Measurement and Simulation of n -Heptane Mixture Autoignition

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Laminar flame speed and autoignition are two fundamental characteristics of a hydrocarbon’s combustion. These two characteristics are extensively studied due to their importance in designing and developing combustion systems. However, a regime in which these two characteristics simultaneously affect the combustion process is not well understood. Thus, the primary focus of this investigation is to understand the autoignition-assisted flame regime prior to the first-stage heat release. The premixed mixture of n-heptane in oxygen–nitrogen–diluent at an equivalence ratio of 1.0, a compressed gas pressure of 6.85 bar, and compressed gas temperatures of 621 K (when using argon) and 616 K (when using helium) were studied in this work. At these initial conditions, the mixture ignition delays are 32.31 ms (using argon) and 53.92 ms (using helium). The flame-propagating images were recorded using a high-speed camera at different spark ignition times to measure the flame locations. The Rapid Compression MachineFlame (RCM-Flame) apparatus was used to perform experiments, and one-dimensional modeling and three-dimensional numerical modeling were performed. The simulations provided data to understand the effect of stretch on the measured spherical burning velocity. The measured and simulated autoignition-assisted laminar flame speeds were in excellent agreement, the linear method can be used to remove the stretch from the spherical burning velocity, and the autoignition-assisted flame speeds show a negligible dependency on the spark ignition times when the first-stage heat release is negligible.

Section: Experimental Setup and Methodologymentioning

confidence: 99%

Measurement and Simulation of Autoignition-Assisted Flame Speed of N-Heptane Mixture at Elevated Gas Temperature

Goyal

Abianeh

2023

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“…In addition, a creviced piston is used to further lessen the formation of roll-up vortices during the compression stroke. A detailed description of the RCM used in this work can be found in our previous studies, such as refs and . However, a brief description of the apparatus is provided herein and shown in Figure a.…”

Section: Experimental Setup and Methodologymentioning

confidence: 99%

Methane Laminar Flame Speed Measurement at High Gas Temperature Using Rapid Compression Machine-Flame (RCM-Flame)

Goyal

Abianeh

2022

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An outwardly expanding spherical flame speed has been studied using a constant volume chamber, a spherical bomb, and a shock tube. However, the literature reports very few laminar flame speed measurements at high gas temperatures relevant to the combustion system conditions. Therefore, this research primarily focuses on measuring the laminar flame speeds at high gas temperatures using a new experimental technique called rapid compression machine flame (RCM-Flame). The laminar flame speeds were measured using a stoichiometric mixture of methane, oxygen, and argon at an average compressed gas pressure of 3.04 bar and a range of gas temperatures from 740 to 765 K. The effects of the RCM chamber size, ignition energy, and temperature inhomogeneity on the flame propagation process were investigated by performing both one-dimensional (1D) and three-dimensional (3D) numerical modeling. The AramcoMech 3.0-detailed kinetic model was reduced under the studied conditions by considering ignition delay times and flame speeds to perform 3D numerical modeling. The linear extrapolation method of expanding a spherical flame showed that laminar flame speeds obtained through the experiments agree well with simulated data generated using 1D and 3D numerical modeling.

“…The compressed gas temperature was 633 K, and the compressed gas pressure was 13.5 bar. 12 Reproduced or adapted with permission from. and observed cetane numbers, affirming the efficacy of the ANN-based approach in predicting the ignition delay for diesel fuels.…”

Section: Application Of An Artificial Neural Network In Ignition Dela...mentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

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This review explores machine learning approaches for predicting ignition delay in combustion processes. Ignition delay is a vital parameter in optimizing the engine design, fuel formulations, and combustion efficiency. The review examines the applications of artificial neural networks (ANNs) and convolutional neural networks (CNNs) in various combustion processes and equipment, such as engines, boilers, and rapid compression machines. The differences between ANNs and CNNs are discussed, highlighting their capabilities and limitations. Numerous studies are presented, demonstrating the successful application of neural networks in predicting ignition delay for different fuels and engines. Overall, machine learning approaches show great promise in accurately predicting the ignition delay and advancing energy utilization.