Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach

[EN] In this article, a numerical methodology for assessing combustion noise in compression ignition engines is described with the specific purpose of analysing the unsteady pressure field inside the combustion chamber. The numerical results show consistent agreement with experimental measurements in both the time and frequency domains. Nonetheless, an exhaustive analysis of the calculation convergence is needed to guarantee an independent solution. These results contribute to the understanding of in-cylinder unsteady processes, especially of those related to combustion chamber resonances, and their effects on the radiated noise levels. The method was applied to different combustion system configurations by modifying the spray angle of the injector, evidencing that controlling the ignition location through this design parameter, it is possible to decrease the combustion noise by minimizing the resonance contribution. Important efficiency losses were, however, observed due to the injector/bowl matching worsening which compromises the performance and emissions levels.The authors want to express their gratitude to CONVERGENT SCIENCE Inc. and Convergent Science GmbH for their kind support for performing the CFD calculations using CONVERGE software.Torregrosa, AJ.; Broatch, A.; Margot, X.; Gómez-Soriano, J. (2018). Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach. International Journal of Engine Research. 1-13. https://doi.org/10.1177/1468087418803030S113Benajes, J., Novella, R., De Lima, D., & Tribotté, P. (2014). Analysis of combustion concepts in a newly designed two-stroke high-speed direct injection compression ignition engine. International Journal of Engine Research, 16(1), 52-67. doi:10.1177/1468087414562867Costa, M., Bianchi, G. M., Forte, C., & Cazzoli, G. (2014). A Numerical Methodology for the Multi-objective Optimization of the DI Diesel Engine Combustion. Energy Procedia, 45, 711-720. doi:10.1016/j.egypro.2014.01.076Navid, A., Khalilarya, S., & Taghavifar, H. (2016). Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model. Energy Conversion and Management, 126, 385-399. doi:10.1016/j.enconman.2016.08.014Benajes, J., García, A., Pastor, J. M., & Monsalve-Serrano, J. (2016). Effects of piston bowl geometry on Reactivity Controlled Compression Ignition heat transfer and combustion losses at different engine loads. Energy, 98, 64-77. doi:10.1016/j.energy.2016.01.014Masterton, B., Heffner, H., & Ravizza, R. (1969). The Evolution of Human Hearing. The Journal of the Acoustical Society of America, 45(4), 966-985. doi:10.1121/1.1911574Strahle, W. C. (1978). Combustion noise. Progress in Energy and Combustion Science, 4(3), 157-176. doi:10.1016/0360-1285(78)90002-3Flemming, F., Sadiki, A., & Janicka, J. (2007). Investigation of combustion noise using a LES/CAA hybrid approach. Proceedings of the Combustion Institute, 31(2), 3189-3196. doi:10.1016/j.proci.2006.07.060Klos, D., & Kokjohn, S. L. (2014). Investigation of the sources of combustion instability in low-temperature combustion engines using response surface models. International Journal of Engine Research, 16(3), 419-440. doi:10.1177/1468087414556135Cyclic dispersion in engine combustion—Introduction by the special issue editors. (2015). International Journal of Engine Research, 16(3), 255-259. doi:10.1177/1468087415572740Hickling, R., Feldmaier, D. A., & Sung, S. H. (1979). Knock‐induced cavity resonances in open chamber diesel engines. The Journal of the Acoustical Society of America, 65(6), 1474-1479. doi:10.1121/1.382910Torregrosa, A. J., Broatch, A., Margot, X., Marant, V., & Beauge, Y. (2004). Combustion chamber resonances in direct injection automotive diesel engines: A numerical approach. International Journal of Engine Research, 5(1), 83-91. doi:10.1243/146808704772914264Broatch, A., Margot, X., Gil, A., & Christian Donayre, (José). (2007). Computational study of the sensitivity to ignition characteristics of the resonance in DI diesel engine combustion chambers. Engineering Computations, 24(1), 77-96. doi:10.1108/02644400710718583Eriksson, L. J. (1980). Higher order mode effects in circular ducts and expansion chambers. The Journal of the Acoustical Society of America, 68(2), 545-550. doi:10.1121/1.384768Broatch, A., Margot, X., Novella, R., & Gomez-Soriano, J. (2017). Impact of the injector design on the combustion noise of gasoline partially premixed combustion in a 2-stroke engine. Applied Thermal Engineering, 119, 530-540. doi:10.1016/j.applthermaleng.2017.03.081Tutak, W., & Jamrozik, A. (2016). Validation and optimization of the thermal cycle for a diesel engine by computational fluid dynamics modeling. Applied Mathematical Modelling, 40(13-14), 6293-6309. doi:10.1016/j.apm.2016.02.021Payri, F., Benajes, J., Margot, X., & Gil, A. (2004). CFD modeling of the in-cylinder flow in direct-injection Diesel engines. Computers & Fluids, 33(8), 995-1021. doi:10.1016/j.compfluid.2003.09.003Benajes, J., Novella, R., De Lima, D., & Thein, K. (2017). Impact of injection settings operating with the gasoline Partially Premixed Combustion concept in a 2-stroke HSDI compression ignition engine. Applied Energy, 193, 515-530. doi:10.1016/j.apenergy.2017.02.044Lesieur, M., Métais, O., & Comte, P. (2005). Large-Eddy Simulations of Turbulence. doi:10.1017/cbo9780511755507Pope, S. B. (2004). Ten questions concerning the large-eddy simulation of turbulent flows. New Journal of Physics, 6, 35-35. doi:10.1088/1367-2630/6/1/035Silva, C. F., Leyko, M., Nicoud, F., & Moreau, S. (2013). Assessment of combustion noise in a premixed swirled combustor via Large-Eddy Simulation. Computers & Fluids, 78, 1-9. doi:10.1016/j.compfluid.2010.09.034Jamrozik, A., Tutak, W., Kociszewski, A., & Sosnowski, M. (2013). Numerical simulation of two-stage combustion in SI engine with prechamber. Applied Mathematical Modelling, 37(5), 2961-2982. doi:10.1016/j.apm.2012.07.040Qin, W., Xie, M., Jia, M., Wang, T., & Liu, D. (2014). Large eddy simulation of in-cylinder turbulent flows in a DISI gasoline engine. Applied Mathematical Modelling, 38(24), 5967-5985. doi:10.1016/j.apm.2014.05.004Broatch, A., Margot, X., Novella, R., & Gomez-Soriano, J. (2016). Combustion noise analysis of partially premixed combustion concept using gasoline fuel in a 2-stroke engine. Energy, 107, 612-624. doi:10.1016/j.energy.2016.04.045Torregrosa, A. J., Broatch, A., Martín, J., & Monelletta, L. (2007). Combustion noise level assessment in direct injection Diesel engines by means of in-cylinder pressure components. Measurement Science and Technology, 18(7), 2131-2142. doi:10.1088/0957-0233/18/7/045Payri, F., Broatch, A., Margot, X., & Monelletta, L. (2008). Sound quality assessment of Diesel combustion noise using in-cylinder pressure components. Measurement Science and Technology, 20(1), 015107. doi:10.1088/0957-0233/20/1/015107Ihlenburg, F. (2003). The Medium-Frequency Range in Computational Acoustics: Practical and Numerical Aspects. Journal of Computational Acoustics, 11(02), 175-193. doi:10.1142/s0218396x03001900Lapuerta, M., Armas, O., & Hernández, J. J. (1999). Diagnosis of DI Diesel combustion from in-cylinder pressure signal by estimation of mean thermodynamic properties of the gas. Applied Thermal Engineering, 19(5), 513-529. doi:10.1016/s1359-4311(98)00075-1Payri, F., Olmeda, P., Martín, J., & García, A. (2011). A complete 0D thermodynamic predictive model for direct injection diesel engines. Applied Energy, 88(12), 4632-4641. doi:10.1016/j.apenergy.2011.06.005Payri, F., Broatch, A., Tormos, B., & Marant, V. (2005). New methodology for in-cylinder pressure analysis in direct injection diesel engines—application to combustion noise. Measurement Science and Technology, 16(2), 540-547. doi:10.1088/0957-0233/16/2/029Shahlari, A. J., Hocking, C., Kurtz, E., & Ghandhi, J. (2013). Comparison of Compression Ignition Engine Noise Metrics in Low-Temperature Combustion Regimes. SAE International Journal of Engines, 6(1), 541-552. doi:10.4271/2013-01-1659Yakhot, V., & Orszag, S. A. (1986). Renormalization group analysis of turbulence. I. Basic theory. Journal of Scientific Computing, 1(1), 3-51. doi:10.1007/bf01061452Redlich, O., & Kwong, J. N. S. (1949). On the Thermodynamics of Solutions. V. An Equation of State. Fugacities of Gaseous Solutions. Chemical Reviews, 44(1), 233-244. doi:10.1021/cr60137a013Issa, R. . (1986). Solution of the implicitly discretised fluid flow equations by operator-splitting. Journal of Computational Physics, 62(1), 40-65. doi:10.1016/0021-9991(86)90099-9Dukowicz, J. K. (1980). A particle-fluid numerical model for liquid sprays. Journal of Computational Physics, 35(2), 229-253. doi:10.1016/0021-9991(80)90087-xReitz, R. D., & Beale, J. C. (1999). MODELING SPRAY ATOMIZATION WITH THE KELVIN-HELMHOLTZ/RAYLEIGH-TAYLOR HYBRID MODEL. Atomization and Sprays, 9(6), 623-650. doi:10.1615/atomizspr.v9.i6.40Babajimopoulos, A., Assanis, D. N., Flowers, D. L., Aceves, S. M., & Hessel, R. P. (2005). A fully coupled computational fluid dynamics and multi-zone model with detailed chemical kinetics for the simulation of premixed charge compression ignition engines. International Journal of Engine Research, 6(5), 497-512. doi:10.1243/146808705x30503Pal, P., Keum, S., & Im, H. G. (2015). Assessment of flamelet versus multi-zone combustion modeling approaches for stratified-charge compression ignition engines. International Journal of Engine Research, 17(3), 280-290. doi:10.1177/1468087415571006Torregrosa, A. J., Broatch, A., Gil, A., & Gomez-Soriano, J. (2018). Numerical approach for assessing combustion noise in compression-ignited Diesel engines. Applied Acoustics, 135, 91-100. doi:10.1016/j.apacoust.2018.02.006Torregrosa, A., Olmeda, P., Degraeuwe, B., & Reyes, M. (2006). A concise wall temperature model for DI Diesel engines. Applied Thermal Engineering, 26(11-12), 1320-1327. doi:10.1016/j.applthermaleng.2005.10.021Broatch, A., Javier Lopez, J., García-Tíscar, J., & Gomez-Soriano, J. (2018). Experimental Analysis of Cyclical Dispersion in Compression-Ignited Versus Spark-Ignited Engines and Its Significance for Combustion Noise Numerical Modeling. Journal of Engineering for Gas Turbines and Power, 140(10). doi:10.1115/1.4040287Molina, S., García, A., Pastor, J. M., Belarte, E., & Balloul, I. (2015). Operating range extension of RCCI combustion concept from low to full load in a heavy-duty engine. Applied Energy, 143, 211-227. doi:10.1016/j.apenergy.2015.01.03

Modeling cycle-to-cycle variations in spark ignited combustion engines by scale-resolving simulations for different engine speeds

Here, internal combustion engine operating speed effects on combustion cycle-to-cycle variations (CCV) are numerically investigated. The recent study by Ghaderi Masouleh et al. (2018) is extended to higher engine speeds including 560, 800 and 1000 RPM. The 3D scale-resolving simulations are carried out in a spark ignited simplified engine geometry under fuel lean condition. The numerical results include the following main findings. (1) Flow velocity and turbulence levels are noted to increase with RPM. (2) For a fixed spark timing, the combustion duration in CAD time increases with RPM contrasting the respective trend in physical time. (3) The link between early flow conditions around the spark position and the whole cycle combustion rate is demonstrated and explained for all the RPM for the investigated three example cycles. (4) On average, the moderate increase of turbulent flame speed with RPM is not able to compensate the reduced physical time for combustion. Hence, the higher RPM cycles burn typically slower in CAD time. (5) On average, the increased combustion duration in CAD time for higher RPM increases the CAD period, where the spark kernel is highly prone to local turbulence fluctuations. (6) A noted effect of RPM on CCV is the stretched combustion duration in CAD time so that the effect of the initial fluctuations can persist for a longer CAD period. (7) In the present model, the velocity magnitude near the spark largely explains cycle-to-cycle variations in the investigated low RPM range.Peer reviewe

Flow and thermal field effects on cycle-to-cycle variation of combustion

Premixed, spark ignited combustion of lean methane at fuel to air equivalence ratio of 0.58 is numerically investigated in a piston-cylinder assembly. The simplified numerical configuration is tailored to emulate the intake, compression and spark ignition processes in engines. Large-eddy simulation is employed in the core flow along with a zonal hybrid wall treatment in near-wall regions. The G-equation level-set method is used to simulate flame propagation with a detailed chemistry based laminar flame speed correlation developed herein. The main numerical findings of this paper are as follows: (1) Despite the geometrical simplicity, the present set-up is shown to exhibit relatively large cycle-to-cycle variation for the three investigated cycles. (2) The local thermodynamics and fluid dynamic conditions around the spark close to the ignition location initiate the first discrepancies between the cycles. (3) These early variations are then amplified due to the subsequent differences in the early growth of flame area. (4) The cycle-to-cycle variation in the present set-up is shown to be largely a consequence of the local flow fluctuations close to the spark position and timing, while the results indicated a less dominating role of thermal stratification on cycle-to-cycle variation. (5) The asymmetric combustion behavior was explained to be a combined effect of burning rate and convection velocity, while convection velocity proved to be the major contributor. (6) Finally, a numerical test in the present model setup indicated large spark kernels being less prone to cycle-to-cycle variations than small kernels.Peer reviewe

A large-eddy simulation study on the influence of diesel pilot spray quantity on methane-air flame initiation

The present study is a continuation of the previous work by Kahila et al. (2019), in which a dual-fuel (DF) ignition process was numerically investigated by modeling liquid diesel-surrogate injection into a lean methane-air mixture in engine relevant conditions. Earlier, the injection duration (tinj) of diesel-surrogate exceeded substantially the characteristic autoignition time scale. Here, such a pilot spray ignition problem is studied at a fixed mass flow rate but with a varying tinj. The focus is on understanding the influence of pilot quantity on spray dilution process and low- and high-temperature chemistry. In total, ten cases are computed with multiple diesel pilot quantities by utilizing a newly developed large-eddy simulation/finite-rate chemistry solver. The baseline spray setup corresponds to the Engine Combustion Network (ECN) Spray A configuration, enabling an extensive validation of the present numerical models and providing a reference case for the DF computations. Additionally, experimental results from a single-cylinder laboratory engine are provided to discuss the ignition characteristics in the context of a real application. The main results of the present study are: (1) reducing tinj introduces excessive dilution of the DF mixture, (2) dilution lowers the reactivity of the DF mixture, leading to delayed high-temperature ignition and slow overall methane consumption, (3) low enough pilot quantity (tinj < 0.3 ms) may lead to very long ignition delay times, (4) cumulative heat release is dominated by low/high-temperature chemistry at low/high tinj values, (5) analysis of the underlying chemistry manifold implies that the sensitivity of ignition chemistry on mixing is time-dependent and connected to the end of injection time, and 6) long ignition delay times at very low tinj values can be decreased by decreasing injection pressure.Peer reviewe

Semantic ambient media-an introduction

The medium is the message! And the message was literacy, media democracy and music charts. Mostly one single distinguishable medium such as TV, the Web, the radio, or books transmitted the message. Now in the age of ubiquitous and pervasive computing, where information flows through a plethora of distributed interlinked media-what is the message ambient media will tell us? What does semantic mean in this context? Which experiences will it open to us? What is content in the age of ambient media? Ambient media are embedded throughout the natural environment of the consumer-in his home, in his car, in restaurants, and on his mobile device. Predominant sample services are smart wallpapers in homes, location based services, RFID based entertainment services for children, or intelligent homes. The goal of this article is to define semantic ambient media and discuss the contributions to the Semantic Ambient Media Experience (SAME) workshop, which was held in conjunction with the ACM Multimedia conference in Vancouver in 2008. The results of the workshop can be found on: www.ambientmediaassociation.org . © 2009 Springer Science+Business Media, LLC

Large-eddy simulation of highly underexpanded transient gas jets

Large-eddy simulations (LES) based on scale-selective implicit filtering are carried out in order to study the effect of nozzle pressure ratios on the characteristics of highly underexpanded jets. Pressure ratios ranging from 4.5 to 8.5 with Reynolds numbers of the order 75 000-140 000 are considered. The studied configuration agrees well with the classical picture of the structure of highly underexpanded jets. Similarities and differences between simulation and experiments are discussed by comparing the concentration field structures from LES and planar laser induced fluorescence data. The transient stages, leading eventually to the highly underexpanded state, are visualized and investigated in terms of a phase diagram revealing the shock speeds and duration of the transient stages. For the studied nozzle pressure ratio range, the Mach disk dimensions are found to be in good agreement with literature data and experimental observations. It is observed how the nozzle pressure ratio influences the Mach disk width, and thereby the slip line separation, which leads to co-annular jets with inner and outer shear layers at higher pressure ratios. The improved mixing with increasing pressure ratio is demonstrated by the probability density functions of the concentration. The coherent structures downstream of the Mach disk are identified using proper orthogonal decomposition (POD). The structures indicate a helical mode originating from the shear layers of the jet. Despite the relatively low energy content of the dominant PODmodes, the frequencies of the POD time coefficients explain the dominant frequencies in the pressure fluctuation spectra. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4772192