Prediction Of Corrosion Rate Research Articles

Study of flow structure in erosion prone complex geometries

Erosion is a challenge in a wide range of industries where fluid is transferred through pipes, valves and other mechanical arrangements. Wear can occur due to a variety of mechanisms but is often related to the presence of droplets or solid particles in the fluid stream. This article presents a study of flow relevant to solid particle erosion in a converging-diverging axisymmetric geometry. The purpose of the study was firstly to investigate the flow characteristics as a function of changes in geometry and compare with empirical data. Secondly, steady state, transient simulations and geometrical simplifications were compared to investigate methods of reducing computational time. Thirdly, the flow structure from each simulation was compared to identify any discrepancy in the simulated velocities in the near wall areas, where erosion is observed, in the diverging section of the geometry. The accuracy of simplified models compared to experimental data were found to be satisfactory with respect to global parameters such as pressure drop and flow coefficient. However, the comparison of steady state, transient, axisymmetric sectioning and full models identified significant discrepancy in local velocities in erosion prone areas, which would affects any erosion rate prediction significantly.

Prediction and Optimization of Erosion Rate of Carbon Fiber–Reinforced Ebonite Using Fuzzy Logic

Abstract In this study, a fuzzy artificial intelligence approach is utilized to predict the erosion rate of reinforced ebonite composite materials with carbon fiber owing to the significant accuracy of soft computing techniques. Experimental data were used to predict the erosion rate with respect to the input testing conditions, namely, impact velocity, impingement angle, erodent size, and stand-off distance. The size of the erosive element of randomly shaped sand particles (silicon dioxide) is set between 300 and 600 μm. Other input process parameters, such as the impact velocity between 30 and 50 m/s, the impingement angle between 30° and 90°, and the stand-off distance between 15 and 25 mm, are selected. The consistency between the experimental and fuzzy logic model values, with a 94.45 % accuracy, signifies that the proposed fuzzy logic model is suitable for predicting the erosion rate of reinforced ebonite composite materials. The maximum erosion rate is obtained between a 50° and 60° impingement angle. Morphological images are analyzed by scanning electron microscopy to elucidate the erosion mechanisms.

Nearfield erosion at the steart marshes (UK) managed realignment scheme following opening

The ingress and egress of water from managed realignment sites in macrotidal estuaries commonly causes erosion at the breaches and the formation of channels across the fronting intertidal areas. However, the rate of development of these features and their final dimensions have not been well documented. This case study paper details the development of the breach area and the erosional processes in the main and exit channels associated with the Steart Marshes managed realignment site in south west England. The paper shows type of data, along with its accuracy, which is currently available for this type of study. It uses a combination of UAV and LiDAR data that was collected as part of a monitoring campaign to record the development of scheme over a period of 3 years and 7 months following site opening. This data would normally reside within consultancy reports and thus be unavailable to the wider scientific community. The paper demonstrates that further work is needed to improve the prediction of erosion rates associated with managed realignment schemes.The monitoring data at Steart Marshes show the development of a large exit channel along the line of the former channel that led across the existing intertidal mudflats. The highest rates of erosion occurred in the first 3 months following opening. After this time rates declined markedly and, approximately 7 months after opening, the breach and exit channel region began to show periods of accretion as well as erosion. The non-linearity in erosion rates is similar to previously reported sites, but the rates of development were more rapid due to higher flow speeds generated by large tidal prism and the single breach design.Although the volume of the exit channel region appears to have stabilised, a secondary channel is starting to form, which suggests that further changes are likely. Within the site, further recession of the step feature is expected before a stable condition is reached. The paper documents these changes in more detail and draws out those behaviours that are likely to apply to other schemes.

Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach

Wellbore integrity management for oil and gas wells plays a vital role throughout the typical lifespan of a well. Downhole casing leaks in oil- and gas-producing wells significantly affect their shallow water horizon, the environment, and fresh water resources. Additionally, downhole casing leaks may cause seepage of toxic gases to fresh water zones and the surface, through the casing annuli. Forecasting of such leaks and proactive measures of prevention will help eliminate their consequences and, in turn, better protect the environment. The objective of this study is to formulate an effective, robust, and accurate model for predicting the corrosion rate of metal casing string using artificial intelligence (AI) techniques. The input parameters used to train AI models include casing leaks, the percentage of metal loss, casing age, and average remaining barrier ratio (ARBR). The target parameter is the corrosion rate of the metal casing string. The dataset from which the AI models were trained was comprised of 250 data points collected from 218 wells in a giant carbonate reservoir that covered a wide range of practically reasonable values. Two AI tools were used: artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs). A prediction comparison was made between these two tools. Based on the minimum average absolute percentage error (AAPE) and the highest coefficient of determination (R2) between the measured and predicted corrosion rate values, the ANN model proposed here was determined to be best for predicting the corrosion rate. An ANN-based empirical model is also presented in this study. The proposed model is based on the associated weights and biases. After evaluating the new ANN equation using an unseen validation dataset, it was concluded that the ANN equation was able to make predictions with a significantly lower AAPE and higher R2. Use of the proposed new equation is very cost-effective in terms of reducing the number of sequential surveys and experiments conducted. The proposed equation can be utilized without an AI engine. The developed model and empirical correlation are very promising and can serve as a handy tool for corrosion engineers seeking to determine the corrosion rate without training an AI model.

Modeling Reforestation’s Role in Climate-Proofing Watersheds from Flooding and Soil Erosion

The mitigation potential of reforestation for offsetting the deleterious effects of increased flooding and soil erosion projected to occur in Atlantic Canada through future climate change was investigated. Modelling determined a strong but non-linear relationship between extent of vegetative cover and runoff volume and discharge rate for a Nova Scotian watershed, suggesting that reforestation will reduce, but not completely prevent, flooding. Predicted erosion rates were found to be progressively reduced in relation to the extent of upland reforestation. Of three scenarios examined in which 60%, 65%, and 85% of the entire watershed are randomly reforested, only the latter would reduce the elevated erosion expected to occur through climate change back to present-day existing levels. Additional modelling revealed that comparable mitigation of soil erosion can ensue through implementation of 70 m streamside buffer strips, which would only take up 19% of the total surface area. Prioritizing riparian zones for reforestation will therefore subsume less of the overall productive land area and therefore enact a less severe socio-economic impact on agriculture and forestry.

Modeling and Simulation of the Effect of Cathode Gas Flow on the Lifetime and Performance of an Annular-Geometry Ion Engine

The past measurements of the plasma density and potential profiles near the exit of the keeper electrode in a hollow cathode device suggested that turbulent ion acoustic fluctuations and ionization instability in the cathode plume significantly increased the energy of the ions that flow from this region. The lifetime of keeper electrode is limited by sputtering or ion bombardment of the Molybdenum surface exposed to the discharge plasma. Increases in the cathode gas flow reduce the amplitude of the fluctuations and the number and energy of the energetic ions, which decreases the erosion rate of the keeper electrodes. However, as the cathode gas flow is raised for a given discharge current, the performance of a 5-kW Annular-Geometry Ion Engine (AGI-Engine) declines. There is a strong relationship between the performance and the lifetime of the ion thruster. To validate whether the 20-A hollow cathode satisfied China's communication satellite platform's application requirement for North-South station keeping, this paper analyzed the effect of the cathode gas flow on the performance and the lifetime of the AGI-Engine. Different from the previous methods, this paper first tracked the movement of energetic ions generated in the plume of the hollow cathode, predicted erosion rates, found where they hit the keeper electrode, and then determined the amount of material that they sputtered. A review of past experimental results was presented first. Next, based on the existing experimental data, theoretical analysis and numerical calculations were performed to determine the optimum gas flow range and the performance curve of the 20-A hollow cathode. The results showed that the primary erosion mechanism of the keeper electrode was caused by impact from Charge Exchange Xenon (CEX) ions, which caused the cathode orifice to be widened and attenuated over time. However, heavy double xenon (X e ++ ) ions, which struck the keeper electrode more severely than CEX ions, were the most crucial factor that limited the lifetime of the 20-A hollow cathode due to their high energy and large mass.

Decision making through a Bayesian network for a pipeline in design

Decision making for investing in a new pipeline project can be a long and costly process. This is usually due to the uncertainty and missing information regarding the interactions of parameters (e.g. brine chemistry, flow conditions or scale deposition) during internal corrosion assessment. In addition, these interactions result in multiple forms of internal corrosion threats (i.e. uniform corrosion, localised corrosion, erosion-corrosion and microbiologically influenced corrosion). Currently, there are no corrosion models in the market that consider all the different corrosion threats, and the predicted corrosion rates are normally conservative, leading to high overall project cost from the usage of higher grade construction material or strict maintenance regime. To predict a much more accurate internal corrosion rate with consideration of all possible corrosion mechanisms in a pipeline, a Bayesian network (BN) model was created that identifies and quantifies the causal relationships between parameters influencing internal corrosion. The model had previously proven its accuracy in predicting the internal condition of operational pipelines where explicit knowledge is available. However, the model has never been applied for a pipeline in design stage, where the design is based on tacit knowledge. In this study, to evaluate the applicability of this BN model on the pipeline at design stage, an offshore pipeline was assessed for internal corrosion.

Analyses Corrosion Prediction Software for CO2 Corrosion of Carbon Steel Using Statistical Formulas

The statistical formulas are capable tools to find a regression of corrosion rate effectively among combining factors. One type of statistical model which is response surface methodology (RSM) has shown a proven method in minimizing number of running. Through this technique, this research study predicting corrosion rate of carbon steel as effects of pH, CO 2 pressure and temperature. It can be used to run 3 dependent factors, 3 level experiment with only 16 number of running. The result reveals that NORSOK corrosion prediction software with second order model regression has 98 % of coefficient determination. Model prediction of Cassandra has 99.3% of coefficient determination. Second order model also has been verified with experimental data which shows a good correlation

Improvement of radial basis function neural network with accelerated particle swarm optimization for corrosion rate prediction of 3C steel in seawater environment

Improvement of radial basis function neural network with accelerated particle swarm optimization for corrosion rate prediction of 3C steel in seawater environment

Computational Fluid Dynamics (CFD) Simulation of Sand Particle Erosion in Turbulent Gas Fluid Flow in Vertical-Horizontal Elbow

Erosion of sand particles is one of the serious problems in the oil and gas production industry. There is strong evidence that most wells now produce increased sand as the major oil and gas production declines. This is a widespread problem as it can affect the rate of production as we age the asset. Accurate erosion prediction, especially elbow, can be used to improve pipe work design, inspection area, operating limit and others. In this study, we used CFD modeling in ANSYS Student Version 19.0 application to predict erosion in turbulent gas flow with the variation of sand particle size and fluid velocity. The results obtained were erosion profile and erosion rate in pipe elbow compared with the experimental results. Simulation results show that CFD able to predict erosion rates well for 300 μm particle size with error 14.75% compare 150 μm particle size with error 271.5%. Formed erosion profile in the form of 'V' scar on the vertical - horizontal pipe elbow.

Numerical Predictions of Uniform CO2 Corrosion in Complex Fluid Domains Using Low Reynolds Number Models

To get the knowledge of local corrosion, thinning is useful for developing targeted inspection plans for pipe components in the oil/gas industry. Aiming at this object, this work presents a computer fluid dynamics (CFD) method to predict CO2 aqueous corrosion in complex fluid domains. The processes involved in CO2 aqueous corrosion, including flow dynamics, mass transfer, chemical reactions, and electrochemical reactions, are modeled and simulated by a commercial CFD software of Fluent V15.0 (Version, manufacturer, city, country). Mass transfer in the straight pipe flow and jet impinging flow are simulated using three low-Reynolds-number turbulent models (Abe–Kondoh–Nagano k − ε model, Change–Hsieh–Chenk k − ε model, and k − ε shear stress transport model). The flow domains are meshed by grids with the first near-wall node at the position at y+ = 0.1. Comparisons between simulations and experimental data show the Abe–Kondoh–Nagano model provides the best predictions of near-wall flow and mass transfer. Thus, it is used to predict CO2 aqueous corrosion. Corrosion rates of dissolved CO2 in straight pipes and a jet impinging are predicted. The predicted corrosion rates are compared with experimental data and results derived from commercial software, Multicorp V5.2.105. The results show that predicted corrosion rates are reasonable. The locations of the highest corrosion rate for a jet impinging system are revealed.

Prediction of the Maximum Erosion Rate of Gas–Solid Two-Phase Flow Pipelines

Erosion is one of the important reasons for the thickness decrease and perforation of the pipe walls. Understanding the gas–solid two-phase flow pipe erosion mechanism is the basis for monitoring pipe erosion. According to the structural characteristics and working conditions of the gas–solid two-phase flow pipeline in a gas transmission station, a gas–solid two-phase flow pipe erosion finite element model was established and validated by combining it with field test data. Then, the gas–solid two-phase flow pipeline erosion characteristics under different pressures, solid contents, throttle valve openings, and pipe diameters were studied. On this basis, a maximum erosion rate prediction equation was put forward after verification by using actual wall thickness detection data. Results show the following: (1) The absolute error of the maximum erosion rate between the model results and the test datum is ≤10.75%. (2) The outer cambered surface of the bend after the throttle valve is the most seriously eroded areas. (3) The maximum erosion rate increases with pressure, solid content and throttle valve opening increasing, but, along with the change of the pipe diameter, the maximum erosion rate increases at first and then decreases with pipe diameter increasing for throttle valve openings of 20% and 30%, and it decreases with pipe diameter increasing for a throttle valve opening of 50%. (4) A maximum erosion rate prediction equation, involving pressure, solid content, opening of the throttle valve, and pipe diameter, is proposed and is verified that the absolute percentage error between the prediction equation calculation results and the field test datum is ≤11.11%. It would seem that this maximum erosion rate prediction equation effectively improves the accuracy of predicting the gas–solid two-phase flow pipe erosion rate in a gas transmission station.

Numerical Simulation of the Blade Aging Process in an Induced Draft Fan Due to Long Time Exposition to Fly Ash Particles

Erosion issues usually affect fans used for the extraction of exhaust gas in power plants. Because of the presence of fly ash within the exhaust flow, fan blades are subjected to material wear at the leading edge, trailing edge, and blade surface, and this may cause a modification of the blade aerodynamic profile, a reduction of blade chord and effective camber. All these effects result in a deterioration of the aerodynamic performance of the blade. Prediction of erosion process in industrial applications helps to better schedule the maintenance and predict the blade life. However, since usually numerical simulations of erosion process do not account for the change in target geometry, and then the variation in time of the erosion process itself, they can be only used to study a very short part (namely the beginning) of the whole process. To this aim, we report a numerical simulation of the blade aging process due to particle erosion in an induced draft fan. This is done using in-house numerical tools able to iteratively simulate the flow field, compute the particle tracking/dispersion/erosion, and modify the geometry (and mesh) according to the predicted erosion rate. First, we study the effect of the geometry damage due to erosion, for a generic particle flow and a given expected maximum damage. In the second part of the computation, a scale factor is introduced to align the simulation time and particle concentrations to a real application, comparing the results with the on-field observation.

Simulation of corrosion of a dual phase alloy steel based on its constituent phase polarization properties

In this study, the corrosion of dual phase alloy steel namely super duplex stainless steel (SDSS) is simulated by taking into consideration the presence of two distinct phases that are austenite and ferrite. A simulation algorithm is developed by the authors for simulating micro galvanic corrosion by random assignment of anode and cathode zones assuming non-linear polarization curves. The governing equation for electric potential in the electrolyte is Laplace equation owing to charge neutrality implying diffusion limited corrosion rate. The metal-electrolyte interface is allowed to dissolve as a moving boundary problem approach in a computer simulation by assigning ferrite property to anode and austenite property to cathode randomly. To validate this proposition, the true non-linear polarization data and its fluctuations in the form of fitted Butler–Volmer expressions of individual phases of the SDSS alloy are used as the boundary condition on anode and cathode zones corresponding to ferrite and austenite phases. This modeling approach is applied to pure austenite as SS304 and pure ferrite as SS430 and 50–50 austenite-ferrite as SDSS. The obtained simulation results of corrosion rate of the SDSS and its constituent pure phases compared well with the direct polarization prediction of corrosion rate for various concentrations of NaCl in water. The simulation prediction is found to be a good fit than by weighted average method.

Random forest regression prediction of solid particle Erosion in elbows

Solid particle erosion is an inevitable problem in oil and gas production and transportation systems, and it can cause severe damage in pipe fittings in which the flow direction changes suddenly. Erosion occurs in a variety of fittings, and one common fitting which is one of the most vulnerable to erosion is the elbow. Repair and/or replacement of pipe fittings in the appropriate period can avoid catastrophic disasters such as hydrocarbon release events. Therefore, accurate prediction of erosion is a subject of interest for the oil and gas industry. Both experimental and modeling approaches have been conducted in the past under different flow conditions and materials. The goal of this study is to use relevant experimental parameters as predictor variables and utilize a machine learning algorithm to predict erosion rate and validate the prediction under different conditions with test data. It is assumed that important parameters in erosion magnitude are material characteristics, pipe diameter, particles properties and size and carrier fluid properties. 201 experimental data points with a broad range of conditions were selected for the analysis. Predicted erosion rates through different machine learning techniques were compared, and Random Forest regression model was selected as an effective and alternative approach for erosion prediction. This method was examined on data points and compared with experimental results. Good agreement was observed in this method with experiments which provides the framework for a new way to predict solid particle erosion which is simpler yet accurate in comparison with available prediction models.

Neural network modelling of high pressure CO2 corrosion in pipeline steels

Abstract The effect of carbon dioxide (CO2) corrosion on pipelines is of great relevance to the petroleum as well as the Carbon Capture and Storage (CCS) industries. CO2 corrosion is responsible for lost production as it brings about the gradual degradation of pipe internals with time. The cost of general corrosion is said to be between 3 to 5% of an industrialised nation’s gross domestic product ( Schmitt et al., 2009 ; Popoola et al., 2013 ). In the U.S., the cost of corrosion in the production and manufacturing sector was $34.4 billion in 2014, with the oil and gas industry accounting for more than half ( Abbas, 2016 ). The use of neural networks (NN) as an analytic tool for corrosion data has been established however the aim of this paper is to characterise selected Matlab transfer and training functions, and assess their degree of suitability for CO2 corrosion rate prediction. Assessments of the training functions include the evaluation of the correlation coefficient (R2-value) and determination of a cumulative absolute error to indicate the level of precision and the extent of model accuracy. A NN model is developed for predicting CO2 corrosion at high partial pressures by considering the results of the various tests and analyses on the given Matlab functions. The results showed that the model is reliable with all test results falling within the 95% confidence limits. Leave-One-Out Cross-Validation (LOOCV) was implemented as a means for carrying out an additional assessment on model performance as well as for model selection from possible alternatives.

Prediction of Corrosion Rate of Q235 Steel under the Marine Environment

A generalized regression neural network is used to predict the corrosion rate of metals in marine environment. The environmental temperature, oxygen content, pH value, salinity and potential are taken as input, and the corrosion rate is taken as output. A3 steel was selected to test the 25 sets of data, 18 groups of data were selected for training, and 7 sets of data were used as the verification. The results show that the generalized regression neural network prediction, select the default S value, the average prediction error is 5.72%, higher than the BP neural network was used to predict the 6.56%, using cross validation method to select the optimal S value, the optimal value of S under the average prediction error of the forecast is 2.38%. It shows that the prediction of corrosion rate of metal materials in marine environment by generalized regression neural network is feasible in technology, and has high prediction accuracy and application value.

Viewpoint - Understanding Mg corrosion in the body for biodegradable medical implants

Accurate prediction of Mg corrosion rates within the body (in vivo) based on laboratory tests (in vitro) is a key challenge for Mg alloys in medical applications. Estimates based on in vitro measurements overestimate the measured Mg corrosion rate in vivo. This indicates that the medical environment and the biocorrosion mechanism is not fully characterised. We summarise (i) the relevant Mg metallurgy, and (ii) the solution factors that influence Mg corrosion in vitro. We analyse the reasons for the disparity between the corrosion rates in vivo and the expected corrosion rates based on in vitro testing, and suggest promising areas of research.

Test Conditions for Pipeline Materials Selection with High Pressure Sour Gas

Acid gases, such as CO2, H2S, and/or sulfur in oil industry’s production fluids, can be responsible for both general and localized corrosion, acting with different mechanisms, which depend on chemical and physical properties of the produced fluids. Materials selection for handling such fluids is performed by combining experience with suggestions from standards and regulations. A good deal of knowledge is available to predict corrosion rates for CO2-containing hydrocarbons, but the effect of high H2S pressure is less understood, mainly due to the difficulty of performing laboratory tests in such challenging conditions. For instance, the so-called NACE solution to assess SSC (Sulfide Stress Cracking) susceptibility of steels is a water-based solution simulating production fluids in equilibrium with one bar bubbling H2S gas. This solution does not represent environments where high gas pressure is present. Moreover, it does not take into account the corrosive properties of sulfur and its compounds that may deposit in such conditions. Besides, properties of high pressure gases are intermediate between those of a gas and those of a liquid: high pressure gases have superior wetting properties and better penetration in small pores, with respect to liquids. These features could enhance and accelerate damage, and nowadays such conditions are likely to be present in many production fields. This paper is aimed to point out a few challenges in dealing with high pressure gases and to suggest that, for materials selection in sour service, a better correspondence of test conditions with the actual field conditions shall be pursued.

Uncertainty quantification in erosion predictions using data mining methods

The transport of solids in multiphase flows is common practice in energy industries due to the unavoidable extraction of solids from oil and gas bearing reservoirs. The persistent collision of solids to the pipeline can lead to erosion, i.e., the removal of internal surface of the pipeline. Reliable estimates of erosion rates are essential for designing and safely operating pipelines that transport solids. Prediction of erosion rates in multiphase flow is a complex problem due to the lack of accurate models for predicting particle movements in the flow and their impact velocities to the wall. The erosion-rate calculations also depend on the accuracy of the flow regime predictions in the pipeline. The comparisons of existing model predictions to experimental data revealed that the predictions might differ by several orders of magnitude for some operating conditions. The goal of this paper is to introduce a computational framework that estimates the model-prediction uncertainty of erosion-rate models. The inputs are a model predicting erosion rates and a database containing erosion-rate measurements at various operating conditions. The framework utilizes a non-parametric regression analysis, Gaussian Process Modeling (GPM), for estimating the model-prediction uncertainty. We compare two approaches for clustering the data prior to training GPMs: (1) a flow regime based clustering, and (2) a new clustering approach introduced in this paper. The results reveal that the new data clustering approach significantly shrinks the confidence intervals of the uncertainty estimates.