Jeong Hwan Sa

KFLOW Results of Airloads on HART-II Rotor Blades with Prescribed Blade Deformation

A three-dimensional compressible Navier-Stokes solver, KFLOW, using overlapped grids has recently been developed to simulate unsteady flow phenomena over helicopter rotor blades. The blade-vortex interaction is predicted for a descending flight using measured blade deformation data. The effects of computational grid resolution and azimuth angle increments on airloads were examined, and computed airloads and vortex trajectories were compared with HART-II wind tunnel data. The current method predicts the BVI phenomena of blade airloads reasonably well. It is found from the present study that a peculiar distribution of vorticity of tip vortices in an approximate azimuth angle range of 90 to 180 degrees can be explained by physics of the shear-layer interaction as well as the dissipation of numerical schemes.

Loose Fluid-Structure Coupled Approach for a Rotor in Descent Incorporating Fuselage Effects

A loose coupling approach is used to combine a comprehensive structural dynamics code CAMRAD II and a computational fluid dynamics solver KFLOW to validate the data and to identify the effect of fuselage on the aeroelastic behavior of the second higher harmonic rotor acoustic test rotor. The computations are made using isolated rotor and rotor–fuselage models. To demonstrate the effect of a fuselage, the shaft tilt angles remain identical for both configurations. A good correlation has been obtained with the present computational fluid dynamics/comprehensive structural dynamics method. It is observed that a rotor–fuselage model improves the correlation significantly in terms of magnitudes and phases of the airloads solution. All the blade–vortex interaction peaks are captured accurately, and the phase shift in the section normal forces improves significantly, with the inclusion of a fuselage. The sources of improvements are investigated, considering the vorticity distributions and induced velocity fields ...

Modeling and Prediction of the Crossflow Transition Using Transition Transport Equations

In this paper, the Reynolds-Averaged Navier-Stokes (RANS) equations are coupled with transition transport equations to predict the natural transition. The present γ-Reθ transition model has been developed without using boundary layer integral parameters and this model is based on two additional transport equations, one for the intermittency and one for the transition-onset momentum thickness Reynolds number. An engineering transition model for crossflow instability is proposed based on the philosophy of the local-correlation transition transport. The present transition model is applied to a variety of different complex applications. The main goal of the present paper is to validate the prediction capability for aeronautical flows over three-dimensional configurations using the γ-Reθ transition transport model considering crossflow effect. Validations of the transition model are conducted for an infinite NLF(2)-0415 swept wing, a finite ONERA M6 wing, and 6:1 inclined prolate spheroid configuration. In all cases numerical results considering the crossflow effect are in good agreement with the available experimental data and prove that the proposed crossflow transition model is well validated.

Design and implementation of a data-driven simulation service system

Computer simulations are widely used in various fields of science and engineering, including computational fluid dynamics. As the demand for the accuracy and quality of simulations grows, the cost of executing simulations itself is also rapidly increasing. However, until now, the reuse of previously obtained simulation results to improve the execution of later simulations has not been much investigated yet. In this paper, we design and implement a simulation service system, which executes requested simulations and returns the result back to the user. More importantly, our simulation service system is data-driven in the sense that the system utilizes previously obtained simulation results to improve the execution of later simulations. Furthermore, our system provides the ability to predict the result of a requested simulation using statistical machine learning techniques on the previous simulation results. This allows the user to roughly estimate the result of her requested simulation without actually executing it. Using our developed system, scientists and engineers can share simulation results and obtain simulation results very quickly without executing simulations redundantly, resulting in a lower overhead of the simulation service system.