Wind and Structures

  • 제2권2호
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  • Pages.85-94
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  • 1999
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Parametric study based on synthetic realizations of EARPG(1)/UPS for simulation of extreme value statistics

  • Seong, Seung H. (School of Mechanical Engineering, Pusan National University)
  • 발행 : 1999.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

The EARPG(1)/UPS was first developed by Seong (1993) and has been tested for wind pressure time series simulations (Seong and Peterka 1993, 1997, 1998) to prove its excellent performance for generating non-Gaussian time series, in particular, with large amplitude sharp peaks. This paper presents a parametric study focused on simulation of extreme value statistics based on the synthetic realizations of the EARPG(1)/UPS. The method is shown to have a great capability to simulate a wide range of non-Gaussian statistic values and extreme value statistics with exact target sample power spectrum. The variation of skewed long tail in PDF and extreme value distribution are illustrated as function of relevant parameters.

키워드

참고문헌

  1. Gaver, D.P. and Lewis, P.A.W. (1980), "First-order autoregressive gamma sequences and point processes" , Advances on Applied Probability, 12, 727-745. https://doi.org/10.2307/1426429
  2. Grigoriu, M. (1995), Applied Non-Gaussian Processes, Prentice Hall PTR, Englewood Cliffs, N.J.
  3. Lechner, J.A., Simiu, E., and Heckert, N.A. (1993), "Assessment of peaks over threshold methods for estimating extreme value distribution tails" , Structural Safety, 12, 305-314. https://doi.org/10.1016/0167-4730(93)90059-A
  4. Seong, S.H. (1993), "Digital synthesis of wind pressure fluctuations on building surfaces" , Ph.D Thesis, Fluid Mechanics and Wind Engineering Program, Department of Civil Engineering, Colorado State University, Fort Collins, Colorado.
  5. Seong, S.H. and Peterka, J.A. (1993), "Computer simulation of non-Gaussian wind pressure fluctuations" , Proc. of Seventh U.S. National Conf. on Wind Engineering, Los Angeles, California, 623-632.
  6. Seong, S.H. and Peterka, J.A. (1997), "Computer simulation of non-Gaussian multiple time series" , J. Wind Eng. Ind. Aerodyn., 72, 95-105. https://doi.org/10.1016/S0167-6105(97)00243-2
  7. Seong, S.H. and Peterka, J.A. (1998), "Digital generation of surface-pressure fluctuations with spiky features", J. Wind Eng. Ind. Aerodyn., 73, 181-192. https://doi.org/10.1016/S0167-6105(97)00283-3
  8. Shinozuka, M. and Jan, C.-M. (1972), "Digital simulation of random processes and its applications", Journal of Sound and Vibration, 25, 111-128. https://doi.org/10.1016/0022-460X(72)90600-1
  9. Tong, H. (1990),Non-Linear Time Series: A Dynamical System Approach, Clarendon, Oxford.
  10. Wegman, E.J., Schwartz, S.C., and Thomas, J.B. (Editors) (1989), Topics in Non-Gaussian Signal Processing, Springer-Verlag.
  11. Yamazaki, F. and Shinozuka, M. (1988), "Digital generation of non-Gaussian stochastic fields" , J. Engrg. Mech., ASCE, 114(7).

피인용 문헌

  1. Discussion - Parameteric study based on synthetic realizations of EARPG(1)/UPS for simulation of extreme value statistics vol.3, pp.1, 2000, https://doi.org/10.12989/was.2000.3.1.069
  2. Spectral method for non-Gaussian data generation by phase modeling: White noise phase versus structured phase vol.87, 2015, https://doi.org/10.1016/j.engstruct.2015.01.020