Model-Based Approach To Study Hot Rolling Mills With Data Farming

The paper describes a computer system for simulating metallurgical rolling processes that consist of multiple steps, each of which is performed by a different type of devices. Both devices and processed materials are described with models, which can be dynamically reconfigured between simulation runs to study different device and environment configurations. Such an approach is especially crucial in technology design based on multi-iterative optimization procedures, for which an objective function uses computationally intensive algorithms. Due to the approach proposed in this paper, in the first stage of optimization more general and coarse models can be applied characterized by lower predictive capabilities and higher computational efficiency. Afterwards, when the optimization procedure finds a solution close to the optimal one, very detailed models can be used to obtain high quality solutions in the last few steps of calculations. To achieve such an objective a hybrid computer system able to use High Performance Computing (HPC) infrastructures was designed and implemented. The details of proposed approach are described, which is followed by presentation of a data farming platform responsible for distribution of complex numerical simulations onto various computer clusters. Finally, a concrete use case of a hot rolling mill is presented and analyzed.