Prediction of thermal stratification in an engine-like geometry using a zero-dimensional stochastic reactor model

Franken, Tim; Klauer, Christian; Kienberg, Martin; Matrisciano, Andrea; Mauß, Fabian

doi:10.1177/1468087418824217

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…The current method allows to analyze the knocking tendency of a certain operating point and to investigate possible reason for occurring auto-ignitions. Future steps to extend the capabilities to predict CCV within the SI-SRM are the use of a phenomenological turbulence model to predict the turbulent kinetic energy 38,61 and the introduction of a spark model with consideration of chemistry and turbulence level on the inflammation time. To analyze the reason for different EGR levels and temperatures, the flow in the intake manifold will be investigated.…”

Section: Resultsmentioning

confidence: 99%

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Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines

Netzer

Pasternak²,

Seidel³

et al. 2019

International Journal of Engine Research

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Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels’ tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier–Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier–Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier–Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier–Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.

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Section: Resultsmentioning

confidence: 99%

“…Of special importance for the prediction of auto-ignition is the variation of nitrogen oxides. 18,19 In Franken et al, 38 the stochastic treatment of the mixing and heat transfer was verified successfully against DNS in an engine-like geometry.…”

Section: Modeling Of CCV In 0dmentioning

confidence: 96%

Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines

Netzer

Pasternak²,

Seidel³

et al. 2019

International Journal of Engine Research

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“…In the formula: P Heatexchange represents the heat exchange amount, kW; C min represents the smaller specific heat capacity flow rate of the two heat exchange fluids, kW/ • C; T in1 represents the air inlet temperature, • C; T in2 represents the coolant inlet temperature, • C; ε represents Heat transfer efficiency, %; Nu means Nusselt number; Re means Reynolds number; Pr means Prandtl number; α, β and γ indicate fitting coefficients [13].…”

Section: Mathematical Model Of Internal Combustion Engine Heat Transf...mentioning

confidence: 99%

Establishment of a Coupling Model for the Prediction of Heat Dissipation of the Internal Combustion Engine Based on Finite Element

Mu¹,

Wang²,

Hong³

et al. 2022

Energy Engineering

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The aerodynamics and heat transfer performance in the rear-mounted automobile cabin have an important influence on the engine's safety and the operational stability of the automobile. The article uses STAR-CCM and GT-COOL software to establish the 3D wind tunnel model and engine cooling system model of the internal combustion engine. At the same time, we established a 3D artificial coupling model through parameter transfer. The research results show that the heat transfer coefficient decreases with the increase of the comprehensive drag coefficient of the nacelle. This shows that the cabin flow field has an important influence on the heat transfer coefficient. The mainstream temperature rise of the engine room increases with the increase of the engine load. It is proved that vehicle speed affects the amount of heat dissipation of the engine room internal combustion engine under certain load conditions. The article provides a more effective and fast calculation method for the research on the heat dissipation of the internal combustion engine and the optimization of the cooling system equipment.

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“…The treatment of the flame propagation has been previously introduced by Bjerkborn et al [37] and broadly validated by Pasternak et al [38] and Netzer [39]. The Woschni model [40] is used to evaluate total wall heat transfer, while the distribution of the heat transfer over the SRM particles is calculated using a stochastic approach, explained by Tuner [36] and further validated by Franken et al [41] using Direct A probability density function is used to describe the in-cylinder content and each particle contributes to the realization of the given PDF at each time-step. Since all stochastic particles in the SRM represent a portion of the in-cylinder mass, a Mass Density Function (MDF) is used to solve the main transport equation of the 0-D SRM.…”

Section: The Stochastic Reactor Modelmentioning

confidence: 99%

“…The treatment of the flame propagation has been previously introduced by Bjerkborn et al [37] and broadly validated by Pasternak et al [38] and Netzer [39]. The Woschni model [40] is used to evaluate total wall heat transfer, while the distribution of the heat transfer over the SRM particles is calculated using a stochastic approach, explained by Tuner [36] and further validated by Franken et al [41] using Direct Numerical Simulation (DNS) results from Schmitt et al [42]. A short overview of the turbulence models adopted in the present work is given in the following sub-section.…”

Section: Online Chemistry Solvermentioning

confidence: 99%

Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models

et al. 2020

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The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO2, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns.

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Prediction of thermal stratification in an engine-like geometry using a zero-dimensional stochastic reactor model

Cited by 6 publications

References 23 publications

Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines

Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines

Establishment of a Coupling Model for the Prediction of Heat Dissipation of the Internal Combustion Engine Based on Finite Element

Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models

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