Tackling the challenges of NO-Laser Induced Fluorescence technique in hydrogen detonation

Compared to classical constant volume or constant pressure thermodynamic cycles, the detonation regime of combustion could increase by 40% the efficiency of engines. In line with the Paris agreement, identifying more efficient combustion processes is one of the strategies to limit CO2 emissions that contribute to climate change. For transportation, researchers focus on obtaining and controlling a self-sustained detonation in a specific engine (PDE or RDE). While the measurement of temperature and chemical species is of current practice in conventional combustion process (flames, engines, etc…), the experimental characterization of detonation relies on the determination of the detonation velocity, global pressure, and density gradient structure. These information are limited to validate numerical simulations and to be confident in the phenomenological comprehension extracted from it. While planar laser-induced fluorescence of hydroxyl radical (NO-PLIF) is a powerful technique to characterize reaction fronts, previous studies have shown significant limitations of this technique for detonation visualization. Not only restricted to reaction front visualization, this technique is also of interest as it can give access to 2-D temperature measurements in detonations. Objectives: The main objective of the project is to overcome the current limitations of the NO-PLIF imaging of detonation. This numerical investigation is based on a preexisting PLIF model that will be used (i) to identify the sensitive parameters (excitation line, laser energy, gas composition, etc…) of the PLIF intensity and (ii) to recommend experimental conditions to maximize the overall image quality.

First, the student will have to become familiar with the principle of the NO-PLIF technique and the particularities associated with its usage on detonations, which has high pressure and temperature variations, high-speed flow (up to 2000m/s), etc… Second, the sensitivity analysis of the fluorescence signal will be conducted to identify the most sensitive parameters of the PLIF signal. Third, optimal operating conditions (≠ maximizing the fluorescence signal) will be identified and tested experimentally. Due to both the strong non-linearities between the PLIF signal intensity and each parameter involved, machine-learning approaches may be used to facilitate the identification of the optimal operating conditions.