Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Aceves, Salvador M.; Flowers, Daniel L.; Chen, Jyh-Yuan; Babajimopoulos, Aristotelis

doi:10.4271/2006-01-3298

Cited by 13 publications

(6 citation statements)

References 28 publications

(33 reference statements)

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“…The autoignition integral was previously used to study knock in SI engines with a high degree of success [18]. More recently, it has been applied to HCCI research to study ignition timing, since HCCI is controlled by autoignition [19,20]. This research uses the autoignition integral, with the assumption that the mass in the cylinder follows a self-similar temperature contour, to predict ignition phasing of different temperature zones.…”

Section: Introductionmentioning

confidence: 99%

Development of a Postprocessing Methodology for Studying Thermal Stratification in an HCCI Engine

Lawler¹,

Hoffman²,

Filipi

et al. 2012

Journal of Engineering for Gas Turbines and Power

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Development of a Postprocessing Methodology for Studying Thermal Stratification in an HCCI EngineNaturally occurring thermal stratification signiflcantly impacts the characteristics of homogeneous charge compression ignition (HCCI) combustion. The in-cylinder gas temperature distributions prior to combustion dictate the ignition phasing, burn rates, combustion efficiency, and unburned hydrocarbon and CO emissions associated with HCCI operation. Characterizing the gas temperature fields in an HCCI engine and correlating them to HCCI burn rates is a prerequisite for developing strategies to expand the HCCI operating range. To study the development of thermal stratification in more detail, a new analysis methodology for postprocessing experimental HCCI engine data is proposed. This analysis tool uses the autoignition integral in the context of the mass fraction burned cwve to infer information about the distribution of temperature that exists in the cylinder prior to combustion. An assumption is made about the shape of the charge temperature profiles of tlie unburned gas during compression and after combustion starts elsewhere in the cylinder. Second, it is assumed that chemical reaction rates proceed very rapidly in comparison to tire staggering of ignition phasing from thermal stratification. The autoignition integral is then coupled to the mass fraction burned curve to produce temperature-mass distributions that are representative of a particular combustion event. Due to the computational efficiency associated with this zero-dimensional calculation, a large number of zones can be simulated at very little computational expense. The temperature-mass distributions are then studied over a coolant temperature sweep. The results show that veiy small changes to compression heat transfer can shift the distribution of mass and temperature in the cylinder enough to significantly affect HCCI burn rates and emissions.The proposed methodology breaks the cylinder into a large number of different "regions" or "zones," each with a different temperature. These "zones" are different from the "zones" traditionally described in multizonal HCCI combustion modeling [21][22][23][24] due to the fact that the "zones" in this analysis are not assigned a spatial location and, at least initially, there is no specific amount of mass attached to each "zone." For the lack of a Journal of Engineering for Gas Turbines and Power

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Section: Introductionmentioning

confidence: 99%

Development of a Postprocessing Methodology for Studying Thermal Stratification in an HCCI Engine

Lawler¹,

Hoffman²,

Filipi

et al. 2012

Journal of Engineering for Gas Turbines and Power

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“…This hysteresis method is necessary because the timescales associated with soot formation tend to be much longer than those of combustion chemistry. Furthermore, it should be noted that the concept of tracking the time history was also used by Aceves et al [49] for fast prediction of HCCI combustion with an ANN linked to the KIVA3V fluid mechanics code. In addition, in the work of Christo et al [28], in order to reduce the dependency of the ANN model on the selection of training sets for modeling turbulent flames, the input parameters were integrated over a prescribed reaction time.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

et al. 2020

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Soot formation in combustion systems is a growing concern due to its adverse environmental and health effects. It is considered to be a tremendously complicated phenomenon which includes multiphase flow, thermodynamics, heat transfer, chemical kinetics, and particle dynamics. Although various numerical approaches have been developed for the detailed modeling of soot evolution, most industrial device simulations neglect or rudimentarily approximate soot formation due to its high computational cost. Developing accurate, easy to use, and computationally inexpensive numerical techniques to predict or estimate soot concentrations is a major objective of the combustion industry. In the present study, a supervised Artificial Neural Network (ANN) technique is applied to predict the soot concentration fields in ethylene/air laminar diffusion flames accurately with a low computational cost. To gather validated data, eight different flames with various equivalence ratios, inlet velocities, and burner geometries are modeled using the CoFlame code (a computational fluid dynamics (CFD) parallel combustion and soot model) and the Lagrangian histories of soot-containing fluid parcels are computed and stored. Then, an ANN model is developed and optimized using the Levenberg-Marquardt approach. Two different scenarios are introduced to validate the network performance; testing the prediction capabilities of the network for the same eight flames that are used to train the network, and for two new flames that are not within the training data set. It is shown that for both of these cases the ANN is able to predict the overall soot concentration field very well with a relatively low integrated error.

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“…The review of turbulence combustion models can be found in [23], but if the ignition is formally considered as the process preceding the combustion stage, such models are not used in the ignition description. The most effective separation of ignition and combustion stages is realized in [24] based on the usage of ignition integral calculated with the help of an artificial neural network (ANN) and KIVA-3 V (or KIVA4) code. The ignition integral:…”

Section: Turbulent Combustion Modelingmentioning

confidence: 99%

Chemical Kinetic Study of Nitrogen Oxides Formation Trends in Biodiesel Combustion

Jiang¹,

Golovitchev²,

Lurbe³

et al. 2012

International Journal of Chemical Engineering

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The use of biodiesel in conventional diesel engines results in increasedNOxemissions; this presents a barrier to the widespread use of biodiesel. The origins of this phenomenon were investigated using the chemical kinetics simulation tool: CHEMKIN-2 and the CFD KIVA3V code, which was modified to account for the physical properties of biodiesel and to incorporate semidetailed mechanisms for its combustion and the formation of emissions. Parametric ϕ-T maps and 3D engine simulations were used to assess the impact of using oxygen-containing fuels on the rate of NO formation. It was found that using oxygen-containing fuels allows more O2molecules to present in the engine cylinder during the combustion of biodiesel, and this may be the cause of the observed increase in NO emissions.

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Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Cited by 13 publications

References 28 publications

Development of a Postprocessing Methodology for Studying Thermal Stratification in an HCCI Engine

Development of a Postprocessing Methodology for Studying Thermal Stratification in an HCCI Engine

An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

Chemical Kinetic Study of Nitrogen Oxides Formation Trends in Biodiesel Combustion

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