AI-enhanced environmental control and energy sustainability in multipetawatt laser facilities

High-power laser facilities, such as ELI Beamlines, are highly sensitive to environmental factors like temperature and humidity, which can significantly impact laser performance and experimental outcomes. Poor temperature control can lead to thermal lensing and misalignment of optical components, degrading beam quality and reducing system efficiency. High humidity may cause condensation on optical surfaces, scattering the laser beam and damaging critical components. Additionally, environmental contaminants make it challenging to maintain optimal laser performance. These issues underscore the need for precise environmental control to ensure reliable operation. This study proposes a digital twin framework specifically tailored to high-power laser facilities. Acting as a virtual model, the digital twin will continuously collect real-time data from the facility’s physical environment, enabling predictive analysis and adaptive control. By simulating different environmental conditions, the digital twin can help optimize facility operations and minimize the risk of disruptions and equipment failures. This dynamic framework aims to improve the overall management of the laser facility’s environmental conditions and operational stability. In connection with the EU FlexRICAN project, this research aims to increase energy flexibility, reduce consumption, and lower CO₂ emissions in these energy-intensive facilities. Several methods will be evaluated for their effectiveness, including Mixed-Integer Linear Programming (MILP) with OMEGAlpes software for energy modeling, Gaussian processes for predictive modeling and uncertainty quantification, and machine learning techniques for enhancement of decision making. The findings will contribute to the optimization of energy efficiency and the maintenance of an optimal and stable indoor environment, ultimately enhancing laser performance while achieving sustainability objectives.