Abstract:This paper presents a numerical analysis of gas flow in the annular combustion chamber of a Proto X-3 Bioenergy micro gas turbine for green building applications. The computational fluids dynamics (CFD) simulation was conducted in two dimensions, turbulent flow and gas phase combustion, with the goal of comparing the effects of different models in real conditions. Two different turbulence models, standard (STD) k-ε and renormalization group (RNG) k-ε, were applied for simulations. The fuel used was biogas prod… Show more
“…Prior studies recommend the two-equations RNG 𝑘-𝜀 turbulence model compared to the standard two-equations 𝑘-𝜀 and the two-equations SST 𝑘-𝜔 model for the CFT CFD simulations due to its accuracy among [21,22]. However, the recently founded four-equations Transitional SST turbulence model is more promising, with a competitive accuracy and ability to quickly converge the iteration process based on other previous studies [13,14].…”
Harvesting the kinetic energy from the small dam’s spillway downstream flow in agricultural areas is interesting. On the other hand, a cross-flow turbine (CFT) is a unique impulse turbine because it works at a higher specific speed, which means this turbine works at a lower head and higher water flow rate. Thus, there is an opportunity to use a CFT to harvest the energy in the spillway. There are two possible scenarios: at the flat horizontal and the 30° slop flow. Several computational fluid dynamics (CFD) simulations were conducted to test the hypothesis. The simulations were performed with five variations of the ratio between the cross-sectional depth parameter of the spillway’s flow before approaching the turbine for each scenario. The total head in the present study case is 3.0 meters with 120 l/s of water discharge. The simulations used ANSYS® Fluent™ for 2D CFD simulation. The tests found that the CFT could attain 80.36% efficiency. Moreover, some water flows over the turbine at a higher rotational speed, leading to a significant loss in turbine performance, called potential loss. This finding indicates that the CFT could harvest the spillway’s flow kinetic energy when the flow is not too deep.
“…Prior studies recommend the two-equations RNG 𝑘-𝜀 turbulence model compared to the standard two-equations 𝑘-𝜀 and the two-equations SST 𝑘-𝜔 model for the CFT CFD simulations due to its accuracy among [21,22]. However, the recently founded four-equations Transitional SST turbulence model is more promising, with a competitive accuracy and ability to quickly converge the iteration process based on other previous studies [13,14].…”
Harvesting the kinetic energy from the small dam’s spillway downstream flow in agricultural areas is interesting. On the other hand, a cross-flow turbine (CFT) is a unique impulse turbine because it works at a higher specific speed, which means this turbine works at a lower head and higher water flow rate. Thus, there is an opportunity to use a CFT to harvest the energy in the spillway. There are two possible scenarios: at the flat horizontal and the 30° slop flow. Several computational fluid dynamics (CFD) simulations were conducted to test the hypothesis. The simulations were performed with five variations of the ratio between the cross-sectional depth parameter of the spillway’s flow before approaching the turbine for each scenario. The total head in the present study case is 3.0 meters with 120 l/s of water discharge. The simulations used ANSYS® Fluent™ for 2D CFD simulation. The tests found that the CFT could attain 80.36% efficiency. Moreover, some water flows over the turbine at a higher rotational speed, leading to a significant loss in turbine performance, called potential loss. This finding indicates that the CFT could harvest the spillway’s flow kinetic energy when the flow is not too deep.
“…The value of inverse-turbulent Prandtl number (α) 1.1 is best used to simulate turbulent flow in a curved pipe using the RNG k-ε model at Re 63800 and the r/D 1,607 (Budiarso et al, 2015). k-ε and RNG k-ε could be used to represent the combustion process phenomenon without any significant differences for the numerical analysis of gas flow in the annular combustion chamber of a Proto X-3 (Daryus et al, 2016). Three turbulence models compared in wind tunnels to predict turbulence parameters are validated with test data, revealing that the k-ε model is effective because its results are comparable to the RSM model (Gunadi et al, 2016).…”
Section: Turbulent Flow Occurs At Reynolds Number Values Abovementioning
Using the CFD method as the initial analysis for experiments has more benefits, including saving time and costs. The variable of flow parameters and geometry can be easily developed to get the desired results. However, research is needed to improve the accuracy of the results and the optimality of the calculation process; the study of complex turbulent flow modelling becomes very important. The k-ε model and renormalization group (RNG) k-ε model are widely used in research to produce the appropriate models and develop the constants value. This turbulent flow modelling research was conducted to improve the result accuracy and the calculation process optimality in the turbulent flow of crossflow turbine. Research was done by comparing the simulation results of k-ε model with different constants and RNG k-ε model. The k-ε model with kinetic Prandtl 0.8, 0.9, 1, 1.1, 1.2 and the RNG k-ε model show different results for predicting the average pressure and velocity distribution in the turbulent flow of crossflow turbine, and likewise for turbulent parameters. The RNG k-ε model has more accuracy than the k-ε model, although the k-ε model's simulation time is quite short. Therefore, complex fluid flow recommends RNG k-ε model.
“…Finally, to combining the computational fluid dynamic (CFD) and experimental method, treated as calibrated input data for numerical models and source for newly created models [22], e.g., in the mixing of glycerol and ethanol [23], the homogenisation of two mutually dissolvable fluids with very different properties [24,25], the water and ethanol system [26] and the blending of two or more miscible liquids with very different density and a viscosity [27].…”
The mixing process in a mechanically agitated vessel is a widespread phenomenon which plays an important role among industrial processes. In that process, one of the crucial parameters, the mixing efficiency, depends on a large number of geometrical factors, as well as process parameters and complex interactions between the phases which are still not well understood. In the last decade, large progress has been made in optimisation, construction and numerical and experimental analysis of mechanically agitated vessels. In this review, the current state in this field has been presented. It shows that advanced computational fluid dynamic techniques for multiphase flow analysis with reactions and modern experimental techniques can be used with success to analyse in detail mixing features in liquid-liquid, gas-liquid, solid-liquid and in more than two-phase flows. The objective is to show the most important research recently carried out.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.