Drag and moment coefficients measured during flight testing of a 2.75-in. rocket

Brown, Thomas G.; Brandon, Fred J.; Harkins, Thomas E.; Hathaway, Wayne

doi:10.2514/6.1997-634

Cited by 5 publications

(1 citation statement)

References 1 publication

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“…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%

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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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.

View full text Add to dashboard Cite

show abstract

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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Strap-down microelectromechanical (MEMS) sensors for high-g munition applications

et al. 2001

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Drag and moment coefficients measured during flight testing of a 2.75-in. rocket

Cited by 5 publications

References 1 publication

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

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

Strap-down microelectromechanical (MEMS) sensors for high-g munition applications

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