A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements

Ankur Srivastava,Andrew J Meade

doi:10.1155/2015/183712

Abstract

Use of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.

Highlights

INTRUSIVE aircraft anemometer booms have long been successfully used to measure air data parameters for subsonic and supersonic flight
Wind tunnel data for the Runway Assisted Landing Site (RALS) tower was available at speeds ranging from 40 fps to 120 fps as wind direction was varied from −180 to +180 degrees at increments of 2 degrees
A hypercone is approximated with radial basis functions (RBFs)

Summary

Introduction

INTRUSIVE aircraft anemometer booms have long been successfully used to measure air data parameters for subsonic and supersonic flight. The authors concluded that the developed nonintrusive technique was clearly superior to the probe based air data systems They showed that FADS were unaffected by dynamic flight maneuvers and they performed well in high angle of attack flights. Panel methods [9] and other popular computational fluid dynamic (CFD) based approaches solve the forward problem on more complex geometries given the freestream wind speed and direction. This approach requires guessing the freestream velocity and solving for the pressure distribution until it matches the measured values. These sampling procedures are essentially sequential in nature and help the engineers to accelerate through the test matrix, and allow the learning algorithm to have better generalization capability

Data-Model Fusion

Freestream Wind Speed and Direction Estimation

Sequential Experiment Design

Conclusions

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