A numerical study of the laser induced evaporation and ionization process during pulsed laser deposition is presented. The process is separated into three domains: (i) conduction inside the solid, (ii) a discontinuity layer between solid and vapor, and (iii) expansion of high temperature vapor/plasma. A quasi-one-dimensional model is solved to predict the temperature field inside the solid. Mass, momentum, and energy are conserved across the discontinuity layer. Equations of mass, momentum, and energy conservation are solved simultaneously to provide boundary conditions for the expansion process. Euler equations are used to model the expansion of high temperature vapor/plasma. The Euler equations are integrated numerically using a Runge–Kutta scheme combined with flux vector splitting. The density, pressure, temperature, and velocity contours of the vapor phase are calculated and the results are analyzed.
Aerodynamic data modeling plays an important role in aerospace and industrial fluid engineering. Support vector machines (SVMs), as a novel type of learning algorithms based on the statistical learning theory, can be used for regression problems and have been reported to perform well with promising results. The work presented here examines the feasibility of applying SVMs to the aerodynamic modeling field. Mainly, the empirical comparisons between the SVMs and the commonly used neural network technique are carried out through two practical data modeling cases -performance-prediction of a new prototype mixer for engine combustors, and calibration of a five-hole pressure probe. A CFD-based diffuser optimization design is also involved in the article, in which an SVM is used to construct a response surface and hereby to make the optimization perform on an easily computable surrogate space. The obtained simulation results in all the application cases demonstrate that SVMs are the potential options for the chosen modeling tasks.
The objective in this aerodynamic shape design effort is to minimize total pressure loss across the two-dimensional linear airfoil cascade row while satisfying a number of constraints. They included fixed axial chord, total torque, inlet and exit flow angles, and blade cross-section area, while maintaining thickness distribution greater than a minimum specified value. The aerodynamic shape optimization can be performed by using any available flow-field analysis code. For the analysis of the performance of intermediate cascade shapes we used an unstructured grid based compressible Navier-Stokes flow-field analysis code with k-e turbulence model. A robust genetic optimization algorithm was used for optimization and a constrained sequential quadratic programming was used enforcement of certain constraints. The airfoil geometry was parameterized using conic section parameters and B-splines thus keeping the number of geometric design variables to a minimum while achieving a high degree of geometric flexibility and robustness. Significant reductions of the total pressure loss were achieved using this constrained method for a supersonic exit flow axial turbine cascade.
This paper presents the development of a numerical algorithm for the computation of axial thrust load on a centrifugal compressor. An unstructured flow solver has been developed for the computation of a hybrid, structured and unstructured grid. The computational domain of the impeller has been discretized using a structured mesh, while the computational domain on the back side of the wheel has been discretized using an unstructured mesh. The two grids are merged and a median dual-mesh is generated. The Navier-Stokes equations are discretized using a finite volume method. Roe’s flux-difference scheme is used for inviscid fluxes and directional derivatives along edges are used for viscous fluxes. The gradients at the mesh vertices are calculated using the Least-squares method. An explicit scheme is used for time integration. Convergence is accelerated using a local time-step and implicit residual smoothing. The results of the numerical simulation include the axial thrust load of the centrifugal compressor. In addition, details of the leakage flow are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.