# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 6 Turbulent Combustion Simulation Approach
### CFD with Different Combustion Models
#### 6.2.2 Combustion Models with Detailed Mechanisms
Even though the CMC model has several advantages in the field of NOx prediction, the CMC model's calculation is enormous when there are complex chemical reactions, and the series expansion would introduce errors in calculating the time-average reaction rate. This method for turbulent premixed flames depends on the precision of the conditional scalar dissipation-rate model [92]. Finally, the FLM model was applied to the combustion models. The thermodynamic and chemical parameters are mapped to several control variables under the flame-let hypothesis, and a flame-let lookup table for turbulent combustion simulation is constructed based on the PDF function. The thermodynamic and chemical parameters of the flow field are obtained by solving the control variable in the flow field and looking up the table. This method realizes the decoupling of turbulence and chemical reaction, greatly reduces the degree of freedom in flow field solution, simplifies the complexity of combustion simulation, and can consider complex chemical-reaction mechanisms. [PERSON] et al. [93] applied two different flame-let libraries with GRI-Mech 2.2 that were generated by one-dimensional unstretched premixed flame and the one-dimensional counterflow diffusion flame, respectively, to predict NOx emissions in Sandia Flame D. To improve the accuracy of NOx emission prediction, two additional variables of mixture fraction variance and enthalpy loss were added. The mixture fraction used for the presumed probability density function integration, and the enthalpy loss took into account the non-adiabatic effect. Compared with the experimental data, the results applying the counter-flow diffusion flame library model to simulate over-estimate 4.2 times at the location of the maximum error occurred; as a comparison, the unstretched premixed flame library only over-estimated by 0.5 times. [PERSON] et al. [94] used the steady-diffusion flame-let (SDF) model with detailed mechanisms to predict the generation of NOx; however, this method is not very suitable for the prediction of NOx emissions. This is because, in most examples, the mass percentage of NOx is overestimated by this method. This is because the elementary reactions involved in the production of NOx are relatively slow. Therefore, using the FLM model will cause the results to be too large. However, the prediction of the overall trend of NOx production is accurate. Above all, the combustion models with detailed mechanisms would increase the predict accuracy and also increase the calculation cost. Several references are listed to exhibit the CFD method in the field of NOx generation in the Table 1. The table shows that the CFD method could evaluate NOx emissions in different combustion chambers. The CFD method could predict the tendency of NOx emissions successfully [95], but the deviation in prediction NOx would approach around 10-20%, even though, in some specific cases, the deviation would be approximately 6%. Moreover, the species of the fuel commonly are methane, natural gas, or hydrogen, which has limited quantities of reactants and reaction mechanisms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Through the above review, it could be found that CFD as a prediction of NOx-generation calculation methods contained many pathways. Although each of these methods has its advantages, they still have the same shortcomings in some respects:  
First, the number of reactants involved in the production of NOx is relatively small in most examples. In most of the examples, it is considered that the formation of NOx has no strong influence on the combustion process. Therefore, with a few exceptions, only a limited number of compound concentrations are used in most of the examples to represent the thermal mechanism, prompt mechanism, and N\({}_{2}\)O mechanism on the formation of NOx.