# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 4 Statistical Overview
A numerical summary of these relations is shown in Figure 9.  
The data indicates that gasoline and hydrogen are the fuels for which the widest range of modeling tools were tested. In the case of hydrogen, AI methods (ANN, SVM) dominated, while for gasoline, almost all techniques appeared, from classical (regression, PCA) to optimization (GA) and classification (RF, SVM, decision trees). With regard to research approaches, the vast majority of publications--89 out of 97, i.e., as much as 91.8%--were experimental. The largest number of such studies concerned gasoline (28), hydrogen (16), natural gas (16), and coal (14), reflecting their industrial importance and the availability of research equipment. Concept work was the second most represented category--70 cases, with the largest share in research on gasoline (25), natural gas (14), and coal (10). Literature reviews appeared mainly in the context of hydrogen (6), biomass (5), and gasoline (6). This data is summarized in Figure 10, which illustrates the frequency of use of each type of approach for the fuel tested. The information in Figures 9 and 10 is derived from the aggregate data presented in Table 4, which lists in detail the number of publications using specific methods for each fuel type and the methodological breakdown. According to the chi-square test, the value of \(\chi^{2}\) = 54.69 at 40 degrees of freedom was obtained for the \"Methods and Algorithms\" group, which translates to \(p\) = 0.06. This result is close to the conventional significance threshold (\(\alpha\) = 0.05), which may suggest the existence of a relationship between the choice of method and the type of fuel. In particular, it can be noted that ANNs and SVM dominated with hydrogen, while GAs and CFD dominated with gasoline and natural gas. For the \"Research Methodology\" group, the value of \(p\) = 0.95 at 10 degrees of freedom indicates a lack of statistically significant relationships regardless of the type of fuel. These conclusions indicate that although calculation methods are often chosen depending on the specificity of the fuel, the type of testing itself does not depend on this factor and is universal in nature. Particular attention is drawn to the high number of experiments and the growing popularity of neural networks and genetic algorithms as the dominant methods in the analysis of fuel combustion. Summing up the results of the quantitative analysis, a statistical assessment of the relationship between the type of fuel and two research aspects was carried out: the selection of calculation methods (ML/GAs) and the choice of methodological approach. The results obtained allow for unambiguous answers to the formulated statistical questions. The fourth question concerned the potential relationship between the type of fuel and the calculation method used. The chi-square test performed for this group (\(\chi^{2}=54.69\); df = 40) yielded a value of \(p\) = 0.06, which is just below the conventional significance threshold (\(\alpha\) = 0.05). Although this does not allow for full confirmation of a statistically significant relationship, this result indicates a clear thematic trend, with a preference for specific methods observed in the analysis of specific fuels. For example, SVM and ANN algorithms were more commonly used in hydrogen research, while CFD and genetic algorithms dominated in gasoline and natural gas combustion analyses. This suggests that the choice of calculation method largely depends on the characteristics of the fuel and the specifics of the problem being analyzed. The fifth question concerned the relationship between the type of fuel and the choice of research approach (experiment, concept, literature analysis). The result of the chi-square test (\(\chi^{2}=3.86\); df = 10; \(p=0.95\)) clearly showed no statistically significant relationship between these variables. Regardless of the type of fuel studied, experimental studies were by far the dominant approach, present in over 90% of publications.