Yooyen, Soemsak
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Yooyen, Soemsak
Alternative Name
Yooyen, S.
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soemsak.yo@kmitl.ac.th
5 results
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Item type:Publication, Numerically thermal analysis of a turbine vane at high temperature(2019-11-26); ; Sleesongsom, S.Using conjugate heat transfer, thermal analysis of a turbine vane coated with thermal barrier coating (TBC) at a high temperature is presented. Numerical results are carried out at two turbine inlet temperatures (T <inf>∞</inf>) i.e. 783 K (low) and 1566 K (high) under two turbulence intensities (Tus) i.e. 8.3% and 16.6%. The main findings of this research are that for both Tus, the metal surface temperature reduction at the high temperature is higher than that at the low temperature because of the lower heat-flux ratio at the higher temperature. Based on the metal temperature reduction, the increasing inlet temperature has a greater influence than the increasing turbulence intensity. The results also indicate that at T <inf>∞</inf> = 783 K, on the pressure side (PS) the metal surface temperature reduction at Tu = 8.3% is lower than that at Tu = 16.6%, while on the suction side (SS) no significant difference happens when Tu increases. Interestingly, an inverse phenomenon happens for both PS and SS, that is the metal surface temperature reduction at Tu = 8.3% increases above that at Tu = 16.6% when T <inf>∞</inf> increases. This discrepancy may suggest the instability of the surface heat-flux ratio due to complex heat convection at the different inlet temperatures. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CHT/CFD analysis of thermal sensitivity of a transonic film-cooled guide vane(2019-01-01); ; Sleesongsom, SuwinThermal parameters are important variables that have great influence on life time of turbine vanes. Therefore, accurate prediction of the thermal parameters is essential. In this study, a numerical approach for conjugate heat transfer (CHT) and computational fluid dynamics (CFD) is used to investigate thermal sensitivity of a transonic guide vane which is fully film-cooled by 199 film holes. Thermal barrier coating (TBC), i.e., the typical TBC and a new one as the candidate TBC, and turbulence intensity (Tu), i.e., Tu=3.3%, 10% and 20%, are two variables used for the present study. At first the external surface temperatures of the vane material are compared. Next, the TBC surface temperatures are considered. Results show the major role of the lower thermal conductivity of TBC which results in the lower and more uniform temperature on the external surface of the vane substrate. Finally, the thermal sensitivity is presented in terms of the percentage reduction of the external surface temperatures of the vane material and the structural temperatures of the vane material at midspan, including the variations of average and maximum vane temperatures. Results show that TBC and Tu have significant effects on the external surface and structural temperatures of the vane substrate. The lower thermal conductivity of TBC leads to the higher difference between the thermal conductivity of the vane substrate and TBC, the reduction of heat transfer and the more uniform temperature within the vane structure. The results also show more effective protection for the average vane temperature from the two TBCs at higher Tus. However, Tu does not significantly affect the reduction of the maximum vane temperature even though the new TBC, which has the very low thermal conductivity, is used. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investigation of cooling performances of a non-film-cooled turbine vane coated with a thermal barrier coating using conjugate heat transfer(2018-04-01); ; ; Xu, HuazhaoThe aim of this paper is to numerically investigate cooling performances of a non-film-cooled turbine vane coated with a thermal barrier coating (TBC) at two turbulence intensities (Tu = 8.3% and 16.6%). Computational fluid dynamics (CFD) with conjugate heat transfer (CHT) analysis is used to predict the surface heat transfer coefficient, overall and TBC effectiveness, as well as internal and average temperatures under a condition of a NASA report provided by Hylton et al. [NASA CR-168015]. The following interesting phenomena are observed: (1) At each Tu, the TBC slightly dampens the heat transfer coefficient in general, and results in the quantitative increment of overall cooling effectiveness about 16-20%, but about 8% at the trailing edge (TE). (2) The protective ability of the TBC increases with Tu in many regions, that is, the leading edge (LE) and its neighborhoods on the suction side (SS), as well as the region from the LE to the front of the TE on the pressure side (PS), because the TBC causes the lower enhancement of the heat transfer coefficient in general at the higher Tu. (3) Considering the internal and average temperatures of the vane coated with two different TBCs, although the vane with the lower thermal conductivity protects more effectively, its role in the TE region reduces more significantly. (4) For both TBCs, the increment of Tu has a relatively small effect on the reduction of the average temperature of the vane. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of polycrystalline structure on helium plasma irradiation of tungsten materials(2018-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Convolutional Neural Networks for plane identification on Satellite imagery by exploiting transfer learning with a different optimizer(2019-07-01); ; Object identification is on an available problem. Automating plane identification on Satellite imagery can be applied for activity and traffic patterns to monitoring airports, and including defense intelligence issues. This paper implements Deep Convolutional Neural Networks(CNN) to classify a plane in the planesnet dataset. Pre-trained model and transfer learning are deployed to overcome a limitation of computation resources by adding new top layer consists of a fully-connected layer and softmax layer to identify the new classes and re-train it. Besides, the experimental designs for testing an implementation of a pretrained model with some kinds of the optimizer to comparing a result. There are four types of optimizer. The first two are well-known optimizer namely Stochastic Gradient Descent optimizer and Adam Optimizer, while others are PowerSign and AddSign optimizer. PowerSign and AddSign optimizer are methods to minimize cost, which discover by using Recurrent neural network(RNN) and Reinforcement Learning. A result demonstrates that a plane identification on Satellite imagery can be achieved by implementing the pre-trained model and obtains an exceptional result with Adam optimizer.