The accuracy of the calculated drag force in OpenRocket was examined in this investigation for a rocket traveling at subsonic, transonic and supersonic speeds. The idea was to compare the computed drag coefficients in OpenRocket to the drag coefficients obtained by running multiple computational fluid dynamics (CFD) simulations in Ansys Fluent. Specifically, the freestream Mach number and altitude parameters were varied for a geometric model of the Mjöllnir rocket. The results obtained converged several orders of magnitude and were exceptionally stable. Both the physical accuracy and numerical independence of the results was verified through examining the solutions' contour plots and conducting multiple sensitivity analyses. The conclusion was that OpenRocket overpredicts the Mjöllnir rocket's drag coefficients by 12 % to 73 % for all examined freestream Mach numbers and altitudes. Additionally, an empirical relationship was found for how the Mjöllnir rocket's drag coefficient changes with altitude and the freestream Mach number. In particular, it is a multivariate function that can be considered valid for an altitude of h ≤ 10 km and a freestream Mach number of 0.2 ≤ M ≤ 3.0. The empirical relationship fitted exceedingly well with simulation data and merits further investigation.

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 (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 explores remote sensing of planetary atmospheres and their circumstellar surroundings. The terrestrial ionosphere is a highly variable space plasma embedded in the thermosphere. Generated by solar radiation and predominantly composed of oxygen ions at high altitudes, the ionosphere is dynamically and chemically coupled to the neutral atmosphere. Variations in ionospheric plasma density impact radio astronomy and communications. Inverting observations of 83.4 nm photons resonantly scattered by singly ionized oxygen holds promise for remotely sensing the ionospheric plasma density. This hypothesis was tested by comparing 83.4 nm limb profiles recorded by the Remote Atmospheric and Ionospheric Detection System aboard the International Space Station to a forward model driven by coincident plasma densities measured independently via ground-based incoherent scatter radar. A comparison study of two separate radar overflights with different limb profile morphologies found agreement between the forward model and measured limb profiles. A new implementation of Chapman parameter retrieval via Markov chain Monte Carlo techniques quantifies the precision of the plasma densities inferred from 83.4 nm emission profiles. This first study demonstrates the utility of 83.4 nm emission for ionospheric remote sensing. Future visible and ultraviolet spectroscopy will characterize the composition of exoplanet atmospheres; therefore, the second study advances technologies for the direct imaging and spectroscopy of exoplanets. Such spectroscopy requires the development of new technologies to separate relatively dim exoplanet light from parent star light. High-contrast observations at short wavelengths require spaceborne telescopes to circumvent atmospheric aberrations. The Planet Imaging Concept Testbed Using a Rocket Experiment (PICTURE) team designed a suborbital sounding rocket payload to demonstrate visible light high-contrast imaging with a visible nulling coronagraph. Laboratory operations of the PICTURE coronagraph achieved the high-contrast imaging sensitivity necessary to test for the predicted warm circumstellar belt around Epsilon Eridani. Interferometric wavefront measurements of calibration target Beta Orionis recorded during the second test flight in November 2015 demonstrate the first active wavefront sensing with a piezoelectric mirror stage and activation of a micromachine deformable mirror in space. These two studies advance our ``close-to-home'' knowledge of atmospheres and move exoplanetary studies closer to detailed measurements of atmospheres outside our solar system.

Automatic altitude compensation has been a holy grail of rocket propulsion for decades. Current state-of-the-art bell nozzles see large performance decreases at low altitudes, limiting rocket designs, shrinking payloads, and overall increasing costs. Aerospike nozzles are an old idea from the 1960’s that provide superior altitude-compensating performance and enhanced performance in vacuum, but have survivability issues that have stopped their application in satellite propulsion systems. A growing need for CubeSat propulsion systems provides the impetus to study aerospike nozzles in this application. This study built two aerospike nozzles using modern 3D metal printing techniques to test aerospikes at a size small enough to be potentially used on a CubeSat. Results indicated promising in-space performance, but further testing to determine thermal limits is deemed necessary.

A parametric study on Secondary Injection Thrust Vector Control (SITVC) has been accomplished numerically with the help of a commercial Computational Fluid Dynamics (CFD) code called FLUENT&reg / . This study consists of two parts / the first part includes the simulation of three dimensional flowfield inside a test case nozzle for the selection of parameters associated with both computational grid and the CFD solver such as mesh size, turbulence model accompanied with two different wall treatment approaches, and solver type. This part revealed that simulation of internal flowfield by a segregated solver with Realizable k-&amp / #949 / (Rke) turbulence model accompanied by enhanced wall treatment approach is accurate enough to resolve this kind of complex three dimensional fluid flow problems. In the second part a typical rocket nozzle with conical diverging section is picked for the parametric study on injection mass flow rate, injection location and injection angle. A test matrix is constructed / several numerical simulations are run to yield the assessment of performance of SITVC system. The results stated that for a nozzle with a small divergence angle, downstream injections with distances of 2.5-3.5 throat diameters from the nozzle throat lead to higher efficiencies over a certain range of total pressure ratios, i.e., mass flow rate ratios, upstream injections should be aligned more to the nozzle axis, i.e., higher injection angles, to prevent reflection of shock waves from the opposite wall and thus low efficiencies. Injection locations that are too much downstream may result reversed flows on nozzle exit.

Multidisciplinary design optimization of aerospace vehicles has attracted interest of many researchers. Well known aerospace companies are developing tools for the mutlidisciplinary design optimization. However, the multiobjective optimization of the design is a new and important area investigated very little by the researchers. This thesis will examine the approaches to the multiobjective and mutlidisciplinary design optimization of rockets and missiles. In the study, multiobjective optimization method called MC-MOSA will be used.

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&#039 / 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.

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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.