Qiongying Lv
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Qiongying Lv has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Anjun Xu
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
1 shared publication - Bing Jia
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
1 shared publication - Gan Qiu
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
1 shared publication - Lingyu Zhou
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
1 shared publication
Latest Publications
- Open Access
On-Site Dynamic Balancing Optimization of a TPS Rotor System Based on a Hybrid Intelligent Optimization Method
To reduce high 1× vibration during staged speed-up of a Turbine Power Simulator (TPS) rotor, a staged incremental on-site balancing method based on a Genetic Algorithm–Salp Swarm Algorithm (GA–SSA) is proposed. SSA is a swarm-intelligence optimizer inspired by salps, gelatinous marine organisms that move collectively in chains. A one-dimensional Timoshenko-beam rotor model with lumped disks and equivalent bearing supports is established and validated using a three-dimensional ANSYS model. From meshes M3 to M4, the equivalent speed associated with the first lateral natural frequency changes by 0.23%. The first three critical-speed errors are 6.75–8.80%, while baseline 1× vibration-amplitude errors remain below 10% and phase errors below 7.1%. Speed-specific influence coefficients are then extracted to formulate a staged incremental balancing model based on the current measured vibration and cumulative correction state. In GA–SSA, the final GA population initializes SSA, and the historical GA best is used as the initial Food. Under equal function-evaluation budgets and 30 paired runs, GA–SSA shows search performance comparable to GA and improves the stability of standalone SSA. On-site tests at 10,358, 25,558, and 38,333 rpm reduce 1× vibration at both rotor ends by 79.0–86.4%, confirming the method’s engineering applicability.
Open AccessOn-Site Dynamic Balancing Optimization of a TPS Rotor System Based on a Hybrid Intelligent Optimization Method
To reduce high 1× vibration during staged speed-up of a Turbine Power Simulator (TPS) rotor, a staged incremental on-site balancing method based on a Genetic Algorithm–Salp Swarm Algorithm (GA–SSA) is proposed. SSA is a swarm-intelligence optimizer inspired by salps, gelatinous marine organisms that move collectively in chains. A one-dimensional Timoshenko-beam rotor model with lumped disks and equivalent bearing supports is established and validated using a three-dimensional ANSYS model. From meshes M3 to M4, the equivalent speed associated with the first lateral natural frequency changes by 0.23%. The first three critical-speed errors are 6.75–8.80%, while baseline 1× vibration-amplitude errors remain below 10% and phase errors below 7.1%. Speed-specific influence coefficients are then extracted to formulate a staged incremental balancing model based on the current measured vibration and cumulative correction state. In GA–SSA, the final GA population initializes SSA, and the historical GA best is used as the initial Food. Under equal function-evaluation budgets and 30 paired runs, GA–SSA shows search performance comparable to GA and improves the stability of standalone SSA. On-site tests at 10,358, 25,558, and 38,333 rpm reduce 1× vibration at both rotor ends by 79.0–86.4%, confirming the method’s engineering applicability.