scholarly journals Knowledge Transfer from Detailed 3-D CFD Codes to System Simulation Tools – CCV Modeling in SI Engine

2016 ◽  
Vol 14 (1) ◽  
pp. 17-32
Author(s):  
Oldřich Vítek ◽  
Jan Macek ◽  
Christoph Poetsch ◽  
Reinhard Tatschl
Keyword(s):  
Knowledge Transfer ◽  
Reference Data ◽  
System Simulation ◽  
Combustion Model ◽  
Model Parameters ◽  
Good Correspondence ◽  
Flame Kernel ◽  
Cross Correlations ◽  
Initial Flame ◽  
Operating Points

Abstract The paper deals with CCV knowledge transfer from reference data (either experiments or 3-D CFD data) into system simulation SW tools (based on 0-D/1-D CFD). It was verified that CCV phenomenon can be modeled by means of combustion model perturbations. The proposed methodology consists of two major steps. First, individual cycle data have to be matched with the 0-D/1-D model, i.e., combustion model parameters are varied to achieve the best possible match of in-cylinder pressure traces. Second, the combustion model parameters (obtained in previous step) are statistically evaluated to obtain PDFs and cross-correlations. Then such information is imposed to the 0-D/1-D tool to mimic pressure traces CCV. Good correspondence with the reference data is achieved only if both PDFs and cross-correlations are imposed simultaneously. Different engine operating points were evaluated to draw some general conclusions in terms of CCV. It was confirmed that turbulence properties and initial flame kernel development are the dominant factors. However, these factors are neither independent nor random – they seem to be correlated. Operating points with high CCV are more organized in terms of the statistics – they exhibit strong cross-correlations of combustion model parameters.

A quasi-dimensional combustion model for spark ignition engines fueled with gasoline–methanol blends

2017 ◽  
Vol 232 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Duc-Khanh Nguyen ◽  
Louis Sileghem ◽  
Sebastian Verhelst
Keyword(s):  
Ignition Delay ◽  
Burning Velocity ◽  
Measurement Data ◽  
Lewis Number ◽  
Combustion Model ◽  
Laminar Burning Velocity ◽  
Velocity Correlation ◽  
Flame Kernel ◽  
Initial Flame ◽  
Better Than

The current work provides a quasi-dimensional model for the combustion of methanol–gasoline blends. New correlations for the laminar burning velocity of gasoline and methanol are developed and used together with a mixing rule to calculate the laminar burning velocity of the blends. Several factors (such as the laminar burning velocity, initial flame kernel, residual gas fraction, turbulence, etc.) have been investigated and the sensitivity of these factors and the used sub-models on the predictive performance was assessed. The simulation results were compared with measurement data from two engines on different gasoline–methanol blends. The results show the importance of the laminar burning velocity correlation, the method of initializing combustion and the turbulent burning velocity model. The newly developed laminar burning velocity correlation of gasoline performed equally or better than the existing correlations and the newly developed correlation of methanol outperformed the other correlations. The initial flame kernel size had a strong influence on the ignition delay. Changing the initial flame kernel to reproduce the same ignition delay was very effective to improve the simulations. Several turbulent combustion models were tested with the newly developed laminar burning velocity correlations and optimized ignition delay. In conclusion, the model of Bradley reproduced the trend going from gasoline to methanol much better than others due to the inclusion of the Lewis number.

scholarly journals Optical Investigation of a Partial Fuel Stratification Strategy to Stabilize Overall Lean Operation of a DISI Engine Fueled with Gasoline and E30

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 396
Author(s):  
Cinzia Tornatore ◽  
Magnus Sjöberg
Keyword(s):  
Volume Fraction ◽  
Direct Injection ◽  
Combustion Process ◽  
Pilot Injection ◽  
Optical Investigation ◽  
Flame Kernel ◽  
Lean Combustion ◽  
Fuel Stratification ◽  
Spark Plug ◽  
Initial Flame

This paper offers new insights into a partial fuel stratification (PFS) combustion strategy that has proven to be effective at stabilizing overall lean combustion in direct injection spark ignition engines. To this aim, high spatial and temporal resolution optical diagnostics were applied in an optically accessible engine working in PFS mode for two fuels and two different durations of pilot injection at the time of spark: 210 µs and 330 µs for E30 (gasoline blended with ethanol by 30% volume fraction) and gasoline, respectively. In both conditions, early injections during the intake stroke were used to generate a well-mixed lean background. The results were compared to rich, stoichiometric and lean well-mixed combustion with different spark timings. In the PFS combustion process, it was possible to detect a non-spherical and highly wrinkled blue flame, coupled with yellow diffusive flames due to the combustion of rich zones near the spark plug. The initial flame spread for both PFS cases was faster compared to any of the well-mixed cases (lean, stoichiometric and rich), suggesting that the flame propagation for PFS is enhanced by both enrichment and enhanced local turbulence caused by the pilot injection. Different spray evolutions for the two pilot injection durations were found to strongly influence the flame kernel inception and propagation. PFS with pilot durations of 210 µs and 330 µs showed some differences in terms of shapes of the flame front and in terms of extension of diffusive flames. Yet, both cases were highly repeatable.

scholarly journals A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Riccardo De Blasis ◽  
Giovanni Batista Masala ◽  
Filippo Petroni
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
Wind Farm ◽  
Central Italy ◽  
High Order ◽  
Distribution Model ◽  
Spot Price ◽  
Model Parameters ◽  
Price Series ◽  
Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

Spark Ignition of SPP Injector Under Sub-Atmospheric Conditions

2021 ◽  
Author(s):  
Qianpeng Zhao ◽  
Yong Mu ◽  
Jinhu Yang ◽  
Yulan Wang ◽  
Gang Xu
Keyword(s):  
High Speed ◽  
Spark Ignition ◽  
Test Facility ◽  
Propagation Mode ◽  
Inlet Temperature ◽  
Reacting Flow ◽  
Atmospheric Conditions ◽  
Flame Kernel ◽  
Flame Development ◽  
Initial Flame

Abstract The sub-atmospheric ignition performance of an SPP (Stratified Partially Premixed) injector and combustor is investigated experimentally on the high-altitude test facility. In order to explore the influence of sub-atmospheric pressure on reignition performance and flame propagation mode, experiments are conducted under different pressures ranging from 19 kPa to 101 kPa. The inlet temperature and pressure drop of the injector (ΔPsw/P3t) are kept constant at 303 K and 3% respectively. The transparent quartz window mounted on the sidewall of the model combustor provides optical access of flame signals. Ignition fuel-air ratio (FAR) under different inlet pressures are experimentally acquired. The spark ignition processes, including the formation of flame kernel, the flame development and stabilization are recorded by a high-speed camera at a rate of 5kHz. Experimental results indicate that the minimum ignition FAR grows rapidly as the inlet air pressure decreases. An algorithm is developed to track the trajectory of flame kernels within 25ms following the spark during its breakup and motion processes. Results show that the calculated trajectory provides a clear description of the flame evolution process. Under different inlet air pressures, the propagation trajectories of flame kernels share similarities in initial phase. It is pivotal for a successful ignition that the initial flame kernel keeps enough intensity and moves into CTRZ (Center-Toroidal Recirculation Zone) along radial direction. Finally, the time-averaged non-reacting flow field under inlet pressure of 54kPa and fuel mass flow of 8kg/h is simulated. The effects of flow structure and fuel spatial distribution on kernel propagation and flame evolution are analyzed.

scholarly journals The information content of Cosmic Infrared Background anisotropies

2019 ◽  
Author(s):  
Robert Reischke ◽  
Vincent Desjacques ◽  
Saleem Zaroubi
Keyword(s):  
Power Spectrum ◽  
Cosmological Parameters ◽  
Model Parameters ◽  
Mass Relation ◽  
Infrared Background ◽  
Cross Correlations ◽  
The Mean ◽  
Cosmic Infrared Background ◽  
Almost All ◽  
Halo Occupation Distribution

Abstract We use analytic computations to predict the power spectrum as well as the bispectrum of Cosmic Infrared Background (CIB) anisotropies. Our approach is based on the halo model and takes into account the mean luminosity-mass relation. The model is used to forecast the possibility to simultaneously constrain cosmological, CIB and halo occupation distribution (HOD) parameters in the presence of foregrounds. For the analysis we use wavelengths in eight frequency channels between 200 and 900 GHz with survey specifications given by Planck and LiteBird. We explore the sensitivity to the model parameters up to multipoles of ℓ = 1000 using auto- and cross-correlations between the different frequency bands. With this setting, cosmological, HOD and CIB parameters can be constrained to a few percent. Galactic dust is modeled by a power law and the shot noise contribution as a frequency dependent amplitude which are marginalized over. We find that dust residuals in the CIB maps only marginally influence constraints on standard cosmological parameters. Furthermore, the bispectrum yields tighter constraints (by a factor four in 1σ errors) on almost all model parameters while the degeneracy directions are very similar to the ones of the power spectrum. The increase in sensitivity is most pronounced for the sum of the neutrino masses. Due to the similarity of degeneracies a combination of both analysis is not needed for most parameters. This, however, might be due to the simplified bias description generally adopted in such halo model approaches.

The Effect of Sparkplug Design on Initial Flame Kernel Development and Sparkplug Performance

2006 ◽  
Author(s):  
Terry Alger ◽  
Barrett Mangold ◽  
Darius Mehta ◽  
Charles Roberts
Keyword(s):  
Kernel Development ◽  
Flame Kernel ◽  
Initial Flame

Development and Validation of an Ignition Model for SI Engines

2006 ◽  
Author(s):  
Stefania Falfari ◽  
Gian Marco Bianchi
Keyword(s):  
Cfd Simulation ◽  
Combustion Process ◽  
Three Dimensional ◽  
Electrical Energy ◽  
Combustion Model ◽  
Test Case ◽  
Ignition Process ◽  
Mean Flow ◽  
Flame Kernel ◽  
Si Engines

In SI engines the ignition process strongly affects the combustion process. Its accurate modelling becomes a key issue for a design-oriented CFD simulation of the combustion process. Different approaches to simulate ignition have been proposed. The common base is decoupling the physics related to the very first ignition phase when a plasma is formed from that of the development of the flame kernel. The critical point of ignition models is related to the capability of representing the effect of ignition system characteristics, the criterion used for flame deposit and the initialisation of the combustion model. This paper aims to present and validates extensively an ignition model suited for CFD calculation of premixed combustion. The ignition model implemented in a customized version of the Kiva 3 code is coupled with ECFM Flamelet combustion model. The ignition model simulates the plasma/kernel expansion based on a lump evaluation of main ignition processes (i.e., breakdown, arc-phase and glow phase). A double switch criterion based on physical and numerical consideration is used to switch to the main combustion model. The Herweg and Maly experimental test case has been used to check the model capability. In particular, two different ignition systems having different amount of electrical energy released during spark discharge are considered. Comparisons with experimental results allowed testing the model with respect to its capability to reproduce the effects of mixture equivalence ratio, mean flow, turbulence and spark energy on flame kernel development as never done before in three-dimensional RANS CFD combustion modelling of premixed flames.

scholarly journals Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems

2019 ◽  
Vol 141 (03) ◽  
pp. S16-S23 ◽  
Author(s):  
Qingyuan Tan ◽  
Xiang Chen ◽  
Ying Tan ◽  
Ming Zheng
Keyword(s):  
Optimization Problems ◽  
Model Parameters ◽  
Compression Ignition Engine ◽  
Automotive Systems ◽  
Model Based ◽  
Expected Performance ◽  
And Control ◽  
Parameter Values ◽  
Engine Systems ◽  
Operating Points

Essentially, the performance improvement of automotive systems is a multi-objective optimization problem [1–4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally requires precise tuning and coordination of accessible system inputs. In the past, such optimization problems have been approximately solved through expensive calibration procedures or an off-line local model-based approaches where either a regressive model or a first-principle model is used. The model-based optimization provides the advantage of finding the optimal model parameters to allow the model to be used to predict the real system behavior reasonably [5]. However, other than the model complexities, there are practically two issues facing the integrity of these models: modeling uncertainty due to inaccurate parameter values and/or unmodeled dynamics, and locally effective range around operating points. As a result, the optimum solutions extracted from the model-based approach could be subject to failure of expected performance [6].

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