Effects of Different Injection Strategies on Combustion and Emission Characteristics of Diesel Engine Fueled with Dual Fuel

Luo, Jianbin; Liu, Zhonghang; Wang, Jie; Chen, Heyang; Zhang, Zhiqing; Bo-ying, Qin; Cui, Shuwan

doi:10.3390/pr9081300

Cited by 9 publications

(5 citation statements)

References 61 publications

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“…Additionally, the mixing of evaporated fuel with fresh air is modeled with the transport equations for the unmixed fuel and unmixed oxygen [35,36]. It can be calculated by:…”

Section: Combustion Modelmentioning

confidence: 99%

Effects of Different Biodiesel-Diesel Blend Fuel on Combustion and Emission Characteristics of a Diesel Engine

et al. 2021

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In this paper, biodiesel was used as an alternative fuel to investigate the combustion and emission characteristics of a four-stroke diesel engine, in terms of cylinder pressure, heat release rate, cylinder temperature, brake thermal efficiency, brake specific fuel consumption, nitrogen oxide, soot, carbon monoxide, and hydrocarbon. Firstly, a diesel engine cylinder model was developed by AVL-Fire software coupled with CHEMKIN code to simulate the injection and combustion of biodiesel with a kinetic mechanism with 106 species and 263 reactions. Then, the simulation model was validated by experimental results under 100% and 50% load conditions and used to simulate the combustion process of a diesel engine fueled with pure diesel, biodiesel, and biodiesel–diesel blends with 10%, 20%, 30% biodiesel by volume, respectively. The results showed that the brake specific fuel consumption increased with the increase of mixed biodiesel ratio. The brake specific fuel consumptions of B10, B20 and B30 increased by 1.1%, 2.3% and 3.3%, respectively, compared with that of D100. The combustion and emission characteristics of the diesel engine are improved. Therefore, biodiesel can be used as an alternative fuel for the diesel engine. The diesel–biodiesel fuel can improve the combustion and emission characteristics of the diesel engine.

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“…Additionally, the mixing of evaporated fuel with fresh air is modeled with the transport equations for the unmixed fuel and unmixed oxygen [35,36]. It can be calculated by:…”

Section: Combustion Modelmentioning

confidence: 99%

Effects of Different Biodiesel-Diesel Blend Fuel on Combustion and Emission Characteristics of a Diesel Engine

et al. 2021

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“…Despite this, the conventional methods mentioned earlier lack the capability to harness the temporal correlation characteristics present in time series data, hindering their ability to make more precise and accurate predictions of RUL. To address this challenge, Han et al [ 17 ] implemented an approach that incorporated stacked auto-encoders and RNNs to forecast the RUL. Liu et al [ 18 ] introduced a method for predicting the RUL of lithium-ion battery capacity using the Gated Recurrent Unit (GRU).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory

Yildirim,

Rana

2024

Sensors

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Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%.

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“…Nevertheless, the conventional methods outlined above are incapable of leveraging the temporal correlation properties of time series data to facilitate more accurate and precise predictions of RUL. In order to tackle this issue, Han et al [12] employed a methodology that involved the utilization of stacked auto-encoders and recurrent neural networks to make predictions regarding the remaining lifespan of bearings. Liu et al [13] proposed an RUL prediction method for lithium-ion battery capacity based on the GRU method.…”

Section: Introductionmentioning

confidence: 99%

Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN–Attention-Based Bidirectional LSTM Modeling

Zhang,

Yang,

Yang

2023

Processes

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This paper presents a methodology for predicting the remaining usability of rolling bearings. The method combines a fully adaptive ensemble empirical modal decomposition of noise (CEEMDAN), convolutional neural network (CNN), and attention bidirectional long short-term memory network (ABiLSTM). Firstly, a finite number of intrinsic mode functions (IMFs) are obtained from breaking down the initial vibration signals using CEEMDAN. The IMFs are further screened by combining the correlation criterion and the craggy criterion. Then, time-frequency domain features, which are extracted from the screened IMFs, are reconstructed into a feature set. The SPT is recognized through some features, like the root mean square (RMS), variance, and kurtosis. Secondly, the deterioration character of rolling bearings was extracted using CNN and used to train the ABiLSTM network. Based on the output of the ABiLSTM network, it forecasts how long rolling bearings will last during use. Finally, the XJTU-SY rolling bearing dataset validated the validity of the suggested rolling bearing remaining life prediction method. We compare our algorithm with other algorithms, such as GRU, LSTM, and CNN–BiLSTM, in which the accuracy of MAE, MSE, RMSE, MAPE, and R2_score is significantly improved. Thus, the results of the validation experiments demonstrate that our proposed algorithm has excellent prediction accuracy.

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Effects of Different Injection Strategies on Combustion and Emission Characteristics of Diesel Engine Fueled with Dual Fuel

Cited by 9 publications

References 61 publications

Effects of Different Biodiesel-Diesel Blend Fuel on Combustion and Emission Characteristics of a Diesel Engine

Effects of Different Biodiesel-Diesel Blend Fuel on Combustion and Emission Characteristics of a Diesel Engine

Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory

Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN–Attention-Based Bidirectional LSTM Modeling

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