A predictive 0-D HCCI combustion model for ethanol, natural gas, gasoline, and primary reference fuel blends

Zhou, Yingcong; Hariharan, Deivanayagam; Yang, Ruinan; Mamalis, Sotirios; Lawler, Benjamin

doi:10.1016/j.fuel.2018.10.041

Cited by 26 publications

(8 citation statements)

References 28 publications

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“…The value of t ign for the main heat release is a function only of the main injection timing as combustion of the pilot injection has already started. To find the start of combustion for the pilot injection, an ignition delay correlation with an Arrhenius form was adapted from 28 and has the form where p is the pressure in bar, ϕ pilot is the fresh air equivalence ratio after the pilot injection, χ 0 2 is the oxygen molar percentage, set to 21%, the activation energy E a is coupled with the gas constant R and set to E a R = 8198 K , and T is temperature of the charge. This correlation is used to provide the ignition delay for the Livengood, Wu integral 29 When the auto-ignition integral value reaches 1, t 1 is considered to be t ign .…”

Section: Table A1mentioning

confidence: 99%

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

Drallmeier

Middleton

Siegel

2022

International Journal of Engine Research

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This paper investigates the optimal crankshaft motion for an opposed piston (OP) engine in a novel hybrid architecture to maximize fuel efficiency. The OP engine was selected for this work due to its inherent thermodynamic benefits and the balanced nature of the engine which can achieve downsizing through reducing the number of cylinders rather than the individual cylinder volume. The typical geartrain required on an OP engine was exchanged for two electric motors, reducing friction loss and decoupling the crankshafts. Using the motors to control the crankshaft motion profiles, this architecture introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To leverage these opportunities, an optimization scheme was developed utilizing nonlinear optimization of a 0-D model to compute the crankshaft motion profile that maximizes the work generated by the system. This optimization was then iteratively coupled with a high fidelity model which supplies the cylinder flow boundary conditions. This iterative approach reduces the model complexity used in the optimal control problem (OCP) while capturing the gas exchange dynamics critical to the 2-stroke cycle of the OP engine. By using the rate of change of motor torque as the input to the OCP, the torque fluctuation in a single cycle can be limited to ensure tracking feasibility. The results show crankshaft velocity slows during the compression stroke and conversely accelerates during the expansion stroke, reducing the peak motor torque required for control and thus reducing the motor losses. The extended residence time at top dead center, however, leads to an increase in heat transfer, illustrating the trade-off between the work extraction efficiency and the indicated engine efficiency.

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Section: Table A1mentioning

confidence: 99%

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

Drallmeier

Middleton

Siegel

2022

International Journal of Engine Research

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“…To solve these problems, scholars have adopted various advanced technologies such as intake air heating [2] and increasing the compression ratio of internal combustion engines [3], control strategy with dual fuel [4] and simulation of surrogate fuels [5]. The use of numerical simulation with chemical kinetics as the core is one of the most appropriate means to explore combustion mechanism and help achieve accurate control of HCCI.…”

Section: Introductionmentioning

confidence: 99%

Chemical Kinetic Model of Multicomponent Gasoline Surrogate Fuel with Nitric Oxide in HCCI Combustion

Yang

Zheng

2020

Molecules

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This study presents a simplified mechanism of a five-component gasoline surrogate fuel (TDRF–NO) that includes n-heptane, isooctane, toluene, diisobutylene (DIB) and nitric oxide (NO). The mechanism consists of 119 species and 266 reactions and involves TDRF and NO submechanisms. Satisfactory results were obtained in simulating HCCI combustion in engines. The TDRF submechanism is based on the simplified mechanism of toluene reference fuel (TRF) and adds DIB to form quaternary surrogate fuel for gasoline. A simplified NO submechanism containing 33 reactions was added to the simplified mechanism of TDRF, considering the effect of active molecular NO on the combustion of gasoline fuel. The ignition delay data of the shock tube under different pressure and temperature conditions verified the validity of the model. Model verification results showed that the ignition delay time predicted by the simplified mechanism and its submechanics were consistent with the experimental data. The addition of NO caused the ignition delay time of the mechanism simulation to advance with increasing concentration of NO added. The established simplified mechanism effectively predicted the actual combustion and ignition of gasoline.

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“…However, these will not be explicitly considered in this work. The authors anticipate that the AI phasing will be an input to burn duration models, 16,17 and that overall combustion phasing and AI burn duration provide sufficient information to establish CCV limits.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

Robertson

Prucka

2020

International Journal of Engine Research

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The drive to improve performance and efficiency of internal combustion engines has greatly expanded the degrees of freedom of engine systems. As efficiency objectives exceed the capability of traditional combustion strategies, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process itself. Spark-assisted compression ignition is an efficient production-viable advanced combustion mode characterized by a spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. This research focuses on autoignition modeling for spark-assisted compression ignition combustion phasing control. This work comprehensively evaluates several autoignition model structures and identifies the real-time production control implications of each. The candidate models include four ignition delay correlations, an ignition delay lookup, three polynomial regressions, and an artificial neural network. All are computationally feasible using production controllers, but the artificial neural network model represents autoignition phasing significantly better than the other options evaluated. The polynomial regressions were similar in error and exceeded the accuracy of ignition delay models. The low performance of the induction time integral–based models stems primarily from the exclusion of low-temperature heat release. The regression models are also exercised on an experimental engine dataset to identify the impact of engine phenomenon such as charge stratification on the performance of each model structure. The trends in the model performance as well as the magnitude of the error were similar when evaluated on both spark-assisted compression ignition simulation data and homogeneous charge compression ignition experimental data.

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A predictive 0-D HCCI combustion model for ethanol, natural gas, gasoline, and primary reference fuel blends

Cited by 26 publications

References 28 publications

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

Chemical Kinetic Model of Multicomponent Gasoline Surrogate Fuel with Nitric Oxide in HCCI Combustion

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

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