Hydrogen is the most common molecule in the universe. It is an excellent fuel for thermal engines: piston, turbojet, rocket, and, going forward, in thermonuclear power plants. Hydrogen is currently used across a range of industrial applications including propulsion systems, e.g., cars and rockets. One obstacle to expanding hydrogen use, especially in the transportation sector, is its low density. This paper explores hydrogen as an addition to liquid fuel in the detonation chamber to generate thermal energy for potential use in transportation and generation of electrical energy. Experiments with liquid kerosene, hexane, and ethanol with the addition of gaseous hydrogen were conducted in a modern rotating detonation chamber. Detonation combustion delivers greater thermal efficiency and reduced NOx emission. Since detonation propagates about three orders of magnitude faster than deflagration, the injection, evaporation, and mixing with air must be almost instantaneous. Hydrogen addition helps initiate the detonation process and sustain continuous work of the chamber. The presented work proves that the addition of gaseous hydrogen to a liquid fuel–air mixture is well suited to the rotating detonation process, making combustion more effective and environmentally friendly.

Detonation cell size is a very important parameter describing the detonation process, used both for explosion safety analysis and for the design of detonation combustion chambers. Typically it has been studied either experimentally or by CFD simulations; both options are costly in terms of money and time. However, progress in the machine learning (ML) methods opened a third way of obtaining cell size. When trained properly, such models are capable of giving rapid, accurate predictions. Utilization of machine learning in the combustion field is gaining more attention from the research community. In this study, the process of training, testing, and evaluation of three different machine learning models for predicting biogas–oxygen mixture detonation cell size is presented. The models include: linear regression (LR), support vector regression (SVR), and neural network (NN). The dataset used for training and testing comes from the experimental studies conducted previously by the authors. It was shown that all the models give very good results with support vector regression proving to be the best.

Interest in alternative and renewable energy sources has risen significantly in recent years. Biogas is a prime example of a promising, alternative fuel that might be a possible replacement for fossil fuels. It is a mixture consisting mainly of CH₄ and CO₂ with various additions. Biogas is easily storable and as such is a more reliable and stable source of energy than solar and wind sources, which suffer from unreliability due to their dependence on weather conditions. In this paper, the authors report experimental results of detonation of a biogas-oxygen mixture. The composition of the biogas was 70% CH₄ + 30% CO₂ and the experiments were carried out for a range of equivalence ratios (Φ = 0.5 ÷ 1.5) and initial pressures (0.6 ÷ 1.6 bar). The aim of the research was to analyze the cellular structure of detonation. The soot foil technique was used to determine the width of the detonation cells (λ). The conducted experiments and subsequent analysis of the detonation cell size confirm that both the increase in the initial pressure of the mixture or move away from stoichiometric (Φ = 1) composition is accompanied by a decrease in the width of the detonation cell. The authors also argue that due to the unstable cellular structure of the detonation, it is insufficient to report only the average cell size. Instead, the researchers propose more detailed statistical description assured values.