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Статті в журналах з теми "Hybrid propellant rockets – Combustion"
Izham Izzat Ismail, Norhuda Hidayah Nordin, Muhammad Hanafi Azami, and Nur Azam Abdullah. "Metals and Alloys Additives as Enhancer for Rocket Propulsion: A Review." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 90, no. 1 (December 25, 2021): 1–9. http://dx.doi.org/10.37934/arfmts.90.1.19.
Повний текст джерелаPalacz, Tomasz, and Jacek Cieślik. "Experimental Study on the Mass Flow Rate of the Self-Pressurizing Propellants in the Rocket Injector." Aerospace 8, no. 11 (October 26, 2021): 317. http://dx.doi.org/10.3390/aerospace8110317.
Повний текст джерелаAbdelraouf, A. M., O. K. Mahmoud, and M. A. Al-Sanabawy. "Thrust termination of solid rocket motor." Journal of Physics: Conference Series 2299, no. 1 (July 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2299/1/012018.
Повний текст джерелаBandyopadhyay, Atri, and Ankit Kumar Mishra. "Comparative Study on Hybrid Rocket Fuels for Space Launch Vehicles Moving in Higher Orbits." 4 1, no. 4 (December 2, 2022): 13–19. http://dx.doi.org/10.46632/jame/1/4/3.
Повний текст джерелаSunil, Rahul, Aditya Virkar, M. Vignesh Kumar, Iynthezhuthon Krishnamoorthy, and Vinayak Malhotra. "Combustion and propulsive characteristics of potential hybrid rocket propellant." IOP Conference Series: Materials Science and Engineering 912 (September 12, 2020): 042023. http://dx.doi.org/10.1088/1757-899x/912/4/042023.
Повний текст джерелаWhitmore, Stephen A., Cara I. Frischkorn, and Spencer J. Petersen. "In-Situ Optical Measurements of Solid and Hybrid-Propellant Combustion Plumes." Aerospace 9, no. 2 (January 23, 2022): 57. http://dx.doi.org/10.3390/aerospace9020057.
Повний текст джерелаUrrego, Jose Alejandro, Fabio Arturo Rojas, and Jaime Roberto Muñoz. "Variability analysis of ABS solid fuel manufactured by fused deposition modeling for hybrid rocket motors." Journal of Mechanical Engineering and Sciences 15, no. 2 (June 10, 2021): 8029–41. http://dx.doi.org/10.15282/jmes.15.2.2021.08.0633.
Повний текст джерелаTian, Hui, Xianzhu Jiang, Yudong Lu, Yu Liang, Hao Zhu, and Guobiao Cai. "Numerical Investigation on Hybrid Rocket Motors with Star-Segmented Rotation Grain." Aerospace 9, no. 10 (October 9, 2022): 585. http://dx.doi.org/10.3390/aerospace9100585.
Повний текст джерелаLee, Dongeun, and Changjin Lee. "Fuel-rich Combustion with AP added Propellant in a Staged Hybrid Rocket Engine." Journal of the Korean Society for Aeronautical & Space Sciences 44, no. 7 (July 1, 2016): 576–84. http://dx.doi.org/10.5139/jksas.2016.44.7.576.
Повний текст джерелаD’Alessandro, Simone, Marco Pizzarelli, and Francesco Nasuti. "A Hybrid Real/Ideal Gas Mixture Computational Framework to Capture Wave Propagation in Liquid Rocket Combustion Chamber Conditions." Aerospace 8, no. 9 (September 4, 2021): 250. http://dx.doi.org/10.3390/aerospace8090250.
Повний текст джерелаДисертації з теми "Hybrid propellant rockets – Combustion"
Fernandez, Margaret Mary. "Propellant tank pressurization modeling for a hybrid rocket /." Online version of thesis, 2009. http://hdl.handle.net/1850/10631.
Повний текст джерелаChakravarthy, Satyanarayanan R. "The role of surface layer processes in solid propellant combustion." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/13264.
Повний текст джерелаMatta, Lawrence Mark. "Investigation of the flow turning loss in unstable solid propellant rocket motors." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15938.
Повний текст джерелаChen, Tzengyuan. "Driving of axial acoustic fields by sidewall stabilized diffusion flames." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/12969.
Повний текст джерелаMcDonald, Brian Anthony. "The Development of an Erosive Burning Model for Solid Rocket Motors Using Direct Numerical Simulation." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4973.
Повний текст джерелаBarnard, Paul Werner. "The prediction of the emission spectra of flares and solid propellant rockets." Thesis, Stellenbosch : University of Stellenbosch, 2003. http://hdl.handle.net/10019.1/16254.
Повний текст джерелаENGLISH 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.
Foss, David T. "Development and modeling of a dual-frequency microwave burn rate measurement system for solid rocket propellant." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/45962.
Повний текст джерелаA 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
Cengiz, Kenan. "Development Of An Iterative Method For Liquid-propellant Combustion Chamber Instability Analysis." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612753/index.pdf.
Повний текст джерелаMasquelet, Matthieu M. "Simulations of a Sub-scale Liquid Rocket Engine: Transient Heat Transfer in a Real Gas Environment." Thesis, Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11102006-082702/.
Повний текст джерелаHamp, Niko. "The modelling of IR emission spectra and solid rocket motor parameters using neural networks and partial least squares." Thesis, Stellenbosch : University of Stellenbosch, 2003. http://hdl.handle.net/10019.1/16334.
Повний текст джерелаENGLISH 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.
Книги з теми "Hybrid propellant rockets – Combustion"
Rocker, M. Modeling on nonacoustic combustion instability in simulations of hybrid motor tests. Marshall Space Flight Center, Ala: National Aeronautics and Space Administration, Marshall Space Flight Center, 2000.
Знайти повний текст джерелаDean, David L. Hybrid fuel formulation and technology development: Final report. Huntsville, AL: McDonnell Douglas, 1995.
Знайти повний текст джерелаVigor, Yang, Brill Thomas B, Ren Wu-Zhen, and Zarchan Paul, eds. Solid propellant chemistry, combustion, and motor interior ballistics. Reston, Va: American Institute of Aeronautics and Astronautics, 2000.
Знайти повний текст джерелаDranovsky, Mark L. Combustion instabilities in liquid rocket engines: Testing and development practices in Russia. Reston, Va: American Institute of Aeronautics and Astronautics, 2007.
Знайти повний текст джерелаMeeting, JANNAF Combustion Subcommittee. 31st JANNAF Combustion Subcommittee meeting: Lockheed Missiles and Space Company, Sunnyvale, CA, 17-21 October 1994. Columbia, MD: Johns Hopkins University, Chemical Propulsion Information Agency, 1994.
Знайти повний текст джерелаYe ti huo jian fa dong ji ran shao guo cheng jian mo yu shu zhi fang zhen: Modeling and numerical simulations of internal combustion process of liquid rocket engines. Beijing: Guo fang gong ye chu ban she, 2012.
Знайти повний текст джерелаYe ti huo jian fa dong ji ran shao dong li xue mo xing yu shu zhi ji suan. Beijing Shi: Guo fang gong ye chu ban she, 2011.
Знайти повний текст джерела(Editor), Martin J. Chiaverini, and Kenneth K. Kuo (Editor), eds. Fundamentals of Hybrid Rocket Combustion and Propulsion (Progress in Astronautics and Aeronautics). AIAA (American Institute of Aeronautics & Ast, 2007.
Знайти повний текст джерелаD, Cruit W., Smith A. W, George C. Marshall Space Flight Center., and AIAA/ASME/SAE/ASEE Joint Propulsion Conference (32nd : 1996 : Lake Buena Vista, Fla.), eds. Cold-flow study of hybrid rocket motor flow dynamics. [Huntsville, AL]: NASA Marshall Space Flight Center, 1996.
Знайти повний текст джерелаD, Cruit W., Smith A. W, and George C. Marshall Space Flight Center., eds. Cold-flow study of hybrid rocket motor flow dynamics: 32nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference, July 1-3, 1996, Lake Buena Vista, FL. [Huntsville, Ala: NASA Marshall Space Flight Center, 1996.
Знайти повний текст джерелаЧастини книг з теми "Hybrid propellant rockets – Combustion"
Kara, Ozan, and Arif Karabeyoglu. "Hybrid Propulsion System: Novel Propellant Design for Mars Ascent Vehicles." In Propulsion - New Perspectives and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96686.
Повний текст джерела"Vortex Injection Hybrid Rockets." In Fundamentals of Hybrid Rocket Combustion and Propulsion, 247–76. Reston ,VA: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/5.9781600866876.0247.0276.
Повний текст джерела"Analytical Models for Hybrid Rockets." In Fundamentals of Hybrid Rocket Combustion and Propulsion, 207–46. Reston ,VA: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/5.9781600866876.0207.0246.
Повний текст джерела"High-Speed Flow Effects in Hybrid Rockets." In Fundamentals of Hybrid Rocket Combustion and Propulsion, 277–322. Reston ,VA: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/5.9781600866876.0277.0322.
Повний текст джерела"Metals, Energetic Additives, and Special Binders Used in Solid Fuels for Hybrid Rockets." In Fundamentals of Hybrid Rocket Combustion and Propulsion, 413–56. Reston ,VA: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/5.9781600866876.0413.0456.
Повний текст джерелаТези доповідей конференцій з теми "Hybrid propellant rockets – Combustion"
Naoumov, Viatcheslav I., Nicole Knochenhauer, Peggy Sansevero, Goldreich Adam, Corey Freeto, Tyler Kimiecik, and Oaty Frye. "Research on the Combustion of Bio-Derived Fuels in Hybrid Propellant Rocket Engine." In 52nd Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-0309.
Повний текст джерелаGontijo, Maurício S., Renato d. Filho, and Caio H. Domingos. "Design of Pre-Combustion Chambers for Hybrid Propellant Rocket Motors and Related Aspects." In AIAA SCITECH 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-2183.
Повний текст джерелаNaoumov, Viatcheslav, Alexander Haralambous, Adam Goldreich, and Elaine Monsy. "Hybrid Propellant Rocket Engine Test Fixture and Research on the Combustion of Non-Conventional Fuels." In 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-450.
Повний текст джерелаNaoumov, Viatcheslav I., and Nidal A. Al Masoud. "Senior Capstone Design Research Project on Combustion of Bio-Derived Fuels in Hybrid Propellant Rocket Engine." In 2018 AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-0807.
Повний текст джерелаNaoumov, Viatcheslav I., Huy Nguyen, and Beatriz Alcalde. "Study of the Combustion of Beeswax and Beeswax With Aluminum Powder in Hybrid Propellant Rocket Engine." In 54th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-2145.
Повний текст джерелаNaoumov, Viatcheslav I., Nidal Al Masoud, Piotr Skomin, and Patryk Deptula. "Undergraduate Research on Peculiarities of the Combustion of Ecologically Clean Paraffin Wax Fuels in Hybrid Propellant Rocket Engines." In 53rd AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1513.
Повний текст джерелаNaoumov, Viatcheslav, Alexander Haralambous, Adam Goldreich, Thomas Boynton, and Murat Koc. "Student-Faculty Research on the Combustion in Hybrid-Propellant Rocket Engine for Aerospace Specialization in Mechanical Engineering Curriculum." In 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-436.
Повний текст джерелаNaoumov, Viatcheslav I., Piotr Skomin, and Patryk Deptula. "Combustion of Bio-derived Fuels With Additives and Research on the Losses of Unburned Fuel in Hybrid Propellant Rocket Engines." In 53rd AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0923.
Повний текст джерелаNaoumov, Viatcheslav I., Nidal A. Al Masoud, Kristine Sherman, Matthew Doolittle, Matthew Ziegler, and Daniel Thorne. "Study of the Combustion of Pure Bio-Derived Fuels and Bio-Derived Fuels with Additives in Hybrid Propellant Rocket Engine." In 55th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0833.
Повний текст джерелаNaoumov, Viatcheslav I., Nidal A. Al Masoud, Jalal Butt, Calvin Correa, David Parmelee, Michael Couillard, Hoan Nguyen, Jeffrey Ampofo, and Keval Patel. "Student-Faculty Research on the Combustion of Non-Conventional Fuels in Hybrid Propellant Rocket Engine in a Wide Range of Oxidizer-to-Fuel Ratios." In AIAA Scitech 2020 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-0068.
Повний текст джерелаЗвіти організацій з теми "Hybrid propellant rockets – Combustion"
Price, E. W., and G. A. Flandro. Combustion Instability in Solid Propellant Rockets. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada179701.
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