A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs

The steelSteel industry is constantly looking for ways to automate processesProcess and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steelSteel industry processesProcess such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to processProcess, resulting in challengesChallenges for the implementation of the models in real-world operational environments. In recent years, deep learningDeep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial processProcess inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.