The focus of this study is the effects of combustion dynamics, performance optimisation, and emissions profile on compression-ignition (diesel) engines operating on ternary fuel formulations comprising waste cooking oil (WCO), butanol, and conventional petroleum-based (diesel) fuel. The primary physicochemical characteristics of these WCO-butanol-diesel fuel formulations were almost identical to those observed with conventional petroleum-based diesel fuel, thereby conforming to both ASTM D6751 and EN 14214 specifications; these include density, kinematic viscosity, and flash point. The variation of the butanol concentration from 10% to 30%, as well as the volume fractional distribution of the WCO from 10% to 40% vol., resulted in significant differences in the thermodynamic response of the engine test rig used for this lab evaluation. Additionally, the D60B20Bu20 ternary blend produced a 7.14% increase in brake thermal efficiency (BTE), from pure (D100) diesel fuel to the D60B20Bu20 baseline; there was also a corresponding reduction in brake-specific fuel consumption (BSFC) by 6.84% and brake-specific energy consumption (BSEC) by 6.00%. The exhaust gas temperature (EGT) for the alcohol-treated ternary coatings was significantly lower than that of pure diesel due to the high latent heat of vaporisation of butanol. The application of advanced environmental profiles on these results demonstrated significant reductions in emissions of regulated pollutants. The most pronounced results were seen with the use of the D60B20Bu20 formulation, which resulted in the largest reduction in both carbon monoxide (CO) emissions (15.94%), and unburned hydrocarbons (HC) (22.85%), respectively; there was also a significant reduction in smoke opacity at high load operating conditions. In contrast to the above, there was an average overall improvement of 7.74% in thermal nitrogen oxide (NOx) emissions across all blends due to both the very high premixed heating phase and the availability of fuel-bound oxygen. Additionally, transient combustion data demonstrated that the D60B20Bu20 matrix achieved the highest peak in-cylinder pressure, maximum mean gas temperature and greatest sharpness of net heat release rate (HRR). This study provides preliminary evidence that a multi-criteria optimisation framework can be used to identify the best overall compromise between aggressively reducing carbonaceous emissions while achieving improved thermodynamic performance with the D60B20Bu20 blend.
One important issue that arises in the engineering application of reduced-order models of thermoacoustic instabilities is that of obtaining a comprehensive representation of the multidimensional combustion dynamics in the form of a flame describing functions (FDFs) that cover the whole domain of operation. By systematically measuring FDFs over this domain, it was recently shown that it was possible to delineate the unstable region of operation of an annular combustor. However, this instability prediction process requires a comprehensive sweep of operational conditions, which can be challenging and costly in practice. The present study explores the use of a supervised Machine Learning method, specifically Gaussian Process Regression (GPR), to predict FDFs from a subset of experimentally determined FDFs. It is shown that a small number of FDF datasets (typically 7 out of 35) can be used for training to predict the remaining datasets and forecast the FDF ensemble corresponding to the whole domain of operation. The method achieves high accuracy, with $ R<^>2 $ R2 scores exceeding 0.9 for the FDF gain and 0.99 for the phase. A physics-based reduced-order model is then employed to calculate instability growth rate maps, thereby inferring the domain of azimuthal instability of an annular combustor. Comparisons with measured FDFs across all operating points indicate that this method, which requires only a few measured FDFs, suitably determines the growth rates in the domain of operation of the system so that the uncertainties associated with FDF prediction by GPR only have a minor impact on the prediction of the instability domain layout.
In previous experiments, two characteristic regimes of upstream flame propagation were observed in model porous media. At the pore scale, the flame can exhibit oscillations similar to those of a flame with repetitive extinction and ignition (FREI-like oscillations) or demonstrate stepwise propagation. The conditions determining the emergence of one propagation regime or the other remained unknown. This work presents a parametric study investigating the influence of parameters on flame behaviour at the pore scale. A reduced 1D model and a detailed 2D model are used for numerical simulation. The results establish that the flame propagation regime is governed primarily by the inlet flow velocity and the maximum pore channel width. Thermophysical properties and pore arrangement have negligible impact on the emergence of oscillations. Despite its quantitative evaluation limitations, the 1D model successfully reproduces the overall flame behaviour at the pore scale and the regime diagram in good agreement with 2D simulations. Thus, the utility of the reduced 1D model for screening governing parameters in pore-scale combustion dynamics is demonstrated.
In the present paper, improvements of the linear forcing method in physical space are developed for both isotropic and anisotropic turbulence. The enhancements achieved are then applied to direct numerical simulations of premixed methane-air flame simulations with inflow-outflow conditions. The first enhancement introduces an individual forcing scheme for each momentum equation, which - unlike previous approaches - removes the constraint that the three space-averaged normal Reynolds stresses must increase or decrease synchronously in time. The second enhancement targets engineering applications involving premixed flames with inflow-outflow conditions. Here, the linear forcing scheme is extended for a flow in a divergent domain, enabling the flame to better stabilise within the computational domain. The third enhancement involves adapting the linear forcing scheme for anisotropic turbulence and applying it to a flame within the divergent domain. Results show that the proposed enhancements - each based on the numerical scheme suggested by Bassenne et al. (2016, Physics of Fluids 28, 035114) - are computationally inexpensive, requiring only 0.26% of the total CPU-time of the simulation. Moreover, it is demonstrated that for anisotropic turbulence, starting from isotropic conditions, the normal Reynolds stresses attain the prescribed levels rapidly, within just one characteristic time interval. When applied to the premixed flame setup, the proposed scheme is used only in the cold, near-inflow region of the domain, which continuously changes shape and size depending on the instantaneous flame position. Results show that with the proposed forcing scheme, the cold volume occupies only a small portion of the domain (11.5-18.0%), demonstrating its numerical efficiency. The consumption flame speed correlates well with the energy dissipation rate (correlation coefficient of 0.86) but negatively with the integral length scale (correlation coefficient of -0.70). Anisotropic turbulence of the same intensity reduces the consumption speed by a factor of 1.11.
The present study examines the combustion and emission properties of hydrogen-enriched Jet A-1 non-premixed flames for sustainable aviation applications. Despite hydrogen's potential for carbon-free combustion, logistical limitations hinder its prompt adoption in gas turbines. Adding hydrocarbon fuels with hydrogen offers a feasible way to improve combustion efficiency and reduce emissions. Experiments were conducted in a swirl-stabilised combustor at an intake temperature of 300 K, atmospheric pressure, and a thermal input of 20 kW. The addition of hydrogen, up to 30% by mass, was assessed throughout equivalence ratios (Phi) from 0.6 to 1.0. Adding hydrogen significantly enhanced the combustion efficiency of Jet A-1, reducing unburned hydrocarbons (UHC) and carbon monoxide (CO) emissions. At 30% H-2 and Phi=0.6, CO diminished to 20 ppm, while unburned hydrocarbons were nearly eradicated (<5 ppm). Elevated combustion temperatures at Phi similar to 1 augmented thermal NOx to 40-45 ppm with 30% H-2, but NOx diminished to 20-25 ppm under lean circumstances (Phi=0.6). Carbon emissions fell by 70%. CFD simulations employed the Reynolds Stress Model (RSM) and beta-PDF techniques, providing detailed insights into the distribution of the reaction zone and the characteristics of turbulent flow structures. There is considerable agreement between simulation results and experimental data for distributed reaction zones and swirling velocities. By demonstrating enhanced efficiency and emissions characteristics for environmentally friendly aviation applications, this work contributes to our understanding of hydrogen-enriched liquid-fuel combustion in gas turbine systems.
This study aims to reveal the detailed bubble development and bursting processes driven by the pyrolysis of plastic fuels. The evolution of a single bubble within the thermoplastic pool consists of two distinct stages: (I) the seed stage, characterised by a tiny constant-radius bubble, and (II) the subsequent growth stage, with a rapid increase in bubble volume. In the seed stage, a bubble core emerges with a fixed initial diameter due to the influence of the strong viscosity of the plastic melts. Simultaneously, the continuous release of gaseous volatiles through the pyrolysis process from the condensed phase results in a substantial pressure increase inside the bubble. After a critical development time, the volume of the bubble grows rapidly, whereas the pressure decreases to slow down growth. The numerical solution of bubble size agrees well with experimental measurements of an externally heated PMMA plate. With bubbles of 0.14-0.18 mm radius seen in a burning PMMA, the bursting jet velocity, estimated by Bernoulli's equation, is around 100-125 m/s. Such velocities could potentially cause the ejection of condensed fuel particles. This study offers a fundamental understanding of polymer bubbling and bursting dynamics and helps quantify the burning rate enhanced by fuel ejection during thermoplastic combustion.
Hydrogen is taking a pivotal role in our near-future energy economy. Because of this, many studies are investigating combustion properties of hydrogen, specifically with respect to its diffusive properties and the resulting thermodiffusive instabilities. In addition to preferential Fickian diffusion, the Soret or thermodiffusion plays an important role in hydrogen flames as well. While most studies are focussed on thermodiffusively unstable flames, either with or without Soret diffusion, this work conducts a systematic study of the impact of Soret diffusion on thermodiffusively stable flames. For rich hydrogen-air flames at atmospheric conditions, which exhibit thermodiffusively stable behaviour, including Soret diffusion generally decreases laminar unstrained flame speeds. This effect is strongest near stoichiomeric conditions and becomes weaker as the flames become richer. The opposite trend is found for strained flame speeds, where Markstein numbers generally increase at richer conditions, but the effect of Soret diffusion is weakest near stoichiometric conditions and largest for very rich flames. For a 2D perturbed rich hydrogen flame, the return to a planar shape at $ \phi =6 $ phi=6 is up to twice as fast when Soret diffusion is included, which also agrees approximately with results from linear stability analysis. The Soret diffusion flux counteracts the Fickian diffusion of the hydrogen species and creates locally leaner conditions in negatively curved parts of the flame, leading to higher heat release rates, and an enrichment of hydrogen towards the burnt gases in regions with positive curvature.
Droplet evaporation is a fundamental process in spray combustion systems, directly influencing ignition delay, combustion efficiency, and emissions. This study presents a comprehensive transient numerical model for single-component n-heptane droplet evaporation in a microgravity environment. The model captures spherically symmetric evaporation dynamics, solving conservation equations for mass, species, energy, and entropy in both gas and liquid phases under varying ambient conditions. Entropy generation, a key indicator of thermodynamic irreversibility, is incorporated to assess transport inefficiencies from heat and mass transfer. Model validation against experimental microgravity data shows good agreement in predicting the temporal evolution of droplet size and surface temperature, especially at higher ambient temperatures, with an average R-2 of 0.83 and an average RMSE of 0.08 across the tested conditions. Results reveal that entropy generation is dominated by heat transfer in the gaseous phase, while mass transfer plays an increasingly significant role over time. The findings highlight the complex interplay between heat conduction, latent heat removal, and species diffusion, providing a thermodynamic basis for improving evaporation modelling in combustion and energy-conversion systems. This study lays the groundwork for future integration of chemical kinetics and combustion into entropy-informed droplet modelling frameworks.
The next generations of gas turbines are expected to be 'fuel-flexible', i.e. to reliably operate with variable fuel mixtures ranging from pure carbon-free fuels such as ammonia and hydrogen to natural gas and blends thereof. These fuel mixtures pose challenges, however, due to variations in combustion dynamics that are primarily caused by the fuels' reactivities and their molecular transport properties. Multiple Mapping Conditioning (MMC) offers a cost-effective simulation approach that can account for realistic species transport and detailed chemical kinetics. Here, an improved mass-based particle mixing method is introduced that can capture differential diffusion by adapting the spatial displacement of the particles and effectuate mixing in different steps. This approach allows accurate modelling with a single particle cloud while maintaining a model-free closure for the chemical source term. The model is tested in a shear layer configuration with various fuel blends. The findings reveal that the new MMC mixing method improves the accuracy of species predictions when compared to standard MMC mixing approaches, and it offers an acceptable estimation of differential diffusion effects for a wide range of fuel blends.
This work provides a systematic evaluation of how different low-dimensional representations of the chemical state space affect the accuracy and computational performance of a latent variable (LV) framework for reactive flows. These latent representations are constructed using Principal Component Analysis (PCA) and Partial Least Squares (PLS) combined with orthogonal and oblique rotation strategies, trained using zero-dimensional reactors with the detailed AramcoMech 1.3 mechanism for CH4/air combustion, and tested on unseen conditions. Performance is assessed in terms of solution accuracy, computational speed-up and metrics describing the latent space and system dynamics. Results show that while all latent-basis configurations achieve computational savings, their numerical behaviour strongly depends on the structure of the latent space. PCA-derived bases provide the lowest reconstruction errors but only a moderate speed-up. Unrotated PLS bases exhibit strong sensitivity to the latent dimension, whereas orthogonal rotations restore stability across compression levels. Among all techniques, the PLS-Quartimax combination provides the best overall performance, yielding smooth source-term manifolds and latent variables explicitly aligned with the fast chemical time scales due to the PLS formulation. This alignment enables larger stable time steps and results in the highest computational speed-up. These findings demonstrate that appropriate combinations of dimensionality-reduction techniques and rotation strategies significantly improve the numerical behaviour of the LV solver, enabling faster chemistry integration while preserving the full thermochemical state.
Modern combustion systems increasingly operate under challenging conditions, including Moderate and Intense Low Oxygen Dilution (MILD) combustion, high Flue Gas Recirculation (FGR), and hydrogen-rich fuels, driven by stringent emission reduction requirements. Traditional chemistry reduction methods, such as the Flamelet Generated Manifold (FGM), rely on restrictive assumptions about flame structure and fail under these emerging operating conditions where classical flame fronts become ill-defined or entirely absent. This work presents a novel Computational Singular Perturbation (CSP)-inspired chemistry reduction framework that addresses these limitations. The methodology employs analysis of chemical timescales to identify dominant species followed by a realtime homogeneous correction method designed to compute the complete chemical species profile from the reduced species basis and local flow conditions. This strategic adaptive retention of chemical information creates an intermediate level of detail based on first principles, eliminating concerns about user fine-tuning and the definitions and tabulation of basis flamelets, while achieving significant computational savings. To further enhance efficiency, machine learning acceleration through deep neural networks is explored as an alternative implementation pathway. Validation of the framework through one-dimensional freely propagating flames demonstrates exceptional accuracy, with flame temperature prediction error of 0.08% and flame speed prediction error of 0.24% using only a third of the participating species. Two-dimensional axisymmetric combustor simulations show significant improvements in flame position and structure prediction compared to detailed chemistry simulations with the Eddy Dissipation Concept (EDC). These results are highly encouraging and warrant first-party implementations of the proposed algorithm with further investigation into more complex industrial reactive flows.
This study outlines and discusses model reduction problems. The concept of Singularly Perturbed Vector Fields (SPVF) is revisited as a framework for handling the decomposition of motions and, in particular, for addressing the fundamental problem of initial-data consistency in reduced models. This issue arises because the initial conditions generally do not lie on the slow invariant manifold that approximates the reduced system. To address this, this study demonstrates how the method of Intrinsic Low-Dimensional Manifolds (ILDMs) can be employed both to approximate the slow manifold and to generate consistent initial data. Two classical benchmark examples in model reduction theory are examined to verify and discuss the approach. The Lindemann mechanism is used to illustrate how SPVF theory reconciles the standard asymptotic limits and thereby captures both pressure dependence and variations in reaction order. The Michaelis-Menten model serves as a key example where standard reduction approaches - such as QSSA and PEA - may fail under certain asymptotic conditions. The SPVF framework provides a systematic way to understand these limitations by clarifying the asymptotic structure and its parameter dependencies. Meanwhile the ILDM method not only offers an efficient way to approximate the slow manifold but also supplies consistent initial data projection procedure, ensuring a physically and mathematically sound reduced system.
This study presents an analysis of the transition-zone in adjacent high explosive (HE) detonation problems which uses a $ D, \kappa , \dot{D} $ D,kappa,D-center dot relationship, where $ D $ D is the detonation front-normal velocity, $ \kappa $ kappa is the detonation front curvature and $ \dot{D} $ D-center dot is the time derivative of detonation front-normal velocity. Our approach extends the traditional ( $ D, \kappa ) $ D,kappa) model to accurately predict the behaviour of both diverging and converging detonation shock fronts. Our findings affirm that a hyperbolic type of front evolution equation, enhanced with wave acceleration, provides a robust framework for modelling complex shock front dynamics in HE materials. This approach not only captures the natural effects of straightness and boundary slope jumps in the transition-zone but also bridges the gap between mathematical predictions and experimental observations, offering insights into the behaviour of both diverging and converging detonation propagations in a homogeneous HE.
The need to transition towards renewable energy sources has led to the use of fuels like biogas in internal combustion (IC) engines. In the present investigation, the combustion of an SI engine is experimentally investigated for various ignition advances (IA) and further evaluated by genetic algorithm (GA). As experimentation takes much time and effort, a soft computing technique is used to predict the cylinder pressure variations for various ranges of IA. The in-cylinder pressure is predicted from the GA model for the IA range of 33 degrees to 47 degrees bTDC and further compared with the data obtained from the SI engine at a compression ratio (CR) of 10:1. Subsequently, a mathematical model is also obtained to establish a mathematical relation between the cylinder pressure variations at each crank angle for various values of IA. The predicted peak cylinder pressure obtained using the genetic algorithm and the mathematical model shows high accuracy (higher than 90% and 94% respectively). The average correlation coefficient for the predicted cylinder pressure using both techniques is found to be 0.99. This can reduce the need to conduct repeated experiments for the biogas-fuelled SI engine used in this study.
A major concern of elementary-step surface reaction mechanisms is the large number of rate parameters to be determined, which however cannot be chosen independently. The resulting surface reactions mechanism must fulfil thermodynamic consistency to yield meaningful results. Here, we present an algorithm implemented in the software $ \mathrm {DETCHEM<^>{ADJUST}} $ DETCHEMADJUST in full detail. The proposed thermodynamic data is then applied to derive a mathematical scheme to describe the steady-state surface coverages. The scheme delivers an explanation for the occurrence of multiple solutions when calculating steady-state surface coverages for given temperature and gas-phase concentration. It offers a new path for numerical modelling of the steady-state behaviour of catalytic reactors such as emission control devices.
Local sensitivity coefficients and parameter uncertainties are commonly used to guide the selection of parameters in the optimisation of combustion kinetic models. In this study, we proposed two additional factors that influence parameter importance and can be utilised for active parameter selection in optimisation workflows: the experimental uncertainty and the deviation between experimental and simulated results. Based on these factors, we proposed four novel impact measures for parameter ranking. These were benchmarked against two commonly used methods by constructing hierarchical optimisation strategies of a methanol/NOx mechanism involving ten parameters and a large experimental dataset. Results showed that measures which did not incorporate parameter uncertainty information led to the steepest initial reduction in the error function. However, over the full course of optimisation, methods considering uncertainty achieved better overall performance, despite following similar improvement trends. Among all evaluated strategies, one of the newly introduced measures - based on error function derivatives and parameter uncertainty information - demonstrated the most consistent performance in reducing posterior uncertainties.
Accurately and efficiently modelling combustion remains a central challenge in the transition to low-carbon energy systems. Virtual chemistry has emerged as a powerful chemistry reduction strategy, relying on optimised species and reactions to reproduce reference flame behaviours at a fraction of the computational cost. However, previous formulations depend on tabulated parameters, limiting their extrapolation capabilities and integration into standard reactive flow solvers. To address these limitations, a new standardised virtual chemistry formalism has been introduced, bridging virtual and detailed chemistry through chemical equilibrium between real and virtual species. The present work extends the framework to accurately capture laminar flame structures by jointly optimising the thermodynamic, kinetic and transport properties. Special attention is given to validating and generalising virtual mechanism structures, with the development of a novel architecture dedicated to pyrolysing fuels and multi-component fuel surrogates. The resulting highly-reduced optimised mechanisms adopt architectures compatible with CHEMKIN and CANTERA solvers. The methodology is applied to representative decarbonised and conventional fuels: Hydrogen, Dodecane, which is a key component of Sustainable Aviation Fuels (SAFs), a multi-component SAF surrogate and a Jet A multi-component surrogate. Particular emphasis is placed on reproducing flame structures across a wide range of combustion regimes, including premixed and non-premixed, adiabatic and non-adiabatic conditions. The results demonstrate the ability of the proposed virtual mechanisms to replicate reference flame structures with drastically reduced complexity, chemical stiffness and CPU time, paving the way for straightforward and efficient high-fidelity combustion simulations.
This study investigates the sensitivity of consumption and displacement speeds of laminar flames to stretch oscillations, focusing on both negative and positive Markstein (Ma) number flames. Joulin's theory (1994), based on constant density and thermal diffusivity, is extended to include different definitions of local consumption and displacement speeds. The theory is validated through detailed numerical simulations of oscillating counter-flow flames using lean hydrogen/air (Ma < 0) and stoichiometric iso-octane/air (Ma > 0) mixtures. An innovative procedure is proposed to compute Markstein lengths from two sets of direct numerical simulations (DNS): one considering only tangential strain rate oscillations and the other involving both strain and curvature oscillations. The results demonstrate good agreement between theory and simulations in terms of high-frequency asymptotic Markstein numbers. It is confirmed that the sensitivity to strain rate vanishes at high frequencies, whereas sensitivity to curvature remains significant, scaling with fuel diffusivity, except for the consumption speed defined as the line integral of the reaction rate normal to the flame. Two additional definitions of consumption speed, which take into account the variation of flame surface area due to curvature, reveal an extra sensitivity at high oscillation frequencies due to geometric effects related to the choice of the reference surface. This leads to different curvature Markstein numbers across all frequencies. It is also found that relaxation rates of Markstein numbers vary significantly between fuels. However, intermediate-frequency discrepancies between theory and DNS, particularly for iso-octane flames, highlight limitations of the model, such as assumptions of constant density and diffusivity and chemistry depending only on the deficient species. This work thus provides crucial insights for the development of turbulent combustion models accounting for flame response time to turbulent stretch, while it shows the need for future studies to incorporate variable density and diffusivity effects through the flame front to improve accuracy of model predictions.
Dual-fuel combustion, where fuels of different reactivity are injected in the combustor separately in time or space, is important for some practical applications. This study employs Large Eddy Simulation (LES) coupled with the Doubly-Conditioned Moment Closure (DCMC) model to investigate dual-fuel flames, where liquid n-heptane ( $ \ce {C7H16} $ C7H16) is injected into a lean premixed methane/air ( $ \ce {CH4} $ CH4/air) swirling stream. Two premixed equivalence ratios, $ \phi _{{\rm pmx}} = 0.14 $ phi pmx=0.14 and 0.56, are studied, representing conditions below and above the lower flammability limit (LFL) of methane. Cold flow simulations and comparison with experimental velocity data suggest that the flow field is reasonably well predicted. Combustion simulations then explore the impact of $ \phi _{{\rm pmx}} $ phi pmx on flame structure, with analysis performed in both physical space and conditional scalar space of DCMC. The results show good agreement with experimental observations in terms of flame shape and reaction zone location. The findings underscore the capability of LES-DCMC to capture key features of complex, multi-modal dual-fuel combustion systems.
The development of computationally efficient kinetic mechanisms for alternative fuels remains a critical bottleneck for large-scale CFD simulations in engine design. This work presents a novel integrated data-driven workflow that automates kinetic mechanism development by coupling chemical lumping, skeletal reduction, and parameter optimisation within a unified framework, demonstrated through a compact OME2 combustion mechanism. Using the SciExpeM data ecosystem, the workflow automatically manages mechanism construction, reduction, and optimisation with minimal manual intervention. The approach treats aggressive skeletal reduction as the foundation for two-stage optimisation, where temporary accuracy loss is systematically recovered through targeted parameter adjustment within physically consistent uncertainty bounds. The integrated workflow achieved a decrease in the number of species from 150 to 55 using DRGEP-based reduction, followed by evolutionary parameter optimisation through OptiSMOKE++. Comprehensive validation against experimental data spanning ignition delay times, jet-stirred reactor speciation, and laminar flame speeds demonstrated reliability across operating conditions relevant to compression ignition engines (650-1700 K, 1-50 atm, phi = 0.3-2.0). The optimised mechanism successfully recovered the accuracy lost during reduction, particularly in the critical intermediate temperature regime (770-910 K). The integrated workflow further improved the traditional size-accuracy trade-off through systematic parameter recalibration, achieving computational efficiency for CFD applications while maintaining chemical fidelity comparable to detailed mechanisms. This methodology establishes a foundation for rapid development of compact kinetic mechanisms for alternative fuels with automated workflows ensuring physical consistency.
