The sensitivity of pressure-based knock threshold values to alternative fuels: A comparison of methanol vs. gasoline

Suijs, Ward; Broekaert, Stijn; De Cuyper, Thomas; Verhelst, Sebastian

doi:10.1016/j.fuel.2023.130850

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Next to cold-start, methanol SI engines also exhibit different oscillation behavior when they knock. As recently discussed by Suijs et al [49] and Robeyn et al [50], the pressure oscillation caused by knock in SI engines on methanol is very different from those on gasoline. This necessitates extra attention and calibration for knock detection algorithms in engine control software.…”

Section: Spark-ignitionmentioning

confidence: 88%

Renewable Methanol as a Fuel for Heavy-Duty Engines: A Review of Technologies Enabling Single-Fuel Solutions

Pu,

Dejaegere,

Svensson

et al. 2024

Energies

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To meet climate targets, a global shift away from fossil fuels is essential. For sectors where electrification is impractical, it is crucial to find sustainable energy carriers. Renewable methanol is widely considered a promising fuel for powering heavy-duty applications like shipping, freight transport, agriculture, and industrial machines due to its various sustainable production methods. While current technological efforts focus mainly on dual-fuel engines in shipping, future progress hinges on single-fuel solutions using renewable methanol to achieve net-zero goals in the heavy-duty sector. This review examines the research status of technologies enabling methanol as the sole fuel for heavy-duty applications. Three main categories emerged from the literature: spark-ignition, compression-ignition, and pre-chamber systems. Each concept’s operational principles and characteristics regarding efficiency, stability, and emissions were analyzed. Spark-ignition concepts are a proven and cost-effective solution with high maturity. However, they face limitations due to knock issues, restricting power output with larger bore sizes. Compression-ignition concepts inherently do not suffer from end-gas autoignition, but encounter challenges related to ignitability due to the low cetane number of methanol. Nonetheless, various methods for achieving autoignition of methanol exist. To obtain stable combustion at all load points, a combination of techniques will be required. Pre-chamber technology, despite its lower maturity, holds promise for extending the knock limit and enhancing efficiency by acting as a distributed ignition source. Furthermore, mixing-controlled pre-chamber concepts show potential for eliminating knock and the associated size and power limitations. The review concludes by comparing each technology and identifying research gaps for future work.

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Section: Spark-ignitionmentioning

confidence: 88%

Renewable Methanol as a Fuel for Heavy-Duty Engines: A Review of Technologies Enabling Single-Fuel Solutions

Pu,

Dejaegere,

Svensson

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

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Multi-Load Knock Detection of a Marine Micro-Ignition Dual-Fuel Engine Based on In-Cylinder Pressure Signals

Hu,

Liu,

Yang

et al. 2024

Preprint

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Exploring the Accuracy of Machine Learning and Deep Learning in Engine Knock Detection

Hameed,

Masood,

Nasim

et al. 2024

BBE

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This study explores the use of machine learning for real-time detection of engine knocking, aiming to enhance early vehicle fault recognition. We extracted frequency modulation amplitude demodulation (FMAD) features from engine sound data and evaluated various machine-learning algorithms using MATLAB. The coarse decision tree algorithm emerged as the most effective, achieving a classification accuracy of 66.01%. Subsequently, by using deep learning models, we significantly improved the accuracy: a convolutional neural network (CNN) achieved 45.16%. accuracy, a deep learning recurrent neural network (RNN) model in LSTM achieved 90% accuracy, and further refinements pushed the accuracy to 93.55%. Additionally, we introduced a knock index to quantify noise levels during each engine cycle. This index, calculated from the integral of the absolute value of the first derivative of a band-pass-filtered vibration signal, provides a visual representation of knock strength. This approach shows promise for early detection of engine knocking, although further refinement of feature extraction methods and algorithm optimization is necessary for practical application. The study highlights the potential of integrating machine learning into real-time vehicle fault detection systems to improve their reliability and effectiveness.

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The sensitivity of pressure-based knock threshold values to alternative fuels: A comparison of methanol vs. gasoline

Cited by 3 publications

References 41 publications

Renewable Methanol as a Fuel for Heavy-Duty Engines: A Review of Technologies Enabling Single-Fuel Solutions

Renewable Methanol as a Fuel for Heavy-Duty Engines: A Review of Technologies Enabling Single-Fuel Solutions

Multi-Load Knock Detection of a Marine Micro-Ignition Dual-Fuel Engine Based on In-Cylinder Pressure Signals

Exploring the Accuracy of Machine Learning and Deep Learning in Engine Knock Detection

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