Numerical study on auto-ignition development and knocking characteristics of a downsized rotary engine under different inlet pressures

Zou, Run; Liu, Jinxiang; Jiao, Huichao; Wang, Nana; Zhao, Jingjing

doi:10.1016/j.fuel.2021.122046

Cited by 17 publications

(5 citation statements)

References 33 publications

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“…The fuel used in this WRE is RON92 gasoline, with 92% (v/v) isooctane and 8% (v/v) n-heptane as the surrogate fuel for the commercial gasoline (#92 Sinopec, China-V). Meanwhile, the reduced primary reference fuel (PRF) skeletal mechanism of 47 species and 142 reactions was selected to achieve the combustion of the surrogate fuel, and the adaptability of this mechanism has been widely verified [34,35,38]. To verify the adaptability of the turbulence model, the combustion model, the initial, and the boundary conditions, a three-dimensional model of the WRE was established according to the literatures [9,39].…”

Section: Model Verificationmentioning

confidence: 99%

Effect of In-Cylinder Low-Pressure Direct Injection Strategy on the Atomization and Combustion Process of a Small-Scaled Gasoline Wankel Rotary Engine

Liu

Huang

et al. 2023

International Journal of Energy Research

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To solve the problem of poor atomization and combustion in a small-scaled gasoline Wankel rotary engine (WRE) with low-pressure port injection mode, the numerical simulation was used to optimize the injection strategy. Firstly, the effects of in-cylinder temperature and pressure on gasoline atomization characteristics were studied, and the optimal injection timing was determined. Based on this, the influence of injection position, injection angle, and installation direction on the atomization, fuel-air mixing, and combustion processes of low-pressure direct injection (DI) small-scaled gasoline WRE was investigated. The results show that the injection angle is a key factor in determining the gasoline atomization characteristics. Injecting along the direction of rotor rotation causes the impingement between fuel bundle and combustion chamber pocket, resulting in the smaller Sauter mean diameter (SMD) and liquid penetration length (LPL). The installation direction of nozzle plays an important role in the airflow movement. When the nozzle is vertical-installed, the airflow repeatedly crosses to form multiple eddies, making the fuel to move more easily towards the front of combustion chamber. When the nozzle is parallel-installed at the lower edge of the installing zone and injecting along the direction of rotor rotation, the peak in-cylinder pressure is the largest and increased by 21% compared to the original port injection. By this injection strategy, the problem of incomplete combustion for the studied small-scaled gasoline WRE could be almost completely solved.

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Section: Model Verificationmentioning

confidence: 99%

Effect of In-Cylinder Low-Pressure Direct Injection Strategy on the Atomization and Combustion Process of a Small-Scaled Gasoline Wankel Rotary Engine

Liu

Huang

et al. 2023

International Journal of Energy Research

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“…Besides, the PSR model was also promoted [18][19][20] for Reynolds-Averaged Navier-Stokes (RANS) simulations of premixed turbulent burning in combustion chambers of piston engines by assuming 19 weak temperature fluctuations in such chambers. Accordingly, the PSR model is widely used [21][22][23][24][25][26][27][28][29][30] in applied Computational Fluid Dynamics (CFD) research into turbulent burning in piston engines and other combustors. In particular, the PSR model is a default complex-chemistry model of flame-turbulence interaction in a popular CFD software such as Converge.…”

Section: Introductionmentioning

confidence: 99%

A priori test of perfectly stirred reactor approach to evaluating mean fuel consumption and heat release rates in highly turbulent premixed flames

Lipatnikov

2023

International Journal of Engine Research

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Unsteady three-dimensional Direct Numerical Simulation (DNS) data obtained earlier by Dave et al. ( J Fluid Mech 2020; 884: A46) from a statistically planar and one-dimensional, highly turbulent, moderately lean hydrogen-air flame propagating in a box are processed to perform a priori test of perfectly stirred reactor model. The test aims in particularly at estimating mesh resolution (or filter width within large eddy simulation framework) required to neglect variations in the temperature and mixture composition within a computational cell when evaluating mean (or filtered) fuel consumption and heat release rates. For this purpose, fuel consumption and heat release rates sampled directly from the DNS data and averaged over a cube of width [Formula: see text] are compared with fuel consumption and heat release rates calculated using the temperature and species concentrations averaged over the same cube. Moreover, turbulent burning velocities computed by integrating the former and latter rates are compared with one another. A ratio of [Formula: see text] to a laminar flame thickness [Formula: see text] is varied from 0.44 to 1.8. The obtained results indicate that the tested simple approach performs reasonably well (poor) if [Formula: see text] ([Formula: see text], respectively). This result is further supported by directly filtering fuel consumption rate in a laminar premixed flame. The values of the thickness [Formula: see text], calculated using detail chemical mechanisms for different fuels under elevated temperatures and pressures associated with combustion in piston engines, indicate that it is difficult to satisfy the constraint of [Formula: see text] in contemporary unsteady multidimensional numerical simulations of turbulent burning in such engines.

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“…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]. As a result, the engine complexity increases [19,20], and therefore data analysis tools need to handle large amounts of data from various physical sensors during engine calibration and runtime operations [21][22][23].…”

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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Numerical study on auto-ignition development and knocking characteristics of a downsized rotary engine under different inlet pressures

Cited by 17 publications

References 33 publications

Effect of In-Cylinder Low-Pressure Direct Injection Strategy on the Atomization and Combustion Process of a Small-Scaled Gasoline Wankel Rotary Engine

Effect of In-Cylinder Low-Pressure Direct Injection Strategy on the Atomization and Combustion Process of a Small-Scaled Gasoline Wankel Rotary Engine

A priori test of perfectly stirred reactor approach to evaluating mean fuel consumption and heat release rates in highly turbulent premixed flames

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

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