Correlation of cylinder pressure–based engine noise metrics to measured microphone data

The understanding of noise generation and source identification is vital in noise control. This research was conducted to experimentally evaluate combustion-induced noise and vibration using coherence and wavelet coherence estimates. A single-cylinder direct-injection diesel engine was chosen for experimental investigation. The independent variables for conducting experiments were injection timing with five levels of 22, 27, 32 (normal), 37, and 42 crank angles before the top dead center, and also the engine torque load with four levels of 55%, 70%, 85%, and 100% of the rated value. The signals of cylinder pressure, liner acceleration, and radiated sound pressure of the test engine were measured and recorded. Then, coherency and wavelet coherency experiments were carried out between cylinder pressure and liner acceleration, cylinder pressure and sound pressure, and liner acceleration and sound pressure signals in MATLAB software. The results indicated that increasing load would increase wavelet coherency between cylinder pressure and liner acceleration signals at frequencies higher than 1 kHz. The coherent regions between cylinder pressure and sound pressure signals were mainly at frequencies higher than 1 kHz while advancing the fuel injection timing had shifted the coherency toward lower frequencies. In general, with advancing injection timing, the coherent regions between liner acceleration and sound pressure signals have appeared at broader time ranges, especially at frequencies between 100 and 500 Hz. Comparing the results of the wavelet coherency and coherency tests, we concluded that wavelet coherency is a more accurate and descriptive tool in evaluating the combustion-induced noise and vibration.

Section: Resultssupporting

confidence: 83%

Evaluation of the combustion-induced noise and vibration using coherence and wavelet coherence estimates in a diesel engine

Ahmadian

Najafi

Ghobadian

et al. 2019

“…This parameter is of interest because it has been shown to be the primary driver in combustion-generated noise in engines. 86,87 In addition, the combustion noise level (CNL) is shown, which was calculated using the algorithm put forward by Shahlari et al 88 Considering these are closed-cycle simulation results, an algorithm also put forward by Shahlari et al 89 was used to calculate the full four-stroke cycle noise from closed-cycle simulation results. As shown in Table 3, CDC operation yields the lowest combustion noise, which is expected due to the mixing limited nature of the majority of the combustion process, which yields a more sedate energy release than volumetric autoignition.…”

Section: Simulated Results Of the Baseline Operating Conditionmentioning

confidence: 99%

A perspective on the range of gasoline compression ignition combustion strategies for high engine efficiency and low NOx and soot emissions: Effects of in-cylinder fuel stratification

Dempsey

Curran

Wagner

2016

136

Many research studies have shown that low temperature combustion in compression ignition engines has the ability to yield ultra-low NOx and soot emissions while maintaining high thermal efficiency. To achieve low temperature combustion, sufficient mixing time between the fuel and air in a globally dilute environment is required, thereby avoiding fuel-rich regions and reducing peak combustion temperatures, which significantly reduces soot and NOx formation, respectively. It has been demonstrated that achieving low temperature combustion with diesel fuel over a wide range of conditions is difficult because of its properties, namely, low volatility and high chemical reactivity. On the contrary, gasoline has a high volatility and low chemical reactivity, meaning it is easier to achieve the amount of premixing time required prior to autoignition to achieve low temperature combustion. In order to achieve low temperature combustion while meeting other constraints, such as low pressure rise rates and maintaining control over the timing of combustion, in-cylinder fuel stratification has been widely investigated for gasoline low temperature combustion engines. The level of fuel stratification is, in reality, a continuum ranging from fully premixed (i.e. homogeneous charge of fuel and air) to heavily stratified, heterogeneous operation, such as diesel combustion. However, to illustrate the impact of fuel stratification on gasoline compression ignition, the authors have identified three representative operating strategies: partial, moderate, and heavy fuel stratification. Thus, this article provides an overview and perspective of the current research efforts to develop engine operating strategies for achieving gasoline low temperature combustion in a compression ignition engine via fuel stratification. In this study, computational fluid dynamics modeling of the in-cylinder processes during the closed valve portion of the cycle was used to illustrate the opportunities and challenges associated with the various fuel stratification levels.

“…Different metrics exist to quantify combustion noise, from the HCCI-suited ringing intensity (RI; MW/m 2 ) indicating the power flux delivered by the pressure waves, 29 to the Noise (dB) indicating the perceived noise outside of the engine (more indicated for conventional engine modes). 30 Given that the RI metric has been specifically developed for HCCI engines and is still being used today (see for example), 31,32 this metric uncertainty will be developed here-under. However, the uncertainty related to any other metric can be obtained following the general methodology developed in this article.…”

Section: Results: Uncertainties On the Physical Quantities Requiring mentioning

confidence: 99%

Uncertainty quantification from raw measurements to post-processed data: A general methodology and its application to a homogeneous-charge compression–ignition engine

Pochet

Jeanmart

Contino

2019

Internal combustion engines have been improved for many decades. Yet, complex phenomena are now resorted to, for which any optimum might be unstable: noise, low-temperature heat release timing, stratification, pollutant sweet spots, and so on. In order to make reliable statements on an improvement, one must specify the uncertainty related to it. Still, uncertainty quantification is generally missing in the piston engine experimental literature. Therefore, we detailed a mathematical methodology to obtain any engine parameter uncertainty and then used it to derive the uncertainty expressions of the physical quantities of the most generic homogeneous-charge compression–ignition research engine (mass-flow-induced mixture with [Formula: see text] fuel). We then applied those expressions on an existing hydrogen homogeneous-charge compression–ignition test bench. This includes the uncertainty propagation chain from sensor specifications, user calibrations, intake control, in-cylinder processes, and post-processing techniques. Directly measured physical quantities have uncertainties of around 1%, depending on the sensor quality (e.g. pressure, volume), but indirectly measured quantities relying on modelled parameters have uncertainties higher than 5% (e.g. wall heat losses, in-cylinder temperature, gross heat release, pressure rise rate). Other findings that such an analysis can bring relate, for example, to the physical quantities driving the uncertainty and to the ones that can be neglected. In the case of the homogeneous-charge compression–ignition engine considered, the effects of blow-by, bottle purity and air moisture content were found negligible; the post-processing for effective compression ratio, effective in-cylinder temperature, and top dead centre offset were found essential; and the pressure and volume uncertainties were found to be the main drivers to a large extent. The obtained numeric values serve the general purpose of alerting the experimenter on uncertainty order of magnitudes. The developed methodology shall be used and adapted by the experimenter willing to study the uncertainty propagation in their setup or willing to assess the adequacy of a sensor performance.