دوره 2، شماره 4 - ( 7-1391 )                   جلد 2 شماره 4 صفحات 206-215 | برگشت به فهرست نسخه ها


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Shojaeefard H, Etghani M, Tahani, Akbari. Artificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine. IJAE. 2012; 2 (4) :206-215
URL: http://www.iust.ac.ir/ijae/article-1-155-fa.html
Artificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine. مجله علمی پژوهشی مهندسی خودرو . 1391; 2 (4) :206-215

URL: http://www.iust.ac.ir/ijae/article-1-155-fa.html


چکیده:   (5044 مشاهده)

In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon monoxide (CO), hydrocarbon (HC) and Soot emissions has been considered. The tests were performed at various injection timings, loads and speeds. It is used artificial neural network (ANN) for predicting and modeling the engine performance and emission. Multi-objective optimization with respect to engine emissions level and engine power was used in order to deter mine the optimum load, speed and injection timing. For this goal, a fast and elitist non-dominated sorting genetic algorithm II (NSGA II) was applied to obtain maximum engine power with minimum total exhaust emissions as a two objective functions.

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نوع مطالعه: پژوهشي | موضوع مقاله: عمومى
دریافت: ۱۳۹۱/۷/۱۲ | پذیرش: ۱۳۹۵/۱۲/۱۳ | انتشار: ۱۳۹۵/۱۲/۱۳

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