%0 Journal Article %1 weber2006diesel %A Weber, Jost %A Peters, Norbert %A Pawlowski, Adam %A Kneer, Reinhold %A Hergart, Carl-Anders %A Thary, Sherif Hussein El %A Lippert, Andreas %D 2006 %J SAE Technical Paper Series %K nrev %T Diesel spray characterization using a micro-genetic algorithm and optical measurements %V 2006-01-1115 %X The non-premixed turbulent combustion and emission formation in a modern DI diesel engine relies mostly on the mixture formation process induced by the diesel fuel spray. Therefore the numerical simulation of this process has to incorporate accurate spray modelling which captures the physics of the spray formation, propagation and vaporization. A widely used framework for spray modelling is the Discrete Droplet Model (DDM) which also is applied in the present work. In the DDM framework, separate submodels account for droplet breakup, droplet-droplet interaction and evaporation. Due to the empirical nature of these submodels (particularly droplet breakup and collision) necessitated by an incomplete representation of the physics, and by the inability to isolate each process under diesel engine relevant conditions, some of the constants controlling the outcomes of these submodels require calibration. From spray chamber experiments at diesel engine relevant conditions (p=5 MPa and T=800 K), shadowgraph imaging resolves the macroscopic spray characteristics, namely the penetration length of the liquid and gaseous phase, respectively. Phase-Doppler Anemometry (PDA) measurements have been also performed to obtain droplet diameter distribution and velocity data at discrete spatial positions which can be interpreted as a spray characterization on a microscopic level. The spray simulation conducted in this work aims to find the optimal agreement with the optical data as expressed by a merit function. Here, 6 parameters are identified that influence the merit function. As a manual search for an optimal point in a 6-dimensional parameter space is nearly impossible, a methodology is presented using a Micro-Genetic Algorithm (μGA) for the search of the optimal agreement with experimental data. The methodology is applied for two 8-hole nozzles with KSfactors of 1.3 and 3.0 respectively, using rail pressures of 60, 90 and 135 MPa. For all 6 cases, the μGA is able to find an optimum where the spray penetration length of liquid and gaseous phase is computed correctly. After the spray model is calibrated, the influence of varying mesh size, mesh topology and time-step size is studied in a "virtual" engine simulation. In the engine simulation, the characteristics of the spray are found to be similar to the spray chamber simulation, requiring no re-adjustment to the spray model parameters and initial conditions.

@article{weber2006diesel, abstract = {The non-premixed turbulent combustion and emission formation in a modern DI diesel engine relies mostly on the mixture formation process induced by the diesel fuel spray. Therefore the numerical simulation of this process has to incorporate accurate spray modelling which captures the physics of the spray formation, propagation and vaporization. A widely used framework for spray modelling is the Discrete Droplet Model (DDM) which also is applied in the present work. In the DDM framework, separate submodels account for droplet breakup, droplet-droplet interaction and evaporation. Due to the empirical nature of these submodels (particularly droplet breakup and collision) necessitated by an incomplete representation of the physics, and by the inability to isolate each process under diesel engine relevant conditions, some of the constants controlling the outcomes of these submodels require calibration. From spray chamber experiments at diesel engine relevant conditions (p=5 MPa and T=800 K), shadowgraph imaging resolves the macroscopic spray characteristics, namely the penetration length of the liquid and gaseous phase, respectively. Phase-Doppler Anemometry (PDA) measurements have been also performed to obtain droplet diameter distribution and velocity data at discrete spatial positions which can be interpreted as a spray characterization on a microscopic level. The spray simulation conducted in this work aims to find the optimal agreement with the optical data as expressed by a merit function. Here, 6 parameters are identified that influence the merit function. As a manual search for an optimal point in a 6-dimensional parameter space is nearly impossible, a methodology is presented using a Micro-Genetic Algorithm (μGA) for the search of the optimal agreement with experimental data. The methodology is applied for two 8-hole nozzles with KSfactors of 1.3 and 3.0 respectively, using rail pressures of 60, 90 and 135 MPa. For all 6 cases, the μGA is able to find an optimum where the spray penetration length of liquid and gaseous phase is computed correctly. After the spray model is calibrated, the influence of varying mesh size, mesh topology and time-step size is studied in a "virtual" engine simulation. In the engine simulation, the characteristics of the spray are found to be similar to the spray chamber simulation, requiring no re-adjustment to the spray model parameters and initial conditions. }, added-at = {2013-03-21T15:13:41.000+0100}, author = {Weber, Jost and Peters, Norbert and Pawlowski, Adam and Kneer, Reinhold and Hergart, Carl-Anders and Thary, Sherif Hussein El and Lippert, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2f2c6efbcabf46a7e8cfb84eff6580544/itv}, interhash = {123a5ce3150493f84d93ea7bef065a3e}, intrahash = {f2c6efbcabf46a7e8cfb84eff6580544}, journal = {SAE Technical Paper Series}, keywords = {nrev}, note = {SAE-Paper Nr. 2006-01-1115}, timestamp = {2014-07-31T07:46:38.000+0200}, title = {Diesel spray characterization using a micro-genetic algorithm and optical measurements}, volume = {2006-01-1115}, year = 2006 }