The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above ...The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above blower becomes relatively small to satisfy the needed operation condition and its performances are considerably degraded as a result of relatively high leakage,disc friction and passage friction loss consequently. The purpose of this paper is to improve its performance through the optimization design of the blade’s profile properly. Based on artificial neural networks (ANN) and hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs),the optimization design approach is established. By conjoining Bezier parameterization and FINE/TURBO solver,the optimized blade is designed by adjusting the profile gradually. An industrial ultra-low specific speed centrifugal blower with parallel hub and shroud has been selected as a reference case for optimization design. The performance investigations of the centrifugal blowers with different types of blades are conducted. The conclusions of the performance improvement of the optimized blade provide positive evidences in the application of the optimization design of the above blower blade.展开更多
This paper proposes an inexact SQP method in association with line search filter technique for solving nonlinear equality constrained optimization. For large-scale applications, it is expensive to get an exact search ...This paper proposes an inexact SQP method in association with line search filter technique for solving nonlinear equality constrained optimization. For large-scale applications, it is expensive to get an exact search direction, and hence the authors use an inexact method that finds an approximate solution satisfying some appropriate conditions. The global convergence of the proposed algorithm is established by using line search filter technique. The second-order correction step is used to overcome the Maratos effect, while the line search filter inexact SQP method has q-superlinear local convergence rate. Finally, the results of numerical experiments indicate that the proposed method is efficient for the given test problems.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.50776056)the National High Technology Research and Development Program of China ("863" Program) (Grant No.2009AA05Z201)
文摘The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above blower becomes relatively small to satisfy the needed operation condition and its performances are considerably degraded as a result of relatively high leakage,disc friction and passage friction loss consequently. The purpose of this paper is to improve its performance through the optimization design of the blade’s profile properly. Based on artificial neural networks (ANN) and hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs),the optimization design approach is established. By conjoining Bezier parameterization and FINE/TURBO solver,the optimized blade is designed by adjusting the profile gradually. An industrial ultra-low specific speed centrifugal blower with parallel hub and shroud has been selected as a reference case for optimization design. The performance investigations of the centrifugal blowers with different types of blades are conducted. The conclusions of the performance improvement of the optimized blade provide positive evidences in the application of the optimization design of the above blower blade.
基金supported by the National Science Foundation Grant under Grant No.10871130the Shanghai Leading Academic Discipline Project under Grant No.T0401
文摘This paper proposes an inexact SQP method in association with line search filter technique for solving nonlinear equality constrained optimization. For large-scale applications, it is expensive to get an exact search direction, and hence the authors use an inexact method that finds an approximate solution satisfying some appropriate conditions. The global convergence of the proposed algorithm is established by using line search filter technique. The second-order correction step is used to overcome the Maratos effect, while the line search filter inexact SQP method has q-superlinear local convergence rate. Finally, the results of numerical experiments indicate that the proposed method is efficient for the given test problems.
