Multiple-objective optimization of a methanol/diesel reactivity controlled compression ignition engine based on non-dominated sorting genetic algorithm-II

Multiple-objective optimization of a heavy-duty compression ignition engine fueled by gasoline/hydrogenated catalytic biodiesel (HCB) blends at low loads was performed by employing the KIVA-3V code and genetic algorithm. In addition, the mechanism of multiple-injection and sensitivity of operating parameters on engine performance of the optimal cases were also explored. The results indicated that efficient combustions for G70H30 (70% gasoline and 30% HCB) and G100 (pure gasoline) with ultra-low nitrogen oxides (NOx) and soot emissions could be obtained after optimization. As HCB fraction increases, the ranges of operating parameters become more extensive, and the required initial temperature for optimal cases can be effectively reduced. When the main injection occurs after the ignition caused by pilot injection, main injection moderates the heat release rate (HRR) by creating concentration and temperature stratifications in the spray area simultaneously, and the exhaust gas recirculation (EGR) rate, pilot, and main start of injections and pilot fraction play dominant roles on engine performance. Moreover, when main injection is much more advanced than the ignition timing, main injection controls the HRR only through the concentration stratification in the reaction zone, and the EGR rate, initial temperature, and pilot faction have dominated effects on engine performance.

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Multiple-objective optimization of methanol/diesel dual-fuel engine at low loads: A comparison of reactivity controlled compression ignition (RCCI) and direct dual fuel stratification (DDFS) strategies

Fuel ◽

10.1016/j.fuel.2019.116673 ◽

2020 ◽

Vol 262 ◽

pp. 116673 ◽

Cited By ~ 10

Author(s):

Yaopeng Li ◽

Ming Jia ◽

Leilei Xu ◽

Xue-Song Bai

Keyword(s):

Multiple Objective ◽

Dual Fuel ◽

Multiple Objective Optimization ◽

Compression Ignition ◽

Reactivity Controlled Compression Ignition ◽

Fuel Stratification

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Effect of Injection Timing and Injection Duration of Manifold Injected Fuels in Reactivity Controlled Compression Ignition Engine Operated with Renewable Fuels

Energies ◽

10.3390/en14154621 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4621

Author(s):

P. A. Harari ◽

N. R. Banapurmath ◽

V. S. Yaliwal ◽

T. M. Yunus Khan ◽

Irfan Anjum Badruddin ◽

...

Keyword(s):

Nitric Oxide ◽

Injection Timing ◽

Compression Ignition ◽

Cylinder Pressure ◽

Compression Ignition Engine ◽

Compressed Natural Gas ◽

Reactivity Controlled Compression Ignition ◽

Brake Thermal Efficiency ◽

Injection Duration ◽

Fuel Mixtures

In the current work, an effort is made to study the influence of injection timing (IT) and injection duration (ID) of manifold injected fuels (MIF) in the reactivity controlled compression ignition (RCCI) engine. Compressed natural gas (CNG) and compressed biogas (CBG) are used as the MIF along with diesel and blends of Thevetia Peruviana methyl ester (TPME) are used as the direct injected fuels (DIF). The ITs of the MIF that were studied includes 45°ATDC, 50°ATDC, and 55°ATDC. Also, present study includes impact of various IDs of the MIF such as 3, 6, and 9 ms on RCCI mode of combustion. The complete experimental work is conducted at 75% of rated power. The results show that among the different ITs studied, the D+CNG mixture exhibits higher brake thermal efficiency (BTE), about 29.32% is observed at 50° ATDC IT, which is about 1.77, 3.58, 5.56, 7.51, and 8.54% higher than D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. The highest BTE, about 30.25%, is found for the D+CNG fuel combination at 6 ms ID, which is about 1.69, 3.48, 5.32%, 7.24, and 9.16% higher as compared with the D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. At all ITs and IDs, higher emissions of nitric oxide (NOx) along with lower emissions of smoke, carbon monoxide (CO), and hydrocarbon (HC) are found for D+CNG mixture as related to other fuel mixtures. At all ITs and IDs, D+CNG gives higher In-cylinder pressure (ICP) and heat release rate (HRR) as compared with other fuel combinations.

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Stochastic Cycle to Cycle Prediction in a Reactivity Controlled Compression Ignition Engine Using Double Wiebe Function

10.4271/2021-01-0374 ◽

2021 ◽

Author(s):

Chinmaya Mishra ◽

P M V Subbarao

Keyword(s):

Compression Ignition ◽

Compression Ignition Engine ◽

Reactivity Controlled Compression Ignition ◽

Wiebe Function

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Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

Volume 1: 25th Design Automation Conference ◽

10.1115/detc99/dac-8584 ◽

1999 ◽

Author(s):

Shapour Azar ◽

Brian J. Reynolds ◽

Sanjay Narayanan

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Optimization Technique ◽

Optimization Techniques ◽

Pareto Set ◽

Design Solution ◽

Multiple Objective ◽

Multiple Objective Optimization ◽

Continuous Variables ◽

Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

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