Numerical Optimization of a Radial Inflow Turbine Based on a Loss Model of a Cryogenic Turboexpander Using the Slime Mould Algorithm
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| Publicado en: | Defence Science Journal vol. 75, no. 4 (2025), p. 512-520 |
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| 024 | 7 | |a 10.14429/dsj.19832 |2 doi | |
| 035 | |a 3228704843 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 202629 |2 nlm | ||
| 100 | 1 | |a Kumar, Anumay | |
| 245 | 1 | |a Numerical Optimization of a Radial Inflow Turbine Based on a Loss Model of a Cryogenic Turboexpander Using the Slime Mould Algorithm | |
| 260 | |b Defence Scientific Information & Documentation Centre |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The major component of the cryogenic turboexpander is the radial inflow turbine; thus, improvements in its design and performance are effective for the system. The inspirations of six design parameters, including velocity ratio, inlet and outlet impeller diameters, mass flow rate, and blade height, are examined in the context of the total-to-static efficiency of the RIT turbine cryogenic turboexpander. A 1-D design of the radial-inflow turbine has been implemented through MATLAB 2020. In this paper, A novel artificial intelligence system slime mould algorithm (SMA) was employed for the numerical optimization of RIT through MATLAB 2020. An innovative MATLAB script was created for this optimization. The parameters of mass flow rate, number of blades, and blade angles were varied in a constrained range for optimization. This paper explores five distinct blade scenarios for design and numerical optimization processes through MATLAB 2020. The optimization of radial inflow turbines will require the development of a greater capacity of the cryogenic liquefaction system. The performance measurement of the radial inflow turbine was done based on total-to-static efficiency. In numerical optimization, the selection of blades in the range of 11–15 resulted in an improvement in the total-to-static efficiency by around 1.46 %, specifically for 13 blades. This enhancement represents a significant 5.0 % improvement over the results presented in the ANN model explored in the available literature. The maximum total-to-static efficiency achieved through SMA optimization is 89.94 % for 15 blades. | |
| 653 | |a Turbines | ||
| 653 | |a Performance measurement | ||
| 653 | |a Inflow | ||
| 653 | |a Mass flow rate | ||
| 653 | |a Liquefaction | ||
| 653 | |a Slime | ||
| 653 | |a Matlab | ||
| 653 | |a Optimization | ||
| 653 | |a Efficiency | ||
| 653 | |a Blades | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Design optimization | ||
| 653 | |a Design parameters | ||
| 653 | |a Turboexpanders | ||
| 700 | 1 | |a Singh, Amrik | |
| 773 | 0 | |t Defence Science Journal |g vol. 75, no. 4 (2025), p. 512-520 | |
| 786 | 0 | |d ProQuest |t Military Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3228704843/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3228704843/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |