Optimizing process outcomes by tuning parameters through an automated system is common in industry. Ideally, this optimization is performed as efficiently as possible, using the minimum number of steps to achieve an optimal configuration. However, care must often be taken to ensure that, in pursuing the optimal solution, the process does not enter an “unsafe” state (for the process itself or its surroundings). Safe Bayesian optimization is a viable method in such contexts, as it guarantees constraint fulfillment during the optimization process, ensuring the system remains safe. This method assumes the constraints are real-valued and continuous functions. However, in some cases, the constraints are binary (true/false) or classification-based (safe/unsafe), limiting the direct application of safe Bayesian optimization. Therefore, a slight modification of safe Bayesian optimization allows for applying the method using a probabilistic classifier for learning classification constraints. However, violation of constraints may occur during the optimization process, as the theoretical guarantees of safe Bayesian optimization do not apply to discontinuous functions. This paper addresses this limitation by introducing an enhanced version of safe Bayesian optimization incorporating a simulation-informed Gaussian process (GP) for handling classification constraints. The simulation-informed GP transforms the classification constraint into a piece-wise function, enabling the application of safe Bayesian optimization. We applied this approach to optimize the parameters of a computational model for the isotope separator online (ISOL) at the MYRRHA facility (Multipurpose Hybrid Research Reactor for High-Tech Applications). The results revealed a significant reduction in constraint violations—approximately $80\%$—compared to safe Bayesian optimization methods that directly learn the classification constraints using Laplace approximation and expectation propagation. The sensitivity to the accuracy of the simulation model was analyzed to determine the extent to which it is advantageous to use the proposed method. These findings suggest that incorporating available information into the optimization process is valuable for reducing the number of unsafe outcomes in constrained optimization scenarios.

International audienceThe windowless target electron beam experimental irradiation (WEBEXPIR) program was set-up as part of the MYRRHA/XT-ADS R&D effort on the spallation target design to investigate the interaction of a proton beam with a liquid lead–bismuth eutectic (LBE) free surface. In particular, possible free surface distortion or shockwave effects in nominal conditions and during sudden beam on/off transient situations, as well as possible enhanced evaporation were assessed. An experiment was conceived at the IBA TT-1000 Rhodotron, where a 7 MeV electron beam was used to simulate the high power deposition at the MYRRHA/XT-ADS LBE free surface. The geometry and the LBE flow characteristics in the WEBEXPIR set-up were made as representative as possible of the actual situation in the MYRRHA/XT-ADS spallation target. Irradiation experiments were carried out at beam currents of up to 10 mA, corresponding to 40 times the nominal beam current necessary to reproduce the MYRRHA/XT-ADS conditions. Preliminary analyses show that the WEBEXPIR free surface flow was not disturbed by the interaction with the electron beam and that vacuum conditions stayed well within the design specifications