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Comparison of Cation-Anion Oxidizer Pairings in Electrically Controllable Solid PropellantsSellards, Emily Rose 13 February 2024 (has links)
Electrically controllable solid propellants are an area of interest as a viable solution to the lack of throttle-ability in solid propellant rocket motors. Existing studies have focused on propellants compositions using hydroxyl-ammonium nitrate, ammonium nitrate, or lithium perchlorate as oxidizers. Additionally, the thermochemical and electrochemical reaction mechanisms have not yet been fully defined. The research in this thesis explores the nitrate and perchlorate oxidizer families to compare their cation-anion relationships. Using these oxidizers, pseudo electrically controllable solid propellant compositions were created with the addition of multi-wall carbon nanotubes to enhance ohmic heating capabilities. These additives were selected based on theory that with a non-complexing polymer, an oxidizer melt layer is required for ions to dissociate and electrically controlled ignition to occur. Using an applied voltage, ignition delay and current draw experiments were performed to expand on prior findings that ignition delay follows oxidizer melt temperature while mobility is associated with the size of the ionic radii. Additionally, neat oxidizer pellets were electrically decomposed to determine their linear regression rate. These results help to characterize the mechanism of reaction. This advances the knowledge of oxidizers in electrically controllable applications. / Master of Science / Solid propellant rocket motors have been extensively studied and used in both space and military applications because they do not use air as the source of oxygen. Their main limitation is the lack of throttle-ability, or inability to control propellant burning. This is because solid propellants, which are generally composed of an ionic oxidizer salt, a polymer fuel, and additives, are pre-combined and stored within the rocket motor. An emerging viable solution to this limitation is electrically controllable solid propellants. With an applied voltage, the oxidizer is heated and melts, allowing ions to dissociate and current to flow between electrodes. This reaction can then be controlled by turning the power supply on and off. Cations, or ions which have a net positive charge, move to the negatively charged cathode while anions, which have a net negative charge, move to the negatively charged anode. The research in this thesis explores different cation-anion oxidizer pairings using both a propellant composition and as a pure oxidizer under an applied voltage. The results help to characterize the mechanism of reaction of each oxidizer in an electrically controllable context and determine their effectiveness in these propellant applications.
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An experimental investigation of the effects of acceleration on the combustion characteristics of an aluminized composite solid propellantNortham, G. Burt January 1965 (has links)
M.S.
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Thermal stresses in a finite solid-propellant grainFrohlich, Jurgen Paul 12 April 2010 (has links)
In order to gain a fundamental understanding of actual solid propellant thermal stress problems, the geometry of the solid propellant baa been idealized as a short, circular cylinder with flat ends. It is felt that the consideration of actual curved ends would only unduly have complicated the analysis.
The method of solution for the thermal stresses in the finite cylinder, that has been presented in this thesis, utilizes an arbitrarily selected set of cylinder end-conditions. Therefore, different end conditions than the ones employed here might have been considered just as easily.
The fundamental difficulties encountered in the thermoelastic analysis of short cylinders are that firstly the problem is at least two-dimensional and secondly, it has mixed boundary conditions since displacements and/or stresses specified along at least four distinct boundaries. It is relatively simple to solve the governing differential equation by the method of separation of variables. The greatest difficulties are encountered in satisfying the various boundary conditions. As a matter of fact the method of solution for the thermal stresses that has been presented in this thesis is applicable only when the temperature distribution throughout the propellant and casing exhibits a particular variation in the axial direction, as shown by Eqs. (39) and (43). With such temperature fields, however the elastic analytic solutions that have been presented are significant since the simultaneous linear algebraic equations, for the arbitrary constants, are easily solved. It is true that, in principle, an infinite number of these arbitrary constants must be determined. From a practical point of view, however, the arbitrary constants can always be reduced to a finite number by truncating the obtained series solutions for the thermal displacements and stresses. / Master of Science
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The prediction of the emission spectra of flares and solid propellant rocketsBarnard, Paul Werner 04 1900 (has links)
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.
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The modelling of IR emission spectra and solid rocket motor parameters using neural networks and partial least squaresHamp, Niko 04 1900 (has links)
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.
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Studies On HTPB Based Copolyurethanes As Solid Propellant Binders : Characterization And Modeling Of Network ParametersSekkar, V 11 1900 (has links) (PDF)
No description available.
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Phase Modification And Combustion Studies On Ammonium Nitrate And Propellant CompositionsOommen, Charlie 07 1900 (has links) (PDF)
No description available.
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CHARACTERIZATION OF SOLID COMPOSITE PROPELLANTS USING TWO-DIMENSIONAL DIGITAL IMAGE CORRELATION AT LARGE AND SMALL SCALESChristopher Matthew Jarocki (12464217) 27 April 2022 (has links)
<p> </p>
<p>Solid composite propellants are used widely in the aerospace industry due to their desirable mechanical and performance characteristics, including their simplicity, high initial thrust and volume specific impulse. Knowledge of the mechanical properties is needed due to the stresses encountered by a solid rocket motor propellant during thermal cycling while in storage, during rapid ignition transients, and dynamic launch and flight phases. These stresses could damage the propellant grain, leading to an unplanned increase in burning surface area and subsequent catastrophic disassembly. </p>
<p>Tensile testing with the conventionally used JANNAF Type C “dogbones” can be material-expensive and time-consuming, particularly if determining the propellant’s response to different strain rates and temperatures. The rapid development of propellants with novel ingredients or formulations is especially hampered by material and time constraints. Using small-scale tests, typically using “micro-dogbone” samples, tensile properties can be characterized with a strong correlation to standard JANNAF Type C samples and only use a fraction of the normally required material. The correlation between the two sample sizes can be demonstrated for a wide range of propellant formulations and environmental conditions, such as extreme temperatures used in test conditions. Propellant characterization can also be relatively slow due to the data analysis time required to ensure that samples did not contain voids or other defects. Using 2-D Digital Image Correlation (DIC) technology, a baseline behavior can be established for propellant samples that contain voids to help screen data faster, leading to a faster characterization time for propellants and reduced cost of the program. Overall, the DIC system is a promising method of non-contact strain measurement that can help characterize and screen solid composite propellants, while micro-dogbones show great promise in being able to reduce the time and cost required for characterizing novel solid propellants. </p>
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Some Effects of Solid Rocket Motor Fuel Exhausts on Avian EmbryosDeGuehery, Lindsey Elliott 01 January 1976 (has links) (PDF)
Fertile White Leghorn (Gallus gallus) and Bobwhite Quail (Colinus virginianus) were subjected to 15 min exposures produced by burning solid rocket motor (SRM) fuel. Comparative mortality data were collected. Chicken eggs were further used to study the effects of exposure on water relations and blood gas parameters. Chicken embryos exposed once on day 19 or incubation demonstrated and LD50 of 204 ppm; the LD50 for quail embryos was 175 ppm. When mortality was regressed on the final exposure concentration, chicken and quail embryos exposed on days 12 and 19 showed LD50's of 127 and 86 ppm respectively, and embryos exposed on days 4, 12, and 19 had LD50's of 75 and 56 ppm. Quail embryos appeared to be more sensitive to SRM exhausts than chicken embryos, probably owing to the larger surface area to volume ration of the egg. Embryos exposed to a small daily concentration had an MLC of 117 ppm and an LD50 of approximately 200 ppm for cumulative exposure concentrations. This suggested that individual exposures were additive in effect. Eggs exposed at temperatures less than 37.5 C showed reduced lethality, while exposures at greater temperatures increased lethality. The rate of water loss from chicken eggs measured over an eight hour period increased 5 times because of a 15 min exposure. Since the increased rate of dehydration occurred during the exposure, the hydroscopic effects of exposure were extreme. When eggs lose the 18% of the initial weight normally lost from evaporation during incubation due to exposure, no more water loss was seen to occur. Blood gas analyses on 12 day embryos showed decreased pH at cumulative exposure concentrations greater than 200 ppm. Carbon monoxide in the exhausts probably increased carboxyhemoglobin, reducing buffering capacity. The acidosis was partially compensated by increased HCO3-, Exogenously derived C1-, plus increased HCO3- may shift intracellular K+, making the serum hyperkatremic. Dehydration effects further increased serum hypertonicity.
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XPS and Carbon-13 NMR spectroscopic analysis of composite rocket propellantsKauffman, Elroy Wayne January 1983 (has links)
In this study the applicability of Carbon-13 NMR and XPS to the detection of chemical changes in a solid composite rocket propellant was studied. Storage at elevated temperatures was used to simulate the propellant ageing process. In the XPS analysis, changes in the sources for the N(1s) and Cl(2p) photopeaks were investigated. The propellant loses "organic" nitrogen as it ages. Changes in the amount of Cl⁻ present are in doubt due to instrumental considerations.
Carbon-13 NMR analysis showed that with increasing age of a sample there is a corresponding loss of vinylic groups from the binder. This loss of vinylic character is preferential in the order pendant>>cis>trans. Due to the long scan times involved this method is of limited utility for ageing analysis. / Master of Science
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