Traditional internal combustion engines have been in use for more than 100 years to power ships, machinery, and automobiles, and will remain in use for the foreseeable future as society transitions to electrification. Stringent emissions regulations highlight the importance of making such engines more efficient and clean. Density, an important means of evaluating fuel quality, is easy to measure. Research has shown that high-density fuel results in higher emissions, but also that changing the density slightly can improve the emissions rate.
Researchers Gang Chen, Xiaoteng Zhang, Yang Zhao, Yafeng Pang, Chao Jin, and Haifeng Liu propose a mathematical model for diesel fuel mixture density prediction that will improve the accuracy of existing models. In their study, “Density Prediction Model of Binary or Ternary Diesel Fuel Blends with Biodiesel and Ethanol for Compression-Ignition Engine Calculations,” the authors wanted to predict the density of binary diesel fuel mixtures or even ternary diesel fuel mixtures at changed temperatures and volume ratios of the component fuels. This study provides important reference values for measuring or designing the density of diesel fuel mixtures. Learn more about this research in the Journal of Energy Engineering at https://doi.org/10.1061/JLEED9.EYENG-5385. The abstract is below.
Abstract
Density is an important indicator for evaluating diesel fuel quality that directly affects the injection timing and injection rule of the engine, and also has a significant impact on the spray broken particle size, the spray penetration distance, the spray cone angle, and so forth, which in turn affects the combustion process and pollutant emission of the compression-ignition engine. Therefore, it is important to accurately predict the diesel fuel mixture density in industrial and compression-ignition engines. However, the mathematical models for predicting the density of diesel fuel mixture with changed temperature are relatively lacking and less accurate, especially for ternary diesel fuel mixtures with different physicochemical properties. This paper proposes a mathematical model including binary and ternary diesel mixtures under changed fuel volume fraction and temperature, and published data were used for verification. The data verification results show that: for the density prediction of binary diesel fuel mixtures at constant temperature, the average relative deviation (ARD) is 0.0245%, the RMS error (RMSE) is 0.000344, and the correlation coefficient (𝑅) is 0.9993. For the density prediction of binary diesel fuel mixtures at changed temperature, the ARD is 0.0609%, the RMSE is 0.000695, and 𝑅 is 0.9980. For the density prediction of ternary diesel fuel mixtures at constant temperature, the ARD is lower than 0.0571%, the RMSE is lower than 0.000610, and 𝑅 is higher than 0.9861. For the density prediction of ternary diesel fuel mixtures at changed temperature, the ARD is 0.0484%, the RMSE is 0.000513, and 𝑅 is 0.9996. The diesel mixed fuel density prediction model proposed in this paper has good accuracy and calculation convenience and provides important reference values for measuring or designing the density of diesel mixed fuel in the field of compression-ignition engines.
Learn more this new formula to change a diesel fuel blend’s density to reduce its emissions in the ASCE Library: https://doi.org/10.1061/JLEED9.EYENG-5385.
