Research ArticleNo Access

A probabilistic model for estimating some design characteristics of aerostructures with an illustration of a Monte Carlo simulation, statistical inferences and applications in aerodynamics

Civil Aviation Authority of Iran, Tehran, Iran

https://doi.org/10.1142/S1793962323500332Cited by:0 (Source: Crossref)

Abstract

This paper examines the performance of a new probability model developed in terms of the surface area and velocity of an aero-structure, and presents some relevant statistical inferences. Several examples will compare the robustness of three kinds of point estimators developed for the lift coefficient. Four different types of confidence intervals for the lift coefficient are also developed, and it is shown that the shortest length confidence interval is an UMA confidence interval. Using the probability model, the correlation coefficient between lift and velocity, and the relevant regression slope are computed. The inferences and the results of this paper can be used particularly in the designing process of an aero-structure, especially when there is some uncertainty about its surface area; this provides some flexibilities for aerodynamicists in determining some of the unknown design characteristics.

Keywords:

AMSC: 60E05, 62F03, 62F10, 62J05, 62P30, 76G25

References

1. Kermode A. C. , Mechanics of Flight, 11th edn., Pearson Education, 2006. Google Scholar
2. Anderson J. , Fundamentals of Aerodynamics, 6th edn., McGraw-Hill Education, 2017. Google Scholar
3. Krasnov N. F., Aerodynamics, Vol. 1, Translated from the Russian by Leib G., Mir Publishers, Moscow, 1985. Google Scholar
4. Liu T. , Evolutionary understanding of airfoil lift, Adv. Aerodyn. 3 (2021) 37. Crossref, Google Scholar
5. Tumse S., Bilgili M., Sahin B. , Lift coefficient estimation of a Delta wing under the ground effect using artificial neural network, Çukurova Univ. J. Faculty Eng. 36(3) (2021) 625–636. Google Scholar
6. Lavicka D., Matas R., Computation of drag and lift coefficients for simple two-dimensional objects with Reynolds number Re = 420 000, EPJ Web of Conferences, 2012. Google Scholar
7. Rohatgi V. K. , An Introduction to Probability Theory and Mathematical Statistics, John Wiley, 1993. Google Scholar
8. Casella G., Berger R. L. , Statistical Inference, 2nd edn., DUXBURY, 2002. Google Scholar
9. Chang K. L. , A Course in Probability Theory, 3rd edn., Academic Press, 2001. Google Scholar
10. Neyman J., Pearson E. S. , On the problem of the most efficient tests of statistical hypotheses, Philos. Trans. R. Soc. A231 :289–337, 1933. Google Scholar
11. Rao C. R. , Linear Statistical Inference and its Applications, 2nd edn., John Wiley, 2002. Google Scholar
12. Boubakar T., Lassina D., Belco T., Abdou F. , The shortest confidence interval for the mean of a normal distribution, Int. J. Stat. Probab. 7(2) (2018) 33–38. Crossref, Google Scholar
13. Draper N. R., Smith H. , Applied Regression Analysis, 3rd edn., Wiley, 2014. Google Scholar
14. Lehmann E. L., Romano J. P. , Testing Statistical Hypotheses, 4th edn., Springer, 2022. Crossref, Google Scholar