Impact of injector nozzle diameter and hole number on performance and emission characteristics of CI engine powered by nanoparticles


Kothiwale G., Akkoli K., Doddamani B., Kattimani S., Ağbulut Ü., Afzal A., ...Daha Fazla

International Journal of Environmental Science and Technology, cilt.20, sa.5, ss.5013-5034, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 5
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s13762-022-04397-0
  • Dergi Adı: International Journal of Environmental Science and Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.5013-5034
  • Anahtar Kelimeler: Biodiesel, Diesel engine, Injector, MWCNT nanoparticles, Transesterification
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

To have energy sustainability and reduce emissions, it is essential to use alternative fuels in IC engines and improve their performance by using fuel combinations. In diesel engines, the fuel atomization process strongly affects combustion and emissions. The injector hole number of a fuel injector nozzle also plays a critical role in influencing the performance and emissions of diesel engines and is an important part of the diesel engine. In general, both parameters affect the spray parameters like droplet size and penetration length and thus the combustion process. In the present work, different injectors (4-hole injector with a nozzle diameter of 0.25 mm, 3-hole injector with a nozzle diameter of 0.20 mm) are used to study the performance and emissions characteristics of DI-CI diesel engine fuelled with a blend of Multi-Walled Carbon Nanotubes and Tallow Oil Methyl Ester. Multi-Walled Carbon Nanotubes were doped at 5, 10, 15, and 20 ppm into the test fuels. The experimental results revealed that the brake thermal efficiency of the engine slightly decreases when the engine is fueled by completely TOME biodiesel. Then the addition of Multi-Walled Carbon Nanotubes into the diesel-Tallow oil biodiesel blend improves the BTE. Furthermore, Multi-Walled Carbon Nanotubes lead to a noteworthy reduction in exhaust pollutants. Accordingly, all emissions (CO, HC, NOx, and smoke) were reduced with Multi-Walled Carbon Nanotubes in the test fuels thanks to the high surface area to volume ratio, higher energy content, catalyst role, accelerating chemical reactions, and oxidization of more unburnt fuels. Diesel–biodiesel blend with 20 ppm Multi-Walled Carbon Nanotubes exhibits superior performance and emissions characteristics among all blends. The BTE of the B40D60C20 blend was almost equivalent to that of diesel and has nearly equal emissions levels compared to diesel fuel under full and part load conditions. The B40D60C20 blend showed a maximum BTE of 30.9% which is 15.53% higher than raw TOME and 3.43% lower than diesel fuel. In addition to that, the blend B40D60C20 showed a significant reduction in CO emissions by 45.46%, HC by 17.29%, NOx by 15.25%, and smoke by 21.28% compared to the raw TOME. Therefore, the optimized fuel blend is B40D60C20 with a dose level of 20 mg/L, where a reasonable improvement in performance and emissions characteristics has been achieved. Additionally, a smaller nozzle diameter for injectors leads to better injection characteristics and a small size for atomized fuel droplets. Accordingly, better results in terms of engine performance and emissions characteristics are achieved for the injector having three-hole with a diameter of 0.20 mm. The optimized fuel combinations with the optimized nozzle geometry will lead to better IC engine performance. The response surface methodology and artificial neural network outcomes demonstrated that these two are excellent modelling techniques, with good accuracy. In addition, the artificial neural network's prediction performance was somewhat better than the response surface methodology.