Abstract:As known, reliable information about underlying turbulence intensity is a mandatory prerequisite to predict the burning rate in quasidimensional combustion models. Based on 3D results reported in the companion part I paper, a quasi-dimensional turbulence model, embedded under the form of "user routine" in the GT-Power™ software, is here presented in detail. A deep discussion on the model concept is reported, compared to the alternative approaches available in the current literature. The model has the potential… Show more
“…A phenomenological K-k- ε turbulence model 55–57 will be implemented in QD SRM to predict the scalar mixing time for different engine operating conditions without using information from 3D CFD simulations.…”
Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.
“…A phenomenological K-k- ε turbulence model 55–57 will be implemented in QD SRM to predict the scalar mixing time for different engine operating conditions without using information from 3D CFD simulations.…”
Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.
“…In 0D tumble models found in previous researches [6,[13][14][15], angular momentum is employed to represent tumble, with the typical expression of its generation rate similar to: Figure 2 shows an example of the mass flux field on a valve curtain area, and an apparent variation can be seen over the surface. This distribution is what eventually determines the structure of charge motion inside the cylinder.…”
Section: Modeling Conceptmentioning
confidence: 99%
“…In 0D tumble models found in previous researches [6,[13][14][15], angular momentum is employed to represent tumble, with the typical expression of its generation rate similar to:…”
Section: Modeling Conceptmentioning
confidence: 99%
“…Turbulence originates from the large-scale flow motion generated by the intake flow, and among different types of large-scale motion, tumble possesses several advantageous structural characteristics for turbulence enhancement: (1) Tumble is spontaneously generated with pentroof cylinder head, which is most common for recent four-valve engines, (2) as a relatively stable mean motion, tumble can store a certain amount of the intake kinetic energy, and (3) having rotation axis perpendicular to the cylinder axis, even well-ordered tumble cannot avoid its breakdown into small-scale motions (or turbulence) near the end of compression, at which high turbulence is desired [4][5][6][7][8]. In summary, the intake-generated tumble can preserve the turbulence potential and release it in a timely manner.…”
Section: Introductionmentioning
confidence: 99%
“…or by using some auxiliary device (shrouded valve [12], tumble flap [4], etc.). In spite of very complicated flow dynamics there must be involved, most 0D tumble models available have used a single coefficient obtained from steady rig test to represent all the influence on valve flow and resultant tumble [6,[13][14][15]. Additionally, in case of tumble decay, fundamental analysis on the effect of chamber geometry and flow structure within it has been hardly implemented, but instead, decay functions for each particular engine are often exploited [14][15][16].…”
Turbulence is one of the most important aspects in spark-ignition engines as it can significantly affect burn rates, heat transfer rates, and combustion stability, and thus the performance. Turbulence originates from a large-scale mean motion that occurs during the induction process, which mainly consists of tumble motion in modern spark-ignition engines with a pentroof cylinder head. Despite its significance, most 0D turbulence models rely on calibration factors when calculating the evolution of tumble motion and its conversion into turbulence. In this study, the 0D tumble model has been improved based on the physical phenomena, as an attempt to develop a comprehensive model that predicts flow dynamics inside the cylinder. The generation and decay rates of tumble motion are expressed with regards of the flow structure in a realistic combustion chamber geometry, while the effects of port geometry on both charging efficiency and tumble generation rate are reflected by supplementary steady CFD. The developed tumble model was integrated with the standard k-ε model, and the new turbulence model has been validated with engine experimental data for various changes in operating conditions including engine speed, load, valve timing, and engine geometry. The calculated results showed a reasonable correlation with the measured combustion duration, verifying this physics-based model can properly predict turbulence characteristics without any additional calibration process. This model can suggest greater insights on engine operation and is expected to assist the optimization process of engine design and operating strategies.
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