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The transition to low-carbon energy systems has heightened interest in hydrogen and ammonia as sustainable alternatives to traditional hydrocarbon fuels. However, the development and operation of combustors utilizing these fuels, like other combustion systems, are challenged by thermoacoustic instabilities arising from the interaction between unsteady heat release and acoustic wave oscillations. Among many different methods for studying thermoacoustic instabilities, thermoacoustic network models have played an important role in analyzing the essential dynamics of these instabilities in combustors operating with low-carbon fuels. This paper provides a comprehensive review of thermoacoustic network modeling techniques, focusing specifically on their application to hydrogen- and ammonia-based combustion systems. We outline the key mathematical frameworks derived from fundamental equations of motion, along with experimental validations and practical applications documented in existing studies. Furthermore, current research gaps are identified, and future directions are proposed to improve the reliability and effectiveness of thermoacoustic network models, contributing to the advancement of efficient and stable low-carbon combustors.

Oxygen-enriched combustion is increasingly recognized as a viable approach for clean energy production and carbon capture, offering substantial benefits for boosting combustion efficiency and mitigating pollutant emissions, which makes it widely adopted in various industrial applications. Liquefied petroleum gas (LPG), predominantly consisting of propane (C3H8), is commonly utilized in numerous combustion systems, yet its emissions of soot particulates have raised considerable environmental concerns. This study delves into the combustion dynamics and soot formation behavior of propane, the principal component of LPG, under oxy-fuel combustion conditions, with the inclusion of H2O and CO2, utilizing both experimental techniques and numerical simulations. The results reveal that CO2 and H2O suppress soot formation through distinct mechanisms. CO2 decreases soot nucleation and surface growth by lowering flame temperature and H atom concentration, but it minimally enhances soot oxidation. H2O significantly reduces soot formation by chemically increasing OH radical concentration, thereby enhancing soot oxidation. A detailed decoupling analysis further shows that CO2’s influence is predominantly thermal and chemical, resulting in lower OH levels and an elongated flame shape. In contrast, H2O’s substantial thermal and chemical effects decrease flame height and promote soot reduction. These insights advance the understanding of soot formation control in oxy-fuel combustion, offering strategies to optimize combustion efficiency and minimize environmental impact.

The need to refuel future missions to Mars and the Moon via in situ resource utilization (ISRU) requires the development of LOX/LCH4 engines, which are complex and expensive to develop and improve. This paper discusses how the use of digital engineering—specifically physics-based modeling (PBM)—can aid in developing, testing, and validating a LOX/LCH4 engine. The model, which focuses on propulsion performance and heat transfer through the engine walls, was created using Siemens’ STAR-CCM+ CFD tool. Key features of the model include Eulerian multiphase physics (EMP), complex chemistry (CC) using the eddy dissipation concept (EDC), and segregated solid energy (SSE) for heat transfer. A comparison between the complete GRI 3.0 and Lu’s reduced combustion mechanisms was performed, with Lu’s mechanism being chosen for its cost-effectiveness and similar output to the GRI mechanism. The model’s geometry represents 1/8th of the engine’s volume, with a symmetric rotational boundary. The performance of this engine was investigated using NASA’s chemical equilibrium analysis (CEA) and STAR-CCM+ simulations, focusing on thrust levels of 125 lbf and 500 lbf. Discrepancies between theoretical predictions and simulations ranged from 1.4% to 28.5%, largely due to differences in modeling assumptions. While NASA CEA has a zero-dimensional, steady-state approach based on idealized conditions, STAR-CCM+ accounts for real-world factors such as multiphase flow, turbulence, and heat loss. For the 125 lbf case, a 9.2% deviation in combustion chamber temperature and a 15.0% difference in thrust were noted, with simulations yielding 113.48 lbf compared to the CEA’s 133.52 lbf. In the 500 lbf case, thrust reached 488 lbf, showing a 2.4% deviation from the design target and an 8.6% increase over CEA predictions. Temperature and pressure deviations were also observed, with the highest engine wall temperature at the nozzle throat. Monte Carlo simulations revealed that substituting LNG for LCH4 affects combustion dynamics. The findings emphasize the need for advanced modeling approaches to enhance the prediction accuracy of rocket engine performance, aiding in the development of digital twins for the CROME.

Drop-in fuels for aviation gas-turbine engines have been introduced recently to mitigate global warming. Despite their similarity to the fossil fuel Jet A-1, their combustion in traditional combustors should be thoroughly analyzed to maintain engine health and low emissions. The paper introduces criteria for assessing the impact of the chemical composition of fuels on combustion in the DEGN 380 turbofan. Based on previous emission-test results, the power functions of carbon monoxide and its emission index were adopted as the model of combustion. Based on the general notation of chemical reactions leading to the production of CO in combustion, the regression coefficients were given a physical meaning by linking them with the parameters of the kinetic equations, i.e., the reaction rate constant of CO and CO2 formation expressed as exponential functions of combustor outlet temperature and the concentration of O2 in the exhaust gas, as well as stoichiometric combustion reactions. The obtained empirical functions show that, in the entire range of engine operating parameters, synthetic components affect the values of the rate constants of CO and CO2 formation. It can be explained by the change in activation energy determined for all chain-of-combustion reactions. The activation energy for the CO formation chain changes in the range between 8.5 kJ/mol for A0 and 24.7 kJ/mol for A30, while for the CO2 formation chain between 29.8 kJ/mol for A0 and 30.8 kJ/mol for A30. The reactivity coefficient lnαiCOACODCO changes between 2.29 for A0 and 6.44 for A30, while lnαiCO2ACO2DCO2 changes between 7.90 for A0 and 8.08 for A30.

Estimation of combustion phasing and power production is essential to ensuring proper combustion and load control. However, archetypal control-oriented physics-based combustion models can become computationally expensive if highly accurate predictive capabilities are achieved. Artificial neural network (ANN) models, on the other hand, may provide superior predictive and computational capabilities. However, using classical ANNs for model-based prediction and control can be challenging, since their heuristic and deterministic black-box nature may make them intractable or create instabilities. In this paper, a hybridized modeling framework that leverages the advantages of both physics-based and stochastic neural network modeling approaches is utilized to capture CA50 (the timing when 50% of the fuel energy has been released) along with indicated mean effective pressure (IMEP). The performance of the hybridized framework is compared to a classical ANN and a physics-based-only framework in a stochastic environment. To ensure high robustness and low computational burden in the hybrid framework, the CA50 input parameters along with IMEP are captured with a Bayesian regularized ANN (BRANN) and then integrated into an overall physics-based 0D Wiebe model. The outputs of the hybridized CA50 and IMEP models are then successively fine-tuned with BRANN transfer learning models (TLMs). The study shows that in the presence of a Gaussian-distributed model uncertainty, the proposed hybridized model framework can achieve an RMSE of 1.3 × 10−5 CAD and 4.37 kPa with a 45.4 and 3.6 s total model runtime for CA50 and IMEP, respectively, for over 200 steady-state engine operating conditions. As such, this model framework may be a useful tool for real-time combustion control where in-cylinder feedback is limited.

The automotive sector is greatly contributing to pollutant emissions and recent regulations introduced the need for a major control of, and reduction of, internal combustion engine emissions. Artificial intelligence (AI) algorithms have proven to hold the potential to be the thrust in the state-of-the-art for engine-out emission prediction, thus enabling tailored calibration modes and control solutions. More specifically, the scientific literature has recently witnessed strong efforts in AI applications for the development of nitrogen oxides (NOx) virtual sensors. These latter replace physical sensors and exploit AI algorithms to estimate NOx concentrations in real-time. Still, the calibration of the algorithms, together with the appropriate choice of the specific metric, strongly affects the prediction capability. In the present paper, a machine learning-based virtual sensor for NOx monitoring in diesel engines was developed, based on the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The latter is commonly used in the literature to deploy virtual sensors due to its high performance, flexibility and robustness. An experimental campaign was carried out to collect data from the engine test bench, as well as from the engine electronic control unit (ECU), for the development and calibration of the virtual sensor at steady-state conditions. The virtual sensor has, since then, been tested throughout on an on-road driving mission to assess its prediction performance in dynamic conditions. In stationary conditions, its prediction accuracy was around 98%, whereas it was 85% in transient conditions. The present study shows that AI-based virtual sensors have the potential to significantly improve the accuracy and reliability of NOx monitoring in diesel engines, and can, therefore, play a key role in reducing NOx emissions and improving air quality.

Techniques such as exhaust gas recirculation (EGR) and water-in-fuel emulsions (WFEs) can significantly decrease NOx emissions in diesel engines. As a disadvantage of adopting EGR, the afterburning period lengthens owing to a shortage of oxygen, lowering thermal efficiency. Meanwhile, WFEs can slightly reduce NOx emissions and reduce the afterburning phase without severely compromising thermal efficiency. Therefore, the EGR–WFE combination was modeled utilizing the KIVA-3V code along with GT power and experimental results. The findings indicated that combining EGR with WFEs is an efficient technique to reduce afterburning and enhance thermal efficiency. Under the EGR state, the NO product was evenly lowered. In the WFE, a considerable NO amount was created near the front edge of the combustion flame. Additionally, squish flow from the piston’s up–down movement improved fuel–air mixing, and NO production was increased as a result, particularly at high injection pressure. Using WFEs with EGR at a low oxygen concentration significantly reduced NO emissions while increasing thermal efficiency. For instance, using 16% of the oxygen concentration and a 40% water emulsion, a 94% drop in NO and a 4% improvement in the Indicated Mean Effective Pressure were obtained concurrently. This research proposes using the EGR–WFE combination to minimize NO emissions while maintaining thermal efficiency.

Thrust regulation is applied to maintain the performance of the liquid propellant rocket engine. The thrust level of a rocket engine can be readily controlled by adjusting the number of propellants introduced into the combustion chamber. In this study, a gas generator design is proposed in which thrust regulation is maintained by performing secondary combustion in the divergent section of the nozzle of a gas generator. Tangential and normal injection techniques have also been studied for better combustion analyses. A normal injection technique is used for the experiment and CFD results are validated with the experimental data. Chemical equilibrium analyses are also performed by minimizing Gibbs free energy with the steepest descent method augmented by the Nelder–Mead algorithm. These equilibrium calculations give the combustion species as obtained through the CFD results. Performance evaluation of the rocket engine, with and without secondary combustion in the gas generator, led to an increase of 42% thrust and 46.15% of specific impulse with secondary combustion in the gas generator.

Superior fuel economy, higher torque and durability have led to the diesel engine being widely used in a variety of fields of application, such as road transport, agricultural vehicles, earth moving machines and marine propulsion, as well as fixed installations for electrical power generation. However, diesel engines are plagued by high emissions of nitrogen oxides (NOx), particulate matter (PM) and carbon dioxide when conventional fuel is used. One possible solution is to use low-carbon gaseous fuel alongside diesel fuel by operating in a dual-fuel (DF) configuration, as this system provides a low implementation cost alternative for the improvement of combustion efficiency in the conventional diesel engine. An initial step in this direction involved the replacement of diesel fuel with natural gas. However, the consequent high levels of unburned hydrocarbons produced due to non-optimized engines led to a shift to carbon-free fuels, such as hydrogen. Hydrogen can be injected into the intake manifold, where it premixes with air, then the addition of a small amount of diesel fuel, auto-igniting easily, provides multiple ignition sources for the gas. To evaluate the efficiency and pollutant emissions in dual-fuel diesel-hydrogen combustion, a numerical CFD analysis was conducted and validated with the aid of experimental measurements on a research engine acquired at the test bench. The process of ignition of diesel fuel and flame propagation through a premixed air-hydrogen charge was represented the Autoignition-Induced Flame Propagation model included ANSYS-Forte software. Because of the inefficient operating conditions associated with the combustion, the methodology was significantly improved by evaluating the laminar flame speed as a function of pressure, temperature and equivalence ratio using Chemkin-Pro software. A numerical comparison was carried out among full hydrogen, full methane and different hydrogen-methane mixtures with the same energy input in each case. The use of full hydrogen was characterized by enhanced combustion, higher thermal efficiency and lower carbon emissions. However, the higher temperatures that occurred for hydrogen combustion led to higher NOx emissions.

Research in the field of Internal Combustion (IC) engines focuses on the drastic reduction of both pollutant and greenhouse gas emissions. A promising alternative to gasoline and diesel fuel is represented by the use of gaseous fuels, above all green hydrogen but also Natural Gas (NG). In previous works, the authors investigated the performance, efficiency, and emissions of a supercharged Spark Ignition (SI) engine fueled with mixtures of gasoline and natural gas; a detailed research involving the combustion process of this kind of fuel mixture has been previously performed and a lot of experimental data have been collected. Combustion modeling is a fundamental tool in the design and optimization process of an IC engine. A simple way to simulate the combustion evolution is to implement a mathematical function that reproduces the mass fraction burned (MFB) profile; the most used for this purpose is the Wiebe function. In a previous work, the authors proposed an innovative mathematical model, the Hill function, that allowed a better interpolation of experimental MFB profiles when compared to the Wiebe function. In the research work presented here, both the traditional Wiebe and the innovative Hill function have been calibrated using experimental MFB profiles obtained from a supercharged SI engine fueled with mixtures of gasoline and natural gas in different proportions; the two calibrated functions have been implemented in a zero-dimensional (0-D) SI engine model and compared in terms of both Indicated Mean Effective Pressure (IMEP) and cyclic pressure variation prediction reliability. It was found that the Hill function allows a better IMEP prediction for all the operating conditions tested (several engine speeds, supercharging pressures, and fuel mixtures), with a maximum prediction error of 2.7% compared to 4.3% of the Wiebe function. A further analysis was also performed regarding the cyclic pressure variation that affects all the IC engines during combustion and may lead to irregular engine operation; in this case, the Hill function proved to better predict the cyclic pressure variation with respect to the Wiebe function.

The use of lifted flames presents some very promising advantages in terms of pollutant emissions and flame stability. The focus here is on a specific low-swirl injection system operated with methane and derived from an air-blast atomizer for aero-engine applications, which is responsible for flame lift-off. The key feature of this concept is the interaction between the swirling jet and the confinement walls, leading to a strong outer recirculation zone and thus to an upstream transport of combustion products from the main reaction region to the flame base. Here, the representation of the physics involved is challenging, since finite-rate effects govern the lift-off occurrence, and only a few numerical studies have been carried out on this test case so far. The aim of the present work is therefore to understand the limits of some state-of-the-art combustion models within the context of LES. Considering this context, two different strategies are adopted: the Flamelet-Generated Manifold (FGM) approach and the Thickened Flame (TF) model. A modified version of the FGM model including stretch and heat loss effects is also applied as an improvement of the standard model. Numerical results are compared with the available experimental data in terms of temperature and chemical species concentration maps, showing that the TF model can better reproduce the lift-off than the FGM approach.

The scale-resolving simulation of a practical gas turbine combustor is performed using a partially premixed finite-rate chemistry combustion model. The combustion model assumes finite-rate chemistry by limiting the chemical reaction rate with flame speed. A comparison of the numerical results with the experimental temperature and species mole fraction clearly showed the superiority of the shear stress transport, K-omega, scale adaptive turbulence model (SSTKWSAS). The model outperforms large eddy simulation (LES) in the primary region of the combustor, probably for two reasons. First, the lower amount of mesh employed in the simulation for the industrial-size combustor does not fit the LES’s explicit mesh size dependency requirement, while it is sufficient for the SSTKWSAS simulation. Second, coupling the finite-rate chemistry method with the SSTKWSAS model provides a more reasonable rate of chemical reaction than that predicted by the fast chemistry method used in LES simulation. Other than comparing with the LES data available in the literature, the SSTKWSAS-predicted result is also compared comprehensively with that obtained from the model based on the unsteady Reynolds-averaged Navier–Stokes (URANS) simulation approach. The superiority of the SSTKWSAS model in resolving large eddies is highlighted. Overall, the present study emphasizes the effectiveness and efficiency of coupling a partially premixed combustion model with a scale-resolving simulation method in predicting a swirl-stabilized, multi-jets turbulent flame in a practical, complex gas turbine combustor configuration.

Within the framework of a Reynolds averaged numerical simulation (RANS) methodology for modeling turbulence, a comparative numerical study of turbulent lifted H2/N2 flames is presented. Three different turbulent combustion models, namely, the eddy dissipation model (EDM), the eddy dissipation concept (EDC), and the composition probability density function (PDF) transport model, are considered in the analysis. A wide range of global and detailed combustion reaction mechanisms are investigated. As turbulence model, the Standard k-ε model is used, which delivered a comparatively good accuracy within an initial validation study, performed for a non-reacting H2/N2 jet. The predictions for the lifted H2/N2 flame are compared with the published measurements of other authors, and the relative performance of the turbulent combustion models and combustion reaction mechanisms are assessed. The flame lift-off height is taken as the measure of prediction quality. The results show that the latter depends remarkably on the reaction mechanism and the turbulent combustion model applied. It is observed that a substantially better prediction quality for the whole range of experimentally observed lift-off heights is provided by the PDF model, when applied in combination with a detailed reaction mechanism dedicated for hydrogen combustion.