Cylinder Pressure Traces Research Articles

A Deep Learning Approach to Predict In-Cylinder Pressure of a Compression Ignition Engine

Abstract As emissions regulations for greenhouse gas emissions become more strict, it is important to increase the efficiency of engines by improving on the design and operation. Current optimization methods involve performing large numbers of experimental investigations on physical engines or making use of detailed Computational Fluid Dynamics modeling efforts to provide visual and statistical insights on in-cylinder behavior. The latter still requires experimental data for model validation. Both of these methods share a common set of problems, that of being monetarily expensive and time consuming. Previous work has proposed an alternative method for engine optimization using machine learning (ML) models and experimental validation data to predict scalar values representing different parameters. With such models developed, one can then quickly iterate on operating conditions to find the point that maximizes an application-dependent reward function. While these ML methods provide information on individual performance parameters, they lack key information of in-cylinder indicators such as cylinder pressure traces and heat release curves that are traditionally used for performance analysis. This work details the process of implement- ing a Multilayer Perceptron (MLP) model capable of accurately predicting crank-angle resolved high-speed in-cylinder pressure using equivalence ratio, fuel injection pressure and injection timing as input features. It was demonstrated that the model was able to approximate engine behavior with mean squared error lower than 0.05 on a 1-55 range in the test set. This approach shows potential for greatly accelerating the optimization process in engine applications.

Combustion noise characterization and optimization of PCCI combustion using response surface methodology

The current work aims at characterizing the premixed charge compression ignition (PCCI) combustion with regression-based approach using response surface methodology. PCCI operating parameters such as load, pilot injection timing, main injection timing, pilot injection quantity, exhaust gas recirculation and injection pressure are considered as input variables. Engine performance indicators such as brake thermal efficiency, brake specific fuel consumption, carbon monoxide (CO), hydrocarbon (HC), oxide of nitrogen (NOx), smoke emissions, combustion phasing and combustion noise metric ringing intensity are considered as output responses. Experimental results validate the optimal solution from response surface methodology approach, and good agreement is found between mathematical models and experimental results. Comparative examination of optimized PCCI combustion versus conventional combustion showed a 66% and 44% decrement in NOx and smoke emissions. Except for CO and HC emissions, the percentage penalty of other responses with PCCI combustion is less than 10%. In addition to ringing intensity another combustion noise metric combustion noise level is computed from Fast Fourier Transform (FFT) analysis of cylinder pressure trace. A combustion noise level of 73.26 dB is obtained at optimized conditions.

Combustion stability, RGF and pressure referencing effect on HRR for a high compression ratio SI engine with natural gas lean mixtures

The knocking and heat release based on cylinder pressure trace is a widespread practice in combustion diagnostic for conventional SI engines. The accuracy depends on quality of measured in-cylinder pressure, thermodynamic models and parameters feeding such models. In non-conventional engine, common models to assess engine operation, do not satisfy properly expected standard, thus application of new methods and models is required. This study presents the determination of knocking intensity by in-cylinder pressure and cycle-to-cycle variability for operation analysis, the effect of referencing pressure and residual gas fraction on heat release for non-conventional SI engines with high compression ratio with natural gas at partial loads and lean mixture. Higher spark timings was achieved at 21 degrees, limited by low occurrence audible knock for KI20 over 0.04 bar. Compression ratio 15.5, engine speed at 1800 rpm, air-cooled system, and equivalence ratio of 0.73 make limited the operation. KI20 of 0.072 was reached at IMEP 3.96 bar and spark timing of 21 degrees. Maximum CoV of KI20 was 27.1. Pressure referencing shows low differences in polytropic coefficients of 1.34. Slight difference for RGF generates differences in the mass burned up to 12%, and around 0.4 degrees in combustion duration.

Experimental investigation of ethanol diesel dual fuel DI diesel engine

This paper investigates the combustion and performance characteristics of an ethanol – diesel dual fuel direct injection diesel engine. An air-cooled constant speed single cylinder diesel engine was operated with dual fuel mode in which ethanol was injected in the inlet manifold and diesel was injected inside the cylinder. An electronic fuel injection system operated by LabView software was used to inject the ethanol. Virtual instrumentation principle was used to generate output pulses for triggering ethanol injection. Diesel fuel was injected by conventional mechanical fuel injection system. The engine was loaded by an eddy current dynamometer. A piezoelectric pressure pickup with charge amplifier connected to PC based data acquistion system recorded cylinder pressure data at every crank angle interval. A crank angle degree marker measures the crank angle position for every crank angle. Tests were conducted on the engine in diesel and ethanol – diesel dual fuel mode. The cylinder pressure traces were used to calculate the heat release rates and combustion parameters. Optimum ethanol quantity was injected at every operating point. An AVL five-gas analyzer was used to measure the different exhaust pollutants. The smoke emission was measured by an AVL smoke meter in Hatridge equivalent percentage. Experimental results show that 40% of diesel can be replaced by ethanol at full load and there was 5.5% reduction in specific energy consumption. Also it was found that 32% reduction in smoke level (Opacity) and 25% reduction in NOx emissions at full load operating condition. It was also observed that the engine was running smoothly without knocking.

A Novel Neural Network with the Ability to Express the Extreme Points Distribution Features of Higher Derivatives of Physical Processes

Higher derivatives are important to interpret the physical process. However, higher derivatives calculated from measured data often deviate from the real ones because of measurement errors. A novel method for data fitting without higher derivatives violating the real physical process is developed in this paper. Firstly, the research on errors’ influence on higher derivatives and the typical functions’ extreme points distribution were conducted, which demonstrates the necessity and feasibility of adopting extreme points distribution features in neural networks. Then, we proposed a new neural network considering the extreme points distribution features, namely, the extreme-points-distribution-based neural network (EDNN), which contains a sample error calculator (SEC) and extreme points distribution error calculator (EDEC). With recursive automatic differentiation, a model calculating the higher derivatives of the EDNN was established. Additionally, a loss function, embedded with the extreme points distribution features, was introduced. Finally, the EDNN was applied to two specific cases to reduce the noise in a second-order damped free oscillation signal and an internal combustion engine cylinder pressure trace signal. It was found that the EDNN could obtain higher derivatives that are more compatible with physical trends without detailed differentiation equations. The standard deviation of derivatives’ error of the EDNN is less than 62.5 percent of that of traditional neural networks. The EDNN provides a novel method for the analysis of physical processes with higher derivatives compatible with real physical trends.

Deep Learning-based Modeling of Gasoline Controlled Auto-Ignition

Internal combustion engines face increasingly stringent pollution and greenhouse gas emission restrictions. In this regard, the technology of low-temperature combustion is the scope of research, as it can reduce pollutant emissions while increasing engine efficiency. However, the system dynamics are very complicated and have a high sensitivity concerning the thermodynamics in the combustion chamber. Modeling with first principle models would be time-consuming and requires accurate parameter estimations. Therefore, this work presents deep learning-based models to capture the dynamics of the cylinder pressure trace for gasoline controlled auto-ignition. These models trained with experimental data can learn the internal pattern of the combustion cycle dynamics. Based on information from the current cycle, they enable the prediction of the combustion pressure trace for the following. The results show that the models precisely predict the pressure traces and ensure high prediction accuracy of engine performance parameters derived from the predicted traces. R2 values of 0.95 or more are obtained for global parameters, whereas values of 0.8 or more are obtained for local parameters.

Development of a phenomenological model for the description of RCCI combustion in a dual-fuel marine internal combustion engine

Increasingly stringent pollutant and CO2 emission standards require engine manufacturers to investigate innovative solutions. Among these techniques, low-temperature combustion (LTC) concepts have a large potential to simultaneously reduce NOx emissions and fuel consumption. A promising manner to realize LTC consists of adopting ultra-lean mixtures, where the combustion evolution is controlled by a proper spatial distribution of fuels with different chemical reactivities. In this context, depending on the proportion and stratification of the fuels, the heat release can primarily depend on chemistry progression, leading to a Reactivity Controlled Compression Ignition (RCCI) mode, or on flame propagation, locally initiated by a high reactivity fuel.In this work, the combustion characteristics of a large-bore research engine are experimentally investigated. Natural gas is supplied into the intake port, while light fuel oil (LFO) is directly injected in the cylinder. An experimental campaign is carried out including sweeps of engine load, air/fuel proportion, LFO amount, valve timing, and intake air temperature. Global engine operating parameters as well as cylinder pressure traces are recorded and analyzed.Based on the available experimental data, a phenomenological model handling both chemistries of fuels with different reactivities and flame propagation is developed and validated. The model is based on a multi-zone approach, where auto-ignition chemistry is solved by a tabulated method to preserve the computational effort. The proposed numerical approach shows the ability to simulate the experimental data with good accuracy, using a fixed tuning constant set. A dedicated correlation is built to reproduce the expected in-cylinder distribution of the directly injected liquid fuel. Global performance and combustion parameters are predicted with an average error below 5%. The model demonstrates to correctly describe the behavior of the tested engine under different operating conditions and to capture the physics behind such advanced combustion concepts.

A Simulation Study on the Transient Behavior of a Gasoline Direct Injection Engine under Cold Start Conditions

<div class="section abstract"><div class="htmlview paragraph">The cold start process is critical to control the emissions in a gasoline direct injection (GDI) engine. However, the optimization is very challenging due to the transient behavior of the engine cold start. A series of engine simulations using CONVERGE CFD™ were carried out to show the detailed process in the very first firing event of a cold start. The engine operating parameters used in the simulations, such as the transient engine speed and the fuel rail pressure (FRP), came from companion experiments. The cylinder pressure traces from the simulations were compared with experiments to help validate the simulation model. The effects of variation of the transient parameters on in-cylinder mixture distribution and combustion are presented, including the effects of the rapidly changing engine speed, the slowly vaporized fuel due to the cold walls, and the low FRP during the first firing cycle of a 4-cylinder engine. Comparison was also made with non-transient steady state operation. It was shown that the injection-induced tumble ratio in the cylinder varied for different engine speed cases, resulting in a better fuel distribution in the low engine speed case. A relatively high turbulence level during the combustion process was seen in the transient engine speed case that led to strengthened combustion. The fuel tracking from the simulation indicated that about 30% of the fuel remained unreacted at a very late crank angle, in which 8% was in the gas phase and 22% in the liquid phase as wall films. As the FRP increased, the fuel droplets became smaller, and more fuel vaporized before hitting the piston. But the splash and rebound fuel fraction off the piston bowl was even less, resulting in a lower overall gas-phase equivalence ratio in the first firing event for the high FRP case. On the other hand, the fuel distribution was more homogeneous under the high FRP condition due to the high injection velocities. There should be an optimal FRP value, but it was difficult to discern since the peak pressures in different FRP cases were similar and turbulence levels varied in a non-monotonic way, as well, which would lead to cycle-to-cycle variations in the real engine.</div></div>

Cycle-to-cycle combustion analysis in hydrogen fumigated common-rail diesel engine

An experimental cycle-to-cycle analysis in multi-cylinder automotive common-rail compression ignition engine was performed to understand better the combustion in the hydrogen fuelled diesel engine. Hydrogen was fumigated in intake line at a wide-ranging from 0 lpm to 50 lpm in steps of 10 lpm. Test engine was operated at three different loads for a constant engine speed. The relative air–fuel ratios were between 1.55 and 3.31. The combustion occurred with an excess of air in all tests. Combustion data of 120 cycles were used to analyse the cyclic variations, determined by standard deviation of the cylinder pressure. Cyclic variations, coefficient of variance (CoV), standard deviation, frequency, and average value of peak combustion pressure (Pmax), maximum pressure rise rate (PRRmax), indicated mean effective pressure (IMEP), mass fraction burned (MFB), and MFB10-90 duration were analysed. Results showed that for all hydrogen addition levels, CoVs of Pmax, IMEP and MFB10-90 were always found below 3% at all loads, and PRRmax values with hydrogen operations in each cycle were under the limit of knocking combustion. Engine load was the most important factor to affect to CoV, standard deviation, and frequency of the combustion parameters. Minor cyclic variations in MFB traces were found at all engine loads and hydrogen addition levels, which agreed well with the cylinder pressure traces. Frequencies of Pmax and IMEP were moderate in low and medium loads, but reduced in high load, and also the values of Pmax and IMEP in high load were distributed in a wider range compared to low and medium loads. MFB10-90 duration increased for dual-fuel modes, its frequency was decreased, and its distribution was extended with hydrogen addition. Trend of standard deviation was mostly similar to that of CoV for the studied combustion parameters. Furthermore, variations of correlation coefficient (R) among Pmax, PRRmax, IMEP, and MFB10-90 were discussed in this paper, and the highest R value was found between IMEP and Pmax.

Trapped mass estimation in automotive diesel engines based on in‐cylinder pressure signal projection

AbstractCylinder pressure signal provides key feedback information that allows for several engine monitoring and control capabilities. During the last few years, the use of cylinder pressure traces for advanced combustion strategies has become the focus of several research works. This is primarily due to the availability of reliable and inexpensive cylinder pressure sensors with expected durability that meets the vehicle lifetime. In this context, several approaches have been proposed to determine the engine trapped mass from the cylinder pressure measurements. A recent innovative approach for trapped charge determination based on a two‐dimensional graphical signature is proposed in a previous work. The resulting estimator uses cylinder pressure signal as a unique input and allows to deduce the trapped mass on a cycle by cycle basis in steady and transient operating conditions. It has been validated in a wide range of engine operating conditions using instrumentation and industrial cylinder pressure sensors. This paper provides a theoretical framework and in‐depth analysis for the signature based trapped mass estimator. A state‐space model for in‐cylinder conditions during the compression phase that complies with the signature modeling structure is developed. Extensive numerical investigations using an experimentally validated simulation platform are then performed. The objective is to select the optimal signature generation interval that reduces the impact of cycle to cycle fluctuations in terms of intake valve closing temperature and polytropic index. The obtained results are promising and clearly show the performance and robustness of the signature based trapped mass estimator that can provide relevant feedback information for adaptive engine control systems. It can be easily implemented for real‐time monitoring and control in industrial automotive applications.

Development of a CFD Model and Validation with PIV-data to Study the Fluid Motion in a Small PFI SI Engine

In-cylinder fluid motion has a substantial impact on air-fuel mixture formation, combustion process and emission formation. In the present paper, a simulation study of the in-cylinder fluid flow is performed using a computational fluid dynamic (CFD) model of a port-fuel-injection (PFI) engine (volume: 110 cc). First, a 1-D model is developed, and validated with the cylinder pressure traces acquired in an optical engine experimentally. The model provided the boundary conditions for multi-dimensional numerical simulations. The predicted velocity fields from CFD are then compared with the measured data obtained using particle image velocimetry (PIV) at various crank angle positions with low throttle opening condition. A good relevance is observed on comparing numerically estimated results with experimental results.

Influence of water port injection on cycle-to-cycle variations in heavy-duty natural gas engine under low load

Water injection is regarded as an effective approach to decrease thermal load and suppress knock onset in heavy-duty natural gas engines. However, the cycle-to-cycle variations in heavy-duty natural gas engines via water injection has rarely been investigated. In this study, the effects of water port injection on cycle-to-cycle variations in a natural gas engine were analyzed experimentally. The engine speed was fixed at 1600 r/min, and engine load was set at a low load with a brake mean effective pressure of 0.387 MPa. Three excess air-fuel ratios (λ = 1.0, 1.2, and 1.4) were defined; and four water to natural gas mass ratios (WNMRs) were set. The results demonstrated that the variations of multicycle cylinder pressure traces consistently increased with the increase in WNMR. The interdependence between the peak in-cylinder pressure (Pmax) and its corresponding crank angle became more dispersed, and the frequency distribution of Pmax covered a wider range with increasing WNMR. The obvious fluctuations and wide range of distribution of indicated mean effective pressure (IMEP) were determined by water addition. The higher the λ, the larger the fluctuations and wider the range of distribution of IMEP. The coefficient of variation of IMEP (COVIMEP) increased with the increase in WNMR and λ. However, the maximum COVIMEP among the three λs and four WNMRs did not exceed 3%. Thus, it can be concluded that no drivability problem is associated with water injection under low load.

Effect of Nitromethane Addition on the Performance of Two-Stroke Spark Ignition Unmanned Aerial Vehicles Piston Engine

Abstract Nitromethane has a stoichiometric air–fuel ratio of 1.7, which is 8.5 times lower than gasoline. For the same amount of air being drawn by the engine, more amount of nitromethane blends and hence more energy can be added. Methanol was used as a medium to mix nitromethane and gasoline, which are normally immiscible. Engine performance tests were carried out to study the effect of nitromethane addition to the methanol-gasoline blend. A large rise in engine torque and brake thermal efficiency (BTE) was obtained during the investigation. However, the brake specific fuel consumption (BSFC) also increased for the nitromethane blends. The engine parameters like spark timing, equivalence ratio, and compression ratio were optimized to further increase the engine power and also bring down the BSFC. A net torque improvement of 42%, BTE improvement of 35%, and BSFC rise of 9% were obtained by adding nitromethane and methanol in small fractions to gasoline. Combustion analysis was carried out using the cylinder pressure trace. High heat release rate and shorter combustion duration with nitromethane addition were observed. Emission measurements showed decrease in HC and CO emissions with nitromethane addition. However, a drastic rise in NO emissions was observed. Hence, it can be concluded that the specific power of small two-stroke spark ignition (SI) engines can be enhanced using nitromethane as a fuel additive to increase the payload of the unmanned aerial vehicles.

A comparative study on the usage of fusel oil and reference fuels in an HCCI engine at different compression ratios

In this study, the effects of the fusel oil on combustion and performance characteristics of an HCCI engine were examined comparatively with reference fuels. 37.5% fusel oil and 62.5% n-heptane were blended (Fusel40). RON40 used as reference fuel and to obtain the same octane number. Experiments were carried out at a fix intake air temperature of 350 K. Engine operation, BSFC, exhaust emission, CA50 and ITE maps were created as well as cylinder pressure and heat release rate traces by sweeping lambda between the misfire and knock limits at each engine speed at three different compression ratios of 11, 12 and 13. Despite its lower heating value, Fusel40 did not lead performance loss for the HCCI engine. Moreover, Fusel40 provided higher brake torque due to better location of the SOC and CA50 at a compression ratio of 13 and minimum BSFC was obtained as 228.6 g/kWh with Fusel40. The maximum ITE was obtained as 44.6% at a compression ratio of 12 and lambda of 2.9 with RON40 fuel. On the other hand, Fusel40 provided higher thermal efficiency than RON40 for a wide operation range of the HCCI engine. ITE ranged between 30 and 43 % for Fusel40 while it was 26–44% for RON40. It is also found that increasing the compression ratio of HCCI engine alone couldn’t be improved thermal efficiency without the start of combustion and combustion phase control. It was determined that fusel oil has a better influence to reduce CO and HC emissions than increasing the compression ratio.

Performance of a direct-injection natural gas engine with multiple injection strategies

This paper presents the experimental results for the performance of a direct-injection natural gas marine engine with different multiple injection strategies. In order to investigate the effects of natural gas post injection proportion and natural gas injection separation, a series of tests were performed at five different injection proportions (8%, 10%, 15%, 20%, 25%) and injection separation from 100μs to 700 μs at every 100 μs. The averaged cylinder pressure traces and combustion parameters (MFB50%, MFB90% and MFB5-90%) of each test point were calculated for combustion analysis. HC, CO and NOx emissions were collected to reveal the emissions characteristics. The results showed that the peak values of cylinder pressure and heat release rate will be lower than that of natural gas single injection strategy when multiple natural gas injection strategies are applied. Though the phase angle of 50% mass fraction burned is advanced at medium natural gas injection separations, the phase angle of 90% mass fraction burned is more likely to occur early at longer natural gas injection separations. With regard to emissions, HC and NOx emissions can benefit from using shorter natural gas injection separations while CO can only be reduced with small post injection proportions with certain natural gas injection separations.

In-cylinder pressure-based convolutional neural network for real-time estimation of low-pressure cooled exhaust gas recirculation in turbocharged gasoline direct injection engines

Low-pressure cooled exhaust gas recirculation is one of the most promising technologies for improving fuel efficiency of turbocharged gasoline direct injection engines. To realize the beneficial effects of the low-pressure cooled exhaust gas recirculation, the accurate estimation of the low-pressure cooled exhaust gas recirculation rate is essential for precise low-pressure cooled exhaust gas recirculation control. In this respect, previous studies have suggested in-cylinder pressure-based low-pressure cooled exhaust gas recirculation models to obtain the low-pressure cooled exhaust gas recirculation rate into the cylinders with fast response. However, these methods require considerable manual process of feature engineering to extract and analyze the combustion characteristics from the cylinder pressure traces. Furthermore, the performance of the entire model is limited solely to certain hand-crafted characteristics and their mathematical formulations. To resolve these limitations, we propose an in-cylinder pressure-based convolutional neural network for low-pressure cooled exhaust gas recirculation estimation. Because the convolutional neural network model automatically learns the complex function between the raw input of the high-dimensional cylinder pressure traces and the low-pressure cooled exhaust gas recirculation rate through an end-to-end deep learning framework, this convolutional neural network model provides a more effective and precise modeling process compared to the conventional combustion characteristics-based regression models. The proposed convolutional neural network model consists of the input layer with the previous consecutive cycles of the pressure traces to resolve the model uncertainty from cycle-to-cycle variations. This input layer is connected to one convolutional layer, two fully connected layers, and the final output layer that is the target low-pressure cooled exhaust gas recirculation rate. The proposed model was trained, validated, and tested using a total of 50,000 cycles of engine experimental data under various transient driving conditions. The remarkable accuracy of the proposed model was evaluated with R2 values over 0.99 and root mean square error values of less than 1.5% under the transient conditions. Moreover, the real-time performance and low memory requirement were also verified on the target embedded platform.

Unsupervised learning and nonlinear identification for in-cylinder pressure prediction of diesel combustion rate shaping process

Combustion Rate Shaping (CRS) offers the potential to control in-cylinder fuel concentration gradient and distribution with new fuel injection strategies, consisting of a higher number of injections and smaller individual injection quantities. Developing a physics-based model for such strategies involves significant development efforts and is also associated with high computational cost. This paper proposes a black-box framework for CRS process control based on artificial neural network and principal components analysis. To identify the nonlinear system behavior of diesel combustion, a multi-input/multi-output empirical model has been developed. The cylinder pressure trace is transformed into principal components coefficients space, and the neural network is used to predict the coefficients from operation parameters of diesel combustion. The in-cylinder pressure trace is then reconstructed by the predicted coefficients and pre-extracted principal components. The model has been evaluated with specific combustion features of CRS diesel engine experiments. The results show that the model successfully captured the combustion characteristics of CRS based diesel combustion processes with sufficient generalization ability.

Cylinder state estimation from measured cylinder pressure traces - A Survey

In the search for improved performance and control of combustion engines there is a search for the sensors that gives information about the combustion profile and the state of the gases in the combustion chamber. A particular interest has been given to the potential use of the cylinder pressure sensor and there is quite a lot of work that has been made in this area. This paper provides a comprehensive list of references and summarizes applications and methods for extracting information from the cylinder pressure sensor about the combustion and the gas state. The summary highlights the following topics related to cylinder pressure: measurement chain, cylinder torque, extraction of the burn profile, combustion placement, knocking, cylinder air mass, air to fuel ratio, residual gas estimation, and cylinder gas temperature estimation. The focus in the summary is on the latter topics about the gas state but thermodynamic analysis of the combustion process also gets a longer treatment since many methods for information extraction rely on the thermodynamic properties.

Robust cylinder pressure estimation in heavy-duty diesel engines

The robustness of a new single-cylinder pressure sensor concept is experimentally demonstrated on a six-cylinder heavy-duty diesel engine. Using a single-cylinder pressure sensor and a crank angle sensor, this single-cylinder pressure sensor concept estimates the in-cylinder pressure traces in the remaining cylinders by applying a real-time, flexible crankshaft model combined with an adaptation algorithm. The single-cylinder pressure sensor concept is implemented on CPU/field-programmable gate array–based hardware. For steady-state engine operating conditions, the added value of the adaptation algorithm is demonstrated for cases in which a fuel quantity change or start of injection change is applied in a single, non-instrumented cylinder. It is shown that for steady-state and transient engine conditions, the cylinder pressure traces and corresponding combustion parameters, indicated mean effective pressure, peak cylinder pressure, and crank angle at 50% heat release, can be estimated with 1.2 bar, 6.0 bar, and 1.1 CAD inaccuracy, respectively.

Comparison of Knock Indexes Based on CFD Analysis

Abstract Recent trends in gasoline engines, such as downsizing, downspeeding and the increase of the compression ratio make knocking combustions a serious limiting factor for engine performance. A detailed analysis of knocking events can help improving the engine performance and diagnostic strategies. An effective way is to use advanced 3D Computational Fluid Dynamics (CFD) simulation for the analysis and prediction of the combustion process. The effects of Cycle to Cycle Variation (CCV) on knocking combustions are taken into account, maintaining a RANS (Reynolds Averaged Navier-Stokes) CFD approach, while representing a complex running condition, where knock intensity changes from cycle to cycle. The focus of the numerical methodology is the statistical evaluation of the local air-to-fuel and turbulence distribution at the spark plugs and their correlation with the variability of the initial stages of combustion. CFD simulations have been used to reproduce knock effect on the cylinder pressure trace. For this purpose, the CFD model has been validated, proving its ability to predict the combustion evolution with respect to SA variations, from non-knocking up to heavy knocking conditions. The pressure traces simulated by the CFD model are then used to evaluate cylinder pressure-based knock indexes. Since the model is able to output other knock intensity tracers, such as the mass of fuel burned in knocking mode, or the local heat transferred to the piston, knock indexes based on the cylinder pressure trace can be related to parameters only available in a simulation environment, that are likely to be more representative of the actual knock intensity, with respect to the local pressure trace for the sensor position. The possibility of simulating hundredths of engine cycle allows using the methodology to compare the indexes quality (correlation with actual knock intensity) on a statistical base.