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Counter-rotating axial-flow compressor (CRAC) is a promising technology to increase the thrust-to-weight ratio of aero-engines. Self-recirculating casing treatment (SRCT) is selected to explore its stability enhancement capacity in the CRAC. In the present work, an effective SRCT is designed by the design of experiment method, and the stability improvement potential of the SRCT and its mechanism of stall margin enhancement are studied. Results show that the optimal SRCT scheme increases the stall margin by about 7.73% and without remarkable peak efficiency loss, and the overall performance of the compressor is enhanced at the near-stall condition. The SRCT improves the quality of the tip flow field by sucking out low-energy fluid and restrains the spillage of the tip leakage flow by the jet effect from the injection port. In addition, the SRCT also reduces the unsteady interference between the adjacent rotors. The effect of speed ratio on the effectiveness of SRCT is further investigated, and results indicate that the SRCT increases the stall margin of the CRAC by about 4.13–5.80% at some off-design speed ratio conditions. The speed ratio affects the first stall stage of the CRAC, thus affecting the effectiveness of the SRCT.

Counter-rotating axial-flow compressor (CRAC) is a promising technology to increase the thrust-to-weight ratio of aero-engines. Self-recirculating casing treatment (SRCT) is selected to explore its stability enhancement capacity in the CRAC. In the present work, an effective SRCT is designed by the design of experiment method, and the stability improvement potential of the SRCT and its mechanism of stall margin enhancement are studied. Results show that the optimal SRCT scheme increases the stall margin by about 7.73% and without remarkable peak efficiency loss, and the overall performance of the compressor is enhanced at the near-stall condition. The SRCT improves the quality of the tip flow field by sucking out low-energy fluid and restrains the spillage of the tip leakage flow by the jet effect from the injection port. In addition, the SRCT also reduces the unsteady interference between the adjacent rotors. The effect of speed ratio on the effectiveness of SRCT is further investigated, and results indicate that the SRCT increases the stall margin of the CRAC by about 4.13–5.80% at some off-design speed ratio conditions. The speed ratio affects the first stall stage of the CRAC, thus affecting the effectiveness of the SRCT. [ABSTRACT FROM AUTHOR]

Compressor design is always a prevalent research issue as nowadays the compressor demands higher performance. Designing afresh a new compressor of higher aerodynamic performance will cost a tremendous amount of time and expense. Consequently, the wiser method of acquiring a higher performance compressor is to optimize a current compressor, discussed in this paper. First, a detailed numerical simulation of al compressor is done. A parametric rotor model is created and the variations of lean and swept parameters are limited. Then, an optimization calculation is carried out based on the combination of an artificial neural network and a genetic algorithm. The optimization iteration is started at three specified working points – a choke point, a design point and a near stall point – to acquire a comprehensive improved solution. A novel penalty function considering both pressure ratio and efficiency is constructed to find the optimal model. Finally, a detailed review is made of the original and the optimized compressor. The computational results show that the isentropic efficiency is successfully increased and the stall margin is remarkably improved. Ultimately, a detailed vortex structure analysis with increasing outlet pressure is discussed. This helps the performance increment mechanism to be understood further.

Compressor design is always a prevalent research issue as nowadays the compressor demands higher performance. Designing afresh a new compressor of higher aerodynamic performance will cost a tremendous amount of time and expense. Consequently, the wiser method of acquiring a higher performance compressor is to optimize a current compressor, discussed in this paper. First, a detailed numerical simulation of al compressor is done. A parametric rotor model is created and the variations of lean and swept parameters are limited. Then, an optimization calculation is carried out based on the combination of an artificial neural network and a genetic algorithm. The optimization iteration is started at three specified working points – a choke point, a design point and a near stall point – to acquire a comprehensive improved solution. A novel penalty function considering both pressure ratio and efficiency is constructed to find the optimal model. Finally, a detailed review is made of the original and the optimized compressor. The computational results show that the isentropic efficiency is successfully increased and the stall margin is remarkably improved. Ultimately, a detailed vortex structure analysis with increasing outlet pressure is discussed. This helps the performance increment mechanism to be understood further. [ABSTRACT FROM AUTHOR]