The Rotation and Curvature(RC)correction is an important turbulence model modifi-cation approach,and the Spalart-Allmaras model with the RC correction(SA-RC)has been exten-sively studied and used.As a multiplier of th...The Rotation and Curvature(RC)correction is an important turbulence model modifi-cation approach,and the Spalart-Allmaras model with the RC correction(SA-RC)has been exten-sively studied and used.As a multiplier of the modelling equation’s production term,the rotation function f_(r1)should have a cautiously designed value range,but its limit varies in different models and flow solvers.Therefore,the need of restriction is discussed theoretically,and the common range of f_(r1)is explored in Burgers vortexes.Afterwards,the SA-RC model with different limits is tested numerically.Negative f_(r1)always appears in the SA-RC model,and the difference between simula-tion results brought by the limits is not negligible.A lower limit of 0 enhances turbulence produc-tion,and therefore the vortex structures are dissipated faster and shrink in size,while an upper limit plays an opposite role.Considering that the lower limit of 0 usually promotes the simulation accu-racy and fixes the numerical defect,whereas the upper limit worsens the predictive performance in most cases,it is recommended to limit f_(r1)non-negative while utilizing the SA-RC model.In addi-tion,the RC-corrected model has a better prediction of the attached flow near curved walls,while the SA-Helicity model largely improves the simulation accuracy of three-dimensional large-scale vortices.The model combining both corrections has the potential to become more adaptive and more accurate.展开更多
The total variation （TV） minimization problem is widely studied in image restora- tion. Although many alternative methods have been proposed for its solution, the Newton method remains not usable for the primal form...The total variation （TV） minimization problem is widely studied in image restora- tion. Although many alternative methods have been proposed for its solution, the Newton method remains not usable for the primal formulation due to no convergence. A previous study by Chan, Zhou and Chan [15] considered a regularization parameter continuation idea to increase the domain of convergence of the Newton method with some success but no robust parameter selection schemes. In this paper, we consider a homotopy method for the same primal TV formulation and propose to use curve tracking to select the regular- ization parameter adaptively. It turns out that this idea helps to improve substantially the previous work in efficiently solving the TV Euler-Lagrange equation. The same idea is also considered for the two other methods as well as the deblurring problem, again with improvements obtained. Numerical experiments show that our new methods are robust and fast for image restoration, even for images with large noisy-to-signal ratio.Mathematics subject classification： 65N06, 65B99.展开更多
Wireless technology provides accurate positioning in indoor environments using time of arrival(TOA) based ranging techniques. However, the positioning accuracy is degraded due to the ranging errors caused by multipath...Wireless technology provides accurate positioning in indoor environments using time of arrival(TOA) based ranging techniques. However, the positioning accuracy is degraded due to the ranging errors caused by multipath and non-line-of-sight(NLOS) propagation. In this paper, a ranging error correction method is proposed to improve positioning performance. A TOA ranging error model(TREM) is built to provide the prior information for ranging error correction first. The mean value of TREM within a certain interval is used as the ranging error correction value(RECV). As the RECV may be unreasonable sometimes, we adjust it according to the actual positioning situation and then exploit the final RECV to correct ranging data. The experimental results show that the proposed method could well reduce ranging errors and the positioning performance is obviously improved when using corrected ranging data.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51976006,51790513)the Aeronautical Science Foundation of China(No.2018ZB51013)+1 种基金the National Science and Technology Major Project,China(2017-II-003-0015)the Open Fund from State Key Laboratory of Aerodynamics,China(No.SKLA2019A0101).
文摘The Rotation and Curvature(RC)correction is an important turbulence model modifi-cation approach,and the Spalart-Allmaras model with the RC correction(SA-RC)has been exten-sively studied and used.As a multiplier of the modelling equation’s production term,the rotation function f_(r1)should have a cautiously designed value range,but its limit varies in different models and flow solvers.Therefore,the need of restriction is discussed theoretically,and the common range of f_(r1)is explored in Burgers vortexes.Afterwards,the SA-RC model with different limits is tested numerically.Negative f_(r1)always appears in the SA-RC model,and the difference between simula-tion results brought by the limits is not negligible.A lower limit of 0 enhances turbulence produc-tion,and therefore the vortex structures are dissipated faster and shrink in size,while an upper limit plays an opposite role.Considering that the lower limit of 0 usually promotes the simulation accu-racy and fixes the numerical defect,whereas the upper limit worsens the predictive performance in most cases,it is recommended to limit f_(r1)non-negative while utilizing the SA-RC model.In addi-tion,the RC-corrected model has a better prediction of the attached flow near curved walls,while the SA-Helicity model largely improves the simulation accuracy of three-dimensional large-scale vortices.The model combining both corrections has the potential to become more adaptive and more accurate.
文摘The total variation （TV） minimization problem is widely studied in image restora- tion. Although many alternative methods have been proposed for its solution, the Newton method remains not usable for the primal formulation due to no convergence. A previous study by Chan, Zhou and Chan [15] considered a regularization parameter continuation idea to increase the domain of convergence of the Newton method with some success but no robust parameter selection schemes. In this paper, we consider a homotopy method for the same primal TV formulation and propose to use curve tracking to select the regular- ization parameter adaptively. It turns out that this idea helps to improve substantially the previous work in efficiently solving the TV Euler-Lagrange equation. The same idea is also considered for the two other methods as well as the deblurring problem, again with improvements obtained. Numerical experiments show that our new methods are robust and fast for image restoration, even for images with large noisy-to-signal ratio.Mathematics subject classification： 65N06, 65B99.
基金supported in part by Huawei Innovation Research Program(Grant No.YB2013020011)
文摘Wireless technology provides accurate positioning in indoor environments using time of arrival(TOA) based ranging techniques. However, the positioning accuracy is degraded due to the ranging errors caused by multipath and non-line-of-sight(NLOS) propagation. In this paper, a ranging error correction method is proposed to improve positioning performance. A TOA ranging error model(TREM) is built to provide the prior information for ranging error correction first. The mean value of TREM within a certain interval is used as the ranging error correction value(RECV). As the RECV may be unreasonable sometimes, we adjust it according to the actual positioning situation and then exploit the final RECV to correct ranging data. The experimental results show that the proposed method could well reduce ranging errors and the positioning performance is obviously improved when using corrected ranging data.
