A series of experiments were performed to investigate the effect of ignition energy (E_ig) and hydrogen addition on the laminar burning velocity (S_u⁰), ignition delay time (t_delay), and flame rising time (t_rising) of lean methane−air mixtures. The mixtures at three different equivalence ratios (ϕ) of 0.6, 0.7, and 0.8 with varying hydrogen volume fractions from 0 to 50% were centrally ignited in a constant volume combustion chamber by a pair of pin-to-pin electrodes at a spark gap of 2.0 mm. In situ ignition energy (E_ig ∼2.4 mJ ÷ 58 mJ) was calculated by integration of the product of current and voltage between positive and negative electrodes. The result revealed that the S_u⁰ value increases non-linearly with increasing hydrogen fraction at three equivalence ratios of 0.6, 0.7, and 0.8, by which the increasing slope of S_u⁰ changes from gradual to drastic when the hydrogen fraction is greater than 20%. t_delay and t_rising decrease quickly with increasing hydrogen fraction; however, t_rising drops faster than t_delay at ϕ = 0.6 and 0.7, and the reverse is true at ϕ = 0.8. Furthermore, t_delay transition is observed when E_ig > E_ig,critical, by which t_delay drastically drops in the pre-transition and gradually decreases in the post-transition. These results may be relevant to spark ignition engines operated under lean-burn conditions.

A better understanding of turbulent premixed flame propagation is the key for improving the efficiency of fuel consumption and reducing the emissions of spark ignition gasoline engines. In this study, we measure turbulent burning velocities (S_T) of pre-vaporized iso-octane/air mixtures over wide ranges of the equivalence ratio (φ = 0.9–1.25, Le ≈ 2.94–0.93), the root-mean-square (r.m.s.) turbulent fluctuating velocity (u′ = 0–4.2 m/s), pressure p = 1–5 atm at T = 358 K and p = 0.5–3 atm at T = 373 K, where Le is the effective Lewis number. Results show that at any fixed p, T and u′, Le < 1 flames propagate faster than Le > 1 flames, of which the normalized iso-octane S_T/S_L data versus u′/S_L are very scattering, where S_L is the laminar burning velocity. But when the effect of Le is properly considered in some scaling parameters used in previous correlations, these large scattering iso-octane S_T/S_L data can be collapsed onto single curves by several modified general correlations, regardless of different φ, Le, T, p, and u′, showing self-similar propagation of turbulent spherical flames. The uncertainty analysis of these modified general correlations is also discussed.

This study introduces a robust approach for denoising pressure signals by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), Continuous Mean Square Error (CMSE) analysis, optimal wavelet selection, and wavelet thresholding techniques. ICEEMDAN decomposes noisy signals into Intrinsic Mode Functions (IMFs), which are classified via CMSE to distinguish noise-dominated IMFs from signal-dominated IMFs. A key innovation is the sparsity-based optimal wavelet selection strategy, which dynamically identifies the most effective wavelet basis and decomposition levels for each noise-dominated IMF. These IMFs undergo wavelet thresholding for noise suppression, while signal-dominated IMFs are preserved to ensure signal integrity during reconstruction. The proposed approach was validated on simulated signals and experimental data from lean syngas/air combustion at equivalence ratios. Results consistently demonstrate superior denoising performance across various metrics compared to traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) and conventional ICEEMDAN-wavelet methods. By dynamically adapting wavelet selection and decomposition levels to varying signal and noise characteristics, this method achieves optimal noise suppression with high signal fidelity. Heursure and Minimaxi methods use hard thresholding and reveal consistent outperformance compared to Sqtwolog and Rigrsure in both simulated and real-world scenarios. This approach offers significant advancements in engine performance optimization, fuel efficiency improvement, combustion diagnostics, and industrial pressure monitoring. By bridging theoretical innovation with practical applications, the proposed method provides a scalable and precise solution for signal reconstruction in complex environments.