Engineering-, Intermediate-, and High-Level Aerodynamic Prediction Methods and Applications

Abstract

Descriptions and applications of engineering-, intermediate-, and high-level missile aerodynamics prediction methods are presented. The engineering-level method is represented by an experimental data-based code, the intermediate-level method includes enhanced panel method-based codes and a modi ed linear theory code, and the high-level methods include a space-marching Euler ow solver. All of the methods contain n and body vorticity models. The engineering-and intermediate-level methods are applied to the prediction of high-angle-of-attackpitch plane and lateral aerodynamic characteristics of missiles at arbitrary roll angle, to aerodynamic loads acting on conventional and chined body shapes, and to assess effects of n-body gap on n loads. The intermediate-level methods are also employed in the design of nonconventional n planforms for minimum hinge moment. The Euler ow solver is applied to the prediction of rolling moments acting on a canard con guration. Comparisons with experimental data are presented. The conclusion is made that the missile aerodynamicist and/or designer should be aware of the availability and should make use of the various levels of missile aerodynamics prediction methodology. NomenclatureC A; C A = axial force coef cient, positive aft along x direction; axial force/q 1 S REF C BM = lifting-surface bending moment coef cient, positive side edge up; bending moment/q 1 S FIN L REF Marnix F. E. Dillenius received his B.S. (with honors), M.S., and Ph.D. degrees in mechanical engineering from the University of California, Berkeley, in 1962, 1964, and 1968, respectively. In 1969, he joined Nielsen Engineering and Research (NEAR), where he was involved in modeling aerodynamic interference, standard takeoff and landing aerodynamics, and trailing-wake vortex studies. He originated and/or made major contributions to analytical investigations concerning supersonic wing loading theories, external store separation characteristics, and detailed missile aerodynamic prediction methods. Dr. Dillenius has been president of NEAR since 1992. He is an Associate Fellow of the AIAA. Daniel J. Lesieutre received his B.S. and M.S. degrees in aeronautical and astronautical engineering from Purdue University in 1983 and 1985, respectively. Since joining NEAR, he has directed research in applied aerodynamics, aerodynamic shape optimization, aeroelastic n design, and unsteady aerodynamics. He has participated in projects involving hydrodynamics, missile, aircraft and launch vehicle aerodynamics, and store separation. He has successfully demonstrated the use of numerical optimization methods for the multidisciplinary design of control ns with geometric and structural constraints. He is a Senior Member of the AIAA. Martin C. Hegedus received his B.S. degree in 1990 from the University of California, Davis, and an M.S. in aeronautics and astronautics from Stanford University in 1992. Since joining NEAR in 1994, he has worked on the development of aerodynamic and store separation prediction codes and graphical user interfaces...

Vertical Wind Tunnel for Prediction of Rocket Flight Dynamics

“…The reason is there is a fin offset which is larger than the 6° that the canards can compensate for. To identify the torque constant β, damping α and disturbance u dist ptq from the roll model of Equations (2)- (8), the first step is to specify the range in the β and α values as given in Equation (19). These ranges are defined:…”

“…The remaining half is essentially disturbances in the wind tunnel, and there are very few canard dynamics so it does not contribute to identifiability. The time points in the disturbance model of Equations (4)- (8) were chosen to be simply the beginning and end points with 1 N = in Equation (4). Since 5 s is analyzed in each data set, and time is reset to 0 each time, the time points are defined:…”

“…In addition to CFD, there are a number of empirical approaches to modelling subsonic and supersonic rocket steady state aerodynamics. These methods are primarily based on NASA wind tunnel data and have been developed into engineering-level and intermediate-level aerodynamic prediction codes [7][8][9]. The models have also been extended to include canard interaction [10,11] but the experimental work is restricted to wind tunnels, so only considers steady state responses to fixed canard angles.…”

“…Data for analysis (a) Roll rate response; (b) Measured canard angle from encoders. To identify the torque constant β, damping α and disturbance ( ) dist u t from the roll model of Equations (2)-(8), the first step is to specify the range in the β and α values as given in Equation(19). These ranges are defined: Roll rate response for Tasha III launch 1 including key points in the launch.…”