Estimation of equivalence ratio distribution in diesel spray using a computational fluid dynamics

2014 ◽  
Author(s):  
Yasumasa Suzuki ◽  
Taku Tsujimura ◽  
Jin Kusaka
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Equivalence Ratio ◽  
Diesel Spray ◽  
Ratio Distribution

scholarly journals Comparison of Transient Diesel Spray Break-Up between Two Computational Fluid Dynamics Codes

2018 ◽  
Author(s):  
Louis Nicholson ◽  
Xiaohang Fang ◽  
Joseph Camm ◽  
Martin Davy ◽  
David Richardson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Diesel Spray ◽  
Break Up

scholarly journals Modelling the generation of temperature inhomogeneities by a premixed flame

2017 ◽  
Vol 10 (2) ◽  
pp. 111-130 ◽  
Author(s):  
Thomas Steinbacher ◽  
Max Meindl ◽  
Wolfgang Polifke
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Heat Release ◽  
Equivalence Ratio ◽  
Transfer Functions ◽  
Mixture Fraction ◽  
Transport Processes ◽  
Premixed Flame ◽  
Series Data ◽  
Entropy Transfer

The response of a laminar, premixed flame to perturbations of upstream equivalence ratio is investigated and modelled, with emphasis on the generation of ‘entropy waves’, i.e. entropic inhomogeneities of downstream temperature. Transient computational fluid dynamics simulations of two adiabatic lean methane-air flames of different Péclet numbers provide guidance and validation data for subsequent modelling. The respective entropy transfer functions, which describe the production of temperature inhomogeneities, as well as transfer functions for the variation of the heat release, are determined from the computational fluid dynamics time series data by means of system identification. The processes governing the dynamics of the entropy transfer functions are segregated into two sub-problems: (1) heat release due to chemical reaction at the flame front and (2) advective and diffusive transport. By adopting a formulation in terms of a mixture fraction variable, these two sub-problems can be treated independently from each other. Models for both phenomena are derived and analysed using simple 0- and 1-dimensional configurations. The heat release process (1) is represented by a fast-reaction-zone model, which takes into account variations of the specific heat capacity with equivalence ratio in order to evaluate the magnitude of downstream temperature fluctuations with quantitative accuracy. For the transport processes (2), two types of models based on mean field data from the computational fluid dynamics simulation are proposed: A semi-analytical, low-order formulation based on stream lines, and a state-space formulation, which is constructed by Finite Elements discretisation of the transport equation for mixture fraction. Model predictions for the entropy transfer functions are found to agree well with the computational fluid dynamics reference data at very low computational costs.

Effects of equivalence ratio and CNG addition on engine performance and emissions in a dual sequential ignition engine

2019 ◽  
Vol 21 (6) ◽  
pp. 1067-1082 ◽  
Author(s):  
Ahmet Alper Yontar ◽  
Yahya Doğu
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Equivalence Ratio ◽  
Three Dimensional ◽  
Engine Performance ◽  
Maximum Torque ◽  
Performance And Emissions ◽  
Increment Rate ◽  
Engine Performance And Emissions ◽  
Dynamics Analyses

Compared to widening usage of CNG in commercial gasoline engines, insufficient but increasing number of studies have appeared in the open literature during last decades, while engine characteristics need to be quantified in exact numbers for each specific fuel and engine. CNG usage in spark-ignition engine offers many advantages such as high specific power outputs, knock resistance, and low CO2 emission. Engine performance and emissions are strong functions of equivalence ratio. This study focuses on determination of the effects of equivalence ratio on engine performance and emissions for a unique commercial engine for three fuels of gasoline, CNG, and gasoline–CNG mixture (90%–10%: G9C1). For this aim, the tests and the three-dimensional in-cylinder combustion computational fluid dynamics analyses were employed in quantification of engine characteristics at wide open throttle position. Equivalence ratios were defined between 0.7 and 1.4. The engine’s maximum torque speed of 2800 r/min was examined. The tested commercial engine is an intelligent dual sequential ignition engine which has unique features such as diagonally positioned two spark-plugs, dual sequential ignition with variable timing and asymmetrical combustion chamber. This gasoline engine was equipped with an independent CNG port-injection system and a specific engine control unit for CNG. In addition, the engine test system has a concomitant dual fuel delivery system that supplies gas fuel into intake airline while liquid gasoline is injected behind the intake valve. Other than testing the engine, the three-dimensional in-cylinder combustion computational fluid dynamics analyses were performed in Star-CD/es-ice software for the three fuels. The CFD model was built by using renormalization group equations, k–ε turbulence model, and G-equation combustion model. Computational fluid dynamics analyses were run for the compression ratio of 10.8:1, equivalence ratio of 1.1, and engine’s maximum torque speed of 2800 r/min. Test results show that brake torque for all fuels increases rapidly from the lean blend to the rich blend. The brake-specific fuel consumption for all fuels decreases from Φ = 0.7 through the stoichiometric region and then slightly increases up to Φ = 1.4. The volumetric efficiencies for three fuels have similar decreasing trend with respect to equivalence ratio. Overall, CNG addition decreases the performance values of torque, brake-specific fuel consumption, volumetric efficiency, brake thermal efficiency, while it decreases emissions of CO2, CO, HC, except NOx. Engine model results show that the maximum in-cylinder pressure is 72 bar at 722 crank angle degree (CAD), 68 bar at 730 CAD, and 60 bar at 735 CAD for gasoline, CNG, and G9C1, respectively. The cumulative heat release for gasoline is 9.09% higher than G9C1, while G9C1 is 15.71% higher than CNG. The CO2 mass fraction for gasoline is about 22.58% lower than G9C1, while it is 40.32% higher than CNG. The maximum mass fraction value of CO is 0.21, 0.17, and 0.08 for gasoline, CNG, and G9C1, respectively. The CO for G9C1 is overall 60.04% lower than CNG and 67.45% lower than gasoline. At maximum point, HC for G9C1 is 31.43% and 71.43% higher than gasoline and CNG, respectively. CNG has the highest level of NOx formation. Maximum NOx mass fractions are 0.0098, 0.0070, and 0.0043 for CNG, G9C1, and gasoline, respectively. After the ignition, the flame development is completed at 1.07, 1.18, and 1.28 ms for gasoline, G9C1, and CNG, respectively. Flame velocities are 28.52, 30.93, and 34.11 m/s for CNG, G9C1, and gasoline, respectively, at 2800 r/min and Φ = 1.1. When the time between ignition moment and top dead center moment is considered, the increment rate of flame center temperature is 904.19, 884.10, and 861.77 K/s for CNG, gasoline, and G9C1, respectively. The highest temperature increment rate occurs for CNG.

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