Interaction between fuel and air in a combustion chamber is one of the main drivers of the mixing process.Experimentally,flow visualizations are limited by high droplet density in the spray.Numerically,the ability of ...Interaction between fuel and air in a combustion chamber is one of the main drivers of the mixing process.Experimentally,flow visualizations are limited by high droplet density in the spray.Numerically,the ability of large eddy simulations(LES)to resolve large scales of flow offers good perspectives on capturing flow structures issued from the interaction between the Lagrangian(fuel droplets)and Eulerian(ambient gas)phases.This study examined these interactions first during a single injection using 3D and 2D criteria for both phases.As for the 3D criteria,the spray shape was analyzed in parallel to the Q-criteria applied to the Eulerian phase,making it possible to relate the spray deformations to some specific Eulerian structures.Secondly,2D criteria were the fuel mass-fraction field and Eulerian streamlines,both taken in the mid-plane of the spray.This last analysis allows for identifying certain mechanisms involved in the Eulerian phase’s structure generation and relates it to high fuel-concentration areas in the fuel mass-fraction visualizations.展开更多
A deep learning-based method for denoising and detecting the gas turbine engine spray droplets in the lightscattered image(Mie scattering)is proposed for the first time.A modified U-Net architecture is employed in the...A deep learning-based method for denoising and detecting the gas turbine engine spray droplets in the lightscattered image(Mie scattering)is proposed for the first time.A modified U-Net architecture is employed in the proposed method to denoise and regenerate the droplets.We have compared and validated the performance of the modified U-Net architecture with standard conventional neural networks(CNN)and modified ResNet architectures for denoising spray images from the Mie scattering experiment.The modified U-Net architecture performed better than the other two networks with significantly lower Mean Squared Error(MSE)on the validation dataset.The modified U-Net architecture also produced images with the highest Power Signal to Noise Ratio(PSNR)compared to the other two networks.This superior performance of the modified U-Net architecture is attributed to the encoder-decoder structure.During downsampling,as part of the encoder,only the most prominent features of the image are selectively retained by excluding any noise.This reconstruction of the noisefree features has produced a more accurate and better denoised image.The denoised images are then passed through a center predictor CNN to determine the location of the droplets with an average error of 1.4 pixels.The trained deep learning method for denoising and droplet center detection takes about 2.13 s on a single graphics processing unit(GPU).This study shows the promise for real-time processing of the experimental data using the well-optimized network.展开更多
Spray evaporation of liquid fuels in a turbulent flow is a common process in various engineering applications such as combustion.Interactions between fuel droplets(discrete phase)and fluid flow(continuous phase)have a...Spray evaporation of liquid fuels in a turbulent flow is a common process in various engineering applications such as combustion.Interactions between fuel droplets(discrete phase)and fluid flow(continuous phase)have a considerable effect on liquid fuel evaporation.In this paper,both the single-and two-phase modeling of liquid fuel injection into a model evaporating chamber are presented.The influences of important issues such as turbulence models,coupling between gas phase and droplets,secondary break-up and air swirling on the current spray simulation are investigated.Accordingly,the shear stress transport turbulence model,Taylor analogy break-up and two-way coupling models are applied to simulate the two-phase flow.Atomization and spray of fuel droplets in hot air are modeled employing an Eulerian-Lagrangian approach.The current results show an acceptable agreement with the experiments.Adjacent the fuel atomizer,bigger droplets are detected near the spray edge and minor droplets are situated in the middle.With increasing the droplets axial position,the droplets diameter decreases with a finite slope.The smaller droplets have a deeper penetration,but their lifetime is smaller and they evaporate sooner.A linear relation between penetration and lifetime of smaller droplets is detected.Maximum droplet penetration and mean axial velocity of gas phase are observed for no air swirling case.The effect of variation of swirl number on the lifetime of droplets is almost negligible.By enhancing the swirl number,the uniformity of droplet size distribution is reduced and some large droplets are formed up in the domain.展开更多
文摘Interaction between fuel and air in a combustion chamber is one of the main drivers of the mixing process.Experimentally,flow visualizations are limited by high droplet density in the spray.Numerically,the ability of large eddy simulations(LES)to resolve large scales of flow offers good perspectives on capturing flow structures issued from the interaction between the Lagrangian(fuel droplets)and Eulerian(ambient gas)phases.This study examined these interactions first during a single injection using 3D and 2D criteria for both phases.As for the 3D criteria,the spray shape was analyzed in parallel to the Q-criteria applied to the Eulerian phase,making it possible to relate the spray deformations to some specific Eulerian structures.Secondly,2D criteria were the fuel mass-fraction field and Eulerian streamlines,both taken in the mid-plane of the spray.This last analysis allows for identifying certain mechanisms involved in the Eulerian phase’s structure generation and relates it to high fuel-concentration areas in the fuel mass-fraction visualizations.
基金This research was funded by the U.S.Federal Aviation Administration Office of Environment and Energy through ASCENT,the FAA Center of Excellence for Alternative Jet Fuels,and the Environment,Project 29A through FAA Award Number 13-C-AJFE-PU-011 under the supervision of Dr.Cecilia Shaw and Dr.Anna Oldani.Any opinions,findings,conclusions,or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.
文摘A deep learning-based method for denoising and detecting the gas turbine engine spray droplets in the lightscattered image(Mie scattering)is proposed for the first time.A modified U-Net architecture is employed in the proposed method to denoise and regenerate the droplets.We have compared and validated the performance of the modified U-Net architecture with standard conventional neural networks(CNN)and modified ResNet architectures for denoising spray images from the Mie scattering experiment.The modified U-Net architecture performed better than the other two networks with significantly lower Mean Squared Error(MSE)on the validation dataset.The modified U-Net architecture also produced images with the highest Power Signal to Noise Ratio(PSNR)compared to the other two networks.This superior performance of the modified U-Net architecture is attributed to the encoder-decoder structure.During downsampling,as part of the encoder,only the most prominent features of the image are selectively retained by excluding any noise.This reconstruction of the noisefree features has produced a more accurate and better denoised image.The denoised images are then passed through a center predictor CNN to determine the location of the droplets with an average error of 1.4 pixels.The trained deep learning method for denoising and droplet center detection takes about 2.13 s on a single graphics processing unit(GPU).This study shows the promise for real-time processing of the experimental data using the well-optimized network.
文摘Spray evaporation of liquid fuels in a turbulent flow is a common process in various engineering applications such as combustion.Interactions between fuel droplets(discrete phase)and fluid flow(continuous phase)have a considerable effect on liquid fuel evaporation.In this paper,both the single-and two-phase modeling of liquid fuel injection into a model evaporating chamber are presented.The influences of important issues such as turbulence models,coupling between gas phase and droplets,secondary break-up and air swirling on the current spray simulation are investigated.Accordingly,the shear stress transport turbulence model,Taylor analogy break-up and two-way coupling models are applied to simulate the two-phase flow.Atomization and spray of fuel droplets in hot air are modeled employing an Eulerian-Lagrangian approach.The current results show an acceptable agreement with the experiments.Adjacent the fuel atomizer,bigger droplets are detected near the spray edge and minor droplets are situated in the middle.With increasing the droplets axial position,the droplets diameter decreases with a finite slope.The smaller droplets have a deeper penetration,but their lifetime is smaller and they evaporate sooner.A linear relation between penetration and lifetime of smaller droplets is detected.Maximum droplet penetration and mean axial velocity of gas phase are observed for no air swirling case.The effect of variation of swirl number on the lifetime of droplets is almost negligible.By enhancing the swirl number,the uniformity of droplet size distribution is reduced and some large droplets are formed up in the domain.
