Abstract Pigging is widely employed to remove deposits from water distribution pipelines, yet technical guidelines for determining the optimal number of passes remain insufficiently established. This study investigates the mechanistic reason why repeated pigging provides superior cleaning performance compared with a single pass by integrating visualization experiments and computational fluid dynamics (CFD) analysis. A serrated scraper pig, consisting of two rows of circumferential serrated rubber pads with a Shore hardness of 60D, was tested in a laboratory-scale recirculating system using a 100 mm diameter transparent acrylic test pipe and artificial sediments designed to simulate strongly adhered pipeline deposits. Direct visualization showed that the second pigging pass achieved a sediment removal efficiency of 37.55%, which was approximately five times higher than that of the first pass (7.46%) and substantially greater than that of conventional flushing (0.55%). Transient CFD simulations (pimpleFoam solver with the γ – Re θ transition model coupled to the shear stress transport (SST) k – ω turbulence model) clarified the physical basis for this enhancement. During the first pass, sediment accumulation within the serrated pad gaps suppresses bypass flow development. After these gaps are partially cleared, accelerated annular flow develops more effectively during the subsequent pass, increasing the local wall shear stress and skin-friction coefficient in the noncontact regions. This combined action of mechanical scraping and hydrodynamic shear explains why the major cleaning effect appears primarily after the first pass. The main practical implication is that at least two pigging passes are recommended for the effective removal of strongly adhered deposits. These findings provide a mechanistic basis for optimizing pigging protocols in water distribution pipeline maintenance.
Abstract This study investigated the fatigue susceptibility of buried gas pipelines under train-induced vibrations, excluding joint behavior and corrosion effects. It assessed material fatigue response and casing mitigation efficacy. Owing to restricted field access, a scaled pipeline network was instrumented and excited with a plate compactor reproducing train vibrations with dominant frequencies of 30–40 Hz. Pipeline ovalization induced by passing trains—the primary stress source—was derived from double-integrated accelerations. Fatigue life was estimated using rainflow counting and Miner’s rule, and yielded peak equivalent stresses of 0.9–1.8 MPa and negligible fatigue damage fractions. Casings accelerated fatigue damage via vibration transmission at contact points. Numerical analyses indicated that stiffer, compacted backfill reduces susceptibility, whereas saturated soils and shallower burial depths increase it. Future work should address joint fatigue and explore alternative protection strategies for pipelines beneath railways or roads in fatigue-prone, high-traffic locations.
Abstract Liquefaction-induced ground failure poses severe risks to the structural integrity of buried lifelines. While pipeline behavior in liquefiable soils has been widely studied, current design guidelines often overlook the complex hydromechanical coupling, specifically local flow and permeability variations, during earthquake loading. This study addresses this gap by implementing a specific state-dependent permeability algorithm (via FISH) within a two-dimensional fully coupled effective stress framework using the PM4Sand constitutive model. Unlike conventional undrained analyses, this approach captures the transient variation in hydraulic conductivity during excess pore-water pressure generation. Results demonstrate that neglecting these dynamic permeability effects can lead to an underestimation of pipeline uplift by up to 30%, significantly increasing the risk of upheaval buckling. Furthermore, the analysis investigates critical design parameters, including the pipeline material, internal operating pressure, relative density, and ground motion characteristics (near-field versus far-field). The findings indicate that internal pressure plays a significant role in stability and that current design protocols may require adjusted safety factors to ensure asset resilience against liquefaction-induced buoyancy.
Abstract To address the issue of suboptimal denoising performance in variational mode decomposition (VMD) due to the reliance on empirical parameter selection, this paper proposes a novel joint denoising method (GVMD) based on the gray wolf optimization (GWO) algorithm for the optimal selection of VMD parameters. First, the GWO algorithm is used to optimize the number of intrinsic mode functions and the penalty factor. Then, the signal is processed by combining the Pearson correlation coefficient (PCC) and dynamic time warping (DTW) to effectively distinguish between noise and valid signals. Finally, the signal is reconstructed to obtain the negative pressure wave (NPW) signal. The simulation results demonstrate that, compared to wavelet transform (WT), VMD, and the VMD optimization algorithm based on northern goshawk [Northern Goshawk optimization (NGO)-VMD], the proposed method achieves the highest signal-to-noise ratio (SNR) of 16.57 dB and the lowest root-mean-square error (RMSE) of 0.151. Additionally, a leakage simulation experimental platform was established to further validate the algorithm’s performance. Experimental results indicate that GVMD produces a smoother NPW signal, preserves the waveform characteristics of the signal, and is capable of identifying distinct NPW inflection points.
Abstract L360QS/Incoloy 825 bimetallic composite pipe (BCP) is widely used for high-sulfur gas transportation in the oil and gas industry. The outer layer of BCPs is susceptible to external soil erosion. However, conventional magnetic flux leakage (MFL) testing has a low recognition rate for defects in the outer layer due to noise interference. In this paper, a Butterworth filter–variational mode decomposition–discrete wavelet transform (BW-VMD-DWT) denoising method is established. Then, the MFL pull test and finite-element simulation are carried out to obtain the original magnetic signal of cylindrical and round pit defects. Finally, the defect detection rate before and after noise filtering is compared via the BW-VMD-DWT method. The results show that the average noise amplitude of cylindrical and round pit defects decreased by 0.35 and 0.20 mT, and the signal-to-noise ratio (SNR) increased by an average of 14.5% and 24.5%, respectively. In addition, the detection rates of cylindrical and round pit defects have increased by an average of 5.5% and 8.0%.
