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Regression-Based Neural Network Simulation for Vibration Frequencies of the Rotating Blade

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Modern Mathematical Methods and High Performance Computing in Science and Technology

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 171))

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Abstract

The aim of this paper is to demonstrate the use of regression-based neural network (RBNN) method to study the problem of the natural frequencies of the rotor blade for micro-unmanned helicopter [3]. The training of the traditional artificial neural network (ANN) model and proposed RBNN model has been implemented in the MATLAB environment using neural network tools (NNT) built-in functions. The graphs for angular velocity (Omega) of the micro-unmanned helicopter are plotted for estimation of the natural frequencies (f1, f2, f3) of transverse vibrations. The results obtained in this research show that the RBNN model, when trained, can give the vibration frequency parameters directly without going through traditional and lengthy numerical solutions procedures. Succeeding this, the numerical results, when plotted, show that with the increase in Omega, there is increase in lagging motion frequencies. Additionally, it is found that the increase in the lower mode natural frequencies is smaller than that of the higher modes. This finding is in agreement with the results reported in earlier research [3–5] carried out by employing Rayleigh–Ritz and FEM, respectively.

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References

  1. Chakravarty, S., Singh, V.P., Sharma, R.K.: Regression-based weight generation algorithm in network for estimation of frequencies of vibrating plates. Comput. Methods Appl. Mech. Eng. J. 195, 4194–4202 (2006)

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  4. Rao, R.S., Gupta, S.S.: Finite element vibration analysis of rotating Timoshenko beams. Sound Vib. J. 242(1), 103–124 (2001)

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Author information

Authors and Affiliations

  1. Professor of Mathematics, Coppin State University, Baltimore, MD, 21216, USA

    Atma Sahu

  2. Professor & Head Mathematics Department, National Institute of Technology, Rourkela, 769 008, Orissa, India

    S. Chakravarty

Authors
  1. Atma Sahu
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  2. S. Chakravarty
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Corresponding author

Correspondence to Atma Sahu .

Editor information

Editors and Affiliations

  1. Department of Applied Mathematics, Raj Kumar Goel Institute of Technology, Ghaziabad, Uttar Pradesh, India

    Vinai K. Singh

  2. Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada

    H.M. Srivastava

  3. Dipartimento di Matematica, University of Turin, Torino, Italy

    Ezio Venturino

  4. High Performance Computing Center (HLRS), University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Michael Resch

  5. Department of Mathematics, Netaji Subhas Institute of Technology, New Delhi, India

    Vijay Gupta

Appendix

Appendix

See Appendix Fig. 5.

figure a
figure b

Leading and trailing edges of a blade

Fig. 5
figure 5

Helicopter blade scheme

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© 2016 Springer Science+Business Media Singapore

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Sahu, A., Chakravarty, S. (2016). Regression-Based Neural Network Simulation for Vibration Frequencies of the Rotating Blade. In: Singh, V., Srivastava, H., Venturino, E., Resch, M., Gupta, V. (eds) Modern Mathematical Methods and High Performance Computing in Science and Technology. Springer Proceedings in Mathematics & Statistics, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-10-1454-3_2

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