Estimation of Overflow Loss of Trailing Suction Hopper Dredger Based on TLBO-PF Algorithm

In order to solve the problem of using PF to estimate the value of overflow loss in the process of replacing the optimal value and reducing the particle difference in the estimation process, this paper proposes an estimation algorithm based on PF and the fusion of the improved TLBO algorithm (TLBO-PF) applied to the overflow Estimation of flow loss. By optimizing the PF reference distribution, the problem of replacing the optimal problem caused by the high probability density region of particle changes during the estimation process is solved. Replace TLBO with PF resampling process to solve the problem of degradation in PF resampling process. By improving the teaching and learning process, the problem of local optimization of probability overlap in the estimation of overflow loss using TLBO instead of PF resampling is solved. TLBO and PF are fused to achieve a large number of sampled particles in the overflow estimation process while making the results easier and excellent, and simulation verification. Compared with the estimated value of PF, the error of the TLBO-PF estimation of the total volume in series is reduced by 4%, and the error of the total mass estimation is reduced by 34%, thus indicating that the TLBO-PF estimation performance is good.