Aiming at improving efficiency in combustion systems, the study on droplet behavior and its trajectory is of crucialimportance. Vortex engine is a kind of internal combustion engine which uses swirl flow to achieve hi...Aiming at improving efficiency in combustion systems, the study on droplet behavior and its trajectory is of crucialimportance. Vortex engine is a kind of internal combustion engine which uses swirl flow to achieve highercombustion efficiency. One of the important advantages of designing vortex engine is to reduce the temperatureof walls by confining the combustion products in the inner vortex. The scopes of this investigation are to studyvortex engine flow field as well as effective parameters on fuel droplet behavior such as droplet diameter, dropletinitial velocity and inlet velocity of the flow field. The flow field is simulated using Reynolds Stress TransportModel (RSM). The Eulerian-Lagrangian method and the one-way coupling approach are employed to simulatetwo phase flow and dispersed phase in the chamber, respectively. A new method, based on computing pressureforce exerted on the droplet surface, is introduced to determine the distinction between using one-way andtwo-way coupling approaches. The results showed that the droplets with smaller diameter are more likely to followthe flow stream lines than bigger droplets, thus evaporate completely in the chamber. Moreover, droplets withgreater initial velocity have higher evaporation rate, yielding the existence of evaporation and combustion in theinner vortex. Additionally, the higher inlet velocity of continuous phase results in higher centrifugal force, leadsdroplets in question to deviate towards the wall faster.展开更多
We propose an algorithm that combines a pre-processing step applied to the a priori state vector prior to retrievals, with the modified damped Newton method （MDNM）, to improve convergence. The initial constraint vec...We propose an algorithm that combines a pre-processing step applied to the a priori state vector prior to retrievals, with the modified damped Newton method （MDNM）, to improve convergence. The initial constraint vector pre-processing step updates the initial state vector prior to the retrievals if the algorithm detects that the initial state vector is far from the true state vector in extreme cases where there are CO2 emissions. The MDNM uses the Levenberg-Marquardt parameter ~,, which ensures a positive Hessian matrix, and a scale factor a, which adjusts the step size to optimize the stability of the convergence. While the algorithm iteratively searches for an optimized solution using observed spectral radiances, MDNM adjusts parameters ）, and a to achieve stable convergence. We present simulated retrieval samples to evaluate the performance of our algorithm and comparing it to existing methods. The standard deviation of our retrievals adding random noise was less than 3.8 ppmv. After pre-processing the initial estimate when it was far from the true value, the CO2 retrieval errors in the boundary layers were within 1.2 ppmv. We tested the MDNM algorithm＇s performance using GOSAT Llb data with cloud screening. Our preliminary validations comparing the results to TCCON FTS measurements showed that the average bias was less than 1.8 ppm and the correlation coefficient was approximately 0.88, which was larger than for the GOSAT L2 product.展开更多
文摘Aiming at improving efficiency in combustion systems, the study on droplet behavior and its trajectory is of crucialimportance. Vortex engine is a kind of internal combustion engine which uses swirl flow to achieve highercombustion efficiency. One of the important advantages of designing vortex engine is to reduce the temperatureof walls by confining the combustion products in the inner vortex. The scopes of this investigation are to studyvortex engine flow field as well as effective parameters on fuel droplet behavior such as droplet diameter, dropletinitial velocity and inlet velocity of the flow field. The flow field is simulated using Reynolds Stress TransportModel (RSM). The Eulerian-Lagrangian method and the one-way coupling approach are employed to simulatetwo phase flow and dispersed phase in the chamber, respectively. A new method, based on computing pressureforce exerted on the droplet surface, is introduced to determine the distinction between using one-way andtwo-way coupling approaches. The results showed that the droplets with smaller diameter are more likely to followthe flow stream lines than bigger droplets, thus evaporate completely in the chamber. Moreover, droplets withgreater initial velocity have higher evaporation rate, yielding the existence of evaporation and combustion in theinner vortex. Additionally, the higher inlet velocity of continuous phase results in higher centrifugal force, leadsdroplets in question to deviate towards the wall faster.
基金supported by the State Key Program of the National Natural Science Foundation of China (Grant No.41130528)the National Natural Science Foundation of China (Grant No.41401387)the Green Path Program of the Beijing Municipal Science and Technology Commission(Grant No.Z161100001116013)
文摘We propose an algorithm that combines a pre-processing step applied to the a priori state vector prior to retrievals, with the modified damped Newton method （MDNM）, to improve convergence. The initial constraint vector pre-processing step updates the initial state vector prior to the retrievals if the algorithm detects that the initial state vector is far from the true state vector in extreme cases where there are CO2 emissions. The MDNM uses the Levenberg-Marquardt parameter ~,, which ensures a positive Hessian matrix, and a scale factor a, which adjusts the step size to optimize the stability of the convergence. While the algorithm iteratively searches for an optimized solution using observed spectral radiances, MDNM adjusts parameters ）, and a to achieve stable convergence. We present simulated retrieval samples to evaluate the performance of our algorithm and comparing it to existing methods. The standard deviation of our retrievals adding random noise was less than 3.8 ppmv. After pre-processing the initial estimate when it was far from the true value, the CO2 retrieval errors in the boundary layers were within 1.2 ppmv. We tested the MDNM algorithm＇s performance using GOSAT Llb data with cloud screening. Our preliminary validations comparing the results to TCCON FTS measurements showed that the average bias was less than 1.8 ppm and the correlation coefficient was approximately 0.88, which was larger than for the GOSAT L2 product.
