Tesi sul tema "Solid propellant"

This dissertation documents the building of computational propellant/ingredient models toward predicting AP/HTPB/Al cookoff events. Two computer codes were used to complete this work; a steady-state code and a transient ignition code Numerous levels of verification resulted in a robust set of codes to which several propellant/ingredient models were applied. To validate the final cookoff predictions, several levels of validation were completed, including the comparison of model predictions to experimental data for: AP steady-state combustion, fine-AP/HTPB steady-state combustion, AP laser ignition, fine-AP/HTPB laser ignition, AP/HTPB/Al ignition, and AP/HTPB/Al cookoff. A previous AP steady-state model was updated, and then a new AP steady-state model was developed, to predict steady-state combustion. Burning rate, temperature sensitivity, surface temperature, melt-layer thickness, surface species at low pressure and high initial temperature, final flame temperature, final species fractions, and laser-augmented burning rate were all predicted accurately by the new model. AP ignition predictions gave accurate times to ignition for the limited experimental data available. A previous fine-AP/HTPB steady-state model was improved to predict a melt layer consistent with observation and avoid numerical divergence in the ignition code. The current fine-AP/HTPB model predicts burning rate, surface temperature, final flame temperature, and final species fractions for several different propellant formulations with decent success. Results indicate that the modeled condensed-phase decomposition should be exothermic, instead of endothermic, as currently formulated. Changing the model in this way would allow for accurate predictions of temperature sensitivity, laser-augmented burning rate, and surface temperature trends. AP/HTPB ignition predictions bounded the data across a wide range of heat fluxes. The AP/HTPB/Al model was based upon the kinetics of the AP/HTPB model, with the inclusion of aluminum being inert in both the solid and gas phases. AP/HTPB/Al ignition predictions bound the data for all but one source. AP/HTPB/Al cookoff predictions were accurate when compared to the limited data, being slightly low (shorter time) in general. Comparisons of AP/HTPB/Al ignition and cookoff data showed that the experimental data might be igniting earlier than expected.

Solid propellant is the highly energetic fuel burnt in the combustion chamber of ballistic weapons. It is manufactured, for this purpose, in either granular or stick form. Internal ballistics describes the behavior within the combustion chamber throughout the ballistic cycle upto projectile exit from the muzzle of the gun barrel. Over the last twenty years this has been achieved by modelling the process using two-phase flow equations. The solid granules or sticks constitute the first phase, which can be assumed to be incompressible over typical pressure ranges within the chamber. The gas-phase is composed of both the original ambient gas contained around the propellant and additional gas produced by the propellant gasifying on heating. Equations can be derived that describe the conservation of mass, momentum and energy in terms of average flow variables. The equations are a highly non-linear system of partial-differential- equations. High-speed flow features are observed in internal ballistics and ordinary fini te- difference methods are unsuitable numerical methods due to inaccurate prediction of discontinuous flow features. Modern shock-capturing methods are employed, which solve the system of equations in conservation form, with the ability to capture shocks and contact discontinuities. However, although the numerical solutions compare well with experiment over the bulk of the combustion chamber, the ignition models used in internal ballistics are unreliable. These are based on either gas or solid-surface temperature achieving some empirically measured 'ignition temperature' after which the propellant burns according to an empirical pressure dependent burning law. Observations indicate that this is not an adequate representation of ignition. Time differences between first solid gasification and ignition imply two distinct processes occurring. ]Further, ignition occurring in gas-only regions indicates that ignition is controlled by a gas-phase reaction. This thesis develops simple ideas to describe possible mechanisms for these physical observations. The aim is to provide an improved model of the ignition of solid propellant. A two stage reaction process is described involving endothermic gasification of the solid, to produce a source of reactant gas, followed by a very exothermic gas-phase ignition reaction. Firstly the gas-phase ignition is considered. A very simple reaction is suggested which is assumed to control the combustion of reactant gas, produced by solid gasification. Ignition is, by definition, the initiation of this exothermic reaction. Chemical kinetics are included in the gas-phase flow equations to explore the evolution of the reactant gas that is subject to changes in temperature and pressure. By assuming spatial uniformity, analytical solutions of the problem are deduced. The physical interpretation of the solution is discussed, in particular, the relationship between temperature, reactant concentration and ignition is explored. Numerical methods are required to solve the one-dimensional flow equations. Development of suitable CFD methods provides a method of solution. Finite-volume schemes, based on the original work by Godunov, are used to solve the conservation form of the equations. A simple test problem is considered whereby reactant gas is injected into a cylindrical combustion chamber. By examining the resulting flow histories, valuable information is gathered about the complicated coupling of chemistry and flow. Chemistry is included into a system of two-phase flow equations. By using standard averaging methods along with an equation for gas-phase species, equations are derived that describe the rate of change of average flo%v variables for both gas and particle phases. Numerical schemes are developed and some of the difficulties involved in two-phase flow systems, that are not an issue in single-phase flow, are presented. An internal ballistics application is considered as a test case and the solution discussed. The other important reaction involved in the combustion cycle, solid gasification, is explored. The model is based on detailed description of interphase mass and energy transfer at the solid-gas interface. This involves the solution of the heat conduction equation with a moving boundary that divides the solid and gas regions. Similar numerical schemes are constructed to solve the equations. Finally, this model is coupled with the equations of gas-phase reaction. This describes the complete cycle whereby increases in gas temperature cause the solid to increase in temperature and gasify. Subsequent gas-phase combustion of the reactant gases produces heat-transfer between the solid and gas and continues to accelerate gasification. Eventually this results in selfsustained combustion of the solid propellant.

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An investigation into the behavior of aluminized solid propellant combustion in a two-dimensional windowed rocket motor was conducted using holographic techniques. Holograms were recorded in the motor port, aft of the propellant grain and at the entrance to the exhaust nozzle for two different propellant compositions at varying operating pressures. Quantitative particle size data for particles larger than 20 microns were obtained from the holograms. From these data, the mean diameters (D32) of the larger particles were calculated and utilized to compare what effects pressure, location in the motor and aluminum content had on the behavior of the aluminum/aluminum oxide particles. D 32 was found to decrease with increasing pressure, but was unaffected by variations in low values of propellant aluminum loading. D 32 at the grain exit was found to be significantly less than within the grain port.
http://archive.org/details/holographicinves00butl
Lieutenant, United States Navy

In this study, experimental investigation of solid propellant combustion instability using an end burning T-Burner setup is performed. For this purpose, a T-Burner setup is designed, analyzed, constructed and tested with all its sub components. T-Burner setup constructed is mainly composed of a base part, a control panel and the T-Burner itself. Combustion chamber, pressure stabilization mechanism, pressurization system, measurement instruments and data acquisition systems form the T-Burner. Pressure stabilization mechanism is utilized in two different alternatives, first of which is by the use of nitrogen gas and a small surge tank with a cavitating venturi. This is a brand new approach for this kind of system. The second alternative is the use of a choked nozzle for pressure stabilization. Resonance frequencies of the system with the two different pressure stabilization mechanisms are experimentally evaluated. Helmholtz frequency of the T-burner constructed is calculated and no Helmholtz instability is observed in the system. Constructed T-Burner setup is operated for a specific solid propellant. System worked successfully and pressure data are obtained. Pressure data revealed oscillatory behaviour. Decay and growth rates of pressure oscillations are used for the calculation of pressure response of the propellant tested. By the use of this T-Burner comparison of the behavior of different propellants can be performed. It can be used as a test device for measuring quantitatively the response of a burning propellant to unsteady motions.

A method for developing an erosive burning model for use in solid propellant design-and-analysis interior ballistics codes is described and evaluated. Using Direct Numerical Simulation, the primary mechanisms controlling erosive burning (turbulent heat transfer, and finite rate reactions) have been studied independently through the development of models using finite rate chemistry, and infinite rate chemistry. Both approaches are calibrated to strand burn rate data by modeling the propellant burning in an environment with no cross-flow, and adjusting thermophysical properties until the predicted regression rate matches test data. Subsequent runs are conducted where the cross-flow is increased from M=0.0 up to M=0.8. The resulting relationship of burn rate increase versus Mach Number is used in an interior ballistics analysis to compute the chamber pressure of an existing solid rocket motor. The resulting predictions are compared to static test data. Both the infinite rate model and the finite rate model show good agreement when compared to test data. The propellant considered is an AP/HTPB with an average AP particle size of 37 microns. The finite rate model shows that as the cross-flow increases, near wall vorticity increases due to the lifting of the boundary caused by the side injection of gases from the burning propellant surface. The point of maximum vorticity corresponds to the outer edge of the APd-binder flame. As the cross-flow increases, the APd-binder flame thickness becomes thinner; however, the point of highest reaction rate moves only slightly closer to the propellant surface. As such, the net increase of heat transfer to the propellant surface due to finite rate chemistry affects is small. This leads to the conclusion that augmentation of thermal transport properties and the resulting heat transfer increase due to turbulence dominates over combustion chemistry in the erosive burning problem. This conclusion is advantageous in the development of future models that can be calibrated to heat transfer conditions without the necessity for finite rate chemistry. These results are considered applicable for propellants with small, evenly distributed AP particles where the assumption of premixed APd-binder gases is reasonable.

A dual-frequency microwave bum rate measurement system for solid rocket motors has been developed and is described. The system operates in the X-band (8.2-12.4 Ghz) and uses two independent frequencies operating simultaneously to measure the instantaneous bum rate in a solid rocket motor. Modeling of the two frequency system was performed to determine its effectiveness in limiting errors caused by secondary reflections and errors in the estimates of certain material properties, particularly the microwave wavelength in the propellant. Computer simulations based upon the modeling were performed and are presented. Limited laboratory testing of the system was also conducted to determine its ability perform as modeled.

Simulations showed that the frequency ratio and the initial motor geometry (propellant thickness and combustion chamber diameter) determined the effectiveness of the system in reducing secondary reflections. Results presented show that higher frequency ratios provided better error reduction. Overall, the simulations showed that a dual frequency system can provide up to a 75% reduction in burn rate error over that returned by a single frequency system. The hardware and software for dual frequency measurements was developed and tested, however, further instrumentation work is required to increase the rate at which data is acquired using the methods presented here. The system presents some advantages over the single frequency method but further work needs to be done to realize its full potential.

Master of Science

In the design and development of solid propellant rocket motors, the use of numerical tools able to simulate, predict and reconstruct the behaviour of a given motor in all its operative conditions is particularly important in order to decrease all the planning and costs. This study is devoted to present an approach to the numerical simulation of a given SPRM internal ballistics, NAWC no. 13, during the quasi steady state by means of a commercial numerical tool, ANSYS FLUENT. The internal ballistics model constructed in this study is a 2-D axisymmetric model, based on several assumptions. Among them is the assumption that there is no contribution of the erosive burning and the dynamic burning in the burning rate model. The results of the internal ballistics simulation are compared with the results found in the bibliographical research, thus validating the model that has been set up. The validation of the results also allows us to conclude that the assumptions made in the construction of the model are reasonable. Suggestions and recommendations for further study are outlined.
Na concepção e desenvolvimento de motores foguete sólidos, o uso de ferramentas numéricas capazes de simular, prever e reconstruir o comportamento de um dado do motor em todas as condições operativas ´e particularmente importante, a fim de diminuir todos os custos e planeamento. Este estudo ´e dedicado a apresentar uma abordagem para a simulação numérica de balística interna de um determinado motor foguete de propelente sólido, Naval Air Warfare Center no. 13, durante a fase quasi steady state por meio de uma ferramenta numérica comercial, ANSYS FLUENT. O modelo de balística interna construído neste estudo é um modelo axissimétrico 2-D. Tem por base vários pressupostos. Entre eles, está o pressuposto de que não há contribuição da queima erosiva e da queima dinâmica no modelo da taxa de queima. Os resultados da simulação balística interna são comparados com os resultados encontrados na pesquisa bibliográfica, validando assim, o modelo que foi construído. A validação dos resultados também nos permite concluir que os pressupostos assumidos na construção do modelo são razoáveis. Sugestões e recomendações para um estudo mais aprofundado são delineadas.

Small-scale solid rocket motors have been extremely successful among propulsion industries in the last decades for improving the accuracy of thrust-time prediction in new motors in order to reduce the amount of experimental tests needed for the characterization of their internal ballistics behaviour. It is of common knowledge the fact that the ballistic response of solid rocket motors depends on several factors, among which the manufacturing process stands out. It is the grain that defines the quality of the propellant. Therefore, the thrust performance can be affected by heterogeneity of grain caused by inclusions (like bubbles, air gaps or cavities) and by the different concentration and orientation of the solid particles during the casting process. The main objectives of the present work are, firstly, the implementation of numerical simulations of small-scale solid motors casting process that helps to characterize the propellant and to study its rheological behaviour and, secondly, the acquisition of an insight of the burning rate anomaly phenomenon, also known as "Hump Effect", through the analysis of particle concentration. This work is divided into two parts: the former is focused on the challenge of bi-dimensional, multi-batch and 3D fluid dynamic simulations of the casting process and the other one focused on the tracking of the particles present inside the propellant. The software that has been used for this work is ANSYS Fluent. Post-processing codes have been implemented with MATLAB in order to obtain the concentration of the particles inside the rocket motor.

Star grain configuration design has been widely used in solid rocket applications for several decades. Although a large number of surface cracks are detected in the rocket motor propellants, the mechanism of these cracks is sull not well known due to the complex geometry of the grain. A stress-freezing photoelastic investigation has been performed to study the deep cracks which emanate from the fingertips of the star-shaped cutout cylinders. Using three-dimensional photoelasticity and proper algorithms in fracture mechanics, the stress intensity factors (SIF's) and the stress singularity orders along the crack front have been calculated. A surface effect on the dominant singularity order is observed and some analytical results are employed as a comparison. Meanwhile, three-dimensional finite element solution to the circular cylinder is used to find the “equivalent” inner radius for the internal star cylinder and the variation of SIF's along the crack border shows a very similar trend to the experimental results once the "equivalent" radius is adopted.
Master of Science

The present research is an experimental and numerical study of aeroacoustic phenomena occurring in large solid rocket motors (SRM) as the Ariane 5 boosters. The emphasis is given to aeroacoustic instabilities that may lead to pressure and thrust oscillations which reduce the rocket motor performance and could damage the payload. The study is carried out within the framework of a CNES (Centre National d'Etudes Spatiales) research program.

Large SRM are composed of a submerged nozzle and segmented propellant grains separated by inhibitors. During propellant combustion, a cavity appears around the nozzle. Vortical flow structures may be formed from the inhibitor (Obstacle Vortex Shedding OVS) or from natural instability of the radial flow resulting from the propellant combustion (Surface Vortex Shedding SVS). Such hydrodynamic manifestations drive pressure oscillations in the confined flow established in the motor. When the vortex shedding frequency synchronizes acoustic modes of the motor chamber, resonance may occur and sound pressure can be amplified by vortex nozzle interaction.

Original analytical models, in particular based on vortex sound theory, point out the parameters controlling the flow-acoustic coupling and the effect of the nozzle design on sound production. They allow the appropriate definition of experimental tests.

The experiments are conducted on axisymmetric cold flow models respecting the Mach number similarity with the Ariane 5 SRM. The test section includes only one inhibitor and a submerged nozzle. The flow is either created by an axial air injection at the forward end or by a radial injection uniformly distributed along chamber porous walls. The internal Mach number can be varied continuously by means of a movable needle placed in the nozzle throat. Acoustic pressure measurements are taken by means of PCB piezoelectric transducers. A particle image velocimetry technique (PIV) is used to analyse the effect of the acoustic resonance on the mean flow field and vortex properties. An active control loop is exploited to obtain resonant and non resonant conditions for the same operating point.

Finally, numerical simulations are performed using a time dependent Navier Stokes solver. The analysis of the unsteady simulations provides pressure spectra, sequence of vorticity fields and average flow field. Comparison to experimental data is conducted.

The OVS and SVS instabilities are identified. The inhibitor parameters, the chamber Mach number and length, and the nozzle geometry are varied to analyse their effect on the flow acoustic coupling.

The conclusions state that flow acoustic coupling is mainly observed for nozzles including cavity. The nozzle geometry has an effect on the pressure oscillations through a coupling between the acoustic fluctuations induced by the cavity volume and the vortices travelling in front of the cavity entrance. When resonance occurs, the sound pressure level increases linearly with the chamber Mach number, the frequency and the cavity volume. In absence of cavity, the pressure fluctuations are damped.

Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

Much Research on composite solid propellants has been performed over the past few decades and much progress has been made, yet many of the fundamental processes are still unknown, and the development of new propellants remains highly empirical. Ways to enhance the performance of solid propellants for rocket and other applications continue to be explored experimentally, including the effects of various additives and the impact of fuel and oxidizer particle sizes on burning behavior. One established method to measure the burning rate of composite propellant mixtures in a controlled laboratory setting is to use a constant-volume pressure vessel, or strand burner. To provide high-pressure burn rate data at pressures up to 360 atm, the authors have installed, characterized and improved a strand burner facility at the University of Central Florida. Details on the facility and its improvements, the measurement procedures, and the data reduction and interpretation are presented. Two common HTPB/AP propellant mixtures were tested in the original strand burner. The resulting burn rates were compared to data from the literature with good agreement, thus validating the facility and related test techniques, the data acquisition, data reduction and interpretation. After more than 380 successful recordings, an upgraded version of the strand burner, was added to the facility. The details of Strand Burner II, its improvements over Strand Burner I, and its characterization study are presented.
M.S.
Department of Mechanical, Materials and Aerospace Engineering
Engineering and Computer Science
Mechanical Engineering MSME

Thesis (MScIng)--University of Stellenbosch, 2003.
ENGLISH ABSTRACT: It was shown in an earlier study that it is possible to predict the spectral radiance of rocket combustion plumes directly from the propellant composition and motor parameters. Little is published in the open literature on this subject, but the current trend is to use determinative methods like computational fluid dynamics and statistical techniques to simulate wide band radiance based on blackbody temperature assumptions. A limitation of these methods is the fact that they are computationally expensive and rather complex to implement. An alternative modeling approach was used which did not rely on solving all the nonlinearities and complex relationships applicable to a fundamental model. A multilayer perceptron based Neural Network was used to develop a parametric functional mapping between the propellant chemical composition and the motor design and the resulting spectral irradiance measured in a section of the plume. This functional mapping effectively models the relationship between the rocket design and the plume spectral radiance. Two datasets were available for use in this study: Emission spectra from solid propellant rockets and flare emission spectra. In the case of the solid rocket propellants, the input to the network consisted of the chemical composition of the fuels and four motor parameters, with the output of the network consisting of 146 scaled emission spectra points in the waveband from 2-5 microns. The four motor parameters were derived from equations describing the mass flow characteristics of rocket motors. The mass flow through the rocket motor does have an effect on the shape of the plume of combustion gases, which in turn has an effect on the infrared signature of the plume. The characteristics of the mass flow through the nozzle of the rocket motor determine the thermodynamic properties of the combustion process. This then influences the kind of chemical species found in the plume and also at what temperature these species are radiating energy.The resultant function describing the plume signature is: Plume signature f {p T A fuel composition} t , , , , 1 1 = ε It was demonstrated that this approach yielded very useful results. Using only 18 basic variables, the spectra were predicted properly for variations in all these parameters. The model also predicted spectra that agree with the underlying physical situation when changing the composition as a whole. By decreasing the Potassium content for example, the model demonstrated the effect of a flame suppressant on the radiance in this wavelength band by increasing the predicted output. Lowering the temperature, which drives the process of molecular vibration and translation, resulted in the expected lower output across the spectral band. In general, it was shown that only a small section of the large space of 2 propellant classes had to be measured in order to successfully generate a model that could predict emission spectra for other designs in those classes. The same principal was then applied to predicting the infrared spectral emission of a burning flare. The brick type flare considered in this study will ignite and the solid fuel will burn on all surfaces. Since there are no physical parameters influencing the plume as in the case of the rocket nozzles it was required to search for parameters that could influence the flare plume. It was possible to calculate thermodynamic properties for the flare combustion process. These parameters were then reduced to 4 parameters, namely: the oxidant-fuel ratio, equilibrium temperature, the molar mass and the maximum combustion temperature. The input variables for the flares thus consisted of the chemical composition and 4 thermodynamic parameters described above. The network proposed previously was improved and optimised for a minimum number of variables in the system. The optimised network marginally improved on the pevious results (with the same data), but the training time involved was cut substantially. The same approach to the optimization of the network was again followed to determine the optimal network structure for predicting the flare emission spectra. The optimisation involved starting out with the simplest possible network construction and continuouslyincreasing the variables in the system until the solution predicted by the network was satisfactory. Once the structure of the network was determined it was possible to optimise the training algorithms to further improve the solution. In the case of the solid rocket propellant emission data it was felt that it would be important to be able to predict the chemical composition of the fuel and the motor parameters using the infrared emission spectra as input. This was done by simply reversing the optimised network and exchanging the inputs with the outputs. The results obtained from the reversed network accurately predicted the chemical composition and motor parameters on two different test sets. The predicted spectra of some of the solid propellant rocket test sets and flare test sets did not compare well with the expected values. This was due to the fact that these test sets were in a sparsely populated area of the variable space. These outliers are normally removed from training data, but in this case there wasn’t enough data to remove outliers. To obtain an indication of the strength of the correlation between the predicted and measured line spectra two parameters were used to test the correlation between two line spectra. The first parameter is the Pearson product moment of coefficient of correlation and gives an indication of how good the predicted line spectra followed the trend of the measured spectral lines. The second parameter measures the relative distance between a target and predicted spectral point. For both the solid propellants and the flares the correlation values was very close to 1, indicating a very good solution. Values for the two correlation parameters of a test set of the flares were 0.998 and 0.992. In order to verify the model it was necessary to prove that the solution yielded by the model is better than the average of the variable space. Three statistical tests were done consisting of the mean-squared-error test, T-test and Wilcoxon ranksum test. In all three cases the average of the variable space (static model) and the predicted values (Neural Network model) were compared to the measured values. For both the T-test and the Wilcoxon ranksum test the null hypothesis is rejected when t < -tα = 1.645 and then thealternative hypothesis is accepted, which states that the error of the NN model will be smaller than that of the static model. The mean squared error for the static model was 0.102 compared to the 0.0167 of the neural net, for a solid propellant rocket test set. A ttest was done on the same test set, yielding a value of –2.71, which is smaller than – 1.645, indicating that the NN model outperforms the static model. The Z value for this test set is Z = -11.9886, which is a much smaller than –1.645. The results from these statistical tests confirm that neural network is a valid conceptual model and the solutions yielded are unique.
AFRIKAANSE OPSOMMING: In ‘n vroeër studie is bewys hoe dit moontlik is om die spektrale irradiansie van ‘n vuurpyl se verbrandingspluim te voorspel vanaf slegs die dryfmiddelsamestelling en vuurpylmotoreienskappe. In die literatuur is daar min gepubliseer oor hierdie onderwerp. Dit wil voorkom asof meer deterministiese metodes gebruik word om die probleem op te los. Metodes soos CFD simulasies en statistiese analises word tans verkies om wyeband radiansie te voorspel gebaseer op perfekte swart ligaam teorie. ‘n Groot beperking van hierdie metodes is die feit dat die berekeninge kompleks is en baie lank neem om te voltooi. ‘n Alternatiewe benadering is gebruik, wat nie poog om al die nie-liniêre en komplekse verbande uit eerste beginsels op te los nie. ‘n Neurale netwerk is gebruik om ‘n funksionele verband te skep tussen die chemiese samestelling van die dryfmiddel, vuurpylmotor ontwerp en die spektrale irradiansie van die vuurpyl se pluim. Die funksionele verband kan nou effektief die afhanklikheid van die dryfmiddelsamestelling, vuurpylmotor ontwerp en die spektrale uitset modelleer. Twee datastelle was beskikbaar vir analise: Emissie spektra van vaste dryfmiddel vuurpyle en ook van vaste dryfmiddel fakkels. Die invoer tot die neurale netwerk van die vuurpyle het bestaan uit die chemiese samestelling van die dryfmiddel en 4 vuurpylmotor eienskappe. Die uitvoer van die netwerk het weer bestaan uit 146 spektrale irradiansie waardes in die golflengte band van 2-5μm. Die 4 vuurpylmotor eienskappe is afgelei uit massavloei teorie vir vuurpyl motors, aangesien die uitvloei van die produkgasse ‘n invloed op die pluim van die motor sal hê. Die massavloei het weer ‘n effek op die spektrale handtekening van die pluim. Die eienskappe van die massavloei deur die mondstuk van die vuurpylmotor bepaal die termodinamiese eienskappe van die verbrandingsproses. Die invloed op die verbrandingsproses bepaal weer watter tipe produkte gevorm word en by watter temperatuur hulle energie uitstraal. Die gevolg is dat ‘n funksie gedefinieer kan word wat die pluim beskryf.Pluim handtekening = f{, temperatuur, mondstuk keël grootte, vernouings verhouding van mondstuk, dryfmiddelsamestelling} Deur net 18 invoer nodes te gebruik kon die netwerk die irradiansie suksesvol voorspel met ‘n variansie in al die invoer waardes. Deur byvoorbeeld die Kalium inhoud van die dryfmiddel samestelling te verminder het die model die vermindering van ‘n vlam onderdrukker suksesvol nageboots deurdat die irradiansie ‘n hoër uitset gehad het. Die sensitiwiteit van die model is verder getoets deur die temperatuur in die verbrandingskamer te verlaag, met ‘n korrekte laer irradiansie uitset, as gevolg van die feit dat die temperatuur die molekulêre vibrasie en translasie beweging beheer. Dieselfde benadering is gebruik om die model te bou vir die voorspelling van die fakkels se infrarooi irradiansie. Anders as die vuurpylmotors vind die verbranding in die geval van die fakkels in die atmosfeer plaas. Dit was dus ook nodig om na die termodinamiese eienskappe van die fakkel verbranding te kyk. Verskeie parameters is bereken, maar 4 parameters, naamlik die brandstof-suurstof verhouding, temperatuur, molêre massa en die maksimum verbrandingstemperatuur, tesame met die dryfmiddel samestelling kon die irradiansie van die fakkels suskesvol voorspel. Die bestaande netwerk struktuur vir die vuurpylmotors is verbeter en geoptimiseer vir ‘n minimum hoeveelheid veranderlikes in die stelsel. Die geoptimiseerde netwerk het ‘n klein verbetering in die voorspellings getoon, maar die oplei het drasties afgeneem. Dieselfde benadering is gebruik om die optimale netwerk vir die fakkels te bepaal. Optimisering van die netwerk struktuur is bereik deur met die eenvoudigste struktuur te begin en die hoeveelheid veranderlikes te vermeerder totdat ‘n bevredigende oplossing gevind is. Na die struktuur van die netwerk bevestig is, kon die oordragfunksies op die nodes verder geoptimiseer word om die model verder te verbeter. Dit het verder geblyk dat dit moonlik is om die netwerk vir die vuurpylmotors om te draai sodat die irradiansie gebruik word om die dryfmiddel samestelling en motor eienskappe te voorspel. Die netwerk is eenvoudig omgedraai en die insette het die uitsette geword.Die resultate van die omgekeerde netwerk het bevestig dat dit wel moontlik is om die dryfmiddel samestelling en motor eienskappe te voorspel vanaf die irradiansie. Die voorspelde spektra van beide die vuurpylmotors en die fakkels het nie altyd goed gekorreleer met die gemete data nie. Van die spektra kom voor in ‘n lae digtheidsdeel van die veranderlike ruimte. Dit het tot gevolg gehad dat daar nie genoeg data vir opleiding van die netwerk in die omgewing van die toetsdata was nie. Hierdie data is eintlik uitlopers en moet verwyder word van die opleidingsdata, maar daar is alreeds nie genoeg data beskikbaar om die uitlopers te verwyder nie. Dit is nodig om te bepaal hoe goed die voorspelde data vergelyk met die gemete data. Twee parameters is gebruik om te bepaal hoe goed die data korreleer. Die eerste is die “Pearson product moment of coefficient of correlation”, wat ‘n goeie aanduiding gee van hoe goed die voorspelde waardes die gemete waardes se profiel volg. Die tweede parameter meet die relatiewe afstand tussen die teiken en die voorspelde waardes. Vir beide die vuurpylmotors en die fakkels het die toetsstelle ‘n korrelasiewaarde van baie na aan 1 gegee, wat ‘n goeie korrelasie is. Die waardes van die twee parameters vir een van die fakkel toetstelle was onderskeidelik 0.998 en 0.992. Die model is geverifieer deur te bepaal of die model ‘n beter oplossing bied as die gemiddeld van die veranderlike ruimte. Drie statistiese toetse is gedoen: “Mean-squarederror” toets, T-toets en ‘n “Wilcoxon ranksum” toets. In al drie gevalle word die gemiddelde van die veranderlike ruimte (statiese model) en die voorspelde waardes (Neurale netwerk model) teen die gemete waardes getoets. Vir beide die T-toets en die “Wilcoxon ranksum” toets word die nul hipotese verwerp indien t < ta = 1.645 en dan word die alternatiewe hipotese aanvaar, wat bepaal dat die fout van die neurale netwerk model kleiner is as die van die statiese model. Die “mean-squared-error” van die statiese model was 0.102, in vergelyking met 0.0167 van die neurale netwerk model vir ‘n vuurpylmotor toetsstel. ‘n T-toets is gedoen vir dieselfde toetsstel, met ‘n resultaat van-2.71, wat kleiner is as –1.645 en aandui dat die neurale netwerk model weereens beter presteer as die statiese model. Die Z waarde uit die “Wilcoxon ranksum” toets is Z=- 11.9886, wat baie kleiner is as –1.645. Die resultate van die statitiese toetse toon dat die neurale netwerk ‘n geldige model is en die oplossings van die model ook uniek is.

Thesis (Mechanical Engineer and M.S. in Mechanical Engineering)--Naval Postgraduate School, June 2010.
Thesis Advisor(s): Brophy, Christopher ; Second Reader: Gannon, Anthony. "June 2010." Description based on title screen as viewed on July 13, 2010. Author(s) subject terms: Solid rocket propellant, two phase flow, erosive burning, alumina agglomeration, laser imaging Includes bibliographical references (p. 87). Also available in print.

Solid propellant rocket motor is the primary propulsion technology used for short and medium range missiles. It is also commonly used as boost motor in many di_erent applications. Its wide spread usage gives rise to diversity of environments in which it is handled and stored. Ability to predict the storage life of solid propellants plays an important role in the design and selection of correct protective environments. In this study a methodology for the prediction of solid propellant storage life using cumulative damage concepts is introduced. Finite element mesh of the solid propellant grain is created with the developed parametric grain geometry generator. Finite element analyses are carried out to obtain the temperature and stress response of the propellant to the environmental thermal loads. Daily thermal cycles are assumed to be sinusoidal cycles represented by their means and amplitudes. With the cumulative damage analyses, daily damage accumulated in the critical locations of the solid propellant grain are investigated. Meta-models relating the daily damage amount with the daily temperature cycles are constructed in order to compute probability of failure. The results obtained in this study imply that it is possible to make numerical predictions for the storage life of solid propellants even in the early design phases. The methodology presented in this study provides a basis for storage life predictions.

Thesis (MScIng)--University of Stellenbosch, 2003.
ENGLISH ABSTRACT: The emission spectrum measured in the middle infrared (IR) band from the plume of a rocket can be used to identify rockets and track inbound missiles. It is useful to test the stealth properties of the IR fingerprint of a rocket during its design phase without needing to spend excessive amounts of money on field trials. The modelled predictions of the IR spectra from selected rocket motor design parameters therefore bear significant benefits in reducing the development costs. In a recent doctorate study it was found that a fundamental approach including quantum-mechanical and computational fluid dynamics (CFD) models was not feasible. This is first of all due to the complexity of the systems and secondly due to the inadequate calculation speeds of even the most sophisticated modern computers. A solution was subsequently investigated by use of the ‘black-box’ model of a multi-layer perceptron feed-forward neural network with a single hidden layer consisting of 146 nodes. The input layer of the neural network consists of 18 rocket motor design parameters and the output layer consists of 146 IR absorbance variables in the range from 2 to 5 μm wavelengths. The results appeared promising for future investigations. The available data consist of only 18 different types of rocket motors due to the high costs of generating the data. The 18 rocket motor types fall into two different design classes, the double base (DB) and composite (C) propellant types. The sparseness of the data is a constraint in building adequate models of such a multivariate nature. The IR irradiance spectra data set consists of numerous repeat measurements made per rocket motor type. The repeat measurements form the pure error component of the data, which adds stability to training and provides lack-of-fit ANOVA capabilities. The emphasis in this dissertation is on comparing the feed-forward neural network model to the linear and neural network partial least squares (PLS) modelling techniques. The objective is to find a possibly more intuitive and more accurate model that effectively generalises the input-output relationships of the data. PLS models are known to be robust due to the exclusion of redundant information from projections made to primary latent variables, similarly to principal components (PCA) regression. The neural network PLS techniques include feed-forward sigmoidal neural network PLS (NNPLS) and radial-basis functions PLS (RBFPLS). The NNPLS and RBFPLS algorithms make use of neural networks to find non-linear functional relationships for the inner PLS models of the NIPALS algorithm. Error-based neural network PLS (EBNNPLS) and radial-basis function network PLS (EBRBFPLS) are also briefly investigated, as these techniques make use of non-linear projections to latent variables. A modification to the orthogonal least squares (OLS) training algorithm of radial-basis functions is developed and applied. The adaptive spread OLS algorithm (ASOLS) allows for the iterative adaptation of the Gaussian spread parameters found in the radial-basis transfer functions. Over-fitting from over-parameterisation is controlled by making use of leaveone- out cross-validation and the calculation of pseudo-degrees of freedom. After cross-validation the overall model is built by training on the entire data set. This is done by making use of the optimum parameterisation obtained from cross-validation. Cross-validation also gives an indication of how well a model can predict data unseen during training. The reverse problem of modelling the rocket propellant chemical compositions and the rocket physical design parameters from the IR irradiance spectra is also investigated. This problem bears familiarity to the field of spectral multivariate calibration. The applications in this field readily make use of PLS and neural network modelling. The reverse problem is investigated with the same modelling techniques applied to the forward modelling problem. The forward modelling results (IR spectrum predictions) show that the feedforward neural network complexity can be reduced to two hidden nodes in a single hidden layer. The NNPLS model with eleven latent dimensions outperforms all the other models with a maximum average R2-value of 0.75 across all output variables for unseen data from cross-validation. The explained variance for the output data of the overall model is 94.34%. The corresponding explained variance of the input data is 99.8%. The RBFPLS models built using the ASOLS training algorithm for the training of the radialbasis function inner models outperforms those using K-means and OLS training algorithms. The lack-of-fit ANOVA tests show that there is reason to doubt the adequacy of the NNPLS model. The modelling results however show promise for future development on larger, more representative data sets. The reverse modelling results show that the feed-forward neural network model, NNPLS and RBFPLS models produce similar results superior to the linear PLS model. The RBFPLS model with ASOLS inner model training and 5 latent dimensions stands out slightly as the best model. It is found that it is feasible to separately find the optimum model complexity (number of latent dimensions) for each output variable. The average R2-value across all output variables for unseen data is 0.43. The average R2-value for the overall model is 0.68. There are output variables with R2-values of over 0.8. The forward and reverse modelling results further show that dimensional reduction in the case of PLS does produce the best models. It is found that the input-output relationships are not highly non-linear. The non-linearities are largely responsible for the compensation of both the DB- and C-class rocket motor designs predictions within the overall model predictions. For this reason it is suggested that future models can be developed by making use of a simpler, more linear model for each rocket class after a class identification step. This approach however requires additional data that must be acquired.
AFRIKAANSE OPSOMMING: Die emissiespektra van die uitlaatpluime van vuurpyle in die middel-infrarooi (IR) band kan gebruik word om die vuurpyle te herken en om inkomende vuurpyle op te spoor. Dit is nuttig om die uitstralingseienskappe van ‘n vuurpyl se IR afdruk te toets, sonder om groot bedrae geld op veldtoetse te spandeer. Die gemodelleerde IR spektrale voorspellings vir ‘n bepaalde stel vuurpylmotor ontwerpsparameters kan dus grootliks bydra om motorontwikkelingskostes te bemoei. In ‘n onlangse doktorale studie is gevind dat ‘n fundamentele benadering van kwantum-meganiese en vloeidinamika-modelle nie lewensvatbaar is nie. Dit is hoofsaaklik as gevolg van die onvoldoende vermoë van selfs die mees gesofistikeerde moderne rekenaars. ‘n Moontlike oplossing tot die probleem is ondersoek deur gebruik te maak van ‘n multilaag perseptron voorwaartse neurale netwerk met 146 nodes in ‘n enkele versteekte laag. Die laag van invoer veranderlikes bestaan uit agtien vuurpylmotor ontwerpsparameters en die uitvoerlaag bestaan uit 146 IR-absorbansie veranderlikes in die reeks golflengtes vanaf 2 tot 5 μm. Dit het voorgekom dat die resultate belowend lyk vir toekomstige ondersoeke. Weens die hoë kostes om die data te genereer bestaan die beskikbare data uit slegs agtien verskillende tipes vuurpylmotors. Die agtien vuurpyl tipes val verder binne twee ontwerpsklasse, naamlik die dubbelbasis (DB) en saamgestelde (C) dryfmiddeltipes. Die yl data bemoeilik die bou van doeltreffende multiveranderlike modelle. Die datastel van IR uitstralingspektra bestaan uit herhaalde metings per vuurpyltipe. Die herhaalde metings vorm die suiwer fout komponent van die data. Dit verskaf stabilitieit tot die opleiding op die data en verder die vermoë om ‘n analise van variansie (ANOVA) op die data uit te voer. In hierdie tesis lê die klem op die vergelyking tussen die voorwaartse neurale netwerk en die lineêre en neurale netwerk parsiële kleinste kwadrate (PLS) modelleringstegnieke. Die doel is om ‘n moontlik meer insiggewende en akkurate model te vind wat effektief die in- en uitvoer verhoudings kan veralgemeen. Dit is bekend dat PLS modelle meer robuus kan wees weens die weglating van oortollige inligting deur projeksies op hoof latente veranderlikes. Dit is analoog aan hoofkomponente (PCA) regressie. Die neurale netwerk PLS-tegnieke sluit in voorwaartse sigmoïdale neurale netwerk PLS (NNPLS) en radiale-basis funksies PLS (RBFPLS). Die NNPLS en RBFPLS algoritmes maak gebruik van die neurale netwerke om nie-lineêre funksionele verbande te kry vir die binne PLS-modelle van die nie-lineêre iteratiewe parsiële kleinste kwadrate (NIPALS) algoritme. Die fout-gebaseerde neurale netwerk PLS (EBNNPLS) en radiale-basis funksies PLS (EBRBFPLS) is ook weens hulle nie-lineêre projeksies na latente veranderlikes kortiliks ondersoek. ‘n Aanpassing tot die ortogonale kleinste kwadrate (OLS) opleidingsalgoritme vir radiale-basis funksies is ontwikkel en toegepas. Die aangepaste algoritme (ASOLS) behels die iteratiewe aanpassing van die verspreidingsparameters binne die Gauss-funksies van die radiale-basis transformasie funksies. Die oormatige parameterisering van ‘n model word beheer deur kruisvalidering met enkele weglatings en die berekening van pseudo-vryheidsgrade. Na kruisvalidering word die algehele model gebou deur opleiding op die volledige datastel. Dit word gedoen deur van die optimale parameterisering gebruik te maak wat deur kruisvalidering bepaal is. Kruisvalidering gee ook ‘n goeie aanduiding van hoe goed ‘n model ongesiende data kan voorspel. Die modellering van die vuurpyle se chemiese en fisiese ontwerpsparameters (omgekeerde probleem) is ook ondersoek. Hierdie probleem is verwant aan die veld van spektrale multiveranderlike kalibrasie. Die toepassings in die veld maak gebruik van PLS en neurale netwerk modelle. Die omgekeerde probleem word dus ondersoek met dieselfde modelleringstegnieke wat gebruik is vir die voorwaartse probleem. Die voorwaartse modelleringsresultate (IR voorspellings) toon dat die kompleksiteit van die voorwaartse neurale netwerk tot twee versteekte nodes in ‘n enkele versteekte laag gereduseer kan word. Die NNPLS model met elf latente dimensies vaar die beste van alle modelle, met ‘n maksimum R2-waarde van 0.75 oor alle uitvoer veranderlikes vir die ongesiende data (kruisvalidering). Die verklaarde variansie vir die uitvoer data vanaf die algehele model is 94.34%. Die verklaarde variansie van die ooreenstemmende invoer data is 99.8%. Die RBFPLS modelle wat gebou is deur van die ASOLS algoritme gebruik te maak om die PLS binne modelle op te lei, vaar beter in vergelyking met die K-gemiddeldes en OLS opleidingsalgoritmes. Die toetse wat ‘n ‘tekort-aan-passing’ ANOVA behels, toon dat daar rede is om die geskiktheid van die NNPLS model te wantrou. Die modelleringsresultate lyk egter belowend vir die toekomstige ontwikkeling van modelle op groter, meer verteenwoordigde datastelle. Die omgekeerde modellering toon dat die voorwaartse neurale netwerk, NNPLS en RBFPLS modelle soortgelyke resultate produseer wat die lineêre PLS model s’n oortref. Die RBFPLS model met ASOLS opleiding van die PLS binne modelle word beskou as die beste model. Dit is lewensvatbaar om die optimale modelkompleksiteite van elke uitvoerveranderlike individueel te bepaal. Die gemiddelde R2-waarde oor alle uitvoerveranderlikes vir ongesiende data is 0.43. Die gemiddelde R2-waarde vir die algehele model is 0.68. Daar is van die uitvoer veranderlikes wat R2-waardes van 0.8 oortref. Die voor- en terugwaartse modelleringsresultate toon verder dat dimensionele reduksie in die geval van PLS die beste modelle lewer. Daar is ook gevind dat die nie-lineêriteite grootliks vergoed vir die voorspellings van beide DB- en Ctipe vuurpylmotors binne die algehele model. Om die rede word voorgestel dat toekomstige modelle ontwikkel kan word deur gebruik te maak van eenvoudiger, meer lineêre modelle vir elke vuurpylklas nadat ‘n klasidentifikasiestap uitgevoer is. Die benadering benodig egter addisionele praktiese data wat verkry moet word.

This work is aimed at evaluating the effect of a solid propellant’s granulometric composition on its burning rate and, more in general, on its ballistic properties, as packing, density, or specific impulse. To evaluate the burning rate, a combustion model is developed in MatLab. To conclude, a model predicting the viscosity of a bimodal propellant is introduced. The obtained results show a significant agreement between experimental data, used as references, and the predicted ones. This work is the starting point from which other works can arise, improving the combustion model and expanding the viscosity estimation also for trimodal propellants.

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Three propellants with aluminum/silicon weight percentages of 18/0%, 13.5/4.5%, and 12/6% were fired in a subscale motor to determine if the plume infrared signature could be reduced without a significant loss in specific impulse. Spectral measurements from 2.5 to 5.5 micrometers and thermal measurements from 3.5 to 5.0 micrometers were made. Plume particle size measurements showed that only particles with small diameters (less than 1.93 micrometers) were present with any significant volume. Replacing a portion of the aluminum in a highly metallized solid propellant with silicon was found to eliminate the Al2O3 in favor of SiO2 and Al6SiOl3, without any change in particulate mass concentration or any large change in particle size distribution. These particulates were found to have significantly lower absorptivity than Al2O3. An additional investigation was conducted to determine the particle size distribution at the nozzle entrance. Malvern ensemble scattering, phase-Doppler single particle scattering and laser transmittance measurements made through windows in the combustion chamber at the nozzle entrance indicated that large particles were present (to 250 micrometers). However, most of the mass of the particles was contained in particles with diameters smaller than 5 micrometers. Approximate calculations made with the measured data showed that if 100 micrometers particles are present with the smoke (particles with diameters less than 2 micrometers) they could account for only approximately 10% of the article volume

In this thesis, Initial Value Problem of Level-set Method is applied to solid propellant combustion to find the grain burnback. For the performance prediction of the rocket motor, 0-D, 1-D or 3-D flow models are used depending on the type of thre grain configuration.

This project was started in the interest of supplementing existing data on additives to composite solid propellants. The study on the addition of iron and aluminum nanoparticles to composite AP/HTPB propellants was conducted at the Combustion and Energy Systems Laboratory at RPI in the new strand-burner experiment setup. For this study, a large literature review was conducted on history of solid propellant combustion modeling and the empirical results of tests on binders, plasticizers, AP particle size, and additives.

The study focused on the addition of nano-scale aluminum and iron in small concentrations to AP/HTPB solid propellants with an average AP particle size of 200 microns. Replacing 1% of the propellant's AP with 40-60 nm aluminum particles produced no change in combustive behavior. The addition of 1% 60-80 nm iron particles produced a significant increase in burn rate, although the increase was lesser at higher pressures. These results are summarized in Table 2. The increase in the burn rate at all pressures due to the addition of iron nanoparticles warranted further study on the effect of concentration of iron. Tests conducted at 10 atm showed that the mean regression rate varied with iron concentration, peaking at 1% and 3%. Regardless of the iron concentration, the regression rate was higher than the baseline AP/HTPB propellants. These results are summarized in Table 3.

Solid rocket motors consist of a thin metallic or composite shell filled with a soft rubbery propellant. Such motors are vulnerable and prone to buckling due to sudden external pressures produced by nearby detonation. The stability conditions of rocket motors subjected toaxisymmetric, external pressure loading are examined. The outer cases of motors are considered as isotropic (metallic) or anisotropic (composite), thin and high-strength shells, which are the main structures of interest in the stability analyses. The inner, low-strength elastic cores are modeled as linear and nonlinear elastic foundations. A general, refined, Sanders' nonlinear shell theory, which accounts for geometric nonlinearity in the form of von Karman type of nonlinear strain-displacement relations, is used to model thin-walled, laminated,composite cylindrical shells. The first order shear deformable concept is adopted in the analyses to include the transverse shear flexibility of composites. A winkler-type of linear and nonlinear elastic foundation is applied to model the internal foundations. Pasternak-foundation constants are also chosen tomodify the proposed elastic foundation model for the purpose of shear interactions. A set of displacement-based finite element codes have been formulated to determine critical buckling loads and mode shapes. The effect of initial imperfections on the structural responses are also incorporated in the formulations. A variety of numerical examples are investigated to demonstrate the validity and efficiency of the purposed theory under various boundary condiitions and loading cases. First, linear eigenvalue analysis is used to examine approximate buckling loads and buckling modes as well as symmetric conditions. An iterative solution procedure, either Newton-Raphson or Riks-Wempner method is employed to trace the nonlinear equilibrium paths for the cases of stress, buckling and post-buckling analyses. Both ring and shell-type models are applied for the structural analyses with different internal elastic foundations and initial imperfections.
Ph. D.

The purpose of this study is to examine the burning behaviour of composite solid propellants (CSP) in the presence of nanoscale, heterogenous catalysts. The study targets the decomposition of am- monium perchlorate (AP) as a key component in the burning profile of these propellants, and seeks to identify parameters of AP decomposition reaction that can be affected by catalytic additives. The decomposition behavior of AP was studied in the presence of titanium dioxide nanoparticles in varying configurations, surface conditions, dopants, morphology, and synthesis parameters with the AP crystals. The catalytic nanoparticles were found to enhance the decomposition rate of the ammonium perchlorate, and promote an accelerated burning rate of CSP propellants containing the additives. Furthermore, different configurations were shown to have varying degrees of effec- tiveness in promoting the decomposition behaviour. To study the effect of the catalyst's configuration in the bulk propellant, controlled dispersion con- ditions of the nanoparticle catalysts were created and studied using differential scanning calorime- try, as well as model propellant strand burning. The catalysts were shown to promote the greatest enthalpy of reaction, as well as the highest burn rate, when the AP crystals were recrystalized around the nanoparticle additives. This is in contrast to the lowest enthalpy condition, which cor- responded to catalysts being dispersed upon the AP crystal surface using bio-molecule templates. Additionally, a method of facile, visible light nanoparticle tracking was developed to study the effect of mixing and settling parameters on the nano-catalysts. To accomplish this, the titania nanoparticles were doped with fluorescent europium molecules to track the dispersion of the cat- alysts in the propellant binder. This method was shown to succesfully allow for dispersion and agglomeration monitoring without affecting the catalytic effect of the TiO2 nanoparticles.
M.S.M.S.E.
Masters
Materials Science Engineering
Engineering and Computer Science
Materials Science and Engineering

Solid propellant rocket motors are the most widely used propulsion systems for military applications that require high thrust to weight ratio for relatively short time intervals. Very wide range of magnitude and duration of the thrust can be obtained from solid propellant rocket motors by making some small changes at the design of the rocket motor. The most effective of these design criteria is the geometry of the solid propellant grain. So the most important step in designing the solid propellant rocket motor is determination of the geometry of the solid propellant grain. The performance prediction of the solid rocket motor can be achieved easily if the burnback steps of the rocket motor are known. In this study, grain burnback analysis for some 3-D grain geometries is investigated. The method used is solid modeling of the propellant grain for some predefined intervals of burnback. In this method, the initial grain geometry is modeled parametrically using commercial software. For every burn step, the parameters are adapted. So the new grain geometry for every burnback step is modeled. By analyzing these geometries, burn area change of the grain geometry is obtained. Using this data and internal ballistics parameters, the performance of the solid propellant rocket motor is achieved. To verify the outputs obtained from this study, rocket motor tests are performed. The results obtained from this study shows that, the procedure that was developed, can be successfully used for the preliminary design of a solid propellant rocket motor where a lot of different geometries are examined.

In this study, a detailed service life assessment procedure for solid propellant rockets under random environmental temperature and transportation loads is introduced. During storage and deployment of rocket motors, uncontrolled thermal environments and random vibratory loads due to transportation induce random stresses and strains in the propellant which provoke mechanical damage. In addition, structural capability degrades due to environmental conditions and induced stresses and strains as well as material capability parameters have inherent uncertainties. In this proposed probabilistic service life prediction, uncertainties along with degradation mechanisms are taken into consideration. Vibration loads are accounted by utilizing acceleration spectral density values which are induced during various deployment scenarios of ground, air and sea transportation. Furthermore, thermal loads are represented with a mathematical model being a harmonic function of time. Throughout the finite element analyses, a linear viscoelastic material model is to be used for the propellant. Change in the structural capability of the propellant with time is calculated using Laheru'
s cumulative damage model. Moreover, to include aging effect of the propellant, Layton model is used. To determine the effects of induced stress and strains under variations and uncertainties in the random loads and material constants, mathematical surrogate models are constructed using response surface method. Limit state functions are utilized to predict failure modes of the solid rocket motor. First order reliability method is used to calculate reliability and probability of failure of the propellant grain. With the proposed methodology, instantaneous reliability of the propellant grain is determined within a confidence interval.

Les reactions chimiques preponderantes dans differentes zones sont determinees et conduisent a la formation d'un schema global. La stoechiometrie et les parametres cinetiques sont determines et introduits dans la resolution des equations des quantites transportables dans la zone reactive. Ils conduisent a la determination numerique de la vitesse de combustion normale de ce type de propergol. Evaluation numerique de l'action des additifs de plomb sur la combustion du propergol, a differentes pressions

The work presented in this thesis deals with the application of optimization tools to the design of solid rocket motor grains per internal ballistic requirements. Research concentrated on the development of an optimization strategy capable of efficiently and consistently optimizing virtually an unlimited range of radial burning solid rocket motor grain geometries. Optimization tools were applied to the design process of solid rocket motor grains through an optimization framework developed to interface optimization tools with the solid rocket motor design system. This was done within a programming architecture common to the grain design system, AML. This commonality in conjunction with the object-oriented dependency-tracking features of this programming architecture were used to reduce the computational time of the design optimization process. The optimization strategy developed for optimizing solid rocket motor grain geometries was called the internal ballistic optimization strategy. This strategy consists of a three stage optimization process; approximation, global optimization, and highfidelity optimization, and optimization methodologies employed include DOE, genetic algorithms, and the BFGS first-order gradient-based algorithm. This strategy was successfully applied to the design of three solid rocket motor grains of varying complexity. The contributions of this work was the development and application of an optimization strategy to the design process of solid rocket motor grains per internal ballistic requirements.
M.S.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering

The application of emission spectroscopy to monitor combustion products of solid rocket propellant combustion can potentially yield valuable data about reactions occurring within the volatile environment of a strand burner. This information can be applied in the solid rocket propellant industry. The current study details the implementation of a compact spectrometer and fiber optic cable to investigate the visible emission generated from three variations of solid propellants. The grating was blazed for a wavelength range from 200 to 800 nm, and the spectrometer system provides time resolutions on the order of 1 millisecond. One propellant formula contained a fine aluminum powder, acting as a fuel, mixed with ammonium perchlorate (AP), an oxidizer. The powders were held together with Hydroxyl-Terminated-Polybutadiene (HTPB), a hydrocarbon polymer that is solidified using a curative after all components are homogeneously mixed. The other two propellants did not contain aluminum, but rather relied on the HTPB as a fuel source. The propellants without aluminum differed in that one contained a bimodal mix of AP. Utilizing smaller particle sizes within solid propellants yields greater surface area contact between oxidizer and fuel, which ultimately promotes faster burning. Each propellant was combusted in a controlled, non-reactive environment at a range of pressures between 250 and 2000 psi. The data allow for accurate burning rate calculations as well as an opportunity to analyze the combustion region through the emission spectroscopy diagnostic. It is shown that the new diagnostic identifies the differences between the aluminized and non-aluminized propellants through the appearance of aluminum oxide emission bands. Anomalies during a burn are also verified through the optical emission spectral data collected.
M.S.M.E.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering