Investigation on the autoignition characteristics of propanol and butanol isomers under diluted lean conditions for stratified low temperature combustion

He, Xiaoyu; Wang, Qi; Fernandes, Ravi X.; Shu, Bo

doi:10.1016/j.combustflame.2021.111818

Cited by 10 publications

(1 citation statement)

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“…Internal combustion engine testing is becoming increasingly difficult because of increasingly stringent regulations on pollutant emissions [1][2][3] and increasing performance demands from customers [4][5][6]. Hence, exploring modern combustion strategies such as low-temperature combustions [7,8], expanding the use of renewable fuels [9], and increasing the hybridization level of vehicles are mandatory to meet the demands of sustainable mobility [10,11]. Concerning modern spark-ignition (SI) engines, the path to reduce fuel consumption demands the adoption of high boost levels in conjunction with downsizing [12,13], as well as water injection [14,15] and lean and/or exhaust gas recirculation diluted mixtures [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of a Hybrid LSTM + 1DCNN Approach to Predict In-Cylinder Pressure of Internal Combustion Engines

Ricci,

Petrucci,

Mariani

et al. 2023

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The control of internal combustion engines is becoming increasingly challenging to the customer’s requirements for growing performance and ever-stringent emission regulations. Therefore, significant computational efforts are required to manage the large amount of data coming from the field for engine optimization, leading to increased operating times and costs. Machine-learning techniques are being increasingly used in the automotive field as virtual sensors, fault detection systems, and performance-optimization applications for their real-time and low-cost implementation. Among them, the combination of long short-term memory (LSTM) together with one-dimensional convolutional neural networks (1DCNN), i.e., LSTM + 1DCNN, has proved to be a promising tool for signal analysis. The architecture exploits the CNN characteristic to combine feature classification and extraction, creating a single adaptive learning body with the ability of LSTM to follow the sequential nature of sensor measurements over time. The current research focus is on evaluating the possibility of integrating virtual sensors into the on-board control system. Specifically, the primary objective is to assess and harness the potential of advanced machine-learning technologies to replace physical sensors. In realizing this goal, the present work establishes the first step by evaluating the forecasting performance of a LSTM + 1DCNN architecture. Experimental data coming from a three-cylinder spark-ignition engine under different operating conditions are used to predict the engine’s in-cylinder pressure traces. Since using in-cylinder pressure transducers in road cars is not economically viable, adopting advanced machine-learning technologies becomes crucial to avoid structural modifications while preserving engine integrity. The results show that LSTM + 1DCNN is particularly suited for the prediction of signals characterized by a higher variability. In particular, it consistently outperforms other architectures utilized for comparative purposes, achieving average error percentages below 2%. As cycle-to-cycle variability increases, LSTM + 1DCNN reaches average error percentages below 1.5%, demonstrating the architecture’s potential for replacing physical sensors.

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