is stipulated to occur at a designated uniform rate for a given simulated firing. Individual transient internal ballistic simulation runs for a reference composite-propellant cylindrical-grain motor show the evolution of the axial pressure wave for a given initiating pressure disturbance, and particle loading and size diminishment rate. Particle loading distributions at various locations in the motor chamber's internal flow, at different times into the given firing (pre-and postdisturbance), are presented. The limit pressure wave magnitudes at a later reference time in a given firing simulation run are collected for a series of runs at different particle size reduction rates, in order to assist in the evaluation of identifiable trends. The numerical results demonstrate that the ability of the particles to suppress axial wave development improves as the nominal particle regression rate becomes lower. Nomenclature A= local core cross-sectional area, m 2 a = gas sound speed, m/s a R = longitudinal (or lateral) acceleration, m/s 2 a n = normal acceleration, m/s 2 b = nonequilibrium sound speed of two-phase mixture C = de St. Robert coefficient, m/s-Pa n C m = particle solid specific heat, J/kg-K C p = gas specific heat, J/kg-K C pp = reactive particle gas specific heat, J/kg-K C s = specific heat, solid phase, J/kg-K D i = drag of gas on a particle from i th particle set, N d = local core hydraulic diameter, m d mi = mean particle diameter for i th particle set, m E = local total specific energy of gas in core flow, J/kg E pi = local total specific energy, i th particle set in flow, J/kg f = frequency, Hz, or Darcy-Weisbach friction factor G a = accelerative mass flux, kg/m 2 -s h = convective heat transfer coefficient, W/m 2 -K )H s = net surface heat of reaction, J/kg K b = burn rate limiting coefficient, s -1 k = gas thermal conductivity, W/m-K k s = thermal conductivity, solid phase, W/m-K M a = magnitude of attenuation M R = limit magnitude, cyclic input m pi = mean mass of a particle from i th particle set, kg N i = number of particles from the i th set in a given volume N set = total number of particle sets N tot = total number of particles in a given volume n = exponent, de St. Robert's law p = local gas static pressure, Pa )p d = initial pulse disturbance step ...

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Solid-Propellant Rocket Motors

Greatrix

2012

Powered Flight

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Model for Prediction of Negative and Positive Erosive Burning

Cited by 33 publications

References 9 publications

Regression rate estimation for standard-flow hybrid rocket engines

Regression rate estimation for standard-flow hybrid rocket engines

Effect of Diminishing Particle Size on Solid Rocket Combustion Instability Symptom Suppression

Solid-Propellant Rocket Motors

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