Dissertations / Theses on the topic 'Hybrid propellant rockets – Combustion'

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.

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.

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

Controlling unsteady combustion induced gas flow fluctuations and the resultant motor vibrations is a very significant step in rocket motor design. It occurs when the unsteady heat release due to combustion happens to feed the acoustic oscillations of the closed duct forming a feed-back system. The resultant vibrations concerned may even lead to total failure of the rocket system unless analysed and tested thoroughly. This thesis aims developing a linear numerical analysis method for the growth rate of instabilities and possible mode shape of a liquid-propelled chamber geometry. In particular, A 3-D Helmholtz code, utilizing Culicks spatial averaging linear iterative method, is developed to find the form of deformed mode shapes iteratively to obtain possible effects of heat source and impedance boundary conditions. The natural mode shape phase is solved through finite volume discretization and the open-source eigenvalue extractor, ARPACK, and its parallel implementation PARPACK. The iterative method is particularly used for analyzing the geometries with complex shapes and essentially for disturbances of small magnitudes to natural mode shapes. The developed tools are tested via two simple cases, a duct with inactive flame and a Rijke tube, used as validation cases for the code particularly with only boundary contribution and heat contribution respectively. A sample 2-D and 3-D liquid-propelled combustion chamber is also analysed with heat sources. After comparing with the expected values, it is eventually proved that the method should be only used for determining the modes instability analysis, as to whether it keeps vibrating or decays. The methodology described can be used as a preliminary design tool for the design of liquid-propellant rocket engine combustors, rapidly revealing only the onset of instabilities.

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.

Cette thèse traite des poudres d’aluminium utilisées dans les mélanges énergétiques. Grâce à la technique de broyage planétaire haute énergie, nous avons synthétisé des poudres d’aluminium micrométriques ou nanométriques. Au moyen de techniques d’analyses diverses, nous avons caractérisé leur texture, comportement thermique, structure et microstructure. Des poudres avec un état de division comparable aux échantillons sphériques ont été produites par optimisation du procédé de broyage. Avec des critères de réactivité, nous montrons que le broyage permet la synthèse de poudres de réactivité comparable voire meilleure que celle des poudres commerciales. Nous avons étudié les mécanismes d’oxydation des poudres en réalisant des analyses thermogravimétriques sous air en mode isotherme ou à vitesse de réaction contrôlée. Enfin, la réactivité des poudres élaborées au laboratoire a été évaluée grâce à différents tests de combustion. La formulation de propergol et des tests de propulsions ont été réalisés en collaboration avec ONERA. Les tests de formulation de propergols solides ont été réussi et les tests de combustions moteur ont montré une vitesse de régression élevée pour le propergol contenant les nanoplaquettes. Dans un second temps, les poudres ont été utilisées dans des nanothermites Al/CuO et des tests de combustion ont été menés avec le laboratoire NS3E de l’institut franco-allemand de Saint-Louis. Les tests de combustion de thermites ont montré des vitesses de régressions plus faibles avec des nanothermites contenants des nanoplaquettes. Cependant, ces dernières semblent légèrement désensibilisées à la friction ou aux décharges électrostatiques
This thesis deals with aluminum powders used in energetic materials. Thanks to the high-energy planetary ball milling technique, we have synthesized aluminum powders having micrometric or nanometric dimensions. Their texture, thermal behavior, structure and microstructure before and during oxidation were studied. Nanoflakes with similar specific surface areas than commercial spherical powders have been obtained from the optimization of the milling process. We have established reactivity criteria and we show that high-energy ball milling allows the synthesis of powders with a similar or better reactivity than spherical powders. The oxidation mechanism of aluminum powders was also followed with thermogravimetric analysis under air either in isothermal mode or using a controlled reaction rate. The modeling of powders oxidation was carried out using principles of heterogeneous kinetics and show that morphology does not impact the oxidation. Finally, the reactivity of the powders was evaluated thanks to various combustion tests. Propellant formulation and propulsion tests were carried out in collaboration with ONERA. The solid propellant formulation tests were successful, and the engine combustion tests showed a high regression rate for the propellant containing the nanoflakes. The preparation of nanothermites and their combustion tests were conducted at the NS3E laboratory of the franco-german Institute of St. Louis. Al/CuO nanothermites formulations were successfully carried out and combustion tests showed lower regression rates for nanothermites containing nanoflakes. However, they seem slightly desensitized to friction or electrostatic discharge

This work describes the development of a hybrid rocket propulsion system for a reusable sounding rocket, as part of the first phase of the UKZN Phoenix Hybrid Sounding Rocket Programme. The programme objective is to produce a series of low-to-medium altitude sounding rockets to cater for the needs of the African scientific community and local universities, starting with the 10 km apogee Phoenix-1A vehicle. In particular, this dissertation details the development of the Hybrid Rocket Performance Code (HRPC) together with the design, manufacture and testing of Phoenix-1A’s propulsion system. The Phoenix-1A hybrid propulsion system, generally referred to as the hybrid rocket motor (HRM), utilises SASOL 0907 paraffin wax and nitrous oxide as the solid fuel and liquid oxidiser, respectively. The HRPC software tool is based upon a one-dimensional, unsteady flow mathematical model, and is capable of analysing the combustion of a number of propellant combinations to predict overall hybrid rocket motor performance. The code is based on a two-phase (liquid oxidiser and solid fuel) numerical solution and was programmed in MATLAB. HRPC links with the NASA-CEA equilibrium chemistry programme to determine the thermodynamic properties of the combustion products necessary for solving the governing ordinary differential equations, which are derived from first principle gas dynamics. The combustion modelling is coupled to a nitrous oxide tank pressurization and blowdown model obtained from literature to provide a realistic decay in motor performance with burn time. HRPC has been validated against experimental data obtained during hot-fire testing of a laboratory-scale hybrid rocket motor, in addition to predictions made by reported performance modelling data. Development of the Phoenix-1A propulsion system consisted of the manufacture of the solid fuel grain and incorporated finite element and computational fluid dynamics analyses of various components of the system. A novel casting method for the fabrication of the system’s cylindrical single-port paraffin fuel grain is described. Detailed finite element analyses were performed on the combustion chamber casing, injector bulkhead and nozzle retainer to verify structural integrity under worst case loading conditions. In addition, thermal and pressure loading distributions on the motor’s nozzle and its subsequent response were estimated by conducting fluid-structure interaction analyses. A targeted total impulse of 75 kNs for the Phoenix-1A motor was obtained through iterative implementation of the HRPC application. This yielded an optimised propulsion system configuration and motor thrust curve.
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2013.

碩士
國立成功大學
航空太空工程學系
89
A curing process of Hydroxyl-terminated polybutadiene (HTPB)solid fuels has been developed in Aerospace Science and Technology Research Center (ASTRC)of National Cheng Kung University. Several thermocouples are prefixed inside the HTPB solid fuel in different locations for regression rate measurement. The pyrolysis characteristics of the cured HTPB fuel have also been obtained by thermogravimetry method. The results show that temperature required for the HTPB vaporization is around 400 degree Celsius, therefore a pebble bed heater facility was utilized for conducting combustion and pyrolysis behavior of HTPB solid fuels burning with GOX. To simulate a hybrid rocket internal ballistic characteristics. The facility can provide the mass flow rate up to 0.5 kg/sec at 400 degree C for 8 minutes. The detail configurations of the testing facility are described in the paper and the preliminary experimental data will be also shown in the discussion.

The University of KwaZulu-Natal (UKZN) Phoenix Hybrid Sounding Rocket Programme was established in 2010. The programme’s main objective is to develop a sounding rocket launch capability for the African scientific community, which currently lacks the ability to fly research payloads to the upper atmosphere. In this dissertation, UKZN’s in-house Hybrid Rocket Performance Simulator (HYROPS) software is used to improve the design of the Phoenix-2A vehicle, which is intended to deliver a 5 kg instrumentation payload to an apogee altitude of 100 km. As a benchmarking exercise, HYROPS was first validated by modelling the performance of existing sub-orbital sounding rockets similar in apogee to Phoenix-2A. The software was found to approximate the performance of the published flight data within 10%. A generic methodology was then proposed for applying HYROPS to the design of hybrid propellant sounding rockets. An initial vehicle configuration was developed and formed the base design on which parametric trade studies were conducted. The performance sensitivity for varying propulsion and aerodynamic parameters was investigated. The selection of parameters was based on improving performance, minimising cost, safety and ease of manufacturability. The purpose of these simulations was to form a foundation for the development of the Phoenix-2A vehicle as well as other large-scale hybrid rockets. Design chamber pressure, oxidiser-to-fuel ratio, nozzle design altitude, and fin geometry were some of the parameters investigated. The change in the rocket’s propellant mass fraction was the parameter which was found to have the largest effect on performance. The fin and oxidiser tank geometries were designed to avoid fin flutter and buckling respectively. The oxidiser mass flux was kept below 650 kg/m2s and the pressure drop across the injector relative to the chamber pressure was maintained above 15% to mitigate the presence of combustion instability. The trade studies resulted in an improved design of the Phoenix-2A rocket. The propellant mass of the final vehicle was 30 kg less than the initial conceptual design and the overall mass was reduced by 25 kg. The Phoenix-2A vehicle was 12 m in length with a total mass of 1006 kg. The fuel grain length of Phoenix-2A was 1.27 m which is approximately 3 times that of Phoenix-1A. The benefit of aluminised paraffin wax as a fuel was also investigated. The results indicated that more inert mass can be delivered to the target apogee of 100 km when using a 40% aluminised paraffin wax.
M.Sc.Eng. University of KwaZulu-Natal, Durban 2013.

Sounding rockets find applications in multiple fields of scientific research including meteorology, astronomy and microgravity. Indigenous sounding rocket technologies are absent on the African continent despite a potential market in the local aerospace industries. The UKZN Phoenix Sounding Rocket Programme was initiated to fill this void by developing inexpensive medium altitude sounding rocket modeling, design and manufacturing capacities. This dissertation describes the development of the Hybrid Rocket Performance Simulator (HYROPS) software tool and its application towards the structural design of the reusable, 10 km apogee capable Phoenix-1A hybrid sounding rocket, as part of the UKZN Phoenix programme. HYROPS is an integrated 6–Degree of Freedom (6-DOF) flight performance predictor for atmospheric and near-Earth spaceflight, geared towards single-staged and multi-staged hybrid sounding rockets. HYROPS is based on a generic kinematics and Newtonian dynamics core. Integrated with these are numerical methods for solving differential equations, Monte Carlo uncertainty modeling, genetic-algorithm driven design optimization, analytical vehicle structural modeling, a spherical, rotating geodetic model and a standard atmospheric model, forming a software framework for sounding rocket optimization and flight performance prediction. This framework was implemented within a graphical user interface, aiming for rapid input of model parameters, intuitive results visualization and efficient data handling. The HYROPS software was validated using flight data from various existing sounding rocket configurations and found satisfactory over a range of input conditions. An iterative process was employed in the aerostructural design of the 1 kg payload capable Phoenix-1A vehicle and CFD and FEA numerical techniques were used to verify its aerodynamic and thermo-structural performance. The design and integration of the Phoenix-1A‟s hybrid power-plant and onboard electromechanical systems for recovery parachute deployment and motor oxidizer flow control are also discussed. It was noted that use of HYROPS in the design loop led to improved materials selection and vehicle structural design processes. It was also found that a combination of suitable mathematical techniques, design know-how, human-interaction and numerical computational power are effective in overcoming the many coupled technical challenges present in the engineering of hybrid sounding rockets.
Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.

碩士
國立成功大學
航空太空工程學系
88
The rates of the interfacial exchange of the properties of energy, mass of chemical species and mass, in particular the regression rate of the propellant during the transient ignition and steady state combustion have been the central theme of modern solid and hybrid rocketry. However to this date, a satisfactory level has not known analytical laws of the propellant in side-burning configuration. By applying the method of Canonical theory developed for the analytical determination of eigen-function of the systems of the exchange of the gasification rate and the regression rate in the hybrid propellant. The numerical simulation conceives that a pipe with solid fuel surrounded. Inlet flow is oxidant with high temperature and the flow is laminar. After obtaining the information of the flow field, we can use the canonical formulations of the burning rate and regression rate to analyze all the mechanisms and parameters affecting the rates.

Characteristics of impinging-jet atomizers in the context of application in liquid propulsion systems are studied in this thesis. A review of past studies on impinging jets revealed the necessity of a correlation in terms of injector parameters for predicting Sauter Mean Diameter (SMD) of a spray. So, an experimental study of atomization in doublet and triplet impinging jet injectors is conducted using water as the stimulant? The major injector parameters considered are orifice diameter, impingement angle and jet velocity. Relative influences of these parameters are explained in terms of a single parameter, specific normal momentum. SMD of the spray reduces as specific normal momentum is increased. A universal expression between non-dimensional SMD and specific normal momentum is obtained, which satisfactorily predicts SMD in doublets as well as triplets. Noting that practical impinging injectors are likely to have skewness (partial impingement), the study is extended to understand the behavior of such jets. In perfectly impinging doublet, a high aspect ratio ellipse-like mass distribution pattern is obtained with major axis normal to the plane of two jets whereas in skewed jets the major axis turns from its normal position. A simple correlation is obtained, which shows that this angle of turn is a function of skewness fraction and impingement angle only and is independent of injection velocity. Experimental data from both mass distribution and photographic technique validate this prediction. SMD is found to decrease as skewness is increased. This may be the combined effect of shearing of liquid sheet at the point of impingement and more sheet elongation. Hence, skewness turns out to be an important parameter in controlling drop size.

A variety of methods have been developed to enhance solid propellant burning rates, including adjusting oxidizer particle size, modifying metal additives, tailoring the propellant core geometry, and adding catalysts or wires. Fully consumable reactive wires embedded in propellant have been used to increase the burning rate by increasing the surface area; however, the manufacture of propellant grains and the observation of geometric effects with reactive components has been restricted by traditional manufacturing and viewing methods. In this work, a printable reactive filament was developed that is tailorable to a number of use cases spanning reactive fibers to photosensitive igniters. The filament employs aluminum fuel within a printable polyvinylidene fluoride matrix that can be tailored to a desired burning rate through stoichiometry or aluminum fuel configuration such as particle size and modified aluminum composites. The material is printable with fused filament fabrication, enabling access to more complex geometries such as spirals and branches that are inaccessible to traditionally cast reactive materials. However, additively manufacturing the reactive fluoropolymer and propellant together comes attendant with many challenges given the significantly different physical properties, particularly regarding adhesion. To circumvent the challenges posed by multiple printing techniques required for such dissimilar materials, the reactive fluoropolymer was included within a solid propellant carrier matrix as small fibers. The fibers were varied in aspect ratio (AR) and orientation, with aspect ratios greater than one exhibiting a self-alignment behavior in concordance with the prescribed extrusion direction. The effective burning rate of the propellant was improved nearly twofold with 10 wt.% reactive fibers with an AR of 7 and vertical orientation.

The reactive wires and fibers in propellant proved difficult to image in realistic sample designs, given that traditional visible imaging techniques restrict the location and dimensions of the reactive wire due to the necessity of an intrusive window next to the wire, a single-view dynamic X-ray imaging technique was employed to analyze the evolution of the internal burning profile of propellant cast with embedded additively manufacture reactive components. To image complex branching geometries and propellant with multiple reactive components stacked within the same line of sight, the dynamic X-ray imaging technique was expanded to two views. Topographic reconstructions of propellants with multiple reactive fibers showed the evolution of the burning surface enhanced by the geometric effects caused by the faster burning fibers. These dual-view reconstructions provide a method for accurate quantitative analysis of volumetric burning rates that can improve the accessibility and viability of novel propellant grain designs.