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Article

Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance

by
Bingfeng Huang
1,
Wei Hong
1,
Kun Shao
2 and
Heng Wu
3,*
1
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130052, China
2
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China
3
Weichai Holding Group Co., Weifang 261000, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11107; https://doi.org/10.3390/su162411107
Submission received: 8 November 2024 / Revised: 11 December 2024 / Accepted: 15 December 2024 / Published: 18 December 2024
(This article belongs to the Special Issue Technology Applications in Sustainable Energy and Power Engineering)
Browse Figures
Figure 1
<p>Schematic diagram of the engine stand.</p> "> Figure 2
<p>Schematic digamma of SVM.</p> "> Figure 3
<p>Comparison of training and test data for NOx and CO in SVMs.</p> "> Figure 4
<p>Input parameter impact weights for the ID.</p> "> Figure 5
<p>HC impact weighting analysis.</p> "> Figure 6
<p>Weighting of the impact of each parameter on the indicator parameter.</p> ">
Versions Notes

Abstract

:
With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis for the optimal design and operation of engines to promote the sustainable development of the transportation industry. The performance of an engine is essentially determined by the combustion process, which in turn depends on the fuel characteristics and the work cycle mode suitability of the technical architecture of the engine itself (oil-engine synergy). Currently, there is a lack of theoretical support and means of reference for the sensitivity analysis of the core parameters of oil–engine synergy. Recognizing the problems of unclear methods of defining sensitivity parameters, unclear influence mechanisms, and imperfect model construction, this paper proposes an evaluation method system composed of oil–engine synergistic sensitivity factor determination and quantitative analysis of contribution. The system contains characteristic data acquisition, model construction and research, and sensitivity analysis and application. In this paper, a hierarchical SVM regression model is constructed, with fuel physicochemical characteristics and engine control parameters as input variables, combustion process parameters as an intermediate layer, and diesel engine performance as output parameters. After substituting the characteristic data into the model, the following results were obtained, R2 > 0.9, MSE < 0.014, MAPE < 3.5%, indicating the model has high accuracy. On this basis, a sensitivity analysis was performed using the Sobol sensitivity analysis algorithm. It was concluded that the load parameters had the highest influence on the ID (ignition delay time), combustion duration (CD), and combustion temperature parameters of the combustion elements, reaching 0.24 and above. The influence weight of the main spray strategy was greater than that of the pre-injection strategy. For the sensitivity analysis of the premix ratio, the injection timing, EGR (exhaust gas recirculation) rate, and load have significant influence weights on the premix ratio, while the influence weights of the other parameters are not more than 0.10. In addition, the combustion temperature among the combustion elements has the highest influence weights on the NOx, PM (particulate matter) concentration, and mass, as well as on the BTE (brake thermal efficiency) and BSFC (brake specific fuel consumption). The ID has the highest influence weight on HC and CO at 0.35. Analysis of the influence weights of the index parameters shows that the influence weights of the fuel physicochemical parameters are much lower than those of the engine control parameters, and the influence weights of the fuel CN (cetane number) are about 5% greater than those of the volatility, which is about 3%. From the analysis of the proportion of index parameters, the engine control parameter influence weights are in the following order: load > EGR > injection timing > injection pressure > pre-injection timing> pre-injection ratio.

1. Introduction

With the increasing severity of the global energy crisis and environmental pollution, the sustainable development of engine technology has become a hot research topic. The influence of fuel physical and chemical properties on engine performance is a complex and critical issue while optimizing engine control parameters is an important means of improving engine performance and reducing emissions. This study aims to explore the sensitivity of fuel physicochemical properties and engine control parameters in depth through systematic experiments and theoretical analysis to provide theoretical support and practical guidance for the sustainable development of engine technology. The results of this study will help to promote the progress of engine technology, reduce environmental pollution, improve energy utilization efficiency, and contribute to sustainable development. With the advancement and development of internal combustion engine intelligence, machine learning is widely used to achieve good results. The so-called machine learning utilizes the machine to learn the way human beings think about problems to solve problems in reality. Experimental data or previous experience are used to train the computer, using pre-training methods to recognize the objective test, learn the test’s prediction, and use its mathematical model to learn. Machine learning includes neural networks, SVM, and genetic algorithms [1].
SVM has been used in the field of diesel engine research due to its accuracy and ability to analyze nonlinear problems. Niu investigated SVM for response prediction in a common rail direct-injection system-assisted marine diesel engine. The study aims to identify better prediction methods based on a small amount of training data. The results show that SVM can find the global optimal solution with excellent prediction accuracy and generalization ability under limited experimental data. Meanwhile, the artificial neural network (ANN) may converge to a local minimum and face overfitting problems. [2]. Kiran explored the applicability of SVM regression to relate biodiesel composition to engine characteristics. Two additional biodiesels not used for calibration validated the developed model with an average absolute percentage error of less than 7%. In addition, the model was fed into a genetic algorithm to determine the optimal composition to reduce BSFC as well as HC, CO, and NOx emissions. The tests showed that the optimized biodiesel contained 8% short-chain esters and 92% long-chain esters. The BSFC of sunflower biodiesel was 23% higher than that of diesel, while the BSFC of optimized biodiesel was 4% higher than that of diesel. The HC and CO emissions of the proposed biodiesel are 42% and 2% lower than diesel, respectively, and much lower than other biodiesels. The NOx emission of optimized biodiesel is 42% higher than diesel, while the NOx emission of sunflower biodiesel is 83% higher than diesel [3]. A series of engine calibration tests were conducted on a hydrogen-rich Wankel rotary engine. After recording the required experimental data, a multi-objective regression model was constructed by applying three methods, quadratic polynomials, ANN, and SVM, which provided a unique insight into the mathematical relationship between engine performance and operating and control parameters. In the case of ANN, the effect of the number of nodes in the hidden layer on the regression performance is discussed, and the weights of the ANN are optimized using a genetic algorithm. For SVM, the effects of kernel function and three optimization methods on regression performance are discussed. The results show that the SVM fits best among the three methods. The best R2 for BTE, fuel energy flow, NOx, CO, and HC are 0.9877, 0.9840, 0.9949, 0.9937, and 0.9992, respectively [4]. Ma performed a series of performance predictions for a hydrogen-rich natural gas engine. The compared SVM methods provided higher fitting accuracy and performed better in nonlinear system regression. In further research on engine performance prediction, the optimal SVM model can accurately describe the relationship between engine characteristics and operating parameters by using an intelligent optimization algorithm [5]. In the study of NOx emission prediction using an intelligent algorithm coupled with an SVM regression model, the values of R2 and MSE can be appropriately reduced, and the model can accurately describe the relationship between engine control parameters and performance [6,7].
In order to quantify the specific degree of influence of the input parameters of an engine system on the output parameters, various global sensitivity analysis methods have been introduced and utilized in the engine field [8]. For example, the Sobol method, which is one of the sensitivity analysis methods, was first proposed by I.M. Sobol in the early twenty-first century, and its mathematical modeling process was analyzed in detail [9]. In addition, professionals such as Abbas Sharifi have also utilized various sensitivity analysis methods, including Sobol, to explore the NOx emissions from diesel engines with reliable simulation modules and found that Sobol’s method has a wide range of applicability to linear or nonlinear models [10]. Yuanjiang Pei et al. have recently explored the application of global sensitivity analysis (GSA) to the simulation of diesel engine systems. They selected multiple topics, such as ignition delays and combustion phases, to conduct a more comprehensive evaluation and obtain the expected results. GSA is, indeed, capable of providing solid objects with quantitative information to ensure that the constructed model can be used for the simulation [11]. GSA is also capable of providing quantitative information on solid objects, which, in turn, ensures a high level of accuracy and reliability of the constructed models. In addition, Bertrand Iooss and Paul Lemaître give an overview of the development of GSA, describing the types of sensitivity analysis methods and their scope of application. The Sobol method based on the variance decomposition is well adapted to low-order nonlinear systems with less than tens of variables [12]. Gülmez Y investigates the relationship between exhaust gas back pressure and diesel engine performance indicator parameters. A neural network model was generated for the study to determine the relationship between the input variables and the performance indicators. The results of the study showed that engine back pressure is a key parameter that affects volumetric efficiency and fuel consumption [13]. Nikzadfar K investigated the effect of engine operating parameters on torque, soot, NOx, and BSFC using the Sobol method. A neural network was used for the modeling, which resulted in a comparison of experimental data with simulation results and found an output prediction error of about 6%. The engine performance and emissions were then analyzed using graphical and statistical methods to investigate how different input parameters affect engine emissions and performance. Finally, the relative importance of each parameter on the performance and emission characteristics of the different engines was investigated using the perturbation method, and the parameters that had the greatest impact on the different outputs were derived [14]. Ma K studied the dynamic behavior analysis of the torsional vibration of an engine crankshaft model and considered several nonlinear factors. A closed-loop self-excited coupled oscillation model for nonlinear crankshafts was developed. Based on the comparison with experiments to verify the correctness of the model, an improved Newmark-beta integral method with better convergence was proposed. On this basis, sensitivity analysis used the Sobol method, and dynamic bifurcation characteristics were explored through a series of numerical simulations. As a result, many "jump" phenomena were found, and appropriate parameter ranges were given based on the bifurcation diagrams [15].
In recent years, more scholars have begun to pay attention to how to better match the fuel’s physicochemical characteristics with the engine’s control parameters. This can effectively reduce exhaust gas pollution and improve the engine’s efficiency. Most of the studies have focused on the quantitative study of the fineness of combustion efficiency and the prediction of performance, but few have explored the weighting relationships among the variables. As a multi-input and multi-output system, it is necessary to conduct an in-depth study on the effects of oil–engine synergy on diesel engine performance, model construction and optimization, and sensitivity analysis, given the increasing complexity of the system, as well as the fact that oil–engine synergy, machine learning methods, intelligent optimization algorithms, and sensitivity analysis have their respective strengths and limitations in the study of diesel engine performance and optimization. Such a study aims to meet the increasingly complex research and optimization needs of diesel engine systems and to provide new methods for the application and optimization of more advanced technologies for diesel engines. Therefore, this paper adopts experimental means to study the influence of fuel physical and chemical properties coupled with engine control parameters on diesel engine combustion and emission and establishes a numerical simulation model based on the characteristic data that correlates the performance of diesel engines with the parameters of fuel physical and chemical properties and engine control parameters and carries out the parameter optimization to improve the accuracy. Then, the coupled sensitivity analysis algorithm reveals the correlation and quantitative sensitivity of different layers, which provides theoretical guidance for realizing efficient and clean combustion in diesel engines.

2. Experimental Parts

2.1. Engine Test Stand

This comprehensive testing arrangement primarily utilizes a water-cooled, turbocharged, four-cylinder diesel powertrain with a displacement volume of 2.771 L, designed to deliver an impressive power output of 82 kilowatts while maintaining a compression ratio of 17.2. During the test, the engine working conditions can be calibrated in real time by connecting the ECU through the INCA system. The selection of injection parameters and working conditions can be adjusted through the ECU to meet the test requirements.
The primary fixtures of the bench are an Eddy Current Dynamometer designated as CW440 (branded by Nanfeng Machinery Factory, Shaoxing, Zhejiang Province, China) that serves a pivotal role in data acquisition, a combustion analyzing tool known as AVL-6260 (AVL Corporation, Graz, Austria), an apparatus for precise assessment of fuel consumption through the ToCeiL-CMFD010. The design scheme of our engine test bench can be discerned from Figure 1. Table 1 lists the critical equipment essential to the operation of this extensive system and the uncertainty analysis.
The selected test conditions are shown in Table 1. In this study, all experiments were conducted under isothermal conditions to ensure that the ambient temperature in the laboratory was stabilized at 298 K to simulate standard room temperature conditions. To enhance the reliability of the experimental data and the significance of the statistical analyses, multiple rounds of replicated experiments, each containing three independent measurement sequences, were executed to minimize random errors and to ensure the convergence and reproducibility of the experimental results. The Phantom v611 high-speed camera from AMETEK, Edison, NJ, USA, was mainly used in the experiment to capture the spray development process and process it with software to derive the spray parameters. The resolution of the camera is 600 × 800, and the exposure time is 240 s.

2.2. Test Fuel and Test Methods

These eight different fuels were obtained from the Petrochemical Research Institute of China Petroleum and Chemical Corporation. An authorized and authoritative third-party agency adheres to strict industry standards and follows a carefully developed methodology during the testing process. At the end of the test, a comprehensive report detailing the test results is generated, as shown in Table 2. Among them, #1, #2, and #3 represent the three different volatile fuels, but the cetane numbers of the three volatile fuels are similar. The test process needs to control the cooling water temperature and oil temperature at about 80 °C and 90 °C, respectively, with an error of no more than ±5 °C, and the engine intake temperature is controlled at 25 ± 2 °C. The engine speed is 2200 r/min, and engine loads were 25%, 50%, and 75% of the full load (BMEP = 0.267, 0.535, 0.802 MPa). The injection timing, injection pressure, load, EGR, and pre-injection timing were fixed to study the effect of different pre-injection ratios (5, 10, 15, and 20%) on the combustion and emission characteristics of different diesel fuels. By analogy, the effects of EGR (0, 10, 20, and 30%), pre-injection timing (−30, −35, −40, and −45 °CA ATDC), injection pressure (1260, 1360, 1460, and 1660 bar), and injection timing (−14.5, −12.5, −10.5, −8.5, and −6.5 °CA ATDC) parameters have been investigated.

3. Data Processing Methods

3.1. Modeling Based on SVM

SVM is a supervised learning method developed by Vapnik based on statistical learning theory, which is based on the principle of structural risk minimization of statistical learning theory, which is achieved by combining the principle of structural risk minimization with the SVM model to minimize the upper bound of the error and has good generalization ability. SVM has now been successfully applied in many fields, such as regression analysis prediction, classification, pattern recognition, etc. SVM was initially used to solve classification problems, and by introducing relaxation variables, SVM was enabled to solve regression problems. As shown in Figure 2, by introducing a suitable kernel function, SVM transforms a low-dimensional nonlinear problem into a high-dimensional linear problem. For the current explorative study, we have opted for the radial basis function (RBF) as the kernel function [16], optimizing the mathematical formulation presented in Equation (1). The suitability of RBF in representing complex data relationships ensures efficient resolution of even intricate nonlinear problems.
K ( x i ⋅ x ) = exp ⁡ − γ ⋅ x i − x j 2 , γ > 0
where xi and xj denote the feature vectors and γ denotes the parameters inside the kernel function.
In SVM, C and γ have an important impact on the accuracy of the model, while usually, ε can meet the requirements using the default value; in this paper, we choose the grid method for optimization [17] due to its ease of use, the common normalization methods include [−1, 1] and [0, 1], and there is no fixed standard. The processing method of data normalization is shown below:
y = y m a x − y m i n × x − x m i n x m a x − x m i n + y m i n
where x is the original data, y is the normalized data, and the subscripts min and max are the minimum and maximum values of the data, respectively.
In the realm of mathematical modeling and analysis, there exists a crucial concept known as R2, which succinctly encapsulates the strength of the impact exercised by the input variables on the output variables. As part of such models, mean squared error (MSE) serves as an indispensable metric. It assesses the divergence between the projected value and the actual figure, thus providing vital insight into the accuracy of the proposed model. When the mean absolute percentage error (MAPE) approaches zero, it signifies that the model operates at its most effective level. Conversely, values exceeding one hundred percent denote a substandard performance. Stated differently, models with MAPEs close to 0 exhibit optimal predictive precision, while those beyond 100% are inferior [18], and the way each of them is defined is shown below:
R 2 = n ∑ i = 1 n y ^ i y − ∑ i = 1 n y ^ i ∑ i = 1 n y i 2 n ∑ i = 1 n y ^ i 2 − ∑ i = 1 n y ^ i 2 ⋅ n ∑ i = 1 n y i 2 − ∑ i = 1 n y i 2
M S E = ∑ i = 1 n y ^ i − y i 2
M A P E = 1 n ∑ i = 1 n y ^ i − y i y i × 100 %
where the n, y ^ i , yi are the number of samples, i predicted output values, and i target true values, respectively (i = 1, 2, …, n).

3.2. Sobol’s Sensitivity Analysis Methods

The Sobol method is a global sensitivity analysis method, which is based on variance decomposition and can deal with nonlinear and nonmonotonic problems when the input features of the model are less than tens. Different from the local sensitivity analysis method, the Sobol method considers the interaction between different features and can calculate the first-order effect index and total effect index between any features. The theoretical calculation process is shown below [19]. Decomposing the function into a sum of functions of increasing dimension,
f X 1 , … , X d = f 0 + ∑ i = 1 d f i X i + ∑ i = 1 d ∑ j = i + 1 d f i j X i , X j + ⋯ + f 1 , … , d X 1 , … , X d
where X = (X1, X2, …, Xd) denotes the d dimensions of the input parameters.
The total variance of f(X) consists of the cumulative variance of each term in Equation (6), fi (Xi)is the effect of changing alone, called the first−order effect of Xi, and f(i,j) (Xi,Xj) is the effect of changing both Xi and Xj at the same time after excluding the effect of their own changes, called the second-order interaction and the higher-order terms have similar definitions, and the final expression for the variance is shown below.
Y = ∑ i = 1 d V i + ∑ i < j d V i , j + ⋯ + V 1 , … d
First-order index (S):
S i = V i V Y
The first-order index is a measure of the contribution to the output variance of the individual parameter Xi. For Si = 0, the factor Xi has no effect on the output of the model, and for Si > 0, the factor has a definite effect on the output of the model.
The total-effect index (ST):
S T i = E X ~ i V X i Y X ~ i V Y = 1 − V X ~ i E X i Y X ~ i V Y
The total order index is a measure of the sum of contribution to the model output variance of the individual parameter Xi and the impact on the model output variance of the interaction between parameters Xi and X~i. For STi = 0, the factor Xi has no effect on the output of the model, and for S(Ti) ≅ 0, the factor has no considerable effect in its range of uncertainty of the output variance Y [20].

4. Results and Analysis

4.1. Modeling Results

The acquisition of feature data was conducted at 2200 rpm with four load (25%, 50%, 75%, and 100%) operating conditions. The existing dataset is brought into the program, which is divided into two parts; the first part is the data of the test using the algorithm to randomly select 80% of the test data for training and 20% of the test data for testing, and the second part is to calculate the influence weights using the Sobol global sensitivity. The first layer input parameters are fuel physicochemical properties and engine control parameters, while the middle layer outputs are ID, CD, combustion temperature, and premix ratio, and the third output layer parameters are BTE, BSFC, and emission outputs such as HC, CO, NOx, and particulate matter. Figure 3 shows the predicted fit of the NOx and CO in the SVM model. Table 3 and Table 4 show the valuation parameters of combustion parameters and emissions, BTE, BSFC, etc., in the SVM algorithm. The SVM model accuracy was: R2 > 0.9, MSE < 0.014, and MAPE < 3.5%, which is a relatively high precision model to calculate the accurate sensitivity.

4.2. Sensitivity Analysis

The accuracy of the model was verified in the previous section. In this section, with the help of the Sobol algorithm model, the sensitivity of fuel physicochemical properties and engine control parameters to the combustion parameters of the intermediate layer, as well as the further output of the index parameters through the combustion parameters of the intermediate layer, are investigated to explore the sensitivity analysis of the intermediate layer combustion parameters to emission, BTE, and BSFC. Finally, the sensitivity analysis of the fuel physicochemical properties and engine control parameters to the emission, BTE, and BSFC are further summarized. Figure 4 shows the sensitivity analysis of input parameters on ID, where input variables 1 to 8 are injection pressure, injection timing, pre-injection ratio, pre-injection timing, EGR, load, CN, and volatility. Where S is the weight of the effect of a single parameter on the ID, and ST is the weight of the effect of this parameter coupled to other parameters on the ID. In order to verify the accuracy of the model, the acquisition of feature data was completed at 1600 rpm with four loads (25%, 50%, 75%, and 100%). In this case, the input and output parameters were kept the same as in the 2200 rpm condition. Among them, the input parameters and output parameters have a better fit in the 1600 rpm condition. It can be concluded that R2 > 0.94, MSE < 0.015, and MAPE < 3.4%. It can be concluded that the accuracy of the established SVM model is reliable. Table 5 shows the sensitivity analysis of input parameters to combustion parameters at different speeds. It can be concluded that the sensitivity analysis constructed in this paper is usable and can be generalized. In Table 5, S is the weight of the effect of a single parameter on the combustion parameters, and ST is the weight of the effect of this parameter coupled with other parameters on the combustion parameters.
Table 5 shows that for the ID and CD, as well as the combustion temperature, load has the highest influence on weight, which is 0.24 and above. EGR influence weight is maintained at about 0.20. Injection time is the next highest; the influence weight is maintained at 0.15 and above. Next, the influence weight of injection pressure on ID is higher than the influence weight of pre-injection strategy, as is the influence weight of ST. For example, the analysis of the influence of the input factors for the ID can be obtained as follows, the increase in the load [21], that is, the increase of the injection volume per cycle, the increase in the oil volume in the cylinder, and the improvement in the thermal atmosphere in the cylinder can significantly affect the ID [22]. Higher injection pressures into the combustion chamber shorten the ID because high injection pressures refine the size of the fuel droplets supplied to the cylinder [23]. This not only improves fuel atomization but also indirectly affects lag time by creating a larger premix before combustion begins. When the pre-injection ratio is increased, the total mass of the combustible mixture formed in the cylinder increases, resulting in faster heat release and heating [24]. For the pre-injection timing, increasing the pre-injection before the main injection can result in better quality in the cylinder, which can affect the length of the ID [25]. The influence weight of fuel physicochemical properties is lower than that of engine control parameters on combustion elements [26]. In addition, for the sensitivity analysis of premix proportion, it can be seen that only parameters 2, 5, and 6 have significant influence weights on premix proportion, while the other five parameters, with influence weights lower than 0.10, have low sensitivities. Figure 5 shows the influence of weight analysis on HC. Where S is the weight of the effect of a single parameter on the HC, and ST is the weight of the effect of this parameter coupled with other parameters on the HC. Table 6 shows the influence of weight analysis of combustion elements on indicators, where parameters 9 to 12 are ID, CD, combustion temperature, and combustion temperature, respectively.
From Table 6, it can be concluded that the combustion temperature has the highest influence weight on NOx, the number of PM concentrations, and the total mass concentration of PM and BTE, BSFC, where the combustion temperature has an influence weight of 0.36 on NOx and BTE. The main conditions for the production of NOx are oxygen enrichment and a higher combustion temperature [27], and the calculated weight of the combustion temperature on NOx reaches 0.36, which also directly proves that the combustion temperature plays a decisive factor in NOx generation [28]. The level of BTE depends on the calorific value of the fuel and the combustion temperature, and the weight of the combustion temperature on BTE is calculated to be 0.37, and the increase in combustion temperature provides sufficient conditions for fuel atomization and combustion, which results in a drastic change in BTE [29]. For PM, the increase in combustion temperature improves several collisions between particles, forming particles with larger particle sizes, and the mass of PM increases with it. In addition, the increase in combustion temperature is conducive to the oxidation of PM and a reduction in emissions [30]. The highest influence weight for HC and CO is the ID factor, which has an influence weight of more than 0.35. This is because the increase in the ID causes the fuel and air in the cylinder to be fully mixed [31], and the combustion rate is accelerated; the shortening of the ID causes the fuel–air mixing quality in the cylinder to be lower and the combustion to not be homogeneous [32]. Figure 6 shows the influence weight of each parameter on the index. Using SVM coupled with the Sobol sensitivity algorithm model, the influence weights of the input layer to the intermediate layer and then to the output layer were calculated separately so that the influence weights of the fuel physicochemical properties and engine control parameters on the index parameters can be clarified [33]. In the figure, input variables 1 to 8 are injection pressure, injection timing, pre-injection ratio, pre-injection timing, EGR, load, CN, and volatility. Taking HC as an example, variables 1 through 8 have influence weights of 3.00%, 5.35%, 20.55%, 8.40%, 25.45%, 8.20%, 17.65%, and 11.40% on HC, respectively. It can be seen that the influence weight of the fuel’s physical and chemical characteristics of the parameters is about 8%, which is far lower than the engine control parameters, which is about 92% of the influence weight. The fuel physical and chemical characteristics of the parameters in the cetane value influence weight are about 5% greater than the distillation temperature of about 3% influence weight. The engine control parameters from the index parameter ratio analysis, as a whole, influence the weight in the following order: load > EGR > injection timing > injection pressure > pre-injection timing > pre-injection ratio.

5. Conclusions

This study is dedicated to analyzing the sensitivity of fuel physicochemical properties and engine control parameters to the synergistic efficiency of oil engines, aiming to provide a scientific basis for enhancing the performance of oil engines and promoting their environmental friendliness. Optimizing the fuel physicochemical characteristics and engine control parameters helps to improve the energy conversion efficiency of the oil engine and reduce energy waste. Second, the sensitivity analysis points out effective ways to reduce oil engine emissions and improve fuel economy, which is crucial for reducing greenhouse gases and other harmful emissions, helping to protect the environment, and meeting the environmental goal of sustainable development. The findings of this study provide theoretical support for the research and development of cleaner fuels and advanced engine technologies, which are key to achieving the energy mix transition and reducing dependence on fossil fuels. In this paper, experimental data are further mined by using SVM coupled with the Sobol sensitivity analysis model. The corresponding models were established, and the results of the algorithm were analyzed, and the conclusions obtained are as follows:
(1)
A hierarchical SVM regression model is developed in this paper for the problem of oil–engine synergy performance improvement. Bringing in the feature data yields R2 > 0.9, MSE < 0.014, and MAPE < 3.5%, indicating that the model has high accuracy. On this basis, a sensitivity analysis was performed in conjunction with the Sobol sensitivity analysis algorithm. The method, which can effectively establish the correlation and sensitivity between parameters and performance under the condition of a small amount of characteristic data, provides a reference and basis for parameter selection and program optimization.
(2)
This paper proposes to take the diesel engine mixture formation and combustion process as the entry point to characterize the performance and construct a feature data matrix. The matrix covers physical and chemical properties characterizing fuel ignition, volatility, and engine control parameters. The modeling results show that there is a strong correlation and sensitivity between the constructed feature data matrix and all the performance indicators.
(3)
In this paper, the model validation is carried out for the same type of engine with different rotational speeds, and the results show a high degree of consistency between the experimental data patterns and sensitivities shown in the experiments and the data patterns and sensitivities predicted by the model, which fully proves that the model established in this paper is usable and can generalize.

Author Contributions

B.H.: methodology, software, writing—original manuscript. W.H., K.S.: conceptualization, writing—reviewing and editing. H.W.: visualization. All authors commented on the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

Author Heng Wu was employed by the company Weichai Holding Group Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Najafi, G.; Ghobadian, B.; Moosavian, A.; Yusaf, T.; Mamat, R.; Kettner, M.; Azmi, W.H. SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels. Appl. Therm. Eng 2016, 95, 186–203. [Google Scholar]
  2. Niu, X.; Yang, C.; Wang, H.; Wang, Y. Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine. Appl. Therm. Eng. 2017, 111, 1353–1364. [Google Scholar] [CrossRef]
  3. Bukkarapu, K.R.; Krishnasamy, A. Support vector regression approach to optimize the biodiesel composition for improved engine performance and lower exhaust emissions. Fuel 2023, 348, 128604. [Google Scholar]
  4. Shi, C.; Ji, C.; Wang, H.; Wang, S.; Yang, J.; Ge, Y. Comparative evaluation of intelligent regression algorithms for performance and emissions prediction of a hydrogen-enriched Wankel engine. Fuel 2021, 290, 120005. [Google Scholar]
  5. Huang, Y.; Ma, F. Intelligent regression algorithm study based on performance and NOx emission experimental data of a hydrogen enriched natural gas engine. Int. J. Engine Res. 2016, 41, 11308–11320. [Google Scholar]
  6. Duan, H.; Huang, Y.; Mehra, R.K.; Song, P.; Ma, F. Study on influencing factors of prediction accuracy of support vector machine (SVM) model for NOx emission of a hydrogen enriched compressed natural gas engine. Fuel 2018, 234, 954–964. [Google Scholar]
  7. Hao, D.; Mehra, R.K.; Luo, S.; Nie, Z.; Ren, X.; Fanhua, M. Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition. Int. J. Engine Res. 2020, 45, 5309–5325. [Google Scholar] [CrossRef]
  8. Ebert, N.; Goos, J.C.; Kirschbaum, F.; Yildiz, E.; Koch, T. Methods of sensitivity analysis in model-based calibration. Automot. Engine Technol. 2020, 5, 45–56. [Google Scholar]
  9. Sobol, I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 2001, 55, 271–280. [Google Scholar]
  10. Sharifi, A.; Ahmadi, M.; Badfar, H.; Hosseini, M. Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine. Environ. Sci. Pollut. Res 2019, 26, 25190–25207. [Google Scholar]
  11. Pei, Y.; Shan, R.; Som, S.; Lu, T.; Longman, D.; Davis, M.J. Global Sensitivity Analysis of a Diesel Engine Simulation with Multi-Target Functions; SAE Technical Paper; SAE International: Warrendale, PA, USA, 2014. [Google Scholar]
  12. Iooss, B.; Lemaître, P. A review on global sensitivity analysis methods. In Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications; Springer: Berlin/Heidelberg, Germany, 2015; pp. 101–122. [Google Scholar]
  13. Gülmez, Y.; Özmen, G. Effect of exhaust backpressure on performance of a diesel engine: Neural network based sensitivity analysis. Int. J. Automot. Technol. 2022, 23, 215–223. [Google Scholar] [CrossRef]
  14. Nikzadfar, K.; Shamekhi, A.H. Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network. Fuel 2014, 125, 116–128. [Google Scholar] [CrossRef]
  15. Ma, K.; Du, J.; Liu, Y. Nonlinear dynamic behavior analysis of closed-loop self-excited crankshaft model using improved Newmark-β method. Nonlinear Dyn. 2023, 111, 5107–5124. [Google Scholar] [CrossRef]
  16. Navid, A.; Khalilarya, S.; Abbasi, M. Diesel engine optimization with multi-objective performance characteristics by non-evolutionary Nelder-Mead algorithm: Sobol sequence and Latin hypercube sampling methods comparison in DoE process. Fuel 2018, 228, 349–367. [Google Scholar] [CrossRef]
  17. De Carvalho, R.N.; Machado, G.B.; Colaço, M.J. Estimating gasoline performance in internal combustion engines with simulation metamodels. Fuel 2017, 193, 230–240. [Google Scholar] [CrossRef]
  18. Zhang, Y.; Wang, Q.; Chen, X.; Yan, Y.; Yang, R.; Liu, Z.; Fu, J. The prediction of spark-ignition engine performance and emissions based on the SVR algorithm. Processes 2022, 10, 312. [Google Scholar] [CrossRef]
  19. Ly, H.B.; Le, L.M.; Duong, H.T.; Nguyen, T.C.; Pham, T.A.; Le, T.T.; Le, V.M.; Nguyen-Ngoc, L.; Pham, B.T. Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Appl. Sci. 2019, 9, 2258. [Google Scholar] [CrossRef]
  20. Gordon, D.; Norouzi, A.; Blomeyer, G.; Bedei, J.; Aliramezani, M.; Andert, J.; Koch, C.R. Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine. Int. J. Eng. Res. 2023, 24, 536–551. [Google Scholar] [CrossRef] [PubMed]
  21. Yu, X.; Zhu, L.; Wang, Y.; Filev, D.; Yao, X. Internal combustion engine calibration using optimization algorithms. Appl. Energy 2022, 305, 117894. [Google Scholar] [CrossRef]
  22. Sun, B.; Zhang, L.; Zhao, S.; Liu, Q.; Luo, F.; Wu, H. Effect of Cetane-Coupled Pilot Injection Parameters on Diesel Engine Combustion and Emissions. ACS Omega 2022, 7, 46550–46563. [Google Scholar] [CrossRef]
  23. Zhang, Z.; Lv, J.; Xie, G.; Wang, S.; Ye, Y.; Huang, G.; Tan, D. Effect of assisted hydrogen on combustion and emission characteristics of a diesel engine fueled with biodiesel. Energy 2022, 254, 124269. [Google Scholar] [CrossRef]
  24. Loo, D.L.; Teoh, Y.H.; How, H.G.; Teh, J.S.; Andrei, L.C.; Starčević, S.; Sher, F. Applications Characteristics of Different Biodiesel Blends in Modern Vehicles Engines: A Review. Sustainability 2021, 13, 9677. [Google Scholar] [CrossRef]
  25. Wu, H.; Xie, F.; Han, Y.; Zhang, Q.; Li, Y. Effect of cetane coupled injection parameters on diesel engine combustion and emissions. Fuel 2022, 319, 123714. [Google Scholar] [CrossRef]
  26. Wu, H.; Xie, F.; Han, Y. Effect of cetane coupled with various engine conditions on diesel engine combustion and emission. Fuel 2022, 322, 124164. [Google Scholar] [CrossRef]
  27. Kim, S.Y.; Han, J.E.; Sohn, Y.S. Demand Forecasting for Heavy-Duty Diesel Engines Considering Emission Regulations. Sustainability 2017, 9, 166. [Google Scholar] [CrossRef]
  28. Alptekin, E. Emission, injection and combustion characteristics of biodiesel and oxygenated fuel blends in a common rail diesel engine. Energy 2017, 119, 44–52. [Google Scholar] [CrossRef]
  29. Teoh, Y.H.; How, H.G.; Sher, F.; Le, T.D.; Nguyen, H.T.; Yaqoob, H. Fuel Injection Responses and Particulate Emissions of a CRDI Engine Fueled with Cocos nucifera Biodiesel. Sustainability 2021, 13, 4930. [Google Scholar] [CrossRef]
  30. Ge, J.C.; Kim, J.Y.; Yoo, B.O.; Song, J.H. Effects of Engine Load and Ternary Mixture on Combustion and Emissions from a Diesel Engine Using Later Injection Timing. Sustainability 2023, 15, 1391. [Google Scholar] [CrossRef]
  31. Bao, J.; Qu, P.; Wang, H.; Zhou, C.; Zhang, L.; Shi, C. Implementation of various bowl designs in an HPDI natural gas engine focused on performance and pollutant emissions. Chemosphere 2022, 303, 135275. [Google Scholar] [CrossRef]
  32. Bao, J.; Wang, H.; Wang, R.; Wang, Q.; Di, L.; Shi, C. Comparative experimental study on macroscopic spray characteristics of various oxygenated diesel fuels. Energy Sci. Eng. 2023, 11, 1579–1588. [Google Scholar] [CrossRef]
  33. Shi, C.; Chai, S.; Wang, H.; Ji, C.; Ge, Y.; Di, L. An insight into direct water injection applied on the hydrogen-enriched rotary engine. Fuel 2023, 339, 127352. [Google Scholar] [CrossRef]
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Figure 1. Schematic diagram of the engine stand.
Figure 1. Schematic diagram of the engine stand.
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Figure 2. Schematic digamma of SVM.
Figure 2. Schematic digamma of SVM.
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Figure 3. Comparison of training and test data for NOx and CO in SVMs.
Figure 3. Comparison of training and test data for NOx and CO in SVMs.
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Figure 4. Input parameter impact weights for the ID.
Figure 4. Input parameter impact weights for the ID.
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Figure 5. HC impact weighting analysis.
Figure 5. HC impact weighting analysis.
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Figure 6. Weighting of the impact of each parameter on the indicator parameter.
Figure 6. Weighting of the impact of each parameter on the indicator parameter.
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Table 1. Uncertainty analysis of the main apparatus used in the parameter measurement.
Table 1. Uncertainty analysis of the main apparatus used in the parameter measurement.
PropertyResolutionUncertainty
Dynamometer (speed measurement)1 rpm±0.3%
Dynamometer (torque measurement) 0.01 N·m±0.2%
CO measurement0.01 ppm±0.3%
HC measurement2 ppm<0.2%
NOx measurement1 ppm<0.2%
Table 2. Main characteristics of test fuel.
Table 2. Main characteristics of test fuel.
Fuel PropertiesCN = 5153.955.357.459.3#1#2#3
Calorific value (MJ/kg)42.9642.9242.9942.9543.0142.9442.9542.95
Density (kg/m3)820.9817.8818.8823.6825.6818.8819.3820.6
Initial distillation Temperature (°C)131.5144.3143.2179.2143.6145.1143.5143.2
50% distillation temperature (°C)256.5244.3234.6266.1282.7244.4234.8259.6
90% distillation temperature (°C)335.5300.1338.6328.3341.5342.6339.4360.3
95% distillation temperature (°C) 356.6342.6359.1349.8361.9350.8360.2362.7
Sulfur content (mg/kg)3.82.43.83.54.43.73.94.1
Cyclic aromatic hydrocarbon content (%)18.415.221.214.519.322.420.714.9
Alkane content (%)47.650.247.335.843.946.147.937.8
Table 3. Fitting results for combustion parameters.
Table 3. Fitting results for combustion parameters.
IndicatorsParameterTrainingTest
R2ID0.97650.9573
CD0.91780.9327
Combustion Temperature0.95220.9109
Premix ratio0.90230.9185
MSEID0.00740.0108
CD0.01050.0128
Combustion Temperature0.00950.0110
Premix ratio0.01050.0125
MAPE%ID2.77642.4683
CD2.49212.2347
Combustion Temperature1.55721.3292
Premix ratio1.68751.8523
Table 4. Results of fitting of indicator parameters.
Table 4. Results of fitting of indicator parameters.
IndicatorsParameterTrainingTest
R2BSFC0.96760.9446
BTE0.92350.9215
HC0.97650.9573
NOx0.96910.9535
Amount of PM 0.95360.9705
PM mass0.93840.9271
MSEBSFC0.00980.0133
BTE0.01150.0135
HC0.00740.0118
NOx0.01020.0116
Amount of PM 0.00110.0015
PM mass0.01260.0127
MAPE%BSFC2.59302.8230
BTE1.75732.1259
HC2.12573.0576
NOx2.95763.2573
Amount of PM 3.03243.1153
PM mass3.34823.4638
Table 5. Sensitivity analysis of input parameters to combustion parameters.
Table 5. Sensitivity analysis of input parameters to combustion parameters.
Combustion ParametersInput ParametersSST
IDinjection pressure0.120.14
injection timing0.150.16
pre-injection ratio0.090.12
pre-injection timing0.100.11
EGR0.200.22
load0.250.27
CN 0.060.08
volatility0.030.05
CDinjection pressure0.110.13
injection timing0.180.20
pre-injection ratio0.090.11
pre-injection timing0.080.10
EGR0.210.24
load0.240.26
CN 0.060.08
volatility0.030.04
Combustion Temperatureinjection pressure0.120.14
injection timing0.170.19
pre-injection ratio0.070.08
pre-injection timing0.090.11
EGR0.190.21
load0.280.30
CN 0.050.06
volatility0.030.04
Premix ratioinjection pressure0.090.11
injection timing0.250.27
pre-injection ratio0.080.10
pre-injection timing0.070.09
EGR0.210.23
load0.220.24
CN 0.040.06
volatility0.020.04
Table 6. Combustion parameter impact weighting analysis of indicators.
Table 6. Combustion parameter impact weighting analysis of indicators.
IndicatorInput ParametersSST
HCID0.350.37
CD0.300.31
Combustion temperature0.200.21
Premix ratio0.150.16
COID 0.370.38
CD0.260.28
Combustion temperature0.210.22
Premix ratio0.160.17
NOxID0.180.20
CD 0.210.23
Combustion temperature0.360.38
Premix ratio0.250.26
Amount of PMID 0.180.20
CD 0.210.22
Combustion temperature0.360.38
Premix ratio0.250.26
PM massID0.170.18
CD 0.210.23
Combustion temperature0.390.41
Premix ratio0.230.24
BSFCID 0.180.20
CD 0.310.32
Combustion temperature0.400.42
Premix ratio0.110.13
BTEID0.160.17
CD0.240.26
Combustion temperature0.370.39
Premix ratio0.230.24
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Huang, B.; Hong, W.; Shao, K.; Wu, H. Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance. Sustainability 2024, 16, 11107. https://doi.org/10.3390/su162411107

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Huang B, Hong W, Shao K, Wu H. Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance. Sustainability. 2024; 16(24):11107. https://doi.org/10.3390/su162411107

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Huang, Bingfeng, Wei Hong, Kun Shao, and Heng Wu. 2024. "Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance" Sustainability 16, no. 24: 11107. https://doi.org/10.3390/su162411107

APA Style

Huang, B., Hong, W., Shao, K., & Wu, H. (2024). Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance. Sustainability, 16(24), 11107. https://doi.org/10.3390/su162411107

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