The turbo-pump and turbine are driven by liquid fuel fed into a gas generator, where the fuel is oxidized with a liquid oxidizing agent. For stable operation of the turbine, the combustion temperature of the gas generator must be maintained below 1000 K. The thermodynamic characteristics of kerosene oxidation in the gas generator must be understood to optimize the design and operation conditions of the liquid-fueled rocket engine system. Herein, the 3-species surrogate mixture model for kerosene was selected, and the detailed Dagaut’s kerosene oxidation mechanism consisting of 225 chemical species and 1800 reversible chemical reactions was utilized. The exit gas temperature and product gas composition in the gas generator under fuel-rich conditions were simulated by applying the perfectly stirred reactor model. The perfectly stirred reactor model was used in combination with the liquid spray model for evaporation of the droplets and the two-temperature model for evaluation of the flame temperature separately from the locally averaged reactor temperature. The theoretical prediction of the gas species fraction and soot yield could be improved by applying the tar cracking mechanism, where the reaction characteristics under high temperature were taken into account.

A one-dimensional reactor model was developed to simulate the performance of an entrained flow gasifier under various operating conditions. The model combined the plug flow reactor (PFR) model with the well-stirred reactor (WSR) model. Reaction kinetics was considered together with gas diffusion for the solid-phase reactions in the PFR model, while equilibrium was considered for the gas-phase reactions in the WSR model. The differential and algebraic equations of mass balance and energy balance were solved by a robust ODE solver, i.e., an semi-implicit Runge–Kutta method, and by a nonlinear algebraic solver, respectively. The computed gasifier performance was validated against experimental data from the literature. The difference in product gas concentration from the equilibrium model, and the underlying mechanisms were discussed further. The optimal condition was found after parameter studies were made for various operating conditions.

Rayleigh–Bénard convection (RBC), the buoyancy-driven flow that arises when a fluid layer is heated from below and cooled from above, governs heat transport in engineering systems, geophysical and atmospheric circulation, stellar and planetary interiors, and numerous industrial processes. This review synthesizes theoretical, numerical, and experimental progress on controlling RBC, organizing methods into (i) passive strategies that modify geometry or boundary conditions and apply constant external fields, and (ii) active strategies that dynamically modulate parameters using feedback or machine learning-based controllers. Quantitatively, passive approaches can substantially delay the onset of convection: oscillatory boundary forcing increases the critical Rayleigh number Rac by up to ∼16×, and selected geometric or magnetic/rotational configurations shift stability by factors of order 1.5–3. Active approaches provide greater adaptability and depth of stabilization: Proportional feedback sustains the conductive state up to ∼4× the classical threshold, robust linear-quadratic-Gaussian designs up to ∼14.5×, while reinforcement learning reduces turbulent convective heat transport by 5.1% at Ra∼3×104, extending Rac by ∼30× over the uncontrolled case. We compare these methods on common performance metrics (extension of Rac, percentage change in Nusselt number, advantages and limitations) and discuss practical factors such as sensor placement, actuator bandwidth and saturation, and robustness to disturbances that govern deployability. The hybrid combining classical feedback with nonlinearity, augmented by multi-agent reinforcement learning, quantum-inspired optimization, and programable thermal metamaterials, provides scalable real-time solutions for precise thermal management in a wide range of scientific and industrial applications.