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dc.contributor.authorHamp, N.
dc.contributor.authorAldrich, Chris
dc.contributor.authorMarais, C.
dc.identifier.citationHamp, N. and Aldrich, C. and Marais, C. 2012. Identification of Rocket Motor Characteristics from Infrared Emission Spectra. In Infrared Spectroscopy – Materials Science, Engineering and Technology, 434-452. Croatia: InTech.

The prediction of infrared (IR) emission spectra from the exhaust gases of rocket plumes finds numerous applications in the strategic identification of rockets. These rocket fingerprints could be classified, thus allowing for the distinction between friend and foe. Likewise, the plume radiation intensity could also be reduced for stealth purposes, where accurate prediction of the spectra could be used to determine whether rockets have the required stealth characteristics during their design phase already. This would reduce the high manufacturing and testing costs involved in later stages. The challenge of predicting the plume radiance is describing the thermodynamic combustion process within the rocket chamber, the plume structure and the rocket plume chemical composition. The factors guiding these processes are the rocket motor design parameters, as well as the rocket motor fuel chemistry. In addition, environmental conditions have a significant implact on the plume structure and the plume chemical composition.Previously, attempts were made to model the middle IR band emission spectra (2 to 5.5um) from the rocket fuel chemistry and the physical properties during combustion by making use of techniques such as quantum mechanics and computational fluid dynamids. These methods proved to be too time consuming and the accuracies of the predictions were not acceptable (Roodt, 1998).More recently, Roodt (1998) was the first to show that the IR spectra could be modelled with a multilayer perceptron neural network using the elemental composition and other physical properties of the rocket motor fuel as input. Although these models were successful, there were some indications that they were not optimal and in this investigation the use of multilayer perceptrons similar to the ones used by Roodt (1998), as well as linear partial least squares (PLS) and neural network PLS (with and without weight updating) are considered.In addition, the modelling problem is considered in terms of a forward mapping, i.e. prediction of the emission spectra of the rockets from their design parameters, as well as a reverse mapping, where the rocket design parameters are predicted from the middle-IR spectral absorbances of the rocket plume.

dc.titleIdentification of Rocket Motor Characteristics from Infrared Emission Spectra
dc.typeBook Chapter
dcterms.source.titleInfrared Spectroscopy – Materials Science, Engineering and Technology
curtin.accessStatusOpen access

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