Design and Optimization Approach for Radial Inflow Turbines

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Design and Optimization Approach for Radial Inflow Turbines ( design-and-optimization-approach-radial-inflow-turbines )

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obtained. Figure 4. Genetic algorithm (GA) procedure. Figure 5 shows the flowchart of the GA assisted radial inflow turbine preliminary design Appl. Sci. 2018, 8, 2038 procedure. The GA drives the exploration of the design space defined by the loading coefficient, flow The above-mentioned GA is from a toolbox called Inspyred, which is a free, open source coefficient and rotational speed in a wide range. That is to say, the GA drives each new mean line framework for creating biologically-inspired computational intelligence algorithms in Python. design, and finally, when the convergence condition reaches requirements, the optimal result will be It is original, and no further development was done on it. In this optimization problem, there is only a single objective, total-to-static efficiency of turbines, so it is enough to briefly explore all of the In this paper, two preliminary designs are performed, the first one is a validation design without design space. GA assistance that verifies the mean line design method by comparing the T-100 design parameters, Figure 5 shows the flowchart of the GA assisted radial inflow turbine preliminary design and the second one is the T-100re designed using a GA assisted preliminary design procedure that procedure. The GA drives the exploration of the design space defined by the loading coefficient, aims to achieve the maximum efficiency under specified constraints. The GA will do the screening flow coefficient and rotational speed in a wide range. That is to say, the GA drives each new mean job automatically for better performance design without human intervention. The designer only line design, and finally, when the convergence condition reaches requirements, the optimal result will be obtained. Figure 5. Flowchart of GA assisted preliminary design procedure. Figure 5. Flowchart of GA assisted preliminary design procedure. In this paper, two preliminary designs are performed, the first one is a validation design without needs to specify the constraint that should be satisfied in the design procedure. 7 of 16 3. Verification of Automated Preliminary Design Methodology GA assistance that verifies the mean line design method by comparing the T-100 design parameters, and the second one is the T-100re designed using a GA assisted preliminary design procedure that 3.1. Mean Line Design aims to achieve the maximum efficiency under specified constraints. The GA will do the screening job automatically for better performance design without human intervention. The designer only needs to Table 1 shows the design parameters of the T-100 turbine [27]. Table 2 gives the ranges specified specify the constraint that should be satisfied in the design procedure. for loading coefficient, flow coefficient, and rotational speed. It can be seen that the specified range covers the most design area in Figure 2. 3. Verification of Automated Preliminary Design Methodology 3.1. Mean Line Design Table 1 shows the design parameters of the T-100 turbine [27]. Table 2 gives the ranges specified for loading coefficient, flow coefficient, and rotational speed. It can be seen that the specified range covers the most design area in Figure 2. Table 1. Design parameters of T-100 turbine. Items Fluid Rotational Speed Inlet Total Temperature Inlet Total Pressure Mass Flow Rate Number of Nozzle Vanes Number of Rotor Blades Pressure Ratio Power Unit Value [-] Air [r/min] 106,588 [K] 1056.5 [kPa] 580.4 [kg/s] 0.33 [-] 19 [-] 16 [-] 5.73 [kW] 121

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