The effect of different sampling method integrated in NSGA II optimization on performance and emission of diesel/hydrogen dual-fuel CI engine

• Published in: Applied soft computing 2022-10, Vol.128, p.109434, Article 109434
• Main Authors: Derikvand, Hadis, Shafiey Dehaj, Mohammad, Taghavifar, Hadi
• Format: Article
• Language: English
• Subjects: Combustion; Design of experiment; Hydrogen; Port fuel injection; SOBOL; Taguchi
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.109434

Recent advances in computational tools and techniques have enabled engineers to tackle single-step and iterative complex computer-aided engineering (CAE) problems. They have also helped develop CAE optimization techniques capable of driving design parameters towards regions where selected system characteristics can be further improved. The present work was conducted in two steps; In the first step, the effects of adding hydrogen to diesel fuel in a turbocharged direct injection (TDI) compression ignition (CI) Audi engine was analyzed through 1D simulation in AVL BOOST. With no significant modifications to the engine, the analysis was carried out at 1900 rpm and 2500 rpm under 0.8 MPa nominal loading conditions. Hydrogen was injected with air at 30 L/min through the intake manifold before the turbocharger. The present work considered all the engine components that are thermodynamically involved in the combustion process. The second analysis step coupled the simulated model with modeFRONTIER to study the optimum parameter values for engine performance and emission and determined the effects of various input variables on these parameters. Optimization was carried out by adopting hybrid algorithms to design experiments and the non-dominated sorting genetic algorithm II (NSGA_II). The response level predictive capability of the Taguchi and SOBOL methods increases the convergence speed of the NSGA_II. The results from the first analysis step indicated that the hydrogen diesel blend reduced brake specific fuel consumption (BSFC), engine power and torque, CO2 and NOx emissions, and increased entropy. The results from the second step suggested that in multivariable optimization, the peak pressure and NOx emissions were affected mainly, by combustion initiation time and the hydrogen added to the diesel fuel, respectively. Finally, it was found that hydrogen injection at 20 mg/min resulted in the best engine performance and emission. •Dual fuel hydrogen engine assessment with NSGA II operated by TOA and SOBOL.•0D/1D turbocharged powertrain optimization with DoE method and data processing.•Optimal points 2-level Taguchi creates poor results owing to high number of input variables.•With the same number of runs, the SOBOL offered better output results than the Taguchi.•Hydrogen injection at 20 mg/min resulted in the best engine performance and emission.