Yazar "Alharbi, Sulaiman Ali" seçeneğine göre listele

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    Experimental investigations on in-cylinder flame and emission characteristics of butanol-gasoline blends in SI engine using combustion endoscopic system
    (Elsevier, 2024) Kumaravel, S.; Saravanan, C. G.; Raman, Vallinayagam; Vikneswaran, M.; Sasikala, J.; Josephin, J. S. Femilda; Alharbi, Sulaiman Ali
    The objective of this study is to characterize the in-cylinder flames of butanol-gasoline blends in a spark ignition (SI) engine. The experiments were performed using butanol-gasoline blends prepared in the ratio of 10:90, 20:80, and 30:70 by volume. The in-cylinder combustion was visualized and captured using a combustion endoscopic system. From the captured combustion images, spatial flame distribution was evaluated for butanol-gasoline fuel blends. Furthermore, combustion, emission, and performance characteristics were investigated in a SI engine for the same blends. The engine test results were rationalized from the flame characterization results of butanol-gasoline combustion to improve the fundamental understanding. The experimental outcome is that the flame spread region (%) was found to be higher for butanol blends when compared to sole gasoline fuel. The addition of butanol to gasoline increased the flame speed and consequently increased the combustion burn rate, as well as the pressure and heat release rate within the cylinder. The brake thermal efficiency of the engine increased with increasing butanol concentration in the blend. In addition, the butanol-gasoline blends showed decreased CO and HC emissions when compared to gasoline but reportedly increased NO emission for butanol-blended gasoline blend fuels. Overall, this study concludes that butanol has the potential to be used as a supplement to gasoline due to improved flame and engine characteristics and can be used in the conventional gasoline engine without any major engine modification.
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    Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology
    (Pergamon-elsevier science, 2024) Seetharaman, Sathyanarayanan; Suresh, S.; Shivaranjani, R. S.; Dhamodaran, Gopinath; Bai, Femilda Josephin Joseph Shobana; Alharbi, Sulaiman Ali; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R 2 between 0.99426 and 0.9998 was observed by ANN whereas R 2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN 's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.