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A comparative study of two combustion models based on non-premixed assumption and partially premixed assumptions using the overall models of Zimont Turbulent Flame Speed Closure Method (ZTFSC) and Extended Coherent Flamelet Method (ECFM) are conducted through Reynolds stress turbulence modelling of Tay model gas turbine combustor for the first time. The Tay model combustor retains all essential features of a realistic gas turbine combustor. It is seen that the non-premixed combustion model fails to predict the combustion completely due to an incorrect assumption of diffusion flame scenario invoking infinitely fast chemistry in complicated flow environments while the two partially premixed combustion models accurately predict the flame pattern in the primary region of the combustor. The ZTFSC model outperformed the ECFM model by producing a better temperature agreement with the experimental result. The latter model predicts lower temperature due to the underestimation of reaction progress. Additionally, a cross-comparison of the present RSM prediction invoking ZTFSC model with LES prediction reported in the literature is conducted. The former produces more accurate species concentration and flame pattern than the latter. This is mainly due to the incorrect assumption of nonpremixed combustion used in LES prediction reported in the literature. It is interesting to find that when nonpremixed combustion model is used for both RSM and LES predictions, the LES predicts higher temperature closer to the injection nozzle of combustor than the RSM model, though the flame shape in both cases is incorrect. This is mainly due to the fact that the traditional RANS model dissipates the energy of swirling flow too fast in the primary region of the combustor. The weaker centre recirculation zone (CRZ) created by vortex breakdown recirculate less air to the area near the injection nozzle resulting in fuel rich combustion. It indicates that the temperature difference between predicted results using RSM in conjunction with ZTFC model and experimental results can be improved by using less energy dissipating turbulence models such as scale resolving simulation (SRS). 1. Introduction The advent of Gas-Turbine for military purposes tracks back to 1940s, and it is subsequently used for aviation and later for ground level power [1]. The main challenge of aviation industries nowadays is the efficiency, stability of combustion and pollutant control, such as the emission of carbon dioxide (CO2), nitrogen oxide, sulphur dioxide and etc. In order to design combustors with desired features and meet with relevant criteria, improved understanding of turbulent combustion through both realistic experimental observation and numerical simulation and validation is required. The former alone is expensive for industries before a more cost-effective numerical prediction is performed. However, the accuracy of the numerical simulation is doubtful as it is highly dependent on the turbulence and combustion models, i.e. the mixing and chemical reactions. To i...

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By introducing image recognition and micro-current testing, jet behavior research was conducted, in which the real-time recognition of ejection mode was realized. To study the factors influencing ejection modes and the current variation trends under different modes, an Electrohydrodynamic Direct-Write (EDW) system with functions of current detection and ejection mode recognition was firstly built. Then a program was developed to recognize the jet modes. As the voltage applied to the metal tip increased, four jet ejection modes in EDW occurred: droplet ejection mode, Taylor cone ejection mode, retractive ejection mode and forked ejection mode. In this work, the corresponding relationship between the ejection modes and the effect on fiber deposition as well as current was studied. The real-time identification of ejection mode and detection of electrospinning current was realized. The results in this paper are contributed to enhancing the ejection stability, providing a good technical basis to produce continuous uniform nanofibers controllably.