Development of a method for maximum structural response prediction of a store externally carried by a jet fighter

Abstract

Experimental structural response of equipment mounted in store carried externally by jet type aircraft is investigated, predicted and compared with responses suggested by military standards in this study. A representative store which is similar to Mark-83 warhead with guidance units in terms of mass and geometry is used in this study. The main scope of this study is to evaluate the structural response proposed by military standards with real test conditions and also suggest a new method with an artificial neural network to predict the maximum response. Seventy-five different flight conditions are used to train the network for low and high frequency components. Also, eight flight conditions apart from the training set of flight conditions are used to test the approach. Acceleration levels are collected in real flight conditions by the data storage system. In signal processing, vibration response is expressed as power spectral density functions in the frequency domain. Procedures to predict the maximum response from measurements are determined with statistical limits in the literature. Besides the well-known limits in literature, third-order polynomial normal and logarithmic transform is used, and the performance of the different limits is compared. It is found that the military standard vibration spectrum is conservative. Distribution-free and normal tolerance limits predicted low frequency acceleration spectral density magnitudes more accurately. Their prediction performances were better than those of the other tolerance limits and that of the military standard. Third-order polynomial transform predictions are found to be reasonable with respect to normal prediction limit and envelope approach. Finally, it can be concluded that the response prediction method proposed in this article works well for Mark-83 warheads with guidance unit carried externally by jet fighter.

Keywords

MIL-STD-810 structural response test spectrum

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References

Hall

. Vibration test level criteria for aircraft equipment. OH, USA: Air Force Wright Aeronautical Lab Wright-Patterson AFB, 1980.

Guanjun

Hui

Jing

, et al. Mechanism of intermittent failures in extreme vibration environment and online diagnosis technology. Proc Inst Mech Eng G: J Aerosp Eng 2015; 229(13): 2469–2480.

Jang

Park

J-W

. Simplified vibration PSD synthesis method for MIL-STD-810. Appl Sci 2020; 10(2): 458.

Nevius

Brignac

. Dynamic qualification testing of F-16 equipment. AGARD Dyn Environ Qualification Tech 1981; 15: 5–9.

Das

Kumar

. Tailoring of specifications for random vibration testing of military airborne equipments from measurement. Int J Res Eng Technol 2015; 4(12): 293–299.

Kim

Park

Eun

, et al. Vibratory loads and response prediction for a high-speed flight vehicle during launch events. Int J Aeronaut Space Sci 2016; 17(4): 551–564.

Chung

Krebs

Peebles

. Estimation of payload random vibration loads for proper structure design. In: 19th AIAA Applied Aerodynamics Conference, Anaheim, CA, 11–14 June 2001, p. 1667.

Lalanne

. Mechanical vibration and shock analysis, specification development. Paris, France: John Wiley & Sons, 2013.

Runyan

. Some recent information on aircraft vibration due to aerodynamic sources. J Acoust Soc Am 1968; 44: 364.

10.

AGARDograph

Series-Volume

FTT

. Aircraft/stores compatibility, integration and separation testing. Brussels, Belgium: NATO: S&T Organization, 2014.

11.

Steininger

Haidl

. Vibration qualification of external A/C stores and equipment. AGARD Dyn Environ Qualification Tech 1981; 14.

12.

Corda

. In-flight vibration environment of the NASA F-15B flight test fixture. CA, USA: NASA Dryden Flight Research Center, 2002.

13.

Sevy

Haller

. Computer program for vibration prediction of fighter aircraft equipments. OH, USA: Air Force Flight Dynamics Lab Wright-Patterson AFB, 1977.

14.

Yıldız

. Aeroelastic stability prediction using flutter flight test data. Ankata, Turkey: METU, 2007.

15.

Kutluay

Mahmutyazicioglu

Platin

. An application of equation error method to aerodynamic model identification and parameter estimation of a gliding flight vehicle. In: AIAA atmospheric flight mechanics conference, Chicago, IL, 10–13 August 2009, p. 5724.

16.

Mallick

Mohanta

Kumar

, et al. Prediction of wind-induced mean pressure coefficients using GMDH neural network. J Aerospace Eng 2020; 33(1): 04019104.

17.

Mazhar

Choudhry

Shehryar

. Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data. Proc Inst Mech Eng Part G: J Aerosp Eng 2019; 233(7): 2549–2569.

18.

Quaranta

Dimino

. Experimental training and validation of a system for aircraft acoustic signature identification. J Aircr 2007; 44(4): 1196–1204.

19.

Halle

Thielecke

. Flight loads estimation using local model networks. In: 29th congress of the international council of the aeronautical sciences, St. Petersburg, Russia, 7–12 September 2014.

20.

Caliskan

Aykan

Hajiyev

. Aircraft icing detection, identification, and reconfigurable control based on Kalman filtering and neural networks. J Aerosp Eng 2008; 21(2): 51–60.

21.

Crowther

Cooper

. Flight test flutter prediction using neural networks. Proc Inst Mech Eng Part G: J Aerosp Eng 2001; 215(1): 37–47.

22.

Newland

. An introduction to random vibrations, spectral & wavelet analysis. NY, USA: Courier Corporation, 2012.

23.

Edwards

. Probability of future observations exceeding one-sided, normal, upper tolerance limits. J Spacecr Rockets 2015; 52(2): 622–625.

24.

Hughes

Paez

. Application of the bootstrap statistical method in deriving vibroacoustic specifications. CA, USA: NASA, 2006.

25.

Bethea

Rhinehart

. Applied engineering statistics. FL, USA: CRC Press, 1991, Vol. 121.

26.

Wang

Bai

Wan

, et al.. Review of statistical induction analysis methods on aircraft vibration test data. Proced Environ Sci 2011; 10: 825–830.

27.

Gibbons

Bhaumik

Aryal

. Groundwater monitoring, detection, and compliance. NJ, USA: Wiley StatsRef: Statistics Reference Online, 2014.

28.

Ramu

Arul

. Estimating probabilistic fatigue of Nitinol with scarce samples. Int J Fatigue 2016; 85: 31–39.

29.

Paksoy

Aradag

. Artificial neural network based prediction of time-dependent behavior for lid-driven cavity flows. Isi Bilimi ve Teknigi Dergisi/Journal Therm Sci Technol 2015; 35(2): 1–18.

30.

Zhen

Liu

Zhang

, et al. Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image. IEEE Trans Industry Appl 2020; 56(4): 3385–3396.

31.

Yang

Jiang

Guo

. Time series data classification based on dual path CNN-RNN cascade network. IEEE Access 2019; 7: 155304–155312.

32.

Sharan

Vijayaraju

James

. Synthesis of in-flight strains using flight parameters for a fighter aircraft. J Aircr 2013; 50(2): 469–477.

33.

Winter

Breitsamter

. Nonlinear identification via connected neural networks for unsteady aerodynamic analysis. Aerosp Sci Technol 2018; 77: 802–818.

34.

Durodola

Ramachandra

, et al.. A pattern recognition artificial neural network method for random fatigue loading life prediction. Int J Fatigue 2017; 99: 55–67.

35.

Durodola

Ramachandra

Gerguri

, et al. Artificial neural network for random fatigue loading analysis including the effect of mean stress. Int J Fatigue 2018; 111: 321–332.

36.

Thompson

. MIL-STD-810G environmental engineering considerations and laboratory tests. Washington, USA: US Department of Defense, 2008.

37.

Schmid

. How to use the FFT and Matlab’s pwelch function for signal and noise simulations and measurements. FHNW/IME 2012: 2–13.

38.

Keegan

. Dynamic environmental criteria (NASA report# HDBK-7005) NASA technical handbook. CA, USA: NASA, 2001.

39.

Piersol

. Procedures to compute maximum structural responses from predictions or measurements at selected points. Shock Vib 1996; 3(3): 211–221.

40.

Dreher

. Aircraft equipment random vibration test criteria based on vibrations induced by turbulent airflow across aircraft external surfaces. OH, USA: Air Force Flight Dynamics Lab Wright-Patterson AFB, 1983.

41.

Wafford

Haidl

Lodge

et al. Dynamic environmental qualification techniques. Neuilly Sur Seine, France: Advisory Group fır Aerospace Research and Development Neuilly-Sur-Seine (France), 1979.

42.

Kartman

. Emprical prediction of missile flight random vibrations. Shock Vib Bull 1970; 41: 4.

43.

Cap

C De Baca

Skousen

. The derivation of maximum predicted environments for externally carried stores using a small number of flight tests. Albuquerque, NM: Sandia National Lab.(SNL-NM), 2014.

44.

Maj

. Allied environmental conditions and test publications, AECTP 200. Brussels, Belgium: NATO Standardisation Agency (NSA), 2006.

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