Pipe Wall Thinning Research Articles

Comprehensive study on microwave inspection of internal pipe wall thinning: discontinuities, reflections and signals

Comprehensive study on microwave inspection of internal pipe wall thinning: discontinuities, reflections and signals

Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron

This study entailed the development of a novel method for estimating the depth of wall thinning of pipes using multifrequency (30–65 kHz) reflection coefficients (MRCs) of the T(0,1) mode-guided waves and a multilayer perceptron (MLP). First, this study established why MRCs are a critical feature of the input layer of the MLP for the defect depth estimation of wall thinning. Further, a mathematical model that can quickly collect large amounts of training data was used to calculate the reflection waveforms. The depths of artificial and actual wall thinning were estimated using the MLP based on the MRCs and the mathematical model. Experiments were conducted using the T(0,1) mode-guided waves to obtain the MRCs for 21 artificial and 6 actual wall thinnings to estimate the defect depths. A maximum of 8347 training data points were prepared using the mathematical model. Because the optimization of the MLP strongly depended on the initial weights and biases, 100 random initial values were prepared to evaluate the average estimations and their standard deviations. The classification scheme of the MLP was used, with classification step widths of 0.5 and 0.25 mm. The correct answer rates for the 21 artificial defects were 93%, with a tolerance of ±0.5 mm for the 0.5 mm classification scheme; those for the 0.25 mm classification scheme were 89%. For the six actual defects, the correct answer rates were 100% with a tolerance of ±0.5 mm for both the 0.5 and 0.25 mm classification schemes. Sufficiently high correct answer rates were obtained in all the cases.

Effect of contact blast loading on the plastic deformation forming ability of large steel pipes

Plastic deformation forming with metal pipe blanks by contact blast loading inside pipes is an interesting moldless forming technique, also a complex and error-prone process. Some advantages are very characteristic of this forming technique such as no cost of mold, tooling and low energy consumption, no complicated control equipment compared to other forming techniques such as casting, rolling, tube hydrostatic forming, bending – welding. Up to now, the calculation and design of this forming technique mainly use some existing reference empirical formulas, so the experimental results are only suitable in the range of small pipe diameters, and still there are significant deviations for larger pipe diameters. In order to increase the predictability and accuracy of forming process by contact blast loading inside large pipes, this paper presents a study on the influence of the mass of highly explosive material – TNT to the forming ability of large steel pipes from API-5LX-42 mild steel materials by modern 3D numerical simulation – using Abaqus/Cae software. Four output criteria with maximum values are used to evaluate the efficiency of this forming process, including maximum diameter of the blast zone (Dmax£2D0), Von Mises stress (Smax£UTS), Hoop plastic strain component (PE22max£1), and Pipe wall thinning rate (eT-max£60 %). The results of this research on the plastic deformation forming process using numerical simulation can be used for the next experimental step to evaluate the difference between simulation and experiment, as well as use this data in the calculation and design of pipe products with circular or square cross-sections to save both time and money of trial and error before application in actual manufacturing

Innovative projection acquisition algorithm for optimizing portable LNDCT in oil and gas pipeline imaging

Fluid pipelines, commonly utilized in the oil industry, often face efficiency and reliability issues due to sediment buildup causing erosion, corrosion, and pipe wall thinning. Traditional assessment methods involve disruptive measures like cutting or creating holes and temporarily taking pipelines out of service. A non-destructive alternative, Limited-Number-Detector Computed Tomography (LNDCT), proves cost-effective and superior. Our proposed algorithm enhances data acquisition and projections using discrete detectors, employing Co-60 as a gamma-ray source and thallium-doped sodium iodide, NaI(Tl), detectors in an arc configuration. Monte Carlo simulations aligned closely with experimental data. Optimization involved adjusting the detector aperture angle based on a primary-to-scatter ratio of gamma-ray photons. We investigated the utility of various isotopes (Co-60, Cs-137, Am-241, Ir-192) to determine optimal projection signal amplitude. The algorithm generates a large sinogram matrix, and a filtered back-projection algorithm with a Hamming filter maximizes image quality while ensuring acceptable calculation volume and time. Using four phantoms, including pipelines filled to different scales, our study evaluates LNDCT configuration, performance, and validation. The results highlight its potential for efficiently evaluating sediment in pipelines, confirming the correctness and accuracy of our proposed algorithm.

Noise-robust pipe wall-thinning discrimination system using convolution recurrent neural network model

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression.

Quantitative Evaluation of Probability of Failure and Damage Risks of Piping Using Bayesian Estimation Based on Multipoint Fixed Point Pipe Wall Thinning Measurements

Quantitative Evaluation of Probability of Failure and Damage Risks of Piping Using Bayesian Estimation Based on Multipoint Fixed Point Pipe Wall Thinning Measurements

Effectiveness of unloading a section of NPS pipeline with a pipe wall thinning defect by mounting a band or welded sleeve

Effectiveness of unloading a section of NPS pipeline with a pipe wall thinning defect by mounting a band or welded sleeve

Assessment of flow-accelerated corrosion-induced wall thinning in SA106 pipes with elbow sections

A combination of flow-accelerated corrosion (FAC) tests and corresponding computational fluid dynamics (CFD) tests were performed to determine the hydrodynamic parameters that could help predict the highly susceptible location to FAC in the elbow section. The accelerated FAC tests were performed on a specimen containing elbow sections fabricated using commercial 2-inch carbon steel pipe. The tests were conducted at flow rates of 9 m/s under the following conditions: water temperature of 150 °C, dissolved oxygen <5 ppb, and pH 7. Thickness reduction of the specimen pipe due to FAC was measured using ultrasonic testing. CFD was conducted on the FAC test specimen, and the turbulence intensity, and shear stress were analyzed. Notably, the location of the maximum hydrodynamic parameters, that is, the wall shear stress and turbulent intensity, is also the same location with maximum FAC rate. Therefore, the shear stress and turbulence intensity can be used as hydrodynamic parameters that help predict the FAC-induced wall-thinning rate. The results provide a method to identify locations susceptible to FAC and can be useful for determining inspection priority in piping systems.

XAI for signal analysis of guided wave testing at pipe elbows

Guided wave testing can provide an efficient screening method for local wall thinning of piping owing to its long inspection range and its ability to inspect pipes with limited access. However, there is a problem in signal interpretation of guided wave testing. While the interpretation of echo signals in guided wave testing is relatively simple for straight pipes, it becomes much more difficult when inspected piping includes an elbow because wave propagation becomes more complicated. This study investigates a signal analysis method that utilizes deep learning to evaluate local wall-thinning at an elbow based on echo signals in guided wave testing using multiple frequencies. To this end, we considered the configuration of deep learning system to analyze echo signals in guided wave testing and utilized Shapley additive explanations (SHAP), one of the explainable AI (XAI) techniques, to provide reasoning for prediction results obtained by deep learning models.

Reliability Analysis of Pipe Wall Thinning based on Quantification of Ultrasonic Testing

Piping in nuclear power plants is subject to corrosion and erosion caused by the interaction with fluids it carries. In order to prevent accidents such as pipe rupture, it is necessary to non-destructively inspect the thickness of the pipe wall and predict the remaining pipe life. In contrast, signals obtained by non-destructive inspection are affected by various uncontrollable and unknown factors, which will result in uncertainty in evaluating pipe wall thickness. Ignoring the uncertainty would lead to a large error in the pipe reliability assessment. Therefore, it is important to develop a reasonable pipe wall thinning management that can takes the uncertainty of non-destructive testing into consideration. Based on this background, this study aimed to develop a piping wall thinning prediction model that accounts for the uncertainty in evaluating pipe wall thickness and to evaluate its applicability to ultrasonic testing. At first, ultrasonic tests were performed to measure the thickness of specimens simulating flow accelerated corrosion. The results of the measurements were statistically analysed to obtain a mathematical model correlating the evaluated and true thickness. A numerical model to predict the reduction of wall thinning with its uncertainty is developed. Compared to the conventional method, the proposed method is able to predict wall thickness with high accuracy.

Automation of the procedure for determining the admissibility of erosion- corrosion wear in pipelines of nuclear power plants made of carbon steels

Many pipelines in Ukrainian nuclear power plants have been in operation for over 30 years, and the determination of the admissibility of erosion-corrosion wear (ECW) defects in the pipeline wall metal is currently one of the pressing issues in the nuclear energy sector of Ukraine. A comparative analysis of determining the admissibility of pipe wall thinning due to ECW was conducted using the adopted methodology “MT-T.0.03.224-18” by the State Enterprise “Energoatom” and the guidance based on the use of regulatory documents for designing pipeline elements. The use of the methodology demonstrated significant advantages in terms of reducing the conservatism of ECW assessment, especially for small-scale thinning and pipelines operating at pressures below 60-80 kgf/cm2, which allows for a substantial reduction in repair work. However, the implementation of the methodology in practice may encounter difficulties due to the complexity and labor-intensiveness of the calculation assessment procedure for nuclear power plant personnel. Therefore, to automate the ECW admissibility assessment in accordance with the methodology requirements, software (SW) was developed for use by nuclear power plant personnel, enabling the prompt generation of a protocol for the express assessment procedure of ECW admissibility for a straight section of the pipeline based on the measurement results of wall thinning parameters in the axial direction. The software allows for concluding whether the section can be put into operation, sent for repair, or requires further refined assessment. Additionally, software based on the finite element method (FEM) was developed, which, when necessary, allows for a refined assessment of a straight section, taking into account the depth and angle of thinning in the circumferential direction, and calculates the stress of the limit plastic state of the pipeline section with ECW from the perspective of viscous fracture. The devel- opment is applicable in the field of nuclear energy in Ukraine.

Comparison of random and periodic rough surfaces by ultrasonic attenuation and frequency distribution

Flow accelerated corrosion generates not only pipe-wall thinning but also roughness on an inside wall. In some flow conditions, the increase in roughness can increase the mass transfer coefficient by up to 80%. It is then very important to have an evaluation tool to monitor and quantify the roughness of a pipe. This study aims to estimate the roughness by using ultrasonic attenuation. The advantage of using ultrasonic attenuation is that it utilizes the signals measured by existing ultrasonic probes, and does not require significant changes to the equipment. To evaluate the inner rough surface, this study compares ultrasonic attenuations on random and periodic rough surfaces. We used an ultrasonic broadband probe on specimens with random and periodic rough surfaces in experiments. The results indicate a significant difference between the attenuation values estimated on random and periodic roughness. In addition, attenuation of amplitude at specific frequencies also was observed. These specific frequencies implied having the potential to estimate the pitch of periodic flaws on the rough surface.

Laboratory validation of in-pipe pulsed thermography in the rapid assessment of external pipe wall thinning in buried metallic utilities

This study characterized the in-pipe thermal signature of external pipewall thinning in steel pipes, a common problem that is caused by external corrosion in hostile underground environment. A model system was prepared to imitate the underground environment by milling several holes of various sizes and residual thicknesses into a mild steel plate. Wall thinning was investigated using active infrared thermography. The non-defective side of the steel plate was heated to 27.4 °C through the application of a thermal energy pulse while the ambient temperature was 22°C. Thermograms were captured inside the pipe at a frequency of 0.02 seconds for 5 min. The images of the thinned surface were processed in two steps. First, the peak contrast time algorithm was used to estimate the residual thickness. Second, Gaussian adaptive thresholding was used to estimate the size of the holes. The maximum observable defects had a diameter of 5 mm and a residual thickness of 3 mm. The type of defect interface (steel–sand or steel–air) had no significant effect on the estimation of residual thickness or size. This study developed a rapid approach in classifying defect's residual thickness by only utilizing two well-known parameters from infrared images – defect's peak thermal contrast and estimated area. Thus, the feasibility of non-destructive, in-pipe, quantitative IR thermographic analysis of buried metal pipelines is demonstrated.

Localizing, identification and sizing of inner surface pipe wall thinning by means of domain transformation and spectral analysis

In microwave inspection of inner surface pipe wall thinning (PWT), the signals measured by a vector network analyzer (VNA) are usually single side-banded, complex-valued S-parameters in the frequency domain, whereas what we seek for, the location and dimension of a PWT, are real-valued quantities in spatial domain. In order to realize a more intuitive and direct detection and localization and avoid the intensive work of dispersion compensation, which is required in dealing with dispersive medium, we firstly transformed the S-parameters in the frequency domain into the wavenumber domain, and then to the spatial domain as a distance trace. A discontinuity along the pipe wall showed up as a distinct impulse in the distance trace, from which the location could be read out directly. Moreover, we applied gating to concerned responses and performed spectral analysis to reveal respectively spectral characteristics of flanged connection, pipe-end and PWT. We were thus able to classify PWT from other discontinuities. Spectral analysis of a PWT’s gated responses also revealed that the spectrum’s lob width is correlated to PWT length, whereas the lob’s peak value is a measure of PWT thickness. We also investigated the utmost length of a pipe that can be explored by means of the proposed technology. The experimental results showed the feasibility of the proposed method and procedures for microwave pipeline inspection and monitoring.

Integration of multiple wall thinning parameters into the probability of detection analysis for long-range pipe inspection using microwaves

A three-dimensional probability of detection (POD) model integrating the depth, circumferential angle, and location of pipe wall thinning (PWT) was proposed for the probabilistic analysis of long-range pipe inspection using microwaves. Microwaves in TM01 mode were used to detect PWT with different sizes and locations (up to 13.5 m). The microwave signals reflected from the PWT were measured and processed, and their amplitudes were extracted and treated as the signal response. The results showed that the signal response was rarely influenced by the axial length of the PWT, while its natural logarithm was proportional to the natural logarithm of the PWT depth and circumferential angle. In addition, the signal response decreased exponentially with the PWT location. Subsequently, a multivariate regression model was established, and the related coefficients were estimated using the maximum likelihood method. The POD calculated from the regression model suggests that a small PWT at a far distance (e.g., 36.0 m) can be reliably detected using microwaves. Compared with a two-dimensional POD model that considers only the PWT cross-section area as the size parameter, the proposed POD model clearly shows that the POD is more dependent on the circumferential angle than on the depth of the PWT.

Cover Picture: Materials and Corrosion. 5/2022

Cover:Pipe wall thinning has to be controlled to follow the degradation rate in aggressive environment. Corrosion monitoring is usually done by electrochemical technique in laboratories but is not always suitable on‐site. EMAR is a nondestructive and contactless technique to access the thickness of a material. Steel thickness reduction is measured by electrochemical, EMAR, and ex‐situ profilometry analysis. EMAR is a reliable technique to monitor thickness reduction of material.More detailed information can be found in:Gildas Diguet, Hina Miyauchi, Sho Takeda, Tetsuya Uchimoto, Nicolas Mary, Toshiyuki Takagi, Hiroshi Abe, EMAR monitoring system applied to the thickness reduction of carbon steel in a corrosive environment, Materials and Corrosion 2022, 73, 658.

Fitness-for-service analysis for corrosion resistant alloy clad pipeline damaged due to lodged intelligent pig

This paper investigates the possible failure modes of an incident of ultrasonic (UT) pig lodged in a Corrosion Resistant Alloy (CRA) clad pipeline during a baseline in-line inspection as part of pre-commissioning pipeline activities. The paper establishes the pipeline integrity following the incident and determines whether it remains fit for the intended service. The paper also discusses the suitability of using UT pigging for in-line re-inspection of the damaged pipeline, the effect of hoop stress due to pipe-wall thinning and the possibility of crack growth. Additionally, the possibility of undetected damage is considered alongside rigorous fatigue crack growth and fracture mechanics assessments of the pipeline to ascertain the pipeline integrity. It was concluded that the probability of the pipeline sustaining flaws exceeding a minimum depth of 1.8 mm is minimal. Due to the robust manufacturing process of the CRA clad pipeline, it was also concluded that the gouges left during the pigging incident do not affect the ability of the clad layer to provide corrosion protection.

Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT

ABSTRACT This study investigated the applicability of microwave non-destructive testing, which has been proved effective in quickly detecting the defect location in a long pipe, to the size evaluation of wall thinning defects. Artificial wall thinning defects with different sizes (depths and lengths) and edge profiles were introduced to a flanged brass pipe with a total length of 15 m, and reflected microwave signals were measured in experiments. A signal processing method combining windowing and dispersion compensation was proposed to extract the defect-related reflection signals in the frequency domain. Resonant frequencies, at which the amplitude of extracted signals dropped significantly, decreased with the increase of either wall thinning depth or length. In addition, the results demonstrate that wall thinning location and pipe end conditions have little influence on resonant frequencies after signal processing. A back propagation neural network was trained by simulation data, using resonant frequencies as the input, to simultaneously evaluate defect depth and length, and the performance was validated by experiments. Maximum prediction errors of depth and length of wall thinning were 0.06 mm and 0.57 mm, respectively, which indicated the feasibility of the proposed method to evaluate the wall thinning defect sizes.

Characterization of swirling-flow behavior in complex pipeline using bubble trajectory method with stereo particle tracking/image velocimetry

To understand the swirling-flow behavior in a complex pipeline geometry comprising an elbow and orifice, a quantitative flow visualization technique is introduced into the flow-field observation using a bubble trajectory method combined with stereo particle tracking/image velocimetry. For the chosen bubble sizes of approximately 2 mm un a pipe of 56 mm in diameter, their motion is correlated with the mean swirling flow, and when analyzed in a straight swirling flow, the bubbles cluster close to the point of maximum axial velocity for swirl intensities larger than 0.3. Subsequently, this experimental technique is applied to the characterization of a swirling flow in the Mihama pipeline model, for which the streamwise variation of the location of the maximum axial velocity and its magnitude are measured over the full range of the pipeline model. A spiral swirling flow is observed downstream of the elbow owing to the combined effect of the swirling and secondary flows in the elbow. The spiral swirling flow persists in the following straight pipe down to the orifice, where a highly accelerated flow with a high turbulence is generated towards the top wall of the pipe. This result agrees closely with the observation of pipe-wall thinning in the prototype Mihama pipeline.

Estimation of Pipe Wall Thinning Using a Convolutional Neural Network for Regression

A pipe wall thinning diagnosis method based on vibration characteristics is proposed. Elbow specimens with artificial pipe wall thinning were fabricated and combined in a loop. By running a pump in the loop, vibration was induced by flow, and the vibrational signals were measured with accelerometers. The effect of pipe wall thinning on the vibrational signals was investigated by analyzing the spectral data of the acceleration signals. The analyzed vibration characteristics were difficult to observe because the change in characteristics was small. A convolutional neural network (CNN) specialized for data recognition was applied to recognize the small change in vibrational signal resulting from the pipe wall thinning. A regression model based on CNN was chosen to learn the tendency of change in the vibrational signals with varying thinning. The data types advantageous for training the regression model were identified. An early stopping technique using the validation data set was adopted to regularize the regression model. The trained regression model was able to predict pipe thinning.