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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. [ABSTRACT FROM AUTHOR]

In this work, we explore the combustion mechanism of single micron-sized aluminum particles using a numerical model. The Burcat database and Catoire mechanism is considered as the thermodynamic data and the kinetic mechanism for our numerical model of aluminum combustion. Two independent experiments, including particle temperature profiles, ignition delay and burning time, are selected to evaluate the performance of the numerical model. The model shows great agreement for all considered properties. A parametric study is further conducted to identify the effect of involved physical parameters on the combustion process. The diffusion coefficient (D) of oxidizers and the activation energy of surface kinetics (Esurf) and evaporation coefficient (α) of aluminum impact the particle temperature the most. Burning time is most sensitive to the activation energy of surface kinetics (Esurf). The optical measurement in a solid propellant combustion indicates that the contact angle of the oxide cap on Al particle is between 10° and 20°. It is found that the selection of contact angle of the oxide cap significantly impacts the prediction of combustion time and residual of active aluminum. The current work highlights the importance of physical properties on the prediction of Al combustion, suggesting that more detailed evaluation from experiments and theory is encouraged.

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. [ABSTRACT FROM AUTHOR]

The filtered tabulated chemistry (FTACLES) approach utilizes data from pre-tabulated explicitly filtered 1D flame profiles for closure of the LES-filtered transport terms. Different methodologies are discussed to obtain a suitable progress variable c from detailed chemistry calculations of a methane/air flame. In this context, special focus is placed on the analytical modeling of the reaction source term using series of parameterized Gaussians. For increasing effective filter sizes in LES (i.e. including the flame thickening) the precise shape of the reaction rate profile becomes less and less relevant. In particular, it is shown that for one-step chemistry, a single Gaussian is sufficient to derive an explicitly expressible 1D flame profile with a prescribed laminar flame speed and thermal flame thickness. The resulting artificial flame profile is shown to have similarities with profiles based on carbon chemistry and detailed reaction mechanisms. Next, the behavior of the filtered c-transport equation is analyzed and several possible closure methods are compared for a wide range of filter widths. It is shown that the unclosed contribution of the filtered diffusion term can be combined with the subgrid convection term, thus simplifying the FTACLES formulation. The model is implemented in OpenFOAM and validated in 1D for a variety of LES filter sizes in combination with artificial flame thickening. A power-law-based wrinkling model is modified for use with artificial flame thickening and combined with the FTACLES model to enable 3D simulations of a premixed turbulent Bunsen burner. The comparison of 3D Large Eddy Bunsen flame simulations at increasing levels of turbulence intensity shows a good match to experimental results for most investigated cases. In addition, the results are mostly insensitive to the variation of the mesh size. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-speed microjet hydrogen–air diffusion flames are investigated computationally. The focus is on the prediction of the so-called bottleneck phenomenon. The latter has been previously observed as a specific feature of the present flame class and has not yet been investigated computationally. In the configuration under consideration, the nozzle diameter is 0.5 mm and six cases with mean nozzle injection velocities (U) between 306 m/s and 561 m/s are considered. The flow in the nozzle lance is analyzed separately to obtain detailed inlet boundary conditions for the flame calculations. It is confirmed by calculation that the phenomenon is mainly determined by the transition to turbulence in the initial parts of the free jet. The transitional turbulence proves to be the biggest challenge in predicting this class of flames, as the generally available turbulence and turbulent combustion models reach the limits of their validity in transitional flows. In a Reynolds-Averaged Numerical Simulation framework, the Shear Stress Transport model is found to perform better than alternative two-equation models and is used as the turbulence model. By neglecting the interactions between the turbulence and chemistry (no-model approach), it is possible to predict the morphology of the bottleneck flame and its dependence on U qualitatively. However, the position of the bottleneck is overpredicted for U < 561 m/s. The experimental flames in the considered U range are all attached to the nozzle. This is also predicted by the no-model approach. The Eddy Dissipation Concept (EDC) used as the turbulence combustion model predicts, however, lifted flames (with increasing lift-off height as U decreases). With the EDC, no bottleneck morphology is observed for U = 561 m/s. For lower U, the EDC results for the bottleneck position are generally closer to the measurements. It is demonstrated that accuracy in predicting the bottleneck position can be improved by ad hoc modifications of the turbulent viscosity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Manifold-based models are an efficient modeling framework for turbulent combustion but, in their basic formulation, do not account for the compressibility effects of high-speed flows. To include the effects of compressibility, ad hoc corrections have been proposed but result in an inconsistent thermodynamic state between the manifold and flow simulation. In this work, an iterative algorithm to consistently incorporate compressibility effects into manifold-based models is developed. The manifold inputs (fuel and oxidizer temperatures and pressure) are determined iteratively to reflect the nonnegligible variations in thermodynamic state (expressed in terms of density and internal energy in flow simulations) that are characteristic of supersonic combustion. The algorithm is demonstrated on data from simulations of high-speed reacting mixing layers and is significantly more accurate than established approaches that only partially couple the manifold in compressible flow simulations. The proposed approach eliminates partial coupling approximation errors in excess of 10 and 20% for temperature and water source term. [ABSTRACT FROM AUTHOR]

In this study, the effects of intake port injection of methane and direct of injection diesel on emissions and the combustion of a light-duty RCCI engine were numerically researched. In this way, AVL Boost software was used for 1-dimensional simulation of the combustion process and emission estimation. Higher octane number methane gas was mixed with air from the intake port, while lower octane number diesel fuel was injected directly into the combustion chamber during the compression stroke. Methane gas was injected at a rate of 65% and diesel fuel at a rate of 35%. The diesel injected directly into the combustion chamber was sprayed at a rate of 5% between 0 and 35 °CA with 8 different injection timings after the main injection. In the model engine, 50% high EGR rate was applied in all combustion modes. The results showed that these parameters have significant effects on performance and emissions. The results in summary; NOx emission reduced at all engine speeds with delayed diesel post-injection timing. The maximum drop of NOx emission was 57.34% with Post15. Although the addition of methane slightly increased the soot emission, it was significantly reduced by the simultaneous addition of methane and application of post-injection strategies of diesel fuel. The soot emission was reduced by 58.85% with the Post35 injection strategy.

Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows; however, they tend to generate massive data-sets, rendering conventional analysis intractable and inefficient. To alleviate this problem, machine learning tools may be used to, for example, discover features from the data for downstream modeling and prediction tasks. To this end, this work applies generative adversarial networks (GANs) to generate realistic flame images based on a time-resolved data set of hydroxide concentration snapshots obtained from planar laser induced fluorescence measurements of a model combustor. The generative model is able to generate flames in attached, lifted, and intermediate configurations dictated by the user. Using k-means clustering and proper orthogonal decomposition, the synthetic image set produced by the GAN is shown to be visually similar to the real image set, with recirculation zones and burned/unburned regions clearly present, indicating good GAN performance in capturing the experimental data statistical structure. Combined with techniques for controlling the configuration of generated flames, this work opens new avenues towards tractable statistical analysis and modeling of flame behavior, as well as rapid and inexpensive flame data generation.

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. [ABSTRACT FROM AUTHOR]

In this study, the effects of intake port injection of methane and direct of injection diesel on emissions and the combustion of a light-duty RCCI engine were numerically researched. In this way, AVL Boost software was used for 1-dimensional simulation of the combustion process and emission estimation. Higher octane number methane gas was mixed with air from the intake port, while lower octane number diesel fuel was injected directly into the combustion chamber during the compression stroke. Methane gas was injected at a rate of 65% and diesel fuel at a rate of 35%. The diesel injected directly into the combustion chamber was sprayed at a rate of 5% between 0 and 35 °CA with 8 different injection timings after the main injection. In the model engine, 50% high EGR rate was applied in all combustion modes. The results showed that these parameters have significant effects on performance and emissions. The results in summary; NOx emission reduced at all engine speeds with delayed diesel post-injection timing. The maximum drop of NOx emission was 57.34% with Post15. Although the addition of methane slightly increased the soot emission, it was significantly reduced by the simultaneous addition of methane and application of post-injection strategies of diesel fuel. The soot emission was reduced by 58.85% with the Post35 injection strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper provides a numerical study on n-dodecane flames using Large-Eddy Simulations (LES) along with the Flamelet Generated Manifold (FGM) method for combustion modeling. The computational setup follows the Engine Combustion Network Spray A operating condition, which consists of a single-hole spray injection into a constant volume vessel. Herein we propose a novel approach for the coupling of the energy equation with the FGM database for spray combustion simulations. Namely, the energy equation is solved in terms of the sensible enthalpy, while the heat of combustion is calculated from the FGM database. This approach decreases the computational cost of the simulation because it does not require a precise computation of the entire composition of the mixture. The flamelet database is generated by simulating a series of counterflow diffusion flames with two popular chemical kinetics mechanisms for n-dodecane. Further, the secondary breakup of the droplet is taken into account by a recently developed modified version of the Taylor Analogy Breakup model. The numerical results show that the proposed methodology captures accurately the main characteristics of the reacting spray, such as mixture formation, ignition delay time, and flame lift-off. Additionally, it captures the "cool flame" between the flame lift-off and the injection nozzle. Overall, the simulations show differences between the two kinetics mechanisms regarding the ignition characteristics, while similar flame structures are observed once the flame is stabilised at the lift-off distance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Numerical simulations of combustion processes in rocket engines requires a long run time of supercomputer systems even for a very short physical time. Therefore, creating digital twins of rocket engines needs enormous processor time, and is not very effective. This computational time surpasses the actual physical time of the process in many orders of magnitude. To speed up numerical simulations the paper presents a solution of the chemical kinetics problem using artificial neural network approach. Using the architecture of a multilayer neural network with bypass connections, namely residual network, it is possible to obtain a fast and reliable solution to the problem. The neural network is trained to predict the state of the system only one time step ahead. Using it in a recursive mode, it is possible to forecast for thousands of steps without loss of accuracy. • Simulation of a chemical kinetic problem by the neural network method was carried out. • The network was trained on a wide range of data. • A shortened neural network with preservation of accuracy was obtained. • The considered network used a small amount of RAM (Random Access Memory). [ABSTRACT FROM AUTHOR]

This work analyses, experimentally and numerically, the combustion behavior of three aviation fuels: a standard Jet A-1, a high aromatic content fuel, and an isoparaffinic Alcohol to Jet (ATJ) fuel. The goal is to demonstrate the ability of a chemical kinetic model to capture the chemistry underlying the combustion behavior of a wide range of jet fuels, starting from compositional information. Real fuels containing up to hundreds of components are modeled as surrogates containing less than 10 components, which represent the chemical functionalities of the real fuel. By using an in-house numerical optimizer, the fuel components and their relative quantities are selected, and a semi-detailed kinetic model (containing about 450 species) is used to simulate the formation of the main oxidation products and reaction intermediates. Calculations are compared with species profiles measured in a laminar flow reactor to validate the model and provide insights into the reactivity of the fuels. Finally, starting from the results, general observations on the strengths and limits of the approach are provided, highlighting areas where further investigations are required.

Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to study combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview of studies on the applications of machine learning in combustion science fields over the past several decades. We introduce the fundamentals of machine learning and its usage in aiding chemical reactions, combustion modeling, combustion measurement, engine performance prediction and optimization, and fuel design. The opportunities and limitations of using machine learning in combustion studies are also discussed. This paper aims to provide readers with a portrait of what and how machine learning can be used in combustion research and to inspire researchers in their ongoing studies. Machine learning techniques are rapidly advancing in this era of big data, and there is high potential for exploring the combination between machine learning and combustion research and achieving remarkable results.

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. [ABSTRACT FROM AUTHOR]

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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.

This paper assesses the accuracy of partially-premixed turbulent combustion models based on the tabulation of chemical kinetics, under multi-injection diesel engine-relevant conditions. For this purpose, 2-D direct numerical simulation (DNS) is carried out. Pockets of gaseous n-heptane are randomly distributed in a turbulent field of a partially burnt n-heptane/air mixture. The burnt gases composition and enthalpy correspond to the partial oxidation of a pilot injection that precedes the main injection, represented here by the fresh fuel pockets. The DNS domain is enclosed in a larger volume, permitting quasi-constant pressure autoignition. Chemical kinetics is modeled by a 29-species skeletal reaction mechanism for n-heptane/air mixture autoignition and flame propagation. A homogeneous isotropic turbulence spectrum is used to initialize the velocity field in the domain. A DNS database is generated varying the progress of the pilot injection combustion c 0 and the velocity fluctuation level u ′ of the turbulence spectrum. Three different modeling approaches are tested a priori against the DNS data: (1) the tabulated homogeneous reactor, which is a direct exploitation of the chemistry tabulation ignoring any local mixture heterogeneity; (2) the presumed conditional moment model, which includes a separate statistical description for the mixture and the combustion progress; (3) the approximated diffusion flame model, which considers the heterogeneous turbulent reactor as a diffusion flame. Since the same chemical kinetics mechanism is used for the generation of the chemistry tabulation, the study is entirely focused on the evaluation of the different modeling assumptions. Results show that accounting for initial progress variable of the mixture ( c 0 ) is mandatory for such models. They also indicate the omission of mixture fraction Z and progress variable c heterogeneities and the assumption of the statistical independence of Z and c as the main responsible for model discrepancies under the studied conditions. [ABSTRACT FROM AUTHOR]

This investigation outlines a data-assisted approach that employs random forest classifiers for local and dynamic submodel assignment in turbulent-combustion simulations. This method is demonstrated in simulations of a single-element GOX/GCH4 rocket combustor; a priori as well as a posteriori assessments are conducted to (i) evaluate the accuracy and adjustability of the classifier for targeting different quantities of interest (QoIs), and (ii) assess improvements, resulting from the data-assisted combustion model assignment, in predicting target QoIs during simulation runtime. Results from the a priori study show that random forests, trained with local flow properties as input variables and combustion model errors as training labels, assign three different combustion models – finite-rate chemistry (FRC), flamelet progress variable (FPV) model, and inert mixing (IM) – with reasonable classification performance even when targeting multiple QoIs. Applications in a posteriori studies demonstrate improved predictions from data-assisted simulations, in temperature and CO mass fraction, when compared with monolithic FPV calculations. An additional a posteriori data-assisted simulation of a modified configuration demonstrates that the present approach can be successfully applied to different configurations, as long as thermophysical behavior can be represented by the training data. These results demonstrate that this data-driven framework holds promise for dynamic combustion submodel assignments in reacting flow simulations. [ABSTRACT FROM AUTHOR]

The present paper provides a contribution to the CFD modelling of reacting flows in IC engines fueled with natural gas. Despite the fact that natural gas has been widely investigated into in the last decades, the literature still lacks reliable models and correlations to be exploited so as to efficiently support the design of internal combustion engines. The paper deals with the development of an accurate CFD model, capable of capturing the effects of the engine working conditions and mixture compositions on the combustion process. The CFD model is based on the Extended Coherent Flame Model combustion model coupled to a laminar flame speed one through a user subroutine, which replaces the commonly adopted empirical correlations. The flame speed values have been derived from the application of a reaction mechanism for natural gas-air-residual gases mixtures.In the second part of the paper, the model is validated and applied to the investigation of the dependence of the combustion quality on the fuel doping with hydrogen as well as on the mixture dilution with EGR. As a matter of fact, the attractiveness of the mixture dilution with EGR relies on the potential in containing engine-out NOx emissions as well as in reducing the pumping losses, thus further abating fuel consumption at part loads. Finally, the effects of fuel blending with H2 on the EGR tolerance is discussed in the paper.

Combustion simulations are the key aspect for full-scale 3D simulations of modern and perspective engines for aerospace propulsion. The present work studies an opportunity to solve chemical kinetics problems using artificial neural networks. Using classical numerical methods, sets of training data were produced. Choosing among different architectures of multi-layer neural networks and tuning their parameters, a rather simple model was worked out capable to solve this problem. The neural network obtained works in a recursive mode, and it can predict the behavior of chemical multi-species dynamical system by many steps. • The kinetic mechanism is resolved using the classical numerical method. • Training datasets received. • A neural network was selected and trained. • Recursive neural network can predict the behavior of chemical kinetics. [ABSTRACT FROM AUTHOR]

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.

Supercritical fluid technology finds its application in almost all engineering aspects in one or other way. Technology of clean jet fuel combustion is also seeing supercritical fluids as one of their contender in order to mitigate the challenges related to global warming and health issues occurred due to unwanted emissions which are found to be the by-products in conventional jet engine combustion. As jet fuel is a blend of hundred of hydrocarbons, thus estimation of chemical kinetics and emission characteristics while simulation become much complex. Advancement in supercritical jet fuel combustion technology demands reliable property statistics of jet fuel as a function temperature and pressure. Therefore, in the present work one jet fuel surrogate (n-dodecane) which has been recognized as the constituent of real jet fuel is studied and thermophysical properties of each is evaluated in the supercritical regime. Correlation has been developed for two transport properties namely density and viscosity at the critical pressure and over a wide range of temperatures (TC + 100 K). Further, to endorse the reliability of the developed correlation, two arithmetical parameters have been evaluated which illustrates an outstanding agreement between the data obtained from online NIST Web-Book and the developed correlation.

Chemical flame structures encountered in practical turbulent combustion chambers are complex because multiple regimes such as premixed, stratified or diffusion may coexist. Combustion modeling strategies based on single flame archetype fail to predict pollutant species, such as CO. To account for multiple combustion regimes, at a reduced computational cost, a novel approach based on virtual optimized mechanisms is developed. This method consists in (i) building a kinetic scheme from scratch instead of reducing a detailed mechanism, (ii) using a reduced number of virtual reactions and virtual species that do not represent real entities and (iii) using sub-mechanisms dedicated to the prediction of given flame quantities. In the present work, kinetic rate parameters of the virtual reactions and virtual species thermodynamic properties are optimized through a genetic algorithm to properly capture the flame temperature as well as CO formation in hydrocarbon/air flames. The virtual optimized chemistry approach is first applied to the derivation of methane/air reduced kinetic schemes. The flame solutions obtained with the virtual optimized mechanisms are subsequently compared to the reference flame library showing good predictive capabilities for both premixed and non-premixed flame archetypes. Analysis of the impact of the reference database demonstrated that the quality of CO formation modeling depends on the reference flame library used to train the optimized kinetic scheme. The virtual mechanisms are also tested on 2-D partially-premixed burners showing good agreement between the reference mechanism and the reduced virtual schemes. Finally, the virtual optimized chemistry approach is used to derive virtual optimized mechanisms for heavy fuels oxidation. [ABSTRACT FROM AUTHOR]

Abstract Resistance to extinction by stretch and and laminar flame speed are important properties of any combustible mixture. Recent work has shown that extinction by stretch controls the overall structure of several important types of methane-based turbulent flames. The parameter used to quantify this phenomena, Extinction Strain Rate (ESR), is numerically studied here for methane-based flames across a range of pressures relevant to gas turbines and internal combustion engines, 1-40 atm. The pressure trends are compared with those of laminar flame speed which is historically better studied. Current kinetic models agree that ESR of lean flames is a non-monotonic function of pressure and that ESR of rich flames increases significantly with pressure, but are found to differ significantly in their numerical predictions of ESR, particularly at higher pressures. To better identify the source of model prediction differences and what governs the overall accuracy of the ESR predictions, various model sensitivity analyses were conducted. Pressure-dependent kinetics are shown to be vital to determining ESR pressure trends as are molecular collision efficiencies. Yet, reactions sensitivities for ESR largely mirror those for laminar flame speed calculations. Sensitivity to the transport parameter, Lennard Jones diameter, significantly exceeds reaction sensitivities for the fuel, oxidizer and bath gas. Thermodynamic parameter ESR sensitivities vary widely with pressure, but at least for enthalpy, appear insignificant when uncertainties are considered. This study informs and motivates further efforts to understand the phenomena of flame extinction by stretch at elevated pressures. [ABSTRACT FROM AUTHOR]

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.

Highlights • Increased mixing tube diameter increases aeration throughout firing range. • Decreased injector diameter increases aeration for high firing rates. • Increased burner surface permeability increases aeration for high firing rates. • Increased combustion chamber height increases aeration for low firing rates. • Increased secondary aeration reduces primary aeration for low firing rates. Abstract This work is about a model for the prediction of air entrainment in a full-premix, atmospheric, ejector-pump like gas burner, used for domestic water heating purposes. The model improves current knowledge by including the effects of combustion and buoyancy on the air entrainment mechanism. It predicts the λ ratio (excess of air in relation to stoichiometry) as a function of ambient conditions, burner geometry, type of hydrocarbon fuel and burner firing rate. A prototype burner with variable geometry was assembled to calibrate and validate the model. The combustion products were analyzed with a CO 2 sensor and the λ ratio was compared with model's predictions, showing good accuracy. A parametric study was done to ascertain the impact of key geometric parameters on burner aeration. [ABSTRACT FROM AUTHOR]

Abstract The paper presents a detailed numerical investigation of a pulsed detonation combustor (PDC) coupled with a transonic axial turbine stage. The time-resolved numerical analysis includes detailed chemistry to replicate detonation combustion in a stoichiometric hydrogen–air mixture, and it is fully coupled with the turbine stage flow simulation. The PDC–turbine performance and flow variations are analyzed for different power input conditions, by varying the system purge fraction. Such analysis allows for the establishment of cycle averaged performance data and also to identify key unsteady gas dynamic interactions occurring in the system. The results obtained allow for a better insight on the source and effect of different loss mechanisms occurring in the coupled PDC–turbine system. One key aspect arises from the interaction between the non-stationary PDC outflow and the constant rotor blade speed. Such interaction results in pronounced variations of rotor incidence angle, penalizing the turbine efficiency and capability of generating a quasi-steady shaft torque. [ABSTRACT FROM AUTHOR]

The current political scenario and the concerns for global warming have pushed very harsh regulations on conventional propulsion systems based on the use of fossil fuels. New technologies are being promoted, but their current technological status needs further research and development for them to become a competitive substitute for the ever-present internal combustion engine. Hydrogen-fueled internal combustion engines have demonstrated the potential of being a fast way to reach full decarbonization of the transport sector, but they still have to face some limitations in terms of the operating range of the engine. For this reason, the present work evaluates the potential of reaching full load operation on a conventional diesel engine, assuming the minimum modifications required to make it work under H 2 combustion. This study shows the methodology through which the combustion model was developed and then used to evaluate a multi-cylinder engine representative of the medium to high duty transport sector. The evaluation included different strategies of dilution to control the combustion performance, and the results show that the utilization of EGR brings different benefits to engine operation in terms of efficiency improvement and emissions reduction. Nonetheless, the requisites defined for the needed turbocharging system are harsher than expected and result in a potential non-conventional technical solution. • A new methodology for numerical modeling of hydrogen combustion is proposed. • The boosting system requirements for hydrogen high load operation are identified. • The benefits and limitations of using EGR for combustion control are determined. [ABSTRACT FROM AUTHOR]

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.

An implementation and validation of the coupled Σ-Y ADF model is presented in this work for reacting Diesel spray CFD simulations under a RANS turbulence modeling approach. An Approximated Diffusion Flamelet (ADF) model Michel et al. (2008) implemented in the OpenFOAM CFD open-source library by Winklinger (2014) is fed with the spray description, i.e. mixing formation process, provided by the Σ-Y Eulerian atomization model Garcia-Oliver et al. (2013). In the present investigation, the Engine Combustion Network Spray A reference configuration is used for validation. Specifically, the model can provide accurate predictions of typical reacting spray metrics, such as the ignition delay and the lift-off length. Moreover, the internal structure is also fairly reproduced in terms of quasi-steady spatial distribution of formaldehyde and OH, related with low and high temperature reactions respectively. Additionally, modeling results have been compared to recent Particle image velocimetry (PIV) measurements Garcia-Oliver et al. (2017) under both inert and reacting conditions. Flow response to heat release is quantitatively predicted by the model, both in terms of local velocity increase as well as radial dilation. The model has been used to understand combustion-induced reduction in entrainment, in particular around the lift-off length location. Flow confinement does not seem to influence the global flame behaviour, even though some changes in the local flow hint can be observed when moving from an open to a closed domain. [ABSTRACT FROM AUTHOR]

A correlated dynamic adaptive chemistry and transport (CO-DACT) method is developed to accelerate numerical simulations with detailed chemistry and transport properties in a reactive flow. Different sets of phase parameters, which govern the transport properties and chemical reaction pathways, respectively, are proposed to identify the correlated groups for transport properties and reaction pathways in both temporal and spatial coordinates. The correlated transport properties and reduced chemical mechanisms in phase space are dynamically updated by different user-specified threshold values. For the calculation of detailed transport properties, the mixture-averaged diffusion model is employed. For the on-the-fly generation of reduced chemistry, the multi-generation path flux analysis (PFA) method is used. In the present method, the chemical reduction and transport properties calculation are only conducted once for all the computation cells in the same correlated group within the pre-specified thresholds. Therefore, without sacrificing accuracy within the range of uncertainty of mechanisms and transport properties, the CO-DACT method can eliminate all redundant chemistry reductions and transport properties calculations in temporal and spatial coordinates when the transport properties and chemical reaction pathways are correlated due to the similarities in phase space. The CO-DACT method is further integrated with the hybrid multi-timescale (HMTS) method to achieve efficient integration of chemistry. Simulations of outward propagating spherical premixed flames and one dimensional (1D) diffusion ignitions of a jet fuel surrogate mixture, as well as an unsteady spherical propagating diffusion flame of a DME/air mixture are conducted to validate the present algorithm. The impact of the selection of threshold values as well as the dependence of numerical errors on pressure and equivalent ratio are also examined. The results demonstrate that the CO-DACT method can increase the computation efficiency for transport properties by at least two-order of magnitudes. Moreover, it is robust, accurate, and improves the overall computation efficiency involving a large kinetic mechanism. [ABSTRACT FROM AUTHOR]

In the two limits of a well-mixed charge (e.g. homogenous charge compression ignition (HCCI)) engine, and a well separated charge (e.g. conventional diesel combustion (CDC)) engine, the CHEMKIN coupled computational fluid dynamics (CFD) codes approach has been proved to work well in predicting both chemistry controlled combustion and mixing controlled combustion. In this study, it is shown that for some certain cases in the new combustion mode reactivity controlled compression ignition (RCCI) engine where flame propagation exists, the CHEMKIN approach could fail to predict the combustion characteristics. To extend the capability of the existing CHEMKIN models in combustion, a flame propagation model (FPM) accounting for combustion in partially premixed mixtures was proposed and coupled into the CFD framework KIVA4. In this FPM model, the turbulent flame speed was calculated and the heat release and species conversion rate in the turbulent flame brush was solved with detailed chemical kinetics. A NOx sub-mechanism and a nine-step phenomenological soot model were also coupled into the current integrated model for emission prediction. This model was validated in 3 different dual-fuel experimental engines by comparing the in-cylinder pressure, heat release rate (HRR), NOx emissions and soot emissions. The CHEMKIN and CHEMKIN-FPM were also compared in terms of their capability of predicting the combustion in different combustion regimes. The results show that under certain operating conditions when CHEMKIN failed in the combustion prediction, the current CHEMKIN-FPM shows a superior ability in predicting combustion characteristics. [ABSTRACT FROM AUTHOR]

The ability of flamelet models to reproduce turbulent combustion in devices such as diesel engines or gas turbines has enhanced the usage of these approaches in Computational Fluid Dynamics (CFD) simulations. The models based on turbulent look-up tables generated from counterflow laminar diffusion flames (DF model) permit drastic reduction of the computational cost of the CFD calculation. Nevertheless, for complex molecular fuels, such as n-heptane, the oxidation process involves hundreds of species and the calculation of the transport equations together with the ODE system that models the chemical kinetics for the DF solution becomes unaffordable for industrial devices where hundreds of flamelets are required. In this context, new hypotheses have to be introduced in order to reduce the computational cost maintaining the coherence of the combustion process. Recently, a new model known as Approximated Diffusion Flamelet (ADF) has been proposed with the aim of solving the turbulent combustion for complex fuels in a reduced time. However, the validity of this model is still an open question and has to be verified in order to justify subsequent CFD calculations. This work assesses the ADF model and its ability to reproduce accurately the combustion process and its main parameters for three fuels with different chemical complexity and boundary conditions by its comparison with the DF model. Results show that although some discrepancies arise, the ADF model has the ability to correctly describe the ignition delay and the combustion structure in the auto-ignition zone that is the most relevant one for industrial processes. [ABSTRACT FROM AUTHOR]