Carbon-neutral (CN) fuels will be part of the solution to reducing global warming effects of the transportation sector, along with electrification. CN fuels such as hydrogen, ammonia, biofuels, and e-fuels can play a primary role in some segments (aviation, shipping, heavy-duty road vehicles) and a secondary role in others (light-duty road vehicles). The composition and properties of these fuels vary substantially from existing fossil fuels. Fuel effects on performance and emissions are complex, especially when these fuels are blended with fossil fuels.Predictively modeling the combustion of these fuels in engine and combustor CFD simulations requires accurate representation of the fuel blends. We discuss a methodology for matching the targeted fuel properties of specific CN fuels, using a blend of surrogate fuel components, to form a fuel model that can accurately capture fuel effects in an engine simulation. Fuel components are drawn from a database of surrogates, the Ansys Model Fuel Library (MFL) [1], for this purpose. The database has 73 surrogate components, including n-alkane, iso-alkane, naphthene, aromatic, alkene, iso-alkene, alcohol, ether, cyclic ether, methyl ester, ketone and acid chemical classes, in addition to hydrogen, CO and ammonia. This wide range of components makes it possible to assemble fuel models for hydrogen, ammonia, biofuels, e-fuels, existing fossil-fuels, and any blends thereof. The database of surrogate components includes kinetics derived from self-consistent rate rules that capture combustion behavior, including autoignition, flame propagation and emissions of soot, NOx, CO and unburned hydrocarbons (UHC). We include details of representative validation studies for the kinetics of individual components and some blends, comparing to fundamental experiments. Accompanying software tools for targeted mechanism reduction make the chemistry applicable for engineering CFD simulations. The accurate representation of fuel properties and kinetics of CN fuels from this database facilitates predictive engine simulations, toward the optimization of both fuels and engines.

The altitude relight capability of an aero-engine is a critical requirement that defines the operational flight envelope of the engine. Regulatory requirements from the Federal Aviation Administration (FAA) and European Union Aviation Safety Agency (EASA) ask to establish the altitude and airspeed envelope for in-flight engine restarting and adherence to engine performance. Further, engine manufacturers are changing combustor designs to meet aggressive goals that limit the emission of nitrogen oxides (NOx). While these design changes help reduce the NOx formation, they can be problematic for restart capabilities at high altitudes. Therefore, the engine design process becomes a complex optimization problem with conflicting goals. Test-rig data can provide insights into the performance; however, using testing to explore the entire design space is challenging, expensive, and sometimes infeasible. In this scenario, high fidelity computational fluid dynamics (CFD) simulations can bridge this gap and are, therefore, widely evaluated by designers and simulation engineers. Such simulations need to resolve flow structures, spray distribution, and ignition processes to predict the high-altitude relight accurately. Moreover, no, or limited parameter adjustments should be required for correctly predicting the relight outcome across different operating conditions. In this work, numerical simulations are performed to predict an aviation gas-turbine combustor's relight performance, operating under different conditions, including sea level and 40000 ft operation. The CFD simulations are performed using the unsteady RANS approach for turbulence, solution-adaptive meshing, and finite-rate kinetics for the combustion modeling that tracks the flame propagation during and after the spark event. The results are encouraging and predict accurate behavior of lighting and not lighting operating conditions consistent with the light/no-light outcomes from the experimental tests. The simulation methodology, best practices, and obtained results are discussed in this paper.

Controlling light-around and re-light presents design challenges for gas-turbine manufacturers. Researchers have studied the detailed phenomena in laboratory experiments to elucidate controlling factors and modes of behavior. Several groups have reported high-fidelity simulations of the fluid dynamics, turbulent mixing and light-around phenomena using large eddy simulations (LES) on highly refined computational meshes. While such simulations can reproduce experimental observations, they are computationally expensive and tend to be impractical for routine design analyses. In this work, we present a less computationally intensive CFD approach, which has been tested against laboratory experiments using both gaseous-fuel injections and liquid-fuel injections. Results show that a consistent practice of mesh and model settings can be used for all the test cases considered. The simulations generate light-around sequences and total-ignition times that agree well with experimental measurements. Observed trends are predicted when varying burner spacing as well as the fuel and injection method.

In this paper, the turbulent jet diffusion flame stabilized behind a bluff body (HM1) is simulated using the Flamelet Generated Manifold (FGM) model. Interactions between turbulence and chemistry are detailed in this paper. In HM1 flame, ignition mainly occurs in the outer shear layer while mixing effects dominate in the recirculation zone. Turbulence is modeled by using variants of two-equation Reynolds Averaged Navier Stokes (RANS) models (steady and unsteady RANS), whilst turbulence-chemistry closure is based on FGM approach. Results are compared with experimental data to validate the dynamics and spatial structure of bluff-body flames. Different approaches based on the variants of steady RANS and unsteady RANS are compared for three mesh resolutions. Definitive advantages and disadvantages of each approach are identified on the basis of computational cost and accuracy. The results provide important insights into the simulation of bluff-body flames approaching the blow-off limit.

With advanced gas turbine combustor and internal combustion engine designs, autoignition can happen alongside flame propagation. Laminar flame speeds are required to model flame propagation. Determining laminar flame speeds using simulation assumes flames are freely propagating, an assumption that is not valid when autoignition does occur. From a CFD modeling viewpoint however, it is useful to have extrapolated laminar flame-speed values over a wide range of conditions, to allow CFD to operate smoothly and avoid discontinuities while calculating flame-propagation properties. In this work we focus on developing an approach for generating laminar flame-speed libraries under both nonigniting and autoigniting conditions. Following a test of whether autoignition occurs, laminar flame speeds are either modeled or extrapolated. The details of the approach implemented and its validation are explained. We assess the accuracy of the extrapolation employed by calculating relevant coefficients based on flame speeds from nearby operating points. Recommendations are made for the time scales to be used in determining autoignition occurrence. Fuel effects are also explored in this context.

With incremental High-Performance Computing (HPC) scalabiliOr and performance improvements, hybrid Reynolds Averaged Navier Stokes (RANS)/Large Eddy Simulation (LES) have become popularfor modeling reactiveflow configurations. Hybrid RANS/LES methods like Stress-Blended Eddy Simulation (SBES) are presented in this paper and have been applied to model near-wall flows using two-equation k-omega RANS formulation while switching to LES in the separatedflow region using a blendingfunction. Turbulent combustion in a rearwardfacing step is modeled by using Flamelet Generated Manifold (FGM) with SBES turbulence. A turbulent premixed propane/air flame (phi=0. 57) is stabilized in the turbulent mixing layer (ReH = 22, 100)formed at the rearward-facing step. The results obtained show a good agreement with the experimental data for velocity, temperature and species mass fractions at various locations in the channel.

The Sydney piloted premixed jet burner (PPJB) experiments are numerically simulated to assess the flamelet generated manifold (FGM) model's ability to predict finite-rate and turbulence/chemistry-interaction effects under low Damköhler number.The results are also compared and assessed with the finite rate eddy-dissipation concept (EDC) model.A reduced CH 4 /air mechanism of 71 species, that considers low-and hightemperature chemistry, is derived from the Model Fuel Library (MFL) and compared with the master MFL mechanism.The same mechanism is used for chemistry closure for both FGM and EDC models.The PPJB simulations are found to be sensitive to the inflow profiles and different power-law profiles are adopted for different centerline jet bulk velocity setups.An overall good match with the experimental data is observed for the non-reactive flow cases, with general under-prediction for the turbulent kinetic energy (TKE).For the PM150 flame conditions presented here, the FGM showed reasonable prediction of the temperature and major species, with under-prediction for OH and over-prediction

Hybrid turbulence modeling is a practical approach to efficiently model the wall-bounded turbulentflows. In this paper, a stress-blended eddy simulation (SBES) model is used with the flamelet generated manifold model (FGM) for modeling turbulent combustion. In the current SBES, the near-wall region is modeled using a two-equation k-omega Reynolds-averaged Navier-Strokes (RANS) formulation, and switches to a large eddy simulation (LES) model in the core region using a blending function. Similarly, the turbulence-related combustion modeling parameters, such as the variances in scalar transport equations and scalar dissipation, are also blended using the same blending function. This combined hybrid FGM-SBES approach is implemented into ANSYS Fluent software and then used to model a swirl-stabilized flame. The flame used is a methane-fueled burner, developed at DLR Stuttgart as the PRECCINSTA combustor. The experimental data for this combustor are available for multiple operating conditions. A stable operating point (phi=0.83, P=30 kW) is chosen. The current FGM-SBES results are compared with experimental data as well as with FGM-LES computations. Differences in predictions of mean and variance of reaction progress and mixture fraction in the core versus the near wall region are analyzed and quantified. The impact of the differences in these parameters is then evaluated by comparing temperature and species mass fractions. The findings from the current work, in terms of accuracy, validity and best practices when modeling wall-bounded flows with FGM-SBES are discussed and summarized.

Combustion models can have a significant impact on flame simulations. While solving finite rate chemistry typically yields more accurate predictions, they depend significantly on the detailed kinetics mechanism used. To demonstrate the effect, Large Eddy Simulation (LES) of Sandia Flame D [1] has been performed using various combustion models. Four different detailed kinetics mechanisms have been considered. They include DRM mechanism with 22 species, GRI-mech 2.11 with 49 species, GRI-mech 3.0 with 53 species [2], and Model Fuel Library (MFL) mechanism with 29 species [3]. In addition to the mechanisms, two modeling approaches considered are direct integration of finite rate kinetics (FR) and Flamelet Generated Manifold (FGM). The performance is compared between combinations of the mechanisms and combustion-modeling approaches for prediction of the flame structure and pollutants, including NO and CO. The mesh contains about half a million hexahedral cells and LES statistics were collected over ten flow throughs. Advanced solvers including dynamic cell clustering using the Chemkin-CFD solver in Fluent have been used for faster simulation time. Based on comparison of simulation results to the measurements at various axial and radial positions, we find that the results using the FGM approach were comparable to those using direct integration of FR chemistry, except for NO. In general, the simulation results are in good agreement with the experiment in terms of aerodynamics, mixture fraction and temperature profiles. However, kinetics mechanisms were found to have the most pronounced effect on emissions predictions. NO was especially more sensitive to the kinetics mechanism. Both versions of the GRI-mech fell short in predicting emissions. Overall, the MFL mechanism was found to yield the closest match with the data for flame structure, CO, and NO.

We have developed a surrogate blending methodology to identify surrogates with a desired degree of complexity. Along with estimation methods for various physical and chemical properties for fuel blends, we have assembled and developed a rich library of over 60 fuel components. The components cover a carbon number range from 1 to 20, and chemical classes including linear and branched alkanes, olefins, aromatics with one and two rings, alcohols, esters, and ethers. With these, surrogates can be formulated to represent most gasoline, diesel, gaseous fuels, renewable fuels, and several additives. As part of the library, we have assembled self-consistent and detailed reaction mechanisms for all the components, as well as for emissions including NOx and polycyclic aromatic hydrocarbons and a detailed soot-surface mechanism. An extensive validation suite has been used to improve the kinetics database such that good predictions and agreement to data are achieved for the fuel components and fuel-component blends, within experimental uncertainties. This effectively eliminates the need to tune specific rate parameters when employing the kinetics mechanisms in combustion simulations. For engine simulations, the master mechanisms have been reduced using a combination of available reduction methods while strictly controlling the error tolerances for targeted predictions. This approach has resulted in small mechanisms for efficiently incorporating the validated kinetics into computational fluid dynamics (CFD) applications. The surrogate formulation methodology, the comprehensive fuel library, and mechanism reduction strategies suggested in this work allow the use of CFD to explore design concepts and fuel effects in engines with reliable predictions.

In this work, a scale separation method has been proposed and implemented in the framework of Flamelet Generated Manifold (FGM) model. In this approach, first a list of slow evolving species like NO, N2O etc., are identified. Then, a separate transport equation for each of these species (called FGM scalars) is solved in addition to the mixture fraction and progress variable equations. The forward and reverse reaction rates of these slow forming species are computed in two-dimensional FGM flamelets and pre-tabulated as a function of progress variable, mixture fraction and their respective variances. At run time, the pre-tabulated probability density function (PDF) averaged production rates of these FGM scalars are used, while their tabulated reverse rates are modified with a linear scaling based on the ratio of tabulated values of the FGM scalar and the prevailing values of the FGM scalars from three dimensional CFD solution. This mechanism allows the reverse rates to provide continuous feedback and respond to the slow evolution of scalar. Other than the list of selected scalars, all other species and temperature are still computed as a function of the main progress variable and mixture fraction. Since, a small set of scalars can be used to track key species, this methodology remains computationally efficient. The current approach has been implemented into commercial CFD solver, ANSYS Fluent, and has been validated for two lab scale turbulent flames, the first one is Sandia Flame D, while the second one is a lifted turbulent methane flame in vitiated co-flow. In the current work, two additional FGM scalar transport equations are solved for CO and NO and comparisons have been made against the tabulated values as well as the experimental data. It has been seen that the scale separation methodology of these scalars leads ∼10–15% improvements in the CO mass fraction, while it reduces the peak NO formation up to 4 times leading to better agreement with experimental data compared to tabulated values. The quality of predictions from the current method is also evaluated against finite rate chemistry-based model as well as reduced order NO model. It is found that the current model has consistent results, and is an improvement over current reduced order modeling approach.

A continued challenge to engine combustion simulation is predicting the impact of fuel-composition variability on performance and emissions. Diesel fuel properties, such as cetane number, aromatic content and volatility, significantly impact combustion phasing and emissions. Capturing such fuel property effects is critical to predictive engine combustion modeling. In this work, we focus on accurately modeling diesel fuel effects on combustion and emissions. Engine modeling is performed with 3D CFD using multi-component fuel models, and detailed chemical kinetics. Diesel FACE fuels (Fuels for Advanced Combustion Engines) have been considered in this study as representative of street fuel variability. The CFD modeling simulates experiments performed at Oak Ridge National Laboratory (ORNL) [1] using the diesel FACE fuels in a light-duty single-cylinder direct-injection engine. These ORNL experiments evaluated fuel effects on combustion phasing and emissions. The actual FACE fuels are used directly in engine experiments while surrogate-fuel blends that are tailored to represent the FACE fuels are used in the modeling. The 3D CFD simulations include spray dynamics and turbulent mixing. We first establish a methodology to define a model fuel that captures diesel fuel property effects. Such a model should be practically useful in terms of acceptable computational turnaround time in engine CFD simulations, even as we use sophisticated fuel surrogates and detailed chemistry. Towards these goals, multi-component fuel surrogates have been developed for several FACE fuels, where the associated kinetics mechanisms are available in a model-fuels database. A surrogate blending technique has been employed to generate the multi-component surrogates, so that they match selected FACE fuel properties such as cetane number, chemical classes such as aromatics content, T50 and T90 distillation points, lower heating value and H/C molar ratio. Starting from a well validated comprehensive gas-phase chemistry, an automated method has been used for extracting a reduced chemistry that satisfies desired accuracy and is reasonable for use in CFD. Results show the level of modeling necessary to capture fuel-property trends under these widely varying engine conditions.

A dvanced research in Spark-ignition (SI) engines has been focused on dilute-combustion concepts.For example, exhaust-gas recirculation is used to lower both fuel consumption and pollutant emissions while maintaining or enhancing engine performance, durability and reliability.These advancements achieve higher engine efficiency but may deteriorate combustion stability.One symptom of instability is a large cycle-to-cycle variation (CCV) in the in-cylinder flow and combustion metrics.Large-eddy simulation (LES) is a computational fluid dynamics (CFD) method that may be used to quantify CCV through numerical prediction of the turbulent flow and combustion processes in the engine over many engine cycles.In this study, we focus on evaluating the capability of LES to predict the in-cylinder flows and gas exchange processes in a motored SI engine installed with a transparent combustion chamber (TCC), comparing with recently published data.Numerical simulations are performed using the commercial CFD software, ANSYS Forte, employing a classical Smagorinsky sub-grid-scale (SGS) model for the LES approach.Two important aspects of the model, namely the coefficient of sub-grid viscosity used in the Smagorinsky model, and the numerical scheme for discretizing the convection term in the momentum transport equation, are evaluated.Simulations are performed for 20 consecutive engine cycles after the simulation setup is validated by the predicted in-cylinder pressure, trapped mass, and temperature data.LES-predicted phase-averaged-mean and root-meansquare (RMS) velocity fields are compared with high-speed particle image velocimetry (PIV) data.The comparison and analysis are performed at two crank angles, representing intake and compression strokes, and on two different planes for measurement in the engine combustion chamber.A proper orthogonal decomposition (POD) technique is applied to quantify CCV in both the LES results and the PIV data, to provide a quantitative assessment of the predictions from LES.The flow field statistics predicted by the LES-Smagorinsky model match well with experimental results.Based on these simulation results, optimal practices for the use of Smagorinsky model with respect to the numerical schemes are summarized.