Studying the effect of diesel fuel properties on emissions from engines with the fuzzy regression method

2011 ◽  
Author(s):  
Yuanwang Deng ◽  
Ye Yuan ◽  
Guixian Sun ◽  
Fei Zhou ◽  
Keliang Cheng
Keyword(s):  
Diesel Fuel ◽  
Regression Method ◽  
Fuel Properties ◽  
Fuzzy Regression

Novel approaches for the production of bio-diesel using waste vegetable oil

2014 ◽  
Vol 3 (10) ◽  
pp. 3419
Author(s):  
Mohan Reddy Nalabolu* ◽  
Varaprasad Bobbarala ◽  
Mahesh Kandula
Keyword(s):  
Diesel Fuel ◽  
Oxidation Stability ◽  
Acid Value ◽  
Waste Cooking Oil ◽  
Flash Point ◽  
Carbon Residue ◽  
Fuel Properties ◽  
Present Moment ◽  
Total Acid ◽  
Cooking Oil

At the present moment worldwide waning fossil fuel resources as well as the tendency for developing new renewable biofuels have shifted the interest of the society towards finding novel alternative fuel sources. Biofuels have been put forward as one of a range of alternatives with lower emissions and a higher degree of fuel security and gives potential opportunities for rural and regional communities. Biodiesel has a great potential as an alternative diesel fuel. In this work, biodiesel was prepared from waste cooking oil it was converted into biodiesel through single step transesterification. Methanol with Potassium hydroxide as a catalyst was used for the transesterification process. The biodiesel was characterized by its fuel properties including acid value, cloud and pour points, water content, sediments, oxidation stability, carbon residue, flash point, kinematic viscosity, density according to IS: 15607-05 standards. The viscosity of the waste cooking oil biodiesel was found to be 4.05 mm2/sec at 400C. Flash point was found to be 1280C, water and sediment was 236mg/kg, 0 % respectively, carbon residue was 0.017%, total acid value was 0.2 mgKOH/g, cloud point was 40C and pour point was 120C. The results showed that one step transesterification was better and resulted in higher yield and better fuel properties. The research demonstrated that biodiesel obtained under optimum conditions from waste cooking oil was of good quality and could be used as a diesel fuel.

scholarly journals Effect of the In-Cylinder Back Pressure on the Injection Process and Fuel Flow Characteristics in a Common-Rail Diesel Injector Using GTL Fuel

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 452
Author(s):  
Luka Lešnik ◽  
Breda Kegl ◽  
Eloísa Torres-Jiménez ◽  
Fernando Cruz-Peragón ◽  
Carmen Mata ◽  
...  
Keyword(s):  
Diesel Fuel ◽  
Back Pressure ◽  
Common Rail ◽  
Fuel Properties ◽  
Injection System ◽  
Nozzle Flow ◽  
Average Mass ◽  
Injection Process ◽  
Fuel Flow ◽  
Computational Program

The presented paper aims to study the influence of mineral diesel fuel and synthetic Gas-To-Liquid fuel (GTL) on the injection process, fuel flow conditions, and cavitation formation in a modern common-rail injector. First, the influence on injection characteristics was studied experimentally using an injection system test bench, and numerically using the one-dimensional computational program. Afterward, the influence of fuel properties on internal fuel flow was studied numerically using a computational program. The flow inside the injector was considered as multiphase flow and was calculated through unsteady Computational Fluid Dynamics simulations using a Eulerian–Eulerian two-fluid approach. Finally, the influence of in-cylinder back pressure on the internal nozzle flow was studied at three distinctive back pressures. The obtained numerical results for injection characteristics show good agreement with the experimental ones. The results of 3D simulations indicate that differences in fuel properties influence internal fuel flow and cavitation inception. The location of cavitation formation is the same for both fuels. The cavitation formation is triggered regardless of fuel properties. The size of the cavitation area is influenced by fuel properties and also from in-cylinder back pressure. Higher values of back pressure induce smaller areas of cavitation and vice versa. Comparing the conditions at injection hole exit, diesel fuel proved slightly higher average mass flow rate and velocities, which can be attributed to differences in fluid densities and viscosities. Overall, the obtained results indicate that when considering the injection process and internal nozzle flow, GTL fuel can be used in common-rail injection systems with solenoid injectors.

Calculation of jet and diesel fuel properties using carbon-13 NMR spectroscopy

1990 ◽  
Vol 4 (2) ◽  
pp. 152-156 ◽  
Author(s):  
David J. Cookson ◽  
Brian E. Smith
Keyword(s):  
Nmr Spectroscopy ◽  
Diesel Fuel ◽  
Fuel Properties

scholarly journals Modelling of Diesel fuel properties through its surrogates using Perturbed-Chain, Statistical Associating Fluid Theory

2018 ◽  
Vol 21 (7) ◽  
pp. 1118-1133 ◽  
Author(s):  
Alvaro Vidal ◽  
Carlos Rodriguez ◽  
Phoevos Koukouvinis ◽  
Manolis Gavaises ◽  
Mark A McHugh
Keyword(s):  
Equation Of State ◽  
Diesel Fuel ◽  
Group Contribution ◽  
Operating Conditions ◽  
Fuel Properties ◽  
Liquid Density ◽  
Theory Approach ◽  
Statistical Associating Fluid Theory ◽  
Diesel Fuels ◽  
Fluid Theory

The Perturbed-Chain, Statistical Associating Fluid Theory equation of state is utilised to model the effect of pressure and temperature on the density, volatility and viscosity of four Diesel surrogates; these calculated properties are then compared to the properties of several Diesel fuels. Perturbed-Chain, Statistical Associating Fluid Theory calculations are performed using different sources for the pure component parameters. One source utilises literature values obtained from fitting vapour pressure and saturated liquid density data or from correlations based on these parameters. The second source utilises a group contribution method based on the chemical structure of each compound. Both modelling methods deliver similar estimations for surrogate density and volatility that are in close agreement with experimental results obtained at ambient pressure. Surrogate viscosity is calculated using the entropy scaling model with a new mixing rule for calculating mixture model parameters. The closest match of the surrogates to Diesel fuel properties provides mean deviations of 1.7% in density, 2.9% in volatility and 8.3% in viscosity. The Perturbed-Chain, Statistical Associating Fluid Theory results are compared to calculations using the Peng–Robinson equation of state; the greater performance of the Perturbed-Chain, Statistical Associating Fluid Theory approach for calculating fluid properties is demonstrated. Finally, an eight-component surrogate, with properties at high pressure and temperature predicted with the group contribution Perturbed-Chain, Statistical Associating Fluid Theory method, yields the best match for Diesel properties with a combined mean absolute deviation of 7.1% from experimental data found in the literature for conditions up to 373°K and 500 MPa. These results demonstrate the predictive capability of a state-of-the-art equation of state for Diesel fuels at extreme engine operating conditions.

scholarly journals Diesel and various blending nanoparticles based diesel, fuel properties study

2020 ◽  
Vol 788 ◽  
pp. 012061
Author(s):  
M Norhafana ◽  
M M Noor ◽  
F Y Hagos ◽  
A A Hairuddin
Keyword(s):  
Diesel Fuel ◽  
Fuel Properties

Modeling Fuel Effects in a Diesel Engine Using Multi-Component Fuel Surrogates in CFD

2018 ◽  
Author(s):  
Karthik V. Puduppakkam ◽  
Chitralkumar V. Naik ◽  
Ellen Meeks
Keyword(s):  
Diesel Fuel ◽  
Cetane Number ◽  
Fuel Properties ◽  
Cfd Simulations ◽  
Fuel Property ◽  
Oak Ridge ◽  
Engine Combustion ◽  
Fuel Effects ◽  
The Impact ◽  
Fuel Surrogates

A continued challenge to engine combustion simulation is predicting the impact of fuel-composition variability on performance and emissions. Diesel fuel properties, such as cetane number, aromatic content and volatility, significantly impact combustion phasing and emissions. Capturing such fuel property effects is critical to predictive engine combustion modeling. In this work, we focus on accurately modeling diesel fuel effects on combustion and emissions. Engine modeling is performed with 3D CFD using multi-component fuel models, and detailed chemical kinetics. Diesel FACE fuels (Fuels for Advanced Combustion Engines) have been considered in this study as representative of street fuel variability. The CFD modeling simulates experiments performed at Oak Ridge National Laboratory (ORNL) [1] using the diesel FACE fuels in a light-duty single-cylinder direct-injection engine. These ORNL experiments evaluated fuel effects on combustion phasing and emissions. The actual FACE fuels are used directly in engine experiments while surrogate-fuel blends that are tailored to represent the FACE fuels are used in the modeling. The 3D CFD simulations include spray dynamics and turbulent mixing. We first establish a methodology to define a model fuel that captures diesel fuel property effects. Such a model should be practically useful in terms of acceptable computational turnaround time in engine CFD simulations, even as we use sophisticated fuel surrogates and detailed chemistry. Towards these goals, multi-component fuel surrogates have been developed for several FACE fuels, where the associated kinetics mechanisms are available in a model-fuels database. A surrogate blending technique has been employed to generate the multi-component surrogates, so that they match selected FACE fuel properties such as cetane number, chemical classes such as aromatics content, T50 and T90 distillation points, lower heating value and H/C molar ratio. Starting from a well validated comprehensive gas-phase chemistry, an automated method has been used for extracting a reduced chemistry that satisfies desired accuracy and is reasonable for use in CFD. Results show the level of modeling necessary to capture fuel-property trends under these widely varying engine conditions.

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