Research on the Parameterized Modeling and Simulation Optimization Algorithm of Tower Assembly in Digital Twin Environment

A novel digital twin optimization algorithm is presented for the optimization of complex parameters. Firstly, the parametric model of the tower is built with the digital twin technique, and the high precision physical modeling method is used to simulate each part of the tower. A multi-dimension optimization framework is built. The key variables in the tower assembly process are optimized and adjusted through an adaptive learning mechanism, thereby improving the accuracy and efficiency of the assembly process. The optimization algorithm has strong adaptive ability and can automatically adjust the optimization strategy according to real-time data to ensure that the best optimization effect can be obtained in various complex environments. Experimental results show that compared with traditional optimization methods, the proposed digital twin optimization algorithm improves the computational efficiency and assembly accuracy by 20% and 15% respectively. In addition, this paper also compares the experimental data under different parameter settings and simulation scenarios, further proving the great potential of the algorithm in practical engineering applications.