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A deep learning approach is developed to swiftly evaluate phase change in computational fluid dynamics (CFD) simulations of a multi-component, liquid–gas two-phase injection flow. This method significantly improves computational efficiency by using a deep feedforward neural network (DFNN) to replace the complex iterative solution of multi-species vapor–liquid equilibrium (VLE). The DFNN takes instantaneous pressure, temperature, and system composition as input and predicts the corresponding phase equilibrium state. A parametric study was conducted to optimize the neural network's hyperparameters, including the activation function, number of hidden layers, and neurons per hidden layer. The rate of phase change is then calculated as a linear relaxation toward phase equilibrium, guiding subsequent computational steps in the CFD solver. A case study was performed to test the proposed methodology, involving the injection of a superheated liquid ethanol–water mixture into a gaseous nitrogen environment. The simulation results and computational cost were examined. It is found that the DFNN model, while accurately representing the non-ideal non-equilibrium phase change of a multi-component injection flow, speeds up the VLE solution by four orders of magnitude, leading to a 30%–40% reduction in overall flow simulation time. This model shows promise for injection flow simulations, especially for systems with a large number of compositions, such as sustainable aviation fuels. [ABSTRACT FROM AUTHOR]

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. It is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models' constants. [ABSTRACT FROM AUTHOR]

Accurate predictions of low-temperature heat release (LTHR) are critical for modeling auto-ignition processes in internal combustion engines. While LTHR is typically obscured by deflagration, extremely late ignition phasing can lead to LTHR prior to the spark, a behavior known as pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection spark-ignition engine was studied using 3-D computational fluid dynamics (CFD) and detailed chemical kinetics. The turbulent combustion was modeled via a hybrid approach that incorporates the G -equation model for tracking the turbulent flame front, and the well-stirred reactor model with detailed chemistry for assessing the low-temperature reactions in unburnt gas. Simulations were conducted using Co-Optima alkylate and E30 fuels at operating conditions characterized by different PSHR intensities. The predicted in-cylinder pressure and heat release rate were found to agree well with experiments. It was found the estimate of previous-cycle trapped residuals is of utmost importance for capturing PSHR correctly. A simulation best practice was developed which keeps the detailed chemistry solver active throughout the entire simulation, allowing to track the evolution of intermediate species from one cycle to the next. Following the validation, the dynamics of PSHR were analyzed in detail employing the pressure-temperature (P-T) trajectory framework. It was shown that PSHR correlated with the first-stage ignition delay of the fuel, hence showing close relation to the in-cylinder P-T trajectory and the chemical kinetics. Besides, it was indicated that LTHR is a self-limiting process that has the effect of attenuating the thermal stratification in the combustion chamber. Furthermore, it was observed the occurrence of PSHR caused the P-T trajectory of end-gas to overlap with the negative temperature coefficient region of the fuel's ignition-delay maps. This effect was more significant in the fuel-rich regions where engine knock tendency would be generally higher, with potential implications on knock control and mitigation. [ABSTRACT FROM AUTHOR]