Flush airdata sensing (FADS) system calibration procedures and results for blunt forebodies

Cobleigh, Brent R.; Whitmore, Stephen A.; Haering, Edward A.; Borrer, Jerry

doi:10.2514/6.1999-4816

Cited by 39 publications

(21 citation statements)

References 4 publications

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“…Based on these measurements, the air data can be estimated by using the pressure distribution model and a special algorithm. 17 Since the exposed mechanisms in ADS are replaced with the flushing design in FADS, the FADS has the ability to measure the air data for supersonic vehicle. Besides, the vehicle equipped with the FADS is not easy to be detected.…”

Section: Introductionmentioning

confidence: 99%

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

Ding

Huan

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

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Section: Introductionmentioning

confidence: 99%

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

Ding

Huan

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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show abstract

“…In spite of observations that different port configurations can vastly affect the effectiveness of the estimation [5], past FADS sensors have always been placed in symmetrical annular or cruciform patterns based on engineering judgment rather than computationally based rationale. FADS configurations are also often designed for fixed points in the trajectory (e.g., the sensor configuration is designed for Mach 5 and angle of attack of 2 deg), even though variations from the nominal condition leave these configurations suboptimal for the inverse estimation of parameters.…”

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confidence: 98%

Cramér–Rao Lower-Bound Optimization of Flush Atmospheric Data System Sensor Placement

Dutta

Braun

2014

Journal of Spacecraft and Rockets

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Flush atmospheric data systems take measurements of the pressure distribution on the forebodies of vehicles and improve the estimate of freestream parameters during reconstruction. These systems have been present on many past entry vehicles, but design of the pressure transducer suites and the placement of the sensors on the vehicle forebody have largely relied on engineering judgment and heuristic techniques. This paper develops a flush atmospheric data system design methodology using Cramér-Rao lower-bound optimization to define the smallest theoretical variance possible from the estimation process. Application of this methodology yields Pareto frontiers of possible optimal configurations and identifies the number of ports that serve as the point of diminishing returns. The methodology is tested with a simulated Mars entry, descent, and landing trajectory. Nomenclature f = objective function H = measurement sensitivity matrix k = number of sampled points for the objective function n = number of ports P = state covariance matrix p = parameter vector for the objective function R = measurement noise covariance matrix q ∞ = freestream dynamic pressure, Pa α = angle of attack, rad β = sideslip angle, rad σ = standard deviation Subscripts i, j = indices y = y component z = z component Superscriptŝ = nonnormalized value − = mean value

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“…5 In order to overcome these disadvantages, the Triples Algorithm was developed. 6,7 The Triples Algorithm uses a potential flow pressure model, a cruciform pressure port configuration with the vertical ports on the line of symmetry, and a strategic set of differences to eliminate the pressures from the state vector, allowing a direct solution of the angle of attack and the sideslip angle. The potential flow model is then used to compute the freestream pressure, total pressure, and the Mach number.…”

Section: Nomenclaturementioning

confidence: 99%

“…(20) and (22). 6 A database of the coefficients of pressure at each of the port locations and selected Mach numbers, angles of attack, and sideslip angles is required. Using the above equations, the effective angles are computed, and the calibration corrections are obtained from Typically, for the Orion database, a fourth order polynomial is required to approximate the relationship between the calibration angle δα and the effective angle of attack α e at each Mach number.…”

Section: Angle Calibrationsmentioning

confidence: 99%

A Modified Triples Algorithm for Flush Air Data Systems That Allows a Variety of Pressure Port Configurations

Millman

2017

AIAA Atmospheric Flight Mechanics Conference

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Air Data Systems (FADS) are becoming more prevalent on re-entry vehicles, as evidenced by the Mars Science Laboratory and the Orion Multipurpose Crew Vehicle . A FADS consists of flush-mounted pressure transducers located at various locations on the fore-body of a flight vehicle or the heat shield of a re-entry capsule. A pressure model converts the pressure readings into useful air data quantities. Two algorithms for converting pressure readings to air data have become predominant -the iterative Least Squares State Estimator (LSSE) and the Triples Algorithm. What follows herein is a new algorithm that takes advantage of the best features of both the Triples Algorithm and the LSSE. This approach employs the potential flow model and strategic differencing of the Triples Algorithm to obtain the effective flight angles; however, the requirements on port placement are far less restrictive, allowing for configurations that are considered optimal for a FADS.

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Flush airdata sensing (FADS) system calibration procedures and results for blunt forebodies

Abstract: Blunt-forebody pressure data are used to study the behavior of the NASA Dryden Flight Research Center flush airdata sensing (FADS) pressure model and High Alpha Research Vehicle

Cited by 39 publications

References 4 publications

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

Cramér–Rao Lower-Bound Optimization of Flush Atmospheric Data System Sensor Placement

A Modified Triples Algorithm for Flush Air Data Systems That Allows a Variety of Pressure Port Configurations

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