Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC.

The fossil fuels have been the most practical way to produce energy for industries, vehicles and homes due to their lower prices, good combustion properties and their availability compared to other types of fuels. However, in this era of modernization, one of the biggest challenges of humanity is depletion of fossil fuels may result the increasing of the price. Studies have found out that it is possible to improve physical and chemical characteristics of alternative fuel based biodiesel by using the transesterification process and hence act as an alternative to mineral diesel. Therefore, the study about the potentiality of the rice bran oil as biodiesel feedstock is explained in this paper. The aim of this study is determining the performance and emission characteristics of the rice bran oil biodiesel (RBOBD). In addition, the comparison is made for its combustion and emission characteristics to those of conventional diesel fuel (CDF). Results show that RBOBD is very good at minimizing emissions, especially CO and SO2 with the value 60 ppm and 10 ppm, respectively. The establish information may help Malaysia to recognize the potentiality of the rice bran oil in order to replace the conventional fuel in the future.

Aerospace structures are typically semi-monocoque structures that are made up of thin-walled closed section reinforced with stiffeners. Stress analysis of such closed thin-walled structures which are statically indeterminate is tedious and time consuming. An educational software which can aid students in carrying out stress analysis of such idealized thin-walled closed sections has been developed. The software enables students to select different types of wing torsion box sections with stiffeners, which may be subjected to bending, shear or torsional loads and evaluate the resulting stresses on it. The software allows the student to idealize a selected twin spar unsymmetrical wing section with multiple booms under multiple loads. Results from this software have been validated against the results in the literature. The software has been developed using MATLAB with graphical user interface (GUI) which is very user friendly.