Continuous simulation method and case study based on AMEsim and Python

With the advancement of digital twin technology, there is a growing demand for model refinement and data processing speed, leading to common challenges in many digital twin projects. These challenges include issues such as multi-software interaction methods and achieving continuous simulation. For instance, in the context of rail transport, a high-speed trains digital twin project necessitates the utilization of AMEsim simulation for digital twin platform development. However, single simulation client does not support real-time data input. To tackle this issue, a continuous simulation method utilizing the Python programming language was devised for AMEsim. This method enables continuous assignment of signals to the simulation, ensuring that the final state of the simulation is accurately conveyed, thereby converting the simulation into multiple consecutive time slices. Experimental results affirmed that the proposed method effectively facilitates continuous assignments within AMEsim, thereby addressing a crucial problem of digital twin projects and enhancing convenience and flexibility for AMEsim users.