Quasi-dimensional Combustion Model Research Articles

An experimental study of knock analysis of HCNG fueled SI engine by different methods and prediction of knock intensity by particle swarm optimization-support vector machine

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by knock. The purpose of this study is to evaluate and predict knock at different operating conditions by varying (0 %–40 %) hydrogen amount in HCNG, by varying (25 %–100 %) load on engine and by varying (700 rpm–1700 rpm) speed of engine. Knock is evaluated by knock ratio, knock intensity, exhaust temperature and in-cylinder heat transfer rate. Quasi-dimensional combustion model (QDCM) MATLAB program is used to calculate in-cylinder pressure theoretically. By increasing (0 %–40 %) hydrogen in HCNG 32.7 %, 7.6 %, 33.5 %, 21.5 % & 6.4 % increment observed in, in-cylinder pressure, knock intensity, knock ratio, in-cylinder heat transfer rate and exhaust temperature respectively. By increasing (25 %–100 %) load on engine 75.8 %, 69.8 %, 88.2 %, 77.8 % & 46.7 % increment observed in, in-cylinder pressure, knock intensity, knock ratio, in-cylinder heat transfer rate & exhaust temperature respectively. By increasing speed (1100–1700) rpm of engine aforementioned parameters decreases by 12.19 %, 38.9 %, 36.06 %, 47.2 % & 14.1 % respectively. Knock intensity is predicted by particle swarm optimization-support vector machine (PSO-SVM) algorithm effectively. To minimize the prediction error, 31 combination of different (1–5) inputs applied to predict knock intensity and found combination of 5 input variable is 67.2 % & 71.9 % more accurate than 1 input variable in-terms of mean squared error and mean absolute error respectively. Findings of this study can be used to train electronic control unit of engine and in the development of HCNG fueled engine.

Optimizing heat transfer predictions in HCNG engines: A novel model validation and comparative study via quasi-dimensional combustion modeling and artificial neural networks

Heat transfer from the walls of engine has a significant role on engine combustion, performance and emission characteristics. The study objectives to showcase the efficacy of the new model through an analysis by comparison with existing heat transfer models. New model is based on woschni model in which pressure and temperature is replaced by other fluid properties like density, thermal conductivity and dynamic viscosity. A series of experiments were conducted on a compressed natural gas internal combustion engine across varying hydrogen fractions, EGR ratios, engine speeds and different loads under stoichiometric conditions. The study demonstrates the efficacy of the proposed model by constantly achieving high prediction quality across a wide range of engine calibration coefficients by using Quasi-dimensional Combustion Model (QDCM) on MATLAB. Comparative analyses of new model with different heat transfer models were undertaken to validate the heat transfer rates with experimental results across a broad spectrum of operational conditions. It is observed that heat transfer rate is increased by increasing the engine load as 25%, 50%, 75% and 100% as 90.57J/deg, 130.12J/deg, 200.02J/deg, and 260.26J/deg with new model correspondingly. Heat transfer rate reduced by rise in engine speed with 1100 rpm, 1200 rpm, 1500 rpm and 1700 rpm is as 32.91 kW, 32.16 kW, 25.36 kW and 18.03 kW by new heat transfer model respectively. Artificial neural network (ANN) popular backpropagation algorithm is adopted to predict the heat transfer rate of HCNG engine, the five-input and one-output network structure are used. The values of correlation coefficient (R) and mean square error (MSE) were 0.99957 and 0.22667, 0.99998 and 0.010776, 0.99253 and 4.4762, 0.9961 and 1.2329, 0.99994 and 0.025108 for Woschni, New_Model, Chang, Sitkei and experimental respectively. This research work offers that ANN is a wise option for conventional modeling systems. In this way, the heat transfer rate of hydrogen-added CNG engines may be precisely predicted using ANN modeling.

Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by Knock. The purpose of this study is to investigate knock intensity (KI) by different convective heat transfer models. This study can be used to train electronic control unit (ECU) of engine operating on different loads from low to high speed. In present study, experimentation have been performed on HCNG fueled spark ignition engine at different operating conditions by varying hydrogen amount in HCNG, by varying load of engine, by varying exhaust gas recirculation (EGR) rate in air and by varying speed of engine to calculate in-cylinder pressure, knock intensity (KI) and heat transfer rate (HTR). Six convective heat transfer models (Assanis, Eichelberg, Han, Hohenberg, Nusselt and Woschni) have been incorporated in quasi dimensional combustion model (QDCM) to calculate the parameters of HCNG engine. Comparative and predictive analysis using artificial neural network fitting tool (ANNFT) of aforementioned simulated models have been performed with experimental findings to attain the efficient model for knock intensity of HCNG engine. Woschni model, predicts the minimum error of 0.37 % & 0.86 % b/w experimental and simulated in-cylinder pressure and indicated mean effective pressure respectively. Han model predicts the minimum error of 3.8 % b/w experimental and simulated knock intensity operating at 11.2 % EGR. Maximum error of 20.3 % at 2.4 % EGR is attained by using Woschni model in knock intensity calculation. The best suited algorithm for present data set is Bayesian regularization utilized in ANNFT to predict knock intensity effectively. The findings of this study can be utilized in the development of HCNG engine.

Comparative analysis of different heat transfer models, energy and exergy analysis for hydrogen-enriched internal combustion engine under different operation conditions

The implementation of hydrogen as a fuel in internal combustion engines is widely considered a favorable optimal due to its zero-carbon emissions and high energy content. Heat transfer from engine cylinder walls has a major role on engine combustion, performance and emission characteristics. In this study, heat transfer analysis conducted by different heat transfer models and identify the best model to analyse the energy and exergy analysis of hydrogen enriched internal combustion engine. A series of experiments were conducted on a compressed natural gas internal combustion engine across varying hydrogen fractions, EGR ratios, engine speeds and load conditions to capture in-cylinder pressure data and heat transfer rate. The authors compared six distinct models for heat transfer based on absolute percentage errors of maximum pressure and indicated mean effective pressure. These models systematically incorporated into a quasi-dimensional combustion model (QDCM) on MATLAB for hydrogen-compressed natural gas engine, accounting for diverse operating conditions. Convective heat transfer models, including Woschni, Nusselt, Hohenberg, Han, Eichelberg, and Assanis correlations were employed. Comparative analyses were undertaken to identify the most precise correlation for predicting engine in-cylinder pressure and heat transfer rates with experimental results across a broad spectrum of operational conditions. The Woschni model, as presented, is most suitable for the QDCM of the hydrogen-compressed natural gas fuel blend. This suitability is indicated by the close alignment of Taylor length and turbulent intensity coefficients to 1, as well as the minimal absolute percentage error observed for indicated mean effective pressure. It is also observed that heat transfer rate is increased by increasing the engine load 25 %, 50 %, 75 % and 100 % as 49.57 J/deg, 129.26 J/deg, 245.35 J/deg and 250.62 J/deg respectively and heat transfer rate is decreased by increasing the speed of engine 1100 rpm, 1200 rpm, 1500 rpm and 1700 rpm as 137.34 J/deg, 134.21 J/deg, 95.33 J/deg and 48.57 J/deg respectively. The energy and exergy analyses revealed that elevating the engine load corresponds to a reduction in exergy destruction. Brake energy increased 15 % by increasing the load conditions from 25 % to 100 % and decreased 7 % by increasing the speed from 1100 rpm to 1700 rpm. Another significant finding from this study is that the values of both energy and exergy efficiencies were 38.42 % and 40.12 % respectively obtained by thermodynamic analyses.

Numerical simulation of nitric oxide (NO) emission for HCNG fueled SI engine by Zeldovich’, prompt (HCN) and nitrous oxide (N2O) mechanisms along with the error reduction novel sub-models and their classification through machine learning algorithms

The zero-carbon emission can be achieved with utilization of hydrogen fuel in transportation sector but NOx emission rises. The experiments have been conducted at different operating conditions: engine speed (700 to 2000 RPM), load (25 to 75 %), MAPs = 65 to 178 kPa, spark timings (0 to 60°CA bTDC), EGR ratios, (0 to 0.33), hydrogen percentages 0 to 40 % in CNG fuel at stoichiometric operating condition to measure the in-cylinder pressure and NOx emission. The in-cylinder temperatures have been obtained through calibrated quasi-dimensional combustion model of HCNG engine. The in-cylinder pressure and temperature have been applied in simulation of NO mechanisms such as: thermal, prompt, and nitrous oxide mechanisms. Furthermore, the parametric properties of in-cylinder temperature, pressure, and nitric oxide mechanisms have been studied at different operating conditions.In second part, the traces of nitric oxide mechanisms have been transformed into positive real number with inclusion of six different sub-models that are: arithmetic mean, trapezoid integration, and author’ established three zone models (TD = 50, 100, 150 and 200 K). The three zone sub-model (TD = 100 K) has better prediction accuracy as compared to other sub-models, but it is still quite high. The prediction accuracy is improved with combination of different sub-models: 6C2, 6C3, 6C4, 6C5 and 6C6 iterations. The optimized MAPE is 24.9471 % corresponding to the four combinations of sub-models: arithmetic mean, trapezoid integration, three zone models (TD = 50 & 100 K). The best of each combination has been trained through machine learning classifier with total 6-algorithims that are Tree, Discriminant, Naïve Bayes, Support vector machine, KNN & Ensemble along with 24-functions to identify the range of different sub-models in a specific combination. The best prediction accuracy is corresponding to Ensemble with sub-space KNN classifier.

A novel laminar flame speed equation for quasi-dimensional combustion model refinement in advanced, ultra-lean gasoline spark-ignited engines

In model-based combustion simulations, laminar flame speed (LFS) is a vital parameter for predicting spark-ignited (SI) turbulent combustion, which is unstable under ultra-lean mixtures. Accurate predictions of ultra-lean combustion require a proper LFS correlation. This work proposes a novel LFS equation for a 5-component gasoline surrogate to refine a quasi-dimensional (QD) combustion model predictivity in two highly efficient gasoline engines dedicated to next-generation hybridized vehicles. First, micro-gravity constant volume vessel experiments are performed to measure the LFS of the 5-component gasoline surrogate for equation validations (equivalence ratio ϕ = 0.55 – 1.0 at elevated temperature and pressure). Calculated data using conventional (LFS_conv), refined (LFS_ref), and novel (LFS_nov) functions are compared with the measured, literature, and 1D kinetics data. LFS_conv computes zero or negative flame speeds under ultra-lean and ultra-rich mixtures, and over-predicted LFS values are obtained for the entire range. Computed data of LFS_ref and LFS_nov are properly validated with measured, literature, and 1D kinetics values. The refined and novel LFS correlations are embedded into the QD combustion model. The combustion model fidelities are compared using the three LFS equations in predicting the combustion and performance characteristics of the engines (conventional-port engine A: ϕ = 1.0 to lean-limit, strong-tumble engine B: ϕ = 0.5 – 1.0). For engine A, predicted combustion and performance using LFS_conv are obtained with average relative errors δ¯conv up to 47.6 %. Maximum δ¯ref = 18.2 % and δ¯nov = 12.8 % are obtained using LFS_conv and LFS_ref, respectively. For the long-stroke engine B model, LFS_nov produces the highest prediction accuracy with δ¯nov≤± 6 %. Using LFS_nov, a sensitivity analysis of the combustion model and the method to predict lean combustion under cycle-to-cycle variations are also proposed for engine B.

Study on engineering application method of large-scale reaction mechanism in knock prediction

In this paper, a new engineering application method for gasoline engine knock prediction in a quasi-dimensional combustion model using a large-scale reaction mechanism is explored. First, an accelerated calculation strategy coupled with 1D calculation and large-scale reaction kinetics mechanism was developed to meet the needs of rapid automatic simulation in engineering. Secondly, a gasoline surrogate was created according to the test results of China 92# gasoline. The motor octane number (MON) and the research octane number (RON) values, which were closely related to knock, were calibrated using the detailed reaction mechanism. Then, the prediction of auto-ignition of unburned region was carried out combined with predicted burn rate model and cycle to cycle variation (CCV) model. The relationship between the experimental in-cylinder pressure oscillation and the simulated auto-ignition in the unburned region was established. The multi-conditions verification results of three engines with different displacement, compression ratio and combustion system arrangement show that the average errors of predicted knock CA50 are 1.6°CA, 1.8°CA and 1.9°CA respectively.

Study of NOx emission for hydrogen enriched compressed natural along with exhaust gas recirculation in spark ignition engine by Zeldovich’ mechanism, support vector machine and regression correlation

The hydrogen fuel is an ideal fuel to achieve the zero-carbon emission for heavy-duty vehicles. The experiments have been conducted under a wide range of operating conditions at different ignition timings (0 to 60°CA bTDC), hydrogen fractions (0–40%) in CNG fuel, EGR ratios (0–35.8%), manifold absolute pressures (65–178 kPa), engine speed (700–2000 rpm) at stoichiometric condition. The calibrated quasi-dimensional combustion model has been applied for simulation of in-cylinder pressure and temperature of HCNG engine. These combustion parameters have been utilized in Zeldovich’ mechanism for the prediction of NOx emission. The mean absolute percentage error is 69.1573% and R2 = 0.8353 that is quite high. The support vector machine and regression correlation have been applied for NOx modelling of HCNG engine.The four sets have been formulated at different independent variables: controllable parameters (engine speed, load, ignition timing, EGR ratio and hydrogen fraction in CNG fuel), combustion parameters (maximum pressure, indicated mean effective pressure, coefficient of variation and combustion duration), working medium parameters (inlet temperature, exhaust temperature, manifold absolute pressure and temperature of the coolant) and mixed parameters. The different sets of brake specific NOx emission have been trained by support vector machine algorithm at different independent variables, combinations (5C2, 5C3, 5C4, 5C5), tuning parameters (penalty factor kernel, function width & intensive band loss function) and sample sizes (decrement & increment of sample sizes). The minimum MAPE for SVM is 4.7490% & R2 = 0.9981. The mathematical modelling has been carried out with utilization of suitable independent parameters, combination and sample size. The mean absolute percentage error for regression correlation is 18.2461% and R2 = 0.9619.

Exergy evaluation of equivalence ratio, compression ratio and residual gas effects in variable compression ratio spark-ignition engine using quasi-dimensional combustion modeling

This study is founded on previously validated, in-house, comprehensive, two-zone, quasi-dimensional combustion model, predicting performance and emissions in spark-ignition engines. The model is extended to include exergy terms complementing the first-law analysis results, which provide further useful information by implementation on test results acquired from an experimental, Ricardo E6, variable compression ratio (VCR) gasoline engine at the authors’ laboratory, operated under various CR and equivalence ratio (EQR) values at wide-open throttle. The model consists of unburned and burned zones simulating the combustion process, following strictly the flame-front movement in combustion chamber. The eventual combustion of the unburned mixture turbulent entrainment into burned zone through the flame-front area is followed closely. The exergy terms of each simulated zone are identified and computed, considering also chemical exergy components (diffusion plus reactive). The accurate account of temperature and species histories in burned zone after entrainment from the unburned one, should lead to more precise evaluation. This is important if irreversibility is computed from balance of all other exergy terms. The investigation examines and compares exergy-wise various EQR, CR and residual gas fractions. Presented diagrams of exergy terms for each zone and cylinder charge provide detailed information for chemical exergy, irreversibility and exergy losses.

Computational optimization of CH4/H2/CO blends in a spark-ignition engine using quasi-dimensional combustion model

Recent research has proven that computational fluid dynamics (CFD) modeling in combination with a genetic algorithm (GA) algorithm is an effective methodology to optimize the design of internal combustion (IC) engines. However, this approach is time consuming, which limits the practical application of it. This study addresses this issue by using a quasi-dimensional (QD) model in combination with a GA to find optimal fuel composition in a spark ignition (SI) engine operated with CH4/H2/CO fuel blends. The QD model for the simulation of combustion of the fuel blends coupled with a chemical kinetics tool for ignition chemistry was validated with respect to measured pressure traces and NOx emissions of a small size single-cylinder SI engine operated with CH4/H2 blends. Calibration was carried out to assess the predictive capability of the QD model, and the effect of hydrogen addition on the lean limit extension of the methane fueled engine was studied. A GA approach was then used to optimize the blend composition and engine input parameters based on a fitness function. The QD-GA methodology was implemented to simultaneously investigate the effects of three input parameters, i.e., fuel composition, air–fuel equivalence ratio and spark timing on NOxemissions and indicated thermal efficiency (ITE) for the engine. The results found indicated that this approach could provide optimal fuel blends and operating conditions with considerable lower NOx emissions together with improved thermal efficiencies compared to the methane fueled engine. The presented computationally-efficient methodology can also be used for other fuel blends and engine configurations.

Prediction of Ultra-Lean Spark Ignition Engine Performances by Quasi-Dimensional Combustion Model With a Refined Laminar Flame Speed Correlation

AbstractThe turbulent combustion in gasoline engines is highly dependent on laminar flame speed SL. A major issue of the quasi-dimensional (QD) combustion model is an accurate prediction of the SL, which is unstable under low engine speeds and ultra-lean mixture. This work investigates the applicability of the combustion model with a refined SL correlation for evaluating the combustion characteristics of a high-tumble port gasoline engine operated under ultra-lean mixtures. The SL correlation is modified and validated for a five-component gasoline surrogate. Predicted SL values from the conventional and refined functions are compared with measurements taken from a constant-volume chamber under micro-gravity conditions. The SL data are measured at reference and elevated conditions. The results show that the conventional SL overpredicts the flame speeds under all conditions. Moreover, the conventional model predicts negative SL at equivalence ratio ϕ ≤ 0.3 and ϕ ≥ 1.9, while the revised SL is well validated against the measurements. The improved SL correlation is incorporated into the QD combustion model by a user-defined function. The engine data are measured at 1000–2000 rpm under engine load net indicated mean effective pressure (IMEPn) = 0.4–0.8 MPa and ϕ = 0.5. The predicted engine performances and combustions are well validated with the measured data, and the model sensitivity analysis also shows a good agreement with the engine experiments under cycle-by-cycle variations.

Study of laminar burning speed and calibration coefficients of quasi-dimensional combustion model for hydrogen enriched compressed natural gas fueled internal combustion engine along with exhaust gas recirculation

Utilization of hydrogen fuel in internal combustion engine is regarded as a good choice due to its zero-carbon emission. The experiments have been performed under a wide range of operating conditions and the traces of in-cylinder pressures are obtained. The authors have formulated five laminar burning speed correlations (HCNG/N2, HCNG/N2/P, HCNG/CO2/T, HCNG/CO2/P & HCNG/CO) from the previous experimental work [25,31,33,34,38] while, two correlations (HCNG/N2/O2/T & HCNG/CO/CO2/T) have been imported from the earlier research work [32,35]. The iterations of these models have been staged in the quasi-dimensional combustion program of HCNG engine along with the exhaust gas recirculation but, results were un-satisfactory.Therefore, another laminar burning speed correlation (HCNG/N2/CO2/T) has been developed to improve the quasi-dimensional combustion model of HCNG fuel mixture along with the EGR technique. The kinetic GRI Mech3.0 mechanism has been utilized for the prediction of laminar burning speed of HCNG fuel mixtures at different hydrogen fractions 0 to 20% by volume in compressed natural gas, EGR ratios (0–0.3) and inlet temperatures (300–350 K). The effect of EGR ratio on the laminar burning speed in HCNG fuel mixture is introduced as the simultaneous impact of CO2 and N2 dilution of gases according to the balance chemical equation at stoichiometric condition. The presented model (HCNG/N2/CO2/T) is best suited for the quasi-dimensional combustion code of HCNG fuel mixture along with the exhaust gas recirculation as mean of the sample points of Taylor length & turbulent intensity coefficients are closer to 1 and minimum absolute percentage error for the indicated mean effective pressure.

Development and validation of a novel quasi-dimensional combustion model for un-scavenged prechamber gas engines with numerical simulations and engine experiments

Lean-burn gas engines equipped with an un-scavenged prechamber have proven to reduce nitrogen oxides (NOx) emissions and fuel consumption, while mitigating combustion cycle-to-cycle fluctuations and unburned hydrocarbon (UHC) emissions. However, the performance of a prechamber gas engine is largely dependent on the prechamber design, which has to be optimised for the particular main chamber geometry and the foreseen engine operating conditions. Optimisation of such complex engine components relies partly on computationally efficient simulation tools, such as quasi and zero-dimensional models, since extensive experimental investigations can be costly and time-consuming. This article presents a newly developed quasi-dimensional (Q-D) combustion model for un-scavenged prechamber gas engines, which is motivated by the need for reliable low order models to optimise the principle design parameters of the prechamber. Our fundamental aim is to enhance the predictability and robustness of the proposed model with the inclusion of the following: (i) Formal derivation of the combustion and flow submodels via reduction of the corresponding three-dimensional models. (ii) Individual validation of the various submodels. (iii) Combined use of numerical simulations and experiments for the model validation. The resulting model shows very good agreement with the numerical simulations and the experiments from two different engines with various prechamber geometries using a set of fixed calibration parameters.

Knock Occurrence Prediction and Performance Optimization of a Natural Gas S.I. Engine

Natural gas is used as alternative fuel in speak ignition engines in many countries to satisfy air quality standards. Gas engines can be operated in stoichiometric and lean burn condition with different combustion and emission details. In this study a natural gas spark-ignition engine at stoichiometric condition was optimized to avoid knock occurrence. Compression ratio and engine speed were optimized to obtain the lowest fuel consumption accompanied with high power and low emission. A quasi-dimensional two-zone combustion model was developed to simulate combustion and a knocking model was incorporated with main model to predict any auto-ignition that might occur. The model was validated by comparing with available experimental results. Performance parameters and exhaust emissions were also computed by using this model. It was found that using cooled EGR especially at high compression ratios has a key role in reducing NO emission.

Impact of the laminar flame speed correlation on the results of a quasi-dimensional combustion model for Spark-Ignition engine

In the present study, the impact of the laminar flame speed correlation on the prediction of the combustion process and performance of a gasoline engine is investigated using a 1D numerical approach. The model predictions are compared with experimental data available for full- and part-load operations of a small-size naturally aspirated Spark-Ignition (SI) engine, equipped with an external EGR circuit.A 1D model of the whole engine is developed in the GT-Power™ environment and is integrated with refined sub-models of the in-cylinder processes. In particular, the combustion is modelled using the fractal approach, where the burning rate is directly related to the laminar flame speed.In this work, three laminar flame speed correlations are assessed, including both experimentally- and numerically-derived formulations, the latter resulting from the fitting of laminar flame speeds computed by a chemical kinetic solver.Each correlation is implemented within the combustion sub-model, which is properly tuned to reproduce the experimental performance of the engine at full load. Then, the reliability of the considered flame speed formulations is proved at part-loads, even under external EGR operations.

A new quasi-dimensional flame tracking combustion model for spark ignition engines

A new quasi-dimensional combustion model based on the flame tracking approach is described and presented in the paper. The new quasi-dimensional flame tracking model is able to simulate the turbulent combustion process in premixed fuel/air/residual gas mixtures. A new method for the description of the geometry of the combustion chamber and flame front was developed enabling the visualisation of flame front movement across the combustion chamber. The control of local turbulence quantities in the flame front near the wall enables that the developed flame tracking model can predict the entire turbulent combustion process after the flame kernel development in spark-ignition engines without case-dependent calibration requirement. The developed quasi-dimensional combustion model was validated with the experimental and results of multidimensional model of a single cylinder spark ignition engine on averaged cycles. The model was integrated with the previously developed ignition, mixture stratification and cyclic variability sub-model that enable the simulation of cyclic combustion variability triggered by the stochastic variations of flow angle at the spark plug, mixture stratification and in-cylinder turbulence level. Due to the novelty of the model which includes the control of local integral length scale and turbulent kinetic energy in the flame segments the predictive capability of quasi-dimensional model is achieved with the application of single set of parameters related to average cycle and cyclic combustion variability. Flame tracking model with the low computational time represents a promising tool to calculate the turbulent combustion process including cyclic combustion variability in modern spark ignition engines.

A quasi-dimensional combustion model for spark ignition engines fueled with gasoline–methanol blends

The current work provides a quasi-dimensional model for the combustion of methanol–gasoline blends. New correlations for the laminar burning velocity of gasoline and methanol are developed and used together with a mixing rule to calculate the laminar burning velocity of the blends. Several factors (such as the laminar burning velocity, initial flame kernel, residual gas fraction, turbulence, etc.) have been investigated and the sensitivity of these factors and the used sub-models on the predictive performance was assessed. The simulation results were compared with measurement data from two engines on different gasoline–methanol blends. The results show the importance of the laminar burning velocity correlation, the method of initializing combustion and the turbulent burning velocity model. The newly developed laminar burning velocity correlation of gasoline performed equally or better than the existing correlations and the newly developed correlation of methanol outperformed the other correlations. The initial flame kernel size had a strong influence on the ignition delay. Changing the initial flame kernel to reproduce the same ignition delay was very effective to improve the simulations. Several turbulent combustion models were tested with the newly developed laminar burning velocity correlations and optimized ignition delay. In conclusion, the model of Bradley reproduced the trend going from gasoline to methanol much better than others due to the inclusion of the Lewis number.

Comparative study of the NOx prediction model of HCNG engine

In order to study the nitrous-oxide emissions of HCNG engines a numerical NOx prediction model has been developed and coupled with the most accepted quasi-dimensional combustion model of SI engines. The experiments have been performed at the different hydrogen volumetric percentage (0–40%), ignition timing (22–30oBTDC), A/F ratio (1–1.73) and manifold-absolute pressure (60–137 kPa) at various engine load and speed. In the next phase of study, the simulation has been performed at the different operating conditions and various HCNG blends. The two NOx mechanism: the thermal NOx and prompt NOx mechanism have been used to predict the NOx emission.The presented Model III of NOx prediction is validated by the previous models (Model I by Dawyer et al. and Model II by Kornbluth et al.) and experimental results. The NO packet and three-zone approach are designed and incorporated to Model III, which is based on the temperature duration of NOx formation. The NO curve has been transformed into NO packets through the temperature difference of NOx formation. Whereas, the width of the packet is equal to the ratio of crank angle duration to the total combustion duration. The total combustion duration is divided into three zones (phases). In order to check the accuracy of the model, the percentage error of NOx emission has been evaluated. Three NOx prediction models have been compared, and the model III is the best one. The error range of the model III is within ±15%.

Phenomenological two-phase multi-zone combustion model for direct-injection diesel engines

This paper proposed a quasi-dimensional combustion model from a new observed two-phase penetration and combustion phenomenon in diesel spray. In the model, fuel spray was divided into two of liquid and gas phase areas. Considering the phenomenon that separation of gas and liquid phase in diesel spray occurs during spray penetration, gas and liquid area of spray are discretized respectively. Liquid phase areas play important role in fuel mass transport, however gas phase areas are the main region for fuel combustion in the model. Fuel and air mixing rate of gas phase zone is the key for the calculation of combustion rate. Validation experiments are designed by using optimal Latin hypercube design method. Comparison of calculations and experiments show that the model is able to predict diesel engine performance at different engine speeds, loads, and injection pressure and timing, and provides guidance for the design of engines.

One-dimensional combustion model with detailed chemistry for transient diesel sprays

A new quasi-dimensional multi-zone diesel combustion model was developed, using the Musculus–Kattke transient spray model. The later is modified and extended in order to take into account fuel evaporation, to deal with multiple species and to account for temperature changes due to mixing and combustion. The spray model is then coupled with the CHEMKIN code to calculate the chemical reaction rates and to evaluate the ignition delay and the combustion rate. The results are first compared with those from the original Musculus–Kattke model in the case of a non-vaporizing inert spray. Good agreement is found. Then, experiments from the Engine Combustion Network database are used to evaluate the code on diesel-like combustion realized in a combustion vessel. Several tests are first conducted to set the values of the various numerical parameters of the model. Then a detailed analysis of the results is provided for a test case. Finally, two parametric studies are performed, relative to the ambient temperature and the oxygen concentration. The agreement of the model with the experiments is qualitatively good. However, some quantitative discrepancies appear, especially at low temperatures or for diluted combustions.