Artificial neural networks approach on vibration and noise parameters assessment of flaxseed oil biodiesel fuelled CI en
- PDF / 2,595,560 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 82 Downloads / 147 Views
ORIGINAL PAPER
Artificial neural networks approach on vibration and noise parameters assessment of flaxseed oil biodiesel fuelled CI engine S. Jaikumar1 · V. Srinivas1 · M. R. S. Satyanarayana1 · M. Rajasekhar1 · D. Vamsi Teja1 · Ch. Tej Kamal1 Received: 3 April 2020 / Revised: 30 August 2020 / Accepted: 30 September 2020 © Islamic Azad University (IAU) 2020
Abstract The current research dwells the experimental investigation together with artificial neural networks analysis on vibration and noise intensity of VCR diesel engine energized with flaxseed oil biodiesel blends. The tests were carried out at four different loads, namely 25, 50, 75, and 100% and three compression ratios (16.5, 17.5, and 18.5). The vibration levels were measured at different locations of the engine body which accounts for the development of bulk vibration. The RMS acceleration (vibration) and RMS noise were calculated. The flaxseed biodiesel mixes have shown the positive outcome concerning vibration and noise intensities, and FSOME20 (20% blend) was revealed inferior to leftover fuel samples. Besides, the intensity of vibration and noise was seen superior at higher compression ratios and loads. The identical disparity was observed irrespective of the fuel samples. At peak load, the RMS acceleration of FSOME20 was reduced by 12.2, 12.05, and 16.74%, while the RMS noise was brought down by 6.8, 9.6, and 7.23% at CR16.5, CR17.5, and CR18.5, respectively. Finally, the experimental results were compared with artificial neural networks (ANNs) predicted outcomes. The coefficient of correlation (R2) and root mean square error (RMSE) noticed were 0.965 and 0.12% intended for vibration, whereas 0.989 and 0.56% for noise correspondingly. Keywords Root mean square · Cetane number · Vibrations · Transesterification · Noise Abbreviations FSOME Flax seed oil methyl ester (Flax seed oil biodiesel) FSOME0 0% FSOME in 100% conventional diesel (conventional diesel) FSOME10 10% FSOME in 90% conventional diesel FSOME20 20% FSOME in 80% conventional diesel FSOME30 30% FSOME in 70% conventional diesel CR Compression ratio IC Internal combustion CI Compression ignition ANN/ANNs Artificial neural network(s) RMS Root mean square RMSE Root mean square error R2 Correlation/regression coefficient TRAINLM Levenberge–Marquardt back-propagation Editorial responsibility: Mohamed F. Yassin. * S. Jaikumar [email protected] 1
Department of Mechanical Engineering, GITAM (Deemed to be University), Visakhapatnam 530045, India
TANSIG Tangent sigmoid PURELIN Pure linear yi Output of the neuron bi Bias xj Input vector wij Synaptic weight ti Target value oi Output value dB (A) Decibel units dB (A) (f) Noise with respect to frequency
Introduction On account of durability and high combustion efficiency, compression ignition (CI) engines are used broadly in the farming field and industry for mobile and immobile applications. Fossil fuels are inexpensive, easily combustible, and widely distributed. However, it is evident that these resources ar
Data Loading...
