A Method for Obtaining Aerodynamic Coefficients from Yawsonde and Radar Data

Whyte, Robert H.; Mermagen, William H.

doi:10.2514/3.61895

Cited by 19 publications

(3 citation statements)

References 4 publications

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“…Nonlinear parameter estimation was conducted using a maximum likelihood method [51][52][53][54][55] . This technique seeks to find model parameters which minimize a likelihood function.…”

Section: A Techniquementioning

confidence: 99%

Control Performance, Aerodynamic Modeling and Validation of Coupled Simulation Techniques for Guided Projectile Roll Dynamics

Sahu

Fresconi

Heavey

2014

AIAA Atmospheric Flight Mechanics Conference

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This paper describes a computational study to understand the roll behavior of a canardcontrolled projectile. Numerical computations were performed for this projectile with roll control maneuvers using advanced computational fluid dynamics and coupled aerodynamics/rigid body dynamics techniques. Roll control algorithms were formulated based on aerodynamic model assessment and parameter estimation. Coupled techniques were validated against wind tunnel experiments for roll control maneuvers. Overall, computed roll control results matched well with the wind tunnel test data; this indicated that the coupled calculations seem to capture the relevant physics observed in the experiment. Nomenclature , ,= roll, roll rate, roll acceleration, rad, rad/s, rad/s 2 = axial moment-of-inertia, kg m 2 = mass, kg = diameter, m 4 = reference area, m 2 = velocity, m/s = Mach number , , , = pitch angle of attack, yaw angle of attack, total angle of attack, aerodynamic roll angle, rad = atmospheric density, kg/m 3 1 2 = dynamic pressure, Pa , , = body-fixed coordinate system = roll damping moment coefficient = radial center-of-pressure, calibers = axial center-of-pressure, calibers = center-of-gravity, calibers = roll moment coefficient = number = deflection, rad 2 ⁄ = velocity of projectile center-of-gravity, m/s ⁄ = angular velocity of projectile, rad/s = transformation matrix from body frame to i th lifting surface frame = time constant, s , , , = state, controls, measurement, error vector , , , = system dynamics, controls, forcing function, measurement matrices = cost function = gain matrix , = control error and control effort matrices = Riccati equation matrix = time, s , = aerodynamic roll moment, friction moment, N m = parameter matrix = likelihood function = covariance = error between measurement and calculation , , , = conservative variables, inviscid flux vector, viscous flux vector, source term , = cell volume, cell area Θ = observability map f = factor = Levenberg-Marquardt parameter subscripts= canard, calculated, command = fin = aerodynamic = i th lifting surface 0, 1, 3, 5 = zeroeth, first, third, fifth order terms = delay = bias = maneuver surface, measurement = friction

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“…Nonlinear parameter estimation was conducted using a maximum likelihood method [51][52][53][54][55] . This technique seeks to find model parameters which minimize a likelihood function.…”

Section: A Techniquementioning

confidence: 99%

Control Performance, Aerodynamic Modeling and Validation of Coupled Simulation Techniques for Guided Projectile Roll Dynamics

Sahu

Fresconi

Heavey

2014

AIAA Atmospheric Flight Mechanics Conference

View full text Add to dashboard Cite

show abstract

“…The former-radar tracking-is the most convenient if modifications to the vehicle are infeasible. Focusing on various radar data reduction, numerous studies are available in the open literature [23][24][25][26][27][28][29][30][31][32][33][34][35] with different techniques had been developed. Chahan and Singh [36] collected and reviewed the majority of available techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the most well-defined ones are the least-square, maximum likelihood estimation, and stepwise regression techniques. The least-square (LS) technique was implemented to estimate drag for the 155mm M107 projectile in [23] and in [24] to investigate drag for the 155mm M549 projectile. The second technique, namely the maximum likelihood estimation (MLE) was applied through different studies to obtain drag for different aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

Assessment of drag prediction techniques based on radar data for supersonic vehicles

Doso,

Khali,

Zakaria

et al. 2023

J. Phys.: Conf. Ser.

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Flight testing is probably the most accurate approach for defining the aerodynamic characteristics of a flying vehicle. This is justified by the fact that compared to other approaches, either engineering or computational ones, flight testing is indeed the one that perfectly resembles the real flight environment. Flight data are either obtained by measuring the vehicle’s kinematics via onboard sensors or by tracking the vehicle’s flight via (commonly) radars. The advantages of the latter approach are evident in cases where modifying the vehicle design is not possible or in cases where rival/enemy vehicles are examined. The key issue for this approach is the quality and demands of the technique by which vehicle aerodynamic characteristics are reduced from flight-tracked data. In the open literature, different techniques are used to analyze radar data of vehicle position and utilize them to predict vehicle drag coefficient. Each technique has its strengths and pitfalls. In this paper, the three well-used techniques of drag estimation from radar data namely, Least Square (LS), Maximum Likelihood Estimation (MLE), and Stepwise regression (SR), are considered. The underlying principle, the output, and the range of validity for each technique are addressed with emphasis on what differentiates each of them. The viability and validity of the three techniques are addressed based on the own flight testing of a free-flight supersonic vehicle and using the point-mass flight model. Meteorological data are also recorded and flight conditions are used to enhance the resulting calculations. Based on experimental data available from the literature, a comparison is conducted for the techniques examined. Considering the lack of flight data utilized, and in conjunction with data from the literature, it has been concluded that the SR technique outperforms for a higher sample rate, however, the MLE is more feasible when there is a lack of data.

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Assessment of Drag Prediction Techniques for a Flying Vehicle Based on Radar-Tracked Data

Doso,

Zakaria,

Ahmed

et al. 2023

Int. J. Aeronaut. Space Sci.

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As far as the aerodynamic characterization of a flying vehicle is concerned, flight testing is probably the most accurate approach as it perfectly resembles the real flight environment. Flight data are obtained by tracking the vehicle via radars if modifying the vehicle design is not recommended/attainable. In the open literature, different techniques are used to analyze radar data; the key issue is the computational demands of each technique and the quality of the resulting aerodynamic characteristics. In this paper, three techniques are considered namely, Least-Square (LS), Maximum-Likelihood Estimation (MLE), and Stepwise Regression (SR), with focus on the prediction of the drag coefficient of a case study vehicle. Features for each technique are addressed based on brief previous published data. A new variant of the MLE method is proposed based on the physical segmentation of the available dataset. Predicted point-mass trajectories are compared with own comprehensive flight test to assess the techniques in concern. It is concluded that Stepwise-regression outperforms with a large dataset, while Maximum-Likelihood Estimation is more feasible considering the lack of data. The proposed variant of the MLE method yields more accurate drag prediction compared to the basic one.

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A Method for Obtaining Aerodynamic Coefficients from Yawsonde and Radar Data

Cited by 19 publications

References 4 publications

Control Performance, Aerodynamic Modeling and Validation of Coupled Simulation Techniques for Guided Projectile Roll Dynamics

Control Performance, Aerodynamic Modeling and Validation of Coupled Simulation Techniques for Guided Projectile Roll Dynamics

Assessment of drag prediction techniques based on radar data for supersonic vehicles

Assessment of Drag Prediction Techniques for a Flying Vehicle Based on Radar-Tracked Data

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