The Effects of Blade Count on Boundary Layer Development in a Low-Pressure Turbine

Dorney, Daniel J.; Flitan, Horia C.; Ashpis, David E.; Solomon, W. J.

doi:10.1515/tjj.2001.18.1.1

Cited by 3 publications

(1 citation statement)

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“…The results suggested that blending ethanol with gasoline can increase engine performance by increasing output power. This outcome can result from several factors, including the elevated oxygen content in ethanol, this higher oxygen concentration has the capacity to enhance the combustion process, ultimately resulting in a greater energy release during combustion [44]. In addition, ethanol possesses an increased capacity to absorb heat during its transition from liquid to gas due to its higher heat of vaporization.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of ethanol-gasoline blends in SI engines using experimental and ANN techniques

Hofny,

Ghazaly,

N Shmroukh

et al. 2024

Eng. Res. Express

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Fuel combustion has become a major global concern, with much research focusing on the various emissions resulting from different types of fuels. Due to the harmful pollutant emissions from fossil fuels, the world has turned to renewable and alternative fuels to limit toxic emissions and greenhouse effects. Ethanol is a biofuel that, when used in spark ignition engine with gasoline can improve the octane number, combustion efficiency, and produce less emissions. The current research studies the effect of different ethanol blends E0, E5, E10, and E15 with gasoline 92 on engine performance parameters and emissions of a GX35 four stroke engine at different engine speeds. The results along the speed range reveal that increasing ethanol amount leads to an average increase of 2.7%, 1%, and 1.1% in brake power (BP), brake thermal efficiency (BTE), and CO2 emissions, respectively. Meanwhile, it causes an average decrease of 28 ℃, 3%, 15 ppm, and 0.18% in exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), HC, and CO emissions respectively. Moreover, the current study develops an Artificial Neural Networks (ANN) model for predicting performance and emissions of spark ignition (SI) engine. Python programming language is used for ANN coding to train and validate the ANN model with E15. Regression plots were generated to visualize the correlation between the target and predicted data, indicating outstanding performance. The results confirmed the model's reliability for BP, EGT, CO, CO2, and HC parameters with R2 value more than 0.99 and with acceptable performance for BSFC and BTE with R2 of 0.9339, and 0.9708, respectively.

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Section: Resultsmentioning

confidence: 99%