Development of an efficient solver for detailed kinetics in reactive flows

The use of chemical kinetic mechanisms in CAE tools for reactive flow simulations is of high importance for studying and predicting pollutant formation. However, usage of complex reaction schemes is accompanied by high computational cost in both 1D and 3D-CFD frameworks. The combustion research community has addressed such challenge via two main approaches: 1) tailor made mechanism reduction strategies; 2) pre-tabulation of the chemistry process and look-up during run-time. The present work covers both topics, although much of the methodology development and validation efforts focused on tabulation.In the first eight months of the PhD work, an isomer lumping strategy based on thermodynamic data was developed and applied to a detailed three component reaction mechanism for n-decane, alpha-methylnaphthalene and methyl decanoate comprising 807 species and 7807 reactions. A total of 74 isomer groups were identified within the oxidation of n-decane and methyl-decanoate via analysis of the Gibbs free energy of the isomers. The lumping procedure led to a mechanism of 463 species and 7600 reactions which was compared against the detailed version over several reactor conditions and over a broad range of temperature, pressure and equivalence ratio. In all cases, very good agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%.In the second phase of the PhD work, a tabulated chemistry approach was developed, implemented and validated against an on-the-fly chemistry solver across different simulation frameworks. As a first attempt, a flamelet-based tabulation method for soot source terms was coupled to the stochastic reactor model (SRM) and tested against a well stirred reactor-based approach under Diesel engine conditions. The main purpose was to assess and quantify benefits of tabulation within the 0D-SRM framework with respect to soot formation only. Subsequently, a chemical enthalpy (ℎ298) based approach was developed and implemented within the SRM model to predict both combustion and emission formation. This approach was widely validated against the detailed on-the-fly solver solutions under 0D reactor conditions as well as Diesel engine conditions for a wide range of operating points. Good agreement was found between the two solvers and a remarkable speed-up was obtained by means of computational costs of the simulation. As a last step, the same tabulated chemistry solver was coupled to a commercial CFD solver (CONVERGE v. 2.4) via user defined functions and performances were assessed against the built-in on-the fly chemistry solver (SAGE) under Diesel engine sector simulations. The tabulated chemistry solver proved to be within an acceptable level of accuracy for engineering studies and showed a consistent speed-up in comparison to the SAGE solver.Across all the investigated frameworks, the developed tabulated chemistry solver was found to be a valid solution to speed-up simulation time without compromising accuracy of the solution for combustion and emissions predictions for Diesel engine applications. In fact, the much-reduced CPU times allowed the SRM to be included in broader engine development campaigns where multi-objective optimization methods where efficiently used to explore new engine designs

Development of an efficient solver for detailed kinetics in reactive flows

The use of chemical kinetic mechanisms in CAE tools for reactive flow simulations is of high importance for studying and predicting pollutant formation. However, usage of complex reaction schemes is accompanied by high computational cost in both 1D and 3-D CFD frameworks. The combustion research community has addressed such challenge via two main approaches: 1) tailor made mechanism reduction strategies; 2) pre-tabulation of the chemistry process and look-up during run-time. The present work covers both topics, although much of the methodology development and validation efforts focused on tabulation. In the first phase of the PhD work, an isomer lumping strategy based on thermodynamic data was developed and applied to a detailed three component reaction mechanism for n-decane, alpha-methylnaphthalene and methyl decanoate comprising 807 species and 7807 reactions. A total of 74 isomer groups were identified within the oxidation of n-decane and methyl decanoate via the assessment of the Gibbs free energy of the isomers. The lumping procedure led to a mechanism of 463 species and 7600 reactions, which was compared against the detailed version over several reactor conditions and over a broad range of temperature, pressure and equivalence ratio. In all cases, excellent agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%. In the second phase of the PhD work, a tabulated chemistry approach was developed, implemented and validated against an on-the-fly chemistry solver across different simulation frameworks. As a first attempt, a flamelet-based tabulation method for soot source terms was coupled to the stochastic reactor model and tested against a well stirred reactor-based approach under Diesel engine conditions. The main purpose was to assess and quantify benefits of tabulation within the 0-D SRM framework with respect to soot formation only. Subsequently, a latent enthalpy (h298) based approach was developed and implemented within the SRM model to predict both combustion and emission formation. This approach was widely validated against the detailed on-the-fly solver solutions under 0-D reactor conditions as well as Diesel engine conditions for a wide range of operating points. Good agreement was found between the two solvers and a remarkable speed-up was obtained in terms of computational costs of the simulation. As a last step, the same tabulated chemistry solver was coupled to a commercial CFD software via user defined functions and performances were assessed against the built-in on-the fly chemistry solver under Diesel engine sector simulations. The tabulated chemistry solver proved to be within an acceptable level of accuracy for engineering studies and showed a consistent speed-up in comparison to the online chemistry solver. Across all the investigated frameworks, the developed tabulated chemistry solver was found to be a valid solution to speed-up simulation time without compromising accuracy of the solution for combustion and emissions predictions for engine applications. In fact, the much-reduced CPU times allowed the SRM to be included in broader engine development campaigns where multi-objective optimization methods where efficiently used to explore new engine designs

An a priori thermodynamic data analysis based chemical lumping method for the reduction of large and multi-component chemical kinetic mechanisms

A chemical species lumping approach for reduction of large hydrocarbons and oxygenated fuels is presented. The methodology is based on an a priori analysis of the Gibbs free energy of the isomer species which is then used as main criteria for the evaluation of lumped group. Isomers with similar Gibbs free energy are lumped assuming they present equal concentrations when applied to standard reactor conditions. Unlike several lumping approaches found in literature, no calculation results from the primary mechanism have been employed prior to the application of our chemical lumping strategy. An 807 species and 7807 individual reactions detailed mechanism comprising n-decane, alpha-methylnaphthalene and methyl decanoate has been used. The thermodynamic data have been analyzed and 74 isomer groups have been identified within the oxidation of n-decane and methyl decanoate. The mechanism reduction has led to a mechanism size of 463 species and 7600 reactions. Thereafter the lumped mechanism has been checked under several reactor conditions and over a broad range of temperature, pressure, and equivalence ratio in order to quantify the accuracy of the proposed approach. In all cases, very good agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%. Effects of the lumping procedure on sensitivities and on isomer concentrations were considered to further demonstrate the validity of the proposed approach

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

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

Diesel engine performance mapping using a parametrized mixing time model

A numerical platform is presented for diesel engine performance mapping. The platform employs a zero-dimensional stochastic reactor model for the simulation of engine in-cylinder processes. n-Heptane is used as diesel surrogate for the modeling of fuel oxidation and emission formation. The overall simulation process is carried out in an automated manner using a genetic algorithm. The probability density function formulation of the stochastic reactor model enables an insight into the locality of turbulence–chemistry interactions that characterize the combustion process in diesel engines. The interactions are accounted for by the modeling of representative mixing time. The mixing time is parametrized with known engine operating parameters such as load, speed and fuel injection strategy. The detailed chemistry consideration and mixing time parametrization enable the extrapolation of engine performance parameters beyond the operating points used for model training. The results show that the model responds correctly to the changes of engine control parameters such as fuel injection timing and exhaust gas recirculation rate. It is demonstrated that the method developed can be applied to the prediction of engine load–speed maps for exhaust NO x , indicated mean effective pressure and fuel consumption. The maps can be derived from the limited experimental data available for model calibration. Significant speedup of the simulations process can be achieved using tabulated chemistry. Overall, the method presented can be considered as a bridge between the experimental works and the development of mean value engine models for engine control applications

Correlation Analysis between Students' Cognitive Styles and their Attitude to join Kinematic and Dynamics Open Source Codes Projects

This paper has two main goals. The first one is the presentation to the scientific community of a web site which gathers software tools for kinematic synthesis and dynamic simulation of mechanisms. The second one consists in presenting the method used to evaluate the impact that this site had on the students during its first year of existence. The web site, called KINSYNTH, is open to colleagues who wish use it as open source software library or upload contributions from their students. The system is based on the free access to the source programs and on the open acknowledge to anyone wish authoring one or more codes, or, at least, simply developing new releases of existing codes. The method used to evaluate the impact that KINSYNTH had on the first students involved in this activity is based on the analysis of their answers to a questionnaire. The questions were carefully selected in order to give scores in several different dimensions which try to measure some particular characteristics of the preferred way of learning. The dimensions were tailored on some widespread known model of thinking and learning, namely, Gardner’s Multiple Intelligence model, Cognitive Styles, Learning Styles and Learning preference inventories. The analysis of the students answers have revealed some interesting characteristics of the class, that can be used to improve the teaching activity, as well as the impact of the web site under developments in order to improve its attractiveness and usefulness

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

The prediction of local heat transfer and thermal stratification in the zero-dimensional stochastic reactor model is compared to direct numerical simulation published by Schmitt et al. in 2015. Direct numerical simulation solves the Navier–Stokes equations without incorporating model assumptions for turbulence and wall heat transfer. Therefore, it can be considered as numerical experiment and is suitable to validate approximations in low-dimensional models. The stochastic reactor model incorporates a modified version of the Euclidean Minimum Spanning Tree mixing model proposed by Subramaniam et al. in 1998. To capture the thermal stratification of the direct numerical simulation, the total enthalpy (H) is used as the only mixing limiting scalar within the newly proposed H-Euclidean-Minimum-Spanning-Tree. Furthermore, a stochastic heat transfer model is incorporated to mimic turbulence effects on local heat transfer distribution to the walls. By adjusting the Cϕ mixing time and Ch stochastic heat transfer parameter, the stochastic reactor model predicts accurately the thermal stratification of the direct numerical simulation. Comparing the Woschni, Hohenberg and Heinle heat transfer model shows that the modified Heinle model matches accurately the direct numerical simulation results. Thereby, the Heinle model accounts for the influence of turbulent kinetic energy on the characteristic velocity in the heat transfer coefficient calculation. This highlights the importance of incorporating turbulence effects in low-dimensional heat transfer models. Overall, the zero-dimensional stochastic reactor model with the H-Euclidean-Minimum-Spanning-Tree mixing model, the stochastic heat transfer model and the modified Heinle correlation have proven successfully the prediction of mean quantities like temperature and heat transfer and thermal stratification of the direct numerical simulation

Influence of Nozzle Eccentricity on Spray Structures in Marine Diesel Sprays

Large two-stroke marine Diesel engines have special injector geometries, which differ substantially from the configurations used in most other Diesel engine applications. One of the major differences is that injector orifices are distributed in a highly non-symmetric fashion affecting the spray characteristics. Earlier investigations demonstrated the dependency of the spray morphology on the location of the spray orifice and therefore on the resulting flow conditions at the nozzle tip. Thus, spray structure is directly influenced by the flow formation within the orifice. Following recent Large Eddy Simulation resolved spray primary breakup studies, the present paper focuses on spray secondary breakup modelling of asymmetric spray structures in Euler-Lagrangian framework based on previously obtained droplet distributions of primary breakup. Firstly, the derived droplet distributions were assigned via user coding to RANS 3D-CFD simulation of nozzle bore geometries having 0.0, 0.4 and 0.8 normalized eccentricities. Spray secondary breakup then calculated by using the KH-RT breakup model. The simulations compared to a widely used industrial methodology and validated against experimental measurements performed in a unique Spray Combustion Chamber. Furthermore, effects of nozzle eccentricity were assessed under non-reactive and reactive conditions using a computationally efficient combustion solver. The methodology was found to be promising for future implementation of droplet mapping techniques under marine diesel engine conditions