Optimal Design of Passive Flow Control for a Boundary-Layer-Ingesting Offset Inlet Using Design-of-Experiments

Abstract

This research will investigate the use of Design-of-Experiments (DOE) in the development of an optimal passive flow control vane design for a boundary-layer-ingesting (BLI) offset inlet in transonic flow. This inlet flow control is designed to minimize the engine fan-face distortion levels and first five Fourier harmonic half amplitudes while maximizing the inlet pressure recovery. Numerical simulations of the BLI inlet are computed using the Reynolds-averaged Navier-Stokes (RANS) flow solver, OVERFLOW, developed at NASA. These simulations are used to generate the numerical experiments for the DOE response surface model. In this investigation, two DOE optimizations were performed using a DOptimal Response Surface model. The first DOE optimization was performed using four design factors which were vane height and angles-of-attack for two groups of vanes. One group of vanes was placed at the bottom of the inlet and a second group symmetrically on the sides. The DOE design was performed for a BLI inlet with a free-stream Mach number of 0.85 and a Reynolds number of 2 million, based on the length of the fan-face diameter, matching an experimental wind tunnel BLI inlet test. The first DOE optimization required a fifth order model having 173 numerical simulation experiments and was able to reduce the DC60 baseline distortion from 64% down to 4.4%, while holding the pressure recovery constant. A second DOE optimization was performed holding the vanes heights at a constant value from the first DOE optimization with the two vane angles-of-attack as design factors. This DOE only required a second order model fit with 15 numerical simulation experiments and reduced DC60 to 3.5% with small decreases in the fourth and fifth harmonic amplitudes. The second optimal vane design was tested at the NASA Langley 0.3-Meter Transonic Cryogenic Tunnel in a BLI inlet experiment. The experimental results showed a 80% reduction of DPCP avg , the circumferential distortion level at the engine fanface. NomenclatureA C = inlet capture (highlight) area; area enclosed by inlet highlight and tunnel wall, in. 2A 0 = inlet mass-flow streamtube at free-stream conditions, in. 2A 0 /A C = inlet mass-flow ratio, ratio of actual airflow to the ideal capture airflow D = duct diameter at AIP DPCP avg = average SAE circumferential distortion descriptor DPRP i = SAE radial distortion descriptor for ring i on AIP total-pressure rake h = height of vortex generator, in. i = ring number on AIP total-pressure rake, value increases from 1 in hub region to 5 in tip region M = Mach number P t = total pressure, psi P t,2,avg = area weighted average total pressure at AIP P t,avg /P t = inlet recovery pressure ratio

Numerical Modeling of Flow Control in a Boundary-Layer-Ingesting Offset Inlet Diffuser at Transonic Mach Numbers

“…However the vanes are approximately 30% of the ingested boundary layer height, which is relatively small when compared to the boundary layer. More details of the vane optimization are described by Allan, Owens, and Lin 27 . Figure 5 shows the vane configuration used in the experiment where the vanes are located a distance x/D=0.5 downstream of the inlet lip highlight.…”

“…The flow solver was used to identify candidate jet actuator locations, which were then used in the modification of the baseline BLI inlet model. A DOE was also performed for a VG vane configuration to be tested at transonic Mach numbers performed by Allan, Owens, and Lin 27 . This research will use the experimental wind tunnel results of Owens, Allan, and Gorton 26 for the validation of OVERFLOW.…”

Generation after Next Propulsor Research: Robust Design for Embedded Engine Systems

“…Initiated early in the Robust Design project before its inlet or fan were designed, this effort leveraged the high quality, computational and experimental database which resulted from prior research conducted by NASA Langley. [28][29][30][31][32][33][34][35] That is, its focus was on evaluating the challenging uncontrolled distortion of Inlet A. …”

Passive Flow Control Study in an S-Duct Using Stereo Particle Image Velocimetry

“…A number of previous studies have investigated the use of passive flow control to reduce the steady distortion at the AIP for both rectangular [17][18][19] and circular ducts [11,[20][21][22][23]. Generally, in the form of arrays of co-rotating vortex generators (VGs), passive flow control devices are used to modify the secondary flows inside the duct.…”

“…Recent investigations used the combination of RANS calculation and optimization algorithms to find the optimum VG configuration in terms of distortion reduction at the AIP [23,28,29]. Yi et al [28] performed an optimization for an S-duct geometry (A out /A in = 1.4 , H/L = 0.3 ) using two different reduced-order computational models based on a VG configuration developed by Anderson and Gibbs [21].…”