Date of Award

Spring 1-1-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Aerospace Engineering Sciences

First Advisor

Ryan Starkey

Second Advisor

Brian Argrow

Third Advisor

John Daily

Fourth Advisor

Alireza Doostan

Fifth Advisor

Peter Hamlington

Abstract

The purpose of this research is to examine methods by which quantification of inlet flow distortion may be improved upon. Specifically, this research investigates how data interpolation effects results, optimizing sampling locations of the flow, and determining the sensitivity related to how many sample locations there are. The main parameters that are indicative of a "good" design are total pressure recovery, mass flow capture, and distortion. This work focuses on the total pressure distortion, which describes the amount of non-uniformity that exists in the flow as it enters the engine. All engines must tolerate some level of distortion, however too much distortion can cause the engine to stall or the inlet to unstart. Flow distortion is measured at the interface between the inlet and the engine.

To determine inlet flow distortion, a combination of computational and experimental pressure data is generated and then collapsed into an index that indicates the amount of distortion. Computational simulations generate continuous contour maps, but experimental data is discrete. Researchers require continuous contour maps to evaluate the overall distortion pattern. There is no guidance on how to best manipulate discrete points into a continuous pattern. Using one experimental, 320 probe data set and one, 320 point computational data set with three test runs each, this work compares the pressure results obtained using all 320 points of data from the original sets, both quantitatively and qualitatively, with results derived from selecting 40 grid point subsets and interpolating to 320 grid points. Each of the two, 40 point sets were interpolated to 320 grid points using four different interpolation methods in an attempt to establish the best method for interpolating small sets of data into an accurate, continuous contour map. Interpolation methods investigated are bilinear, spline, and Kriging in Cartesian space, as well as angular in polar space. Spline interpolation methods should be used as they result in the most accurate, precise, and visually correct predictions when compared results achieved from the full data sets.

Researchers were interested if fewer than the recommended 40 probes could be used – especially when placed in areas of high interest - but still obtain equivalent or better results. For this investigation, the computational results from a two-dimensional inlet and experimental results of an axisymmetric inlet were used. To find the areas of interest, a uniform sampling of all possible locations was run through a Monte Carlo simulation with a varying number of probes. A probability density function of the resultant distortion index was plotted. Certain probes are required to come within the desired accuracy level of the distortion index based on the full data set. For the experimental results, all three test cases could be characterized with 20 probes. For the axisymmetric inlet, placing 40 probes in select locations could get the results for parameters of interest within less than 10% of the exact solution for almost all cases. For the two dimensional inlet, the results were not as clear. 80 probes were required to get within 10% of the exact solution for all run numbers, although this is largely due to the small value of the exact result.

The sensitivity of each probe added to the experiment was analyzed. Instead of looking at the overall pattern established by optimizing probe placements, the focus is on varying the number of sampled probes from 20 to 40. The number of points falling within a 1\% tolerance band of the exact solution were counted as good points. The results were normalized for each data set and a general sensitivity function was found to determine the sensitivity of the results. A linear regression was used to generalize the results for all data sets used in this work. However, they can be used by directly comparing the number of good points obtained with various numbers of prob

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