Acoustic Induced Vibration (AIV) refers to the high acoustic energy generated by pressure-reducing devices that excite pipe shell vibration modes, producing excessive dynamic stress. Analysis of this risk is an important part of Asset Integrity Management systems as AIV can cause catastrophic piping failure. Existing methodologies address this risk through an analytical assessment. However, these methodologies are not fully known and input parameters are limited. The aim of this paper is to propose an advanced methodology for AIV risk assessment allowing a better understanding of possible mitigation action.
The approach presented for identifying AIV risk is based on a dynamic stress evaluation at pipe discontinuities in order to assess pipe fatigue. Dynamic stress evaluation is performed through a fluid-structure coupling Finite Element Analysis. Pressure fluctuations inside the pipe are predicted and coupled with a pipe structural analysis. This methodology is provided with its validation through measurement on an actual AIV field case, corresponding to a crack initiation due to AIV on an FPSO flare network tail pipe. Piping dynamic behavior and vibration levels on the pipe predicted by this numerical approach are compared to results obtained in the field case study. The comparison shows good agreement between the experimental and computed data, giving confidence in the pertinence of this approach.
The method is then used to quantitatively assess mitigation action efficiency. Existing guidelines are efficient to perform a quick screening of a large number of pipes. However, when it comes to mitigation measures, the limited input parameters used to quantify the Likelihood of Failure restrain from assessing the impact of existing mitigation measures and therefore demonstrating their efficiency. Consequently, the methodology is applied to existing mitigation measures that cannot be quantitatively assessed through existing guidelines such as: the use of sweepolet, forged tee, and full encirclement wrap branch reinforcement. Comparison between computation results with and without mitigation measures make it possible to quantify the impact of such modifications for reducing AIV risk.