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Hadi Taghavifar

Hadi Taghavifar

Academic rank: Assistant Professor
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Education: PhD.
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Faculty: Technical Engineering
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Research

Title
Optimization of a DI diesel engine to reduce emission and boost power by exergy and NLPQL method
Type
JournalPaper
Keywords
diesel engine, exergy analysis, irreversibility, NLPQL optimization
Year
2019
Journal Environmental Progress and Sustainable Energy
DOI
Researchers Hadi Taghavifar

Abstract

The design and manufacture of efficient diesel engines, which can convert the majority of fuel combustion into a mechanical rotation movement is still growing. Simultaneous investigation of optimum engine configuration and the second law of thermodynamics consideration can best achieve this goal. A baseline 1.8 Ford diesel engine is simulated via AVL Fire software and then the multiobjective nonlinear programming by quadratic Lagrangian method have been applied to find the global optimal design. Then for exergy analysis of the baseline and optimized engine characteristics, a FORTRAN developed code is used. Two basic optimization cases are detected for exergy assessment that are RunID41 (feasible design) and RunID35 (infeasible design) among 55 run points. It was determined that the optimum feasible solution should characterize with low inner wall diameter (Di) with wider half spray cone angle to avoid wall collision in order to stay in the constraint zone and feasible area. The results of exergy also indicates that RunID41 is fuel efficient since the fuel burnt exergy of three design of ID41, ID35, and baseline are 719.52, 694.77, and 643.07 J, respectively. It has also been pointed out that the feasible optimum gives the higher thermo‐mechanical exergy, work exergy, and total exergy, while the infeasible optimum yields a higher heat release rate exergy and chemical exergy as there are no restrictions of injection and combustion chamber geometry set by the designer.