Dispersion of Particle-laden Jet in Supersonic Crossflow(PJSC)is an essential process in many applications,experimental study on which,however,has rarely been reported.In order to gain physical insights into PJSC,a sp...Dispersion of Particle-laden Jet in Supersonic Crossflow(PJSC)is an essential process in many applications,experimental study on which,however,has rarely been reported.In order to gain physical insights into PJSC,a specialized experimental setup capable of producing a supersonic crossflow at Mach 2.6 and a particle-laden jet with particle mass loading up to 60%is developed.Visualization of the particles motion is achieved with the help of high-speed planar laser scattering technology.The dispersion characteristics of PJSC within a supersonic channel structured by cavity are systematically analyzed through six experimental cases.The results indicate that the vortices have a significant influence on particle dispersion,leading to preferential concentration of particles.i.e.particle clusters.The particle dispersion is summarized as the"scale dispersion"pattern.The primary pathways for particles entering the cavity are identified as the shear layer above the cavity and collisions at the cavity rear edge.Among the studied factors,the momentum flux ratio exerts the most substantial influence on the dispersion process.Importantly,a reduction in the injection distance is correlated with less particles entering the cavity.The insights gained from this research provide essential references for furthering understanding particle dispersion mechanisms in supersonic flows and developing highly accurate numerical models.展开更多
For the design and optimization of advanced aero-engines,the prohibitively computational resources required for numerical simulations pose a significant challenge,due to the extensive exploration of design parameters ...For the design and optimization of advanced aero-engines,the prohibitively computational resources required for numerical simulations pose a significant challenge,due to the extensive exploration of design parameters across a vast design space.Surrogate modeling techniques offer a viable alternative for efficiently emulating numerical results within a notably compressed timeframe.This study introduces parametric Reduced-Order Models(ROMs)based on Convolutional Auto-Encoders(CAE),Fully Connected AutoEncoders(FCAE),and Proper Orthogonal Decomposition(POD)to fast emulate spatial distributions of physical variables for a supercritical jet into a supersonic crossflow under different operating conditions.To further accelerate the decision-making process,an optimization model is developed to enhance fuel-oxidizer mixing efficiency while minimizing total pressure loss.Results indicate that CAE-based ROMs exhibit superior prediction accuracy while FCAE-based ROMs show inferior predictive accuracy but minimal uncertainty.The latter may be ascribed to the markedly greater number of hyperparameters.POD-based ROMs underperform in regions of strong nonlinear flow dynamics,coupled with higher overall prediction uncertainties.Both AE-and POD-based ROMs achieve online predictions approximately 9 orders of magnitude faster than conventional simulations.The established optimization model enables the attainment of Pareto-optimal frontiers for spatial mixing deficiencies and total pressure recovery coefficient.展开更多
The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in r...The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in real time merely by relying on traditional approaches.With the rapid advancement of deep learning technology,its powerful data processing capability offers a fast method for the prediction of the transverse jet flow field.Consequently,a prediction model based on deep learning is established,with the aim of obtaining the flow characteristics of a transverse jet under different freestream and jet conditions.This study segments the complex grid into several individual grids and trains them independently.The trained model can successfully establish the nonlinear mapping relationship between the transverse jet flow field and the input parameters.The prediction accuracy of the established model for the wall pressure under different conditions exceeds 99%,and the established model is also capable of reproducing structures such as shock waves and recirculation zones in the overall flow field,thereby achieving highly precise and efficient prediction of the jet structure and flow information.The results suggest that in contrast to the traditional numerical simulation,this deep learning model demonstrates greater efficiency in predicting the transverse jet flow field.展开更多
A comprehensive numerical study on the three-dimensional structure of a turbulent jet in crossflow is performed. The jet-to-crossflow velocity ratio (R) varies in the range of 2 - 16; both vertical jets and inclined j...A comprehensive numerical study on the three-dimensional structure of a turbulent jet in crossflow is performed. The jet-to-crossflow velocity ratio (R) varies in the range of 2 - 16; both vertical jets and inclined jets without excess streamwise momentum are considered. The numerical results of the Standard two-equation k-ε model show that the turbulent structure can be broadly categorised according to the jet-to-crossflow velocity ratio. For strong to moderate jet discharges, i.e. R> 4, the jet is characterized by a longitudinal transition through a bent-over phase during which the jet becomes almost parallel with the main freestream, to a sectional vortex-pair flow with double concentration maxima; the computed flow details and scalar mixing characteristics can be described by self-similar relations beyond a dimensionless distance of around 20-60. The similarity coefficients are only weakly dependent on R. The cross-section scalar field is kidney-shaped and bifurcated, vvith distinct double concentration maxima; the aspect ratio is found to be around 1.2. A loss in vertical momentum is ob-served and the added mass coefficient of the jet motion is found to be approximately 1. On the other hand, for weak jets in strong crossflow, i. e. R ≥ 2, the lee of the jet is characterized by a negative pressure region. Although the double vortex flow can stili be noted, the scalar field becomes more symmetrical and no longer bifurcated. The similarity coeffcients are al-so noticeably different. The predicted jet flovv characteristics and mixing rates are well supported by experimental and field dala展开更多
The transverse injection flow field has an important impact on the flowpath design of scramjet engines. At present a combination of the transverse injection scheme and any other flame holder has been widely employed i...The transverse injection flow field has an important impact on the flowpath design of scramjet engines. At present a combination of the transverse injection scheme and any other flame holder has been widely employed in hypersonic propulsion systems to promote the mixing process between the fuel and the supersonic freestream; combustion efficiency has been improved thereby, as well as engine thrust. Research on mixing techniques for the transverse injection flow field is summarized from four aspects, namely the jet-to-crossflow pressure ratio, the geometric configuration of the injection port, the number of injection ports, and the injection angle. In conclusion, urgent investigations of mixing techniques of the transverse injection flow field are pro- posed, especiaUy data mining in the quantitative analytical results for transverse injection flow field, based on results from multi-objective design optimization theory.展开更多
The mixing and merging characteristics of multiple tandem jets in crossflow are investigated by use of the Computational Fluid Dynamics （CFD） code FI,UENT. The realizable k - ε model is employed for turbulent elosu...The mixing and merging characteristics of multiple tandem jets in crossflow are investigated by use of the Computational Fluid Dynamics （CFD） code FI,UENT. The realizable k - ε model is employed for turbulent elosure of the Reynolds-averaged Navier-Stokes equations. Numerical experiments are performed for 1-, 2- and 4-jet groups, tbr jet-tocrossflow velocity ratios of R = 4.2 ～ 16.3. The computed velocity and scalar concentration field are in good agreement with experiments using Particle Image Velocimetry （PIV） and Laser Induced Fluorescence （LIF）, as well as previous work. The results show that the leading jet behavior is similar to a single free jet in crossflow, while all the downstream rear jets have less bent-over jet trajectories - suggesting a reduced ambient velocity for the rear jets. The concentration decay of the leading jet is greater than that of the rear jets. When normalized by appropriate crossflow momentum length scales, all jet trajectories follow a universal relation regardless of the sequential order of jet position and the nund）er of jets. Supported by the velocity and trajectory measurements, the averaged maximum effective crossflow velocity ratio is computed to be in the range of 0.39 to 0.47.展开更多
基金co-supported by the National Natural Science Foundation of China (No. 12272409)the Scientific Research and Innovation Project of Hunan Province, China (Nos. CX20230058, kq2107001, 2022RC1233 and QL20230015)
文摘Dispersion of Particle-laden Jet in Supersonic Crossflow(PJSC)is an essential process in many applications,experimental study on which,however,has rarely been reported.In order to gain physical insights into PJSC,a specialized experimental setup capable of producing a supersonic crossflow at Mach 2.6 and a particle-laden jet with particle mass loading up to 60%is developed.Visualization of the particles motion is achieved with the help of high-speed planar laser scattering technology.The dispersion characteristics of PJSC within a supersonic channel structured by cavity are systematically analyzed through six experimental cases.The results indicate that the vortices have a significant influence on particle dispersion,leading to preferential concentration of particles.i.e.particle clusters.The particle dispersion is summarized as the"scale dispersion"pattern.The primary pathways for particles entering the cavity are identified as the shear layer above the cavity and collisions at the cavity rear edge.Among the studied factors,the momentum flux ratio exerts the most substantial influence on the dispersion process.Importantly,a reduction in the injection distance is correlated with less particles entering the cavity.The insights gained from this research provide essential references for furthering understanding particle dispersion mechanisms in supersonic flows and developing highly accurate numerical models.
基金supported by the Science Center for Gas Turbine Project,China(No.P2022-B-II-020-001)the National Natural Science Foundation of China(No.52276123).
文摘For the design and optimization of advanced aero-engines,the prohibitively computational resources required for numerical simulations pose a significant challenge,due to the extensive exploration of design parameters across a vast design space.Surrogate modeling techniques offer a viable alternative for efficiently emulating numerical results within a notably compressed timeframe.This study introduces parametric Reduced-Order Models(ROMs)based on Convolutional Auto-Encoders(CAE),Fully Connected AutoEncoders(FCAE),and Proper Orthogonal Decomposition(POD)to fast emulate spatial distributions of physical variables for a supercritical jet into a supersonic crossflow under different operating conditions.To further accelerate the decision-making process,an optimization model is developed to enhance fuel-oxidizer mixing efficiency while minimizing total pressure loss.Results indicate that CAE-based ROMs exhibit superior prediction accuracy while FCAE-based ROMs show inferior predictive accuracy but minimal uncertainty.The latter may be ascribed to the markedly greater number of hyperparameters.POD-based ROMs underperform in regions of strong nonlinear flow dynamics,coupled with higher overall prediction uncertainties.Both AE-and POD-based ROMs achieve online predictions approximately 9 orders of magnitude faster than conventional simulations.The established optimization model enables the attainment of Pareto-optimal frontiers for spatial mixing deficiencies and total pressure recovery coefficient.
基金co-supported by the National Natural Science Foundation of China(No.12202488)the National Postdoctoral Researcher Program(Grants No.GZB20230985)+1 种基金the Natural Science Program of National University of Defense Technology(No.ZK22-30)the Independent Innovation Science Fund of National University of Defense Technology(No.24-ZZCX-BC-05)。
文摘The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in real time merely by relying on traditional approaches.With the rapid advancement of deep learning technology,its powerful data processing capability offers a fast method for the prediction of the transverse jet flow field.Consequently,a prediction model based on deep learning is established,with the aim of obtaining the flow characteristics of a transverse jet under different freestream and jet conditions.This study segments the complex grid into several individual grids and trains them independently.The trained model can successfully establish the nonlinear mapping relationship between the transverse jet flow field and the input parameters.The prediction accuracy of the established model for the wall pressure under different conditions exceeds 99%,and the established model is also capable of reproducing structures such as shock waves and recirculation zones in the overall flow field,thereby achieving highly precise and efficient prediction of the jet structure and flow information.The results suggest that in contrast to the traditional numerical simulation,this deep learning model demonstrates greater efficiency in predicting the transverse jet flow field.
文摘A comprehensive numerical study on the three-dimensional structure of a turbulent jet in crossflow is performed. The jet-to-crossflow velocity ratio (R) varies in the range of 2 - 16; both vertical jets and inclined jets without excess streamwise momentum are considered. The numerical results of the Standard two-equation k-ε model show that the turbulent structure can be broadly categorised according to the jet-to-crossflow velocity ratio. For strong to moderate jet discharges, i.e. R> 4, the jet is characterized by a longitudinal transition through a bent-over phase during which the jet becomes almost parallel with the main freestream, to a sectional vortex-pair flow with double concentration maxima; the computed flow details and scalar mixing characteristics can be described by self-similar relations beyond a dimensionless distance of around 20-60. The similarity coefficients are only weakly dependent on R. The cross-section scalar field is kidney-shaped and bifurcated, vvith distinct double concentration maxima; the aspect ratio is found to be around 1.2. A loss in vertical momentum is ob-served and the added mass coefficient of the jet motion is found to be approximately 1. On the other hand, for weak jets in strong crossflow, i. e. R ≥ 2, the lee of the jet is characterized by a negative pressure region. Although the double vortex flow can stili be noted, the scalar field becomes more symmetrical and no longer bifurcated. The similarity coeffcients are al-so noticeably different. The predicted jet flovv characteristics and mixing rates are well supported by experimental and field dala
基金supported by the Science Foundation of National University of Defense Technology (No. JC11-01-02)the Hunan Provincial Natural Science Foundation of China (No.12jj4047)
文摘The transverse injection flow field has an important impact on the flowpath design of scramjet engines. At present a combination of the transverse injection scheme and any other flame holder has been widely employed in hypersonic propulsion systems to promote the mixing process between the fuel and the supersonic freestream; combustion efficiency has been improved thereby, as well as engine thrust. Research on mixing techniques for the transverse injection flow field is summarized from four aspects, namely the jet-to-crossflow pressure ratio, the geometric configuration of the injection port, the number of injection ports, and the injection angle. In conclusion, urgent investigations of mixing techniques of the transverse injection flow field are pro- posed, especiaUy data mining in the quantitative analytical results for transverse injection flow field, based on results from multi-objective design optimization theory.
基金The workis supported by a grant fromthe Hong Kong Research Grants Council (HKU7347/01E) Programfor NewCentury Excellent Talents in University (NCET-04-0494) the National Natural Science Foundation of China(Grant No.50479068)
文摘The mixing and merging characteristics of multiple tandem jets in crossflow are investigated by use of the Computational Fluid Dynamics （CFD） code FI,UENT. The realizable k - ε model is employed for turbulent elosure of the Reynolds-averaged Navier-Stokes equations. Numerical experiments are performed for 1-, 2- and 4-jet groups, tbr jet-tocrossflow velocity ratios of R = 4.2 ～ 16.3. The computed velocity and scalar concentration field are in good agreement with experiments using Particle Image Velocimetry （PIV） and Laser Induced Fluorescence （LIF）, as well as previous work. The results show that the leading jet behavior is similar to a single free jet in crossflow, while all the downstream rear jets have less bent-over jet trajectories - suggesting a reduced ambient velocity for the rear jets. The concentration decay of the leading jet is greater than that of the rear jets. When normalized by appropriate crossflow momentum length scales, all jet trajectories follow a universal relation regardless of the sequential order of jet position and the nund）er of jets. Supported by the velocity and trajectory measurements, the averaged maximum effective crossflow velocity ratio is computed to be in the range of 0.39 to 0.47.
