Design Exploration for Vortex Generators for Boundary-Layer-Ingesting Inlet

*† ‡ For an efficient automatic design of vortex generators (VGs) deployed for a flushmounted boundary-layer-ingesting (BLI) offset inlet, a surrogate-assisted evolutionary optimization and a data mining strategy are conducted. The objectives for designing an inlet are to minimize flow distortion and maximize total pressure recovery simultaneously. Design parameters are height, width, position and angles of VGs in the circumferential and streamline directions. Unlike conventional inlets, the BLI inlet is top-mounted and embedded in the airframe, resulting in a significant amount of low-momentum boundary layer flow being ingested. Hence, the deployment of VGs becomes more effective in the BLI inlet than in the conventional one and thus the positioning and sizing of these devise are more critical. For example, an array of VGs placed regularly along the spanwise direction may not be the best choice. However, the guideline for the VGs installation is not yet available because of the enormous complexity in the flow. Thus, a systematic and mathematics-based approach for guiding the search for an optimal configuration is desirable and necessary, for which we carry out data mining to reveal the structure in the design space and design knowledge. The efficacy of the present flow control concept is demonstrated using high-fidelity Navier-Stokes simulations of the flow. The optimization is performed by GA or MOGA, evaluated by a kriging model. The flow structures associated with the baseline and optimal configurations are elaborated to shed light on the inlet performance.

[1]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[2]  Oh-Hyun Rho,et al.  Methods for the accurate computations of hypersonic flows: I. AUSMPW + scheme , 2001 .

[3]  Shigeru Obayashi,et al.  Multidisciplinary design optimization of wing shape for a small jet aircraft using kriging model , 2006 .

[4]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[5]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[6]  Brian G. Allan,et al.  Numerical Modeling of Flow Control in a Boundary-Layer-Ingesting Offset Inlet Diffuser at Transonic Mach Numbers , 2006 .

[7]  Brian G. Allan,et al.  Optimal Design of Passive Flow Control for a Boundary-Layer-Ingesting Offset Inlet Using Design-of-Experiments , 2006 .

[8]  F. Menter.  Two-equation eddy-viscosity turbulence models for engineering applications , 1994 .

[9]  Chongam Kim,et al.  Adjoint Based Design Approach for Boundary Layer Ingestion Offset Intake , 2009 .

[10]  Odis C. Pendergraft,et al.  Designing and Testing a Blended Wing Body with Boundary-Layer Ingestion Nacelles , 2006 .

[11]  Chongam Kim,et al.  Cures for the shock instability: development of a shock-stable Roe scheme , 2003 .

[12]  Joongkee Chung,et al.  Comparison of compressor face boundary conditions for unsteady CFD simulations of supersonic inlets , 1995 .

[13]  B. H. Anderson,et al.  Vortex-generator installation studies on steady-state and dynamic distortion , 1996 .

[14]  A. Jirásek,et al.  Developement and application in design strategy for design of vortex generator flow control in inlets , 2006 .

[15]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[16]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[17]  A. Jameson,et al.  Lower-upper Symmetric-Gauss-Seidel method for the Euler and Navier-Stokes equations , 1988 .