Response Surface Methods for Efficient Complex Aircraft Configuration Aerodynamic Characterization

TLDR

Response surface methodology is being investigated at Langley’s full‑scale tunnel to efficiently characterize the aerodynamic behavior of complex aircraft configurations, including a 5 % scale blended‑wing‑body model, as a function of attitude and multiple control‑surface inputs. This paper compares design‑of‑experiments/response‑surface methodology with one‑factor‑at‑a‑time methods for a low‑speed wind‑tunnel test of a blended‑wing‑body aircraft with 11 actuated control surfaces, aiming to capture fundamental stability and control data for simulation models and to benchmark against baseline tests. A modified fractional‑factorial design augmented with center and axial points produced regression models of aerodynamic forces and moments over the design space, with model adequacy and uncertainty quantified by robust statistical methods inherent to response‑surface methodology. The study demonstrates control‑surface allocation optimization for desired responses and discusses key highlights and problems encountered during testing.

Abstract

A response surface methodology approach to wind-tunnel testing of aircraft with complex configurations is being investigated at the Langley full-scale tunnel as part of a series of tests using design of experiments. An exploratory study was conducted using response surface methodology and a 5% scale blended-wing-body model in an effort to efficiently characterize aerodynamic behavior as a function of attitude and multiple control surface inputs. This paper provides a direct comparison of the design of experiments/response surface methodology and one factor at a time methods for a low-speed wind-tunnel test of a blended-wing-body aircraft configuration with 11 actuated control surfaces. A modified fractional factorial design, augmented with center points and axial points, produced regression models for the characteristic aerodynamic forces and moments over a representative design space as a function of model attitude and control surface inputs. Model adequacy and uncertainty levels were described using robust statistical methods inherent to the response surface methodology practice. Experimental goals included the capture of fundamental stability and control data for simulation models and comparisons to baseline data from recent one factor at a time tests. Optimization is demonstrated for control surface allocation for a desired response. A discussion of highlights and problems associated with the test is included.