Time-frequency analysis application to the evaluation of instantaneous combustion noise

D’Ambrosio, Stefano; Ferrari, Alessandro; Jin, Zhiru

doi:10.1016/j.fuel.2021.122655

Cited by 4 publications

(2 citation statements)

References 55 publications

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“…5,6 The resonance intensity is frequently estimated through the maximum amplitude pressure oscillation (MAPO) and it can be useful to identify the levels of vibration and even maintain the knock occurrence with stochastic controls in desired levels. 7,8 The in-cylinder pressure signal can be used as input to feed several models that estimate other control parameters, such as in Khameneian et al 9 to estimate the temperature and air distribution for a GDI spark ignition engine, in Muric´et al 10 to estimate the emitted NOx from the HRR and the unburned temperature, in d'Ambrosio et al 11 to estimate the combustion noise, in Bares et al 12 to predict the knock probability, in Broatch et al 13 and Youssef 14 to estimate the trapped mass, or in Martos et al 15 to estimate the soot by semiempirical models. These models can be used to replace current sensors, to improve the accuracy of the estimation in a sensor data fusion scenario, 16 or to identify the system dynamics in order to design advanced controls.…”

Section: Introductionmentioning

confidence: 99%

In-cylinder pressure smart sampling for efficient data management

Plá

Morena

Bares

et al. 2022

International Journal of Engine Research

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The present paper proposes an optimized algorithm for minimizing the amount of data processed in order to maintain all critical information from the in-cylinder pressure sensor but with minimum computational cost. The algorithm uses singular value decomposition (SVD) for reducing the number of samples and pivoting QR decomposition to identify the optimal sampling locations. Experimental data from four engines with different combustion modes, namely Spark ignition (SI), Compression ignition (CI), turbulent jet ignition (TJI), and dual fuel ignition (DFCI), was used to validate the algorithm. The impact of the different sampling methodologies on different metrics for engine performance has been addressed and studied showing negligible information loss when reducing to 25 samples per cycle the acquisition buffer size.

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Section: Introductionmentioning

confidence: 99%

In-cylinder pressure smart sampling for efficient data management

Plá

Morena

Bares

et al. 2022

International Journal of Engine Research

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“…Combustion noise has been studied for different configurations, e.g., open and confined flames [ 1 , 2 ], gasoline engines [ 3 ], and diesel engines [ 4 , 5 ]. The recent reviews of the computational methods include those that were performed by Zandie et al [ 6 ], Dowling and Mahmoudi [ 7 ], Ihme [ 8 ], and Tam et al [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Validating Confined Flame Noise Simulation Using External Sensor

Williamson

Srivastava²,

Sallam

2022

Sensors

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Advancements in lean premixed combustion have increased the efficiency and reduced the amount of greenhouse gas emissions, but they have led to increased noise emissions due to higher turbulence and mixing fluctuations. This study used an external sensor (microphone) to validate the simulation of the combustion noise of a confined space. An experimental facility with a laboratory-scale furnace was used to carry out the measurement, and the simulation of the confined flame noise was conducted in OpenFOAM. The simulation utilized the Partially Stirred Reactor (PaSR) and a hybrid computational aeroacoustics (CAA) approach using the large eddy simulation (LES)/the Ffwocs Williams–Hawkings (FWH) method. Additionally, unsteady Reynolds-averaged Navier–Stokes (URANS)/the FWH method was tested for a comparison with the LES prediction. A sensor which was placed outside the enclosure for ease of access was then used to validate the results of the numerical model. The sensor data agreed with the LES/FWH results including the amplitude and frequency of the primary combustion peak and the overall sound pressure level (OASPL). This suggested that a sensor which was placed outside the enclosure could serve as a validation tool for the simulation of the confined flames despite the sound reflections from the walls.

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Numerical study of combustion noise of a lean-premixed green H2-ethylene fuel: A machine learning optimization

Hajialigol,

Ahmadi Boyaghchi

2024

International Journal of Hydrogen Energy

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Time-frequency analysis application to the evaluation of instantaneous combustion noise

Cited by 4 publications

References 55 publications

In-cylinder pressure smart sampling for efficient data management

In-cylinder pressure smart sampling for efficient data management

Validating Confined Flame Noise Simulation Using External Sensor

Numerical study of combustion noise of a lean-premixed green H2-ethylene fuel: A machine learning optimization

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