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Journal Article

Development of JP-8 Surrogates and their Validation using Ignition Quality Tester

2014-04-15
2014-01-9077
This paper presents a new approach for the development of six different JP-8 surrogates for application in diesel cycle simulation. The approach involves a step-wise formulation of 2-, 3-, and 4-component surrogates from a list of pure compounds which are selected based on several criteria. A MATLAB code is developed and is used in conjunction with the Ignition Quality Tester (IQT) and HYSYS software in order to formulate optimal surrogates. The first part of the results shows a comparison between the calculated and the measured DCNs for six surrogates. The differences in the properties such as the density, volatility, lower heating value, H/C ratio, molecular weight, and threshold sooting index of the surrogates and the JP-8 are also highlighted. This is followed by the evaluation of the surrogates with respect to the target JP-8 fuel. The evaluation is made in terms of ignition delays and the rate of heat release at three different IQT test temperatures.
Technical Paper

Development of a Surrogate for SASOL IPK and Its Validation in Ignition Quality Tester

2017-03-28
2017-01-0263
SASOL IPK is a low cetane number synthetic fuel formed from coal by the Fischer-Tropsch process which can be used as an extender to JP8, currently used in military ground vehicles. This paper presents two surrogates developed considering the following criteria: (a) availability of kinetic combustion models for each component, (b) smallest number of components to reduce computation time and cost, (c) matching the following properties of target fuel DCN, distillation curve, density, LHV, MW and H/C ratio. The autoignition and combustion characteristics of the surrogates were validated in IQT according to ASTM D6890-10a. Surrogate formulation strategy involves an equation to calculate DCN of the surrogate mixture from the DCN of each component. The linear equation commonly used for such calculations was modified to include a multiplier, based on regression analysis, for each component to produces DCN values that agree well with the measured DCN in the IQT.
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