Research Papers: Piper and Riser Technology

A Data-Driven Mode Identification Algorithm for Riser Fatigue Damage Assessment

[+] Author and Article Information
C. Shi, J. Park, L. Manuel

Department of Civil, Architectural, and
Environmental Engineering,
University of Texas,
Austin, TX 78712

M. A. Tognarelli

BP America Production Co.,
501 Westlake Park Blvd,
Houston, TX 77079

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received October 4, 2011; final manuscript received March 16, 2014; published online April 16, 2014. Assoc. Editor: Jeffrey M. Falzarano.

J. Offshore Mech. Arct. Eng 136(3), 031702 (Apr 16, 2014) (8 pages) Paper No: OMAE-11-1088; doi: 10.1115/1.4027292 History: Received October 04, 2011; Revised March 16, 2014

A well-established empirical procedure, which we refer to as weighted waveform analysis (WWA), is employed to reconstruct a model riser's response over its entire length using a limited number of strain measurements. The quality of the response reconstruction is controlled largely by identification of the participating riser response modes (waveforms); hence, mode selection is vital in WWA application. Instead of selecting a set of consecutive riser vibratory modes, we propose a procedure that automatically identifies a set of nonconsecutive riser modes that can thus account for higher harmonics in the riser response (at multiplies of the Strouhal frequency). Using temporal data analysis of the discrete time-stamped samples, significant response frequencies are identified on the basis of power spectrum peaks; similarly, using the spatial data analysis of the sparse nonuniformly sampled data, significant wavenumbers are identified using Lomb–Scargle periodograms. Knowing the riser length, the most important wavenumber is related to a specific mode number; this dominant mode is, in turn, related to the dominant peak in power spectra based on the temporal data analysis. The riser's fundamental frequency is estimated as the ratio of the empirically estimated dominant spectral frequency to the dominant mode number. Additional mode numbers are also identified as spectral peak frequencies divided by the fundamental frequency. This mode selection technique is an improvement over similar WWA procedures that rely on a priori knowledge of the riser's fundamental frequency or on the knowledge of the physical properties and assumptions on added mass contributions. At selected target locations, we compare fatigue damage rates, estimated based on the riser response reconstructed using the WWA method with the proposed automated mode selection technique (we refer to this as the “improved” WWA) and those based on the “original” WWA method (that relies on a theoretically computed fundamental natural frequency of the riser). In both cases, predicted fatigue damage rates based on the empirical methods and data at various locations (other than the target) are cross-validated against damage rates based directly on measurements at the target location. The results show that the improved WWA method, which empirically estimates the riser's fundamental natural frequency and automatically selects significant modes of vibration, may be employed to estimate fatigue damage rates quite well from limited strain measurements.

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Grahic Jump Location
Fig. 1

Identification of the dominant mode number (wavenumber) for the case example of selecting sensor no. 4 as the target: (a) spatial covariance versus spatial separation between sensors and (b) the Lomb–Scargle periodogram of the spatial covariance

Grahic Jump Location
Fig. 2

Selection of the optimal set of frequencies for the WWA scheme for the case example of selecting sensor no. 4 as the target: (a) PSDs of the strains measured at 23 sensors and (b) summation of the strain PSDs from all 23 sensors and identification of the important frequencies

Grahic Jump Location
Fig. 3

Response reconstruction based on the improved WWA method for the case example of selecting sensor no. 4 as the target: (a) the PSD of the strain at the target sensor location (reconstructed versus measured), (b) the RMS of curvature (reconstructed versus measured), (c) the RMS of displacement (reconstructed versus measured), and (d) off- diagonal elements of the orthogonality matrix for the selected modes

Grahic Jump Location
Fig. 4

Comparison of the damage ratios estimated using the improved and the original WWA methods: (a) NDP2120, uniform current with a peed of 1.4 m/s, (b) NDP2150, uniform current with a speed of 1.7 m/s, (c) NDP2350, sheared current with a maximum speed of 0.7 m/s, and (d) NDP2420, sheared current with a maximum speed of 1.4 m/s




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