scholarly journals Abel inversion of axially-symmetric shock wave flows

Shock Waves ◽  
2005 ◽  
Vol 14 (1-2) ◽  
pp. 21-28 ◽  
Author(s):  
A. F. P. Houwing ◽  
K. Takayama ◽  
Z. Jiang ◽  
T. Hashimoto ◽  
K. Koremoto ◽  
...  
2018 ◽  
Vol 63 (12) ◽  
pp. 530-532 ◽  
Author(s):  
V. A. Levin ◽  
V. V. Markov ◽  
G. B. Sizykh

1990 ◽  
Vol 112 (2) ◽  
pp. 179-184 ◽  
Author(s):  
J. Iwamoto

When an under-expanded sonic jet impinges on a perpendicular flat plate, a shock wave forms just in front of the plate and some interesting phenomena can occur in the flow field between the shock and the plate. In this paper, experimental and numerical results on the flow pattern of this impinging jet are presented. In the experiments the flow field was visualized using shadow-photography and Mach-Zehnder interferometry. In the numerical calculations, the two-step Lax-Wendroff scheme was applied, assuming inviscid, axially symmetric flow. Some of the pressure distributions on the plate show that the maximum pressure does not occur at the center of the plate and that a region of reversed flow exists near the center of the plate.


Author(s):  
A. R. Mitchell ◽  
Francis McCall

SynopsisThe relaxation technique of R. V. Southwell is developed to evaluate mixed subsonic-supersonic flow regions with axial symmetry, changes of entropy being taken into account. In the problem of a parallel supersonic flow of Mach number I·8 impinging on a blunt-nosed axially symmetric obstacle, the new technique is used to determine the complete field downstream of the bow shock wave formed. Lines of constant vorticity and Mach number are shown in the field, and where possible a comparison is made with the corresponding 2-dimensional problem.


Shock Waves ◽  
1997 ◽  
Vol 7 (3) ◽  
pp. 151-162 ◽  
Author(s):  
Z. Jiang ◽  
K. Takayama ◽  
H. Babinsky ◽  
T. Meguro

2015 ◽  
Vol 43 (10) ◽  
pp. 3365-3368
Author(s):  
Alexandra Gurinovich ◽  
Pavel Bogdanovich ◽  
Alexander Komorny

2013 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
G. N. Oliveira ◽  
M. G. Silva ◽  
L. R. Carrocci

In this paper it is addressed the prediction of form and location of detached shock waves ahead of two-dimensional and axially symmetric bodies at an zero angle of attack. As shown in Figs. 5 and 6, results show a very good agreement with experimental data. In this context an approximate method, based on a simplified form of the continuity relation, is developed to predict the location of detached shock waves ahead of two-dimensional and axially symmetric bodies. In order to reduce the problem to an equivalent onedimensional form, it is assumed that: (1) The form of the shock between its foremost point and its sonic point is adequately represented by a hyperbola asymptotic to tile free-stream Mach lines; and (2) the sonic line between the shock and the body is straight and inclined at a constant angle. Although the new methodology has some points of contact with earlier methodologies, the novelty here is that it is used Missile Datcom code as an aid to find out sonic point on body and also it is adopted Parametric System Identification (PSI) in the determination of bow shock shape which uses the Matlab® optimizer fmincon function and an active set strategy to minimize an error in a rms sense subject to simple constraint placed on the parameters by the user. The optimizer function calls a user written function which calculates the shape of the shock wave using the current parameters supplied by optimizer. Also for the shock distance L, the methodology presented here allows to select the value of the mentioned constant angle consistently based either in aerodynamics literature or through physical considerations. As the L value is previously known from measurements or aerodynamics literature, it was used an optimizer to minimize the error between predicted and known result varying a parameter which absorbs all inconsistencies that arise when it is used the basic Moeckel’s model considered here. Once the principal characteristics of the shock wave are calculated, an error value is returned to optimizer function based on the differences between predicted and known results.


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