Abstract
Present studies deal with application of finite element method and intelligent soft computing techniques viz. neural network (NN) and adaptive neuro-fuzzy inference system (ANFIS) for the prediction of short duration impulse, ramp and hat forces. Two blunt bodies viz. a hemisphere and a blunt cone with cylindrical aft body have been considered in these investigations. Training of the NN and ANFIS has been carried out using one axial acceleration and two normal accelerations obtained from known input forces and a moment. It is shown here that the NN is unable to recover the unknown forces and moment. However, ANFIS-based strategy is found better for prediction of the same forces and moment. Thus, novelty of these studies exists in the assessment and successful implementation of the soft computing techniques like NN and ANFIS for prediction of the unknown short-duration force and moment time histories. These predictions are also cross-checked for the error, and it has been observed that the ANFIS can be used for short-duration force prediction in high-speed aerospace applications.
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References
Azari A, Poursina M, Poursina D (2014) Radial forging force prediction through MR, ANN, and ANFIS models. Neural Comput Appl 25(3–4):849–58
Basavaraj T, Kurbet SN, Kuppast VV, Yadwad AM (2015) Modal analysis of a 2-cylinder crankshaft using ANSYS. Int J Eng Res Appl 1(5):26–30
Daniel WJT, Mee DJ (1995) Finite element modelling of a three-component force balance for hypersonic flows. Comput Struct 54(1):35–48 Jan 3
Grillenzoni C (2000) Time-varying parameters prediction. Ann Inst Stat Math 52(1):108–22
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Joshi MV, Reddy NM (1986) Aerodynamic force measurements over missile configurations in IISc shock tunnel at \(M\infty {=}5.5\). Exp Fluids 4(6):338–40
Jurković Z, Jurković M, Buljan S (2006) Optimization of extrusion force prediction model using different techniques. J Achiev Mater Manuf Eng 17(1–2):353–6
Kulkarni V, Reddy KPJ (2010) Accelerometer based force balance for high enthalpy facilities. J Aerosp Eng 23(4):276–280
Mee DJ (2003) Dynamic calibration of force balances for impulse hypersonic facilities. Shock Waves 12(6):443–55
Menezes V, Trivedi S, Kumar A (2011) An accelerometer balance for the measurement of roll, lift and drag on a lifting model in a shock tunnel. Meas Sci Technol 22(6):067003
Naumann KW, Ende H, Mathieu G, George A (1993) Millisecond aerodynamic force measurement with side-jet model in the ISL shock tunnel. AIAA J 31(6):1068–74 Jun
Nayak KC, Tripathy RK, Panda SR, Sahoo SN (2014) Prediction of cutting and feed forces for conventional milling process using adaptive neuro fuzzy inference system (ANFIS). IAES Int J Artif Intell 3(1):24
Panda SS, Chakraborty D, Pal SK (2008) Flank wear prediction in drilling using back propagation neural network and radial basis function network. Appl Soft Comput 8(2):858–71
Ramesh P, Bommana D, Kulkarni V, Sahoo N, Dwivedy SK (2014) Experimental assessment of noncontact type laser-based force measurement technique for impulsive loading. Int J Struct Stab Dyn 14(04):1450003
Rao PS, Venkatesh R (2016) Static and transient analyses of leaf spring using composite material. IUP J Mech Eng 9(1):25
Rath S, Singh AP, Bhaskar U, Krishna B, Santra BK, Rai D, Neogi N (2010) Artificial neural network modeling for prediction of roll force during plate rolling process. Mater Manuf Process 25(1–3):149–53
Release AN. 14.5 Documentation. ANSYS Inc. (2012)
Rizal M, Ghani JA, Nuawi MZ, Haron CH (2013) Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Appl Soft Comput 13(4):1960–8
Robinson M, Hannemann K (2006) Short duration force measurements in impulse facilities. In: 25th AIAA aerodynamic measurement technology and ground testing conference, p 3439
Sahoo N (2003) Simultaneous measurement of aerodynamic forces and convective surface heating rates for large angle blunt cones in hypersonic shock tunnel. Doctoral dissertation, Department of aerospace engineering, Indian Institute of Science, Bangalore, India
Sahoo N, Mahapatra DR, Jagadeesh G, Gopalakrishnan S, Reddy KPJ (2003) An accelerometer balance system for measurement of aerodynamic force coefficients over blunt bodies in a hypersonic shock tunnel. Meas Sci Technol 14(3):260
Saravanan S, Jagadeesh G, Reddy KPJ (2009) Aerodynamic force measurement using 3-component accelerometer force balance system in a hypersonic shock tunnel. Shock Waves 18(6):425–35
Simmons JM, Sanderson SR (1991) Drag balance for hypervelocity impulse facilities. AIAA 29(12):2185–2191
Smith AL, Mee DJ, Daniel WJ, Shimoda T (2001) Design, modelling and analysis of a six component force balance for hypervelocity wind tunnel testing. Comput Struct 79(11):1077–88
Vidal RJ (1956) Model instrumentation techniques for heat transfer and force measurements in a hypersonic shock tunnel. Cal rept. no. AD-917-A-1, PB 138852, WADC TN 56-315, AD 97238
Zhu F, Wu Y (2014) A rapid structural damage detection method using integrated ANFIS and interval modeling technique. Appl Soft Comput 25:473–84
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Ramesh, P., Nanda, S.R., Kulkarni, V. et al. Application of neural-networks and neuro-fuzzy systems for the prediction of short-duration forces acting on the blunt bodies. Soft Comput 23, 5725–5738 (2019). https://doi.org/10.1007/s00500-018-3231-9
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DOI: https://doi.org/10.1007/s00500-018-3231-9