Parametric study and optimization using RSM of DI diesel engine for lower emissions

• Published in: Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 39; no. 3; pp. 671 - 680
• Main Authors: Ganji, Prabhakara Rao, Chintala, Kiran Prasad, Raju, V. R. K, Surapaneni, Srinivasa Rao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017; Springer Nature B.V
• Subjects: Automotive engines; Automotive fuels; Compression ratio; Design of experiments; Diesel engines; Emissions; Engineering; Exhaust gases; Fossil fuels; Fuel injection; Mechanical Engineering; Nitrogen oxides; Norms; Optimization; Parameter modification; Parametric statistics; Peak pressure; Pollutants; Response surface methodology; Soot; Technical Paper; CFD; RSM; Optimization; Emissions
• ISSN: 1678-5878; 1806-3691
• DOI: 10.1007/s40430-016-0600-0

Drastic climatic changes due to pollutants emitted by increased usage of fossil fuels in automotive are the major challenge in the current global scenario. This leads to stricter norms imposing by climatic authorities in terms of emissions. Therefore, the automotive manufactures or service providers have to be more cautious in tuning some of the parameters without modifying the entire geometry of the engine. This study presents the simulation analysis of the CAT3401 engine using CONVERGE™ CFD code by varying the parameters, such as compression ratio (CR) 16.5–12, start of injection (SOI) 30°–0° bTDC, fuel injection pressure (FIP) 500–1400 bar, and exhaust gas recirculation (EGR) 0–25 %. Response surface method (RSM) is adopted for the design of experiments for the selected combinations of the parameters. Results were analyzed in terms of performance (peak pressure) and emissions (NO x and Soot). Higher CR, FIP, and advanced SOI resulted in a decrease in the formation and improvement in the performance, but increased the NO x and vice versa. The EGR has a strong potential to reduce NO x but with increase in soot. The resultant models helped to predict the response parameters, such as peak pressure, NO x , and soot emissions. The optimum combination of the selected parameters is found for higher performance and lower emissions as compared with the existing reference engine.