Prediction Of Corrosion Rate Research Articles

Spatial Assessment of the Effects of Land Cover Change on Soil Erosion in Hungary from 1990 to 2018

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care.

Determination of the Corrosion Rate of Steel Bars in Concrete Based on the Porosity of Interfacial Zone

In the present paper, a simple model to predict the corrosion rate of steel bars in concrete is developed, considering the influence of the porosity of steel-concrete interfacial zone. Firstly, a model for the porosity of the interfacial zone is developed basing on the water-cement ratio gradient in the interfacial zone. Then the relationship between the porosity of the interfacial zone and the corrosion parameters of the steel bar in concrete is discussed, the anode-cathode ratio for the electrochemical corrosion process, the chloride threshold value and the resistivity of concrete are determined by the porosity of steel-concrete interfacial zone. The corrosion rate of steel bars, which is taken as the corrosion current density, is calculated by considering the cathodic concentration polarization of oxygen diffusion and resistance polarization of concrete, basing on the determined parameters above. Finally, the applicability and efficiency of the corrosion rate prediction model is verified by experiments.

Numerical and experimental investigation of the erosion of zirconia particulate‐reinforced epoxy matrix composites by angular silicon carbide particles

AbstractAn smooth particle hydrodynamics/finite element (SPH/FEM) model was developed to simulate the erosion of zirconia particle‐reinforced epoxy composites, due to the overlapping impacts of angular particles at both perpendicular and oblique incidences. For each composite target, multiple sub‐volumes, each having their own random reinforcement distribution, were modeled. The reinforcements were assumed as noneroding and nondeforming and were distributed in the modeled sub‐volumes according to the random distributions measured in the actual samples. Erosion tests were performed to verify the numerical model, and it was found that the erosion rates of the zirconia‐reinforced composites were in all cases significantly lower than the neat epoxy. The experimentally‐observed erosion mechanisms were successfully simulated by the model, and the predicted erosion rates matched the measured ones to within 2% to 11%. It is anticipated that similar models can be used to predict erosion of other particulate composite systems whose reinforcement erosion and fracture is negligible compared to the matrix erosion.

Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

Improvements of turbulence model for analysis of hydrogen stratification erosion by turbulent buoyant jet

Light gas stratification erosion by steam turbulent buoyant jet is the typical phenomenon about containment thermal hydraulics and hydrogen safety. Overprediction of stratification erosion rate has been observed in computational fluid dynamics (CFD) analysis by using current turbulence model based on simple gradient diffusion hypothesis (SGDH). To solve this problem, the effects of buoyancy term, coefficient and turbulent Schmidt number are discussed and a modified k-ε model with buoyancy term based on generalized gradient diffusion hypothesis (GGDH) which contains vertical and horizontal density gradient is developed. In modified k-ε model, besides the value of coefficient, the buoyancy term based on SGDH is modified into GGDH. The strain tensor is modified into rotating tensor to suppress the overestimated local turbulent kinetic energy generated in flow stagnation zone. Comparison between analysis results and experimental data shows that improvement effect of modified model on prediction of light gas stratification erosion rate is significant.

The empirical prediction of weight change and corrosion rate of low-carbon steel

Understanding the corrosion rate of metallic building materials is very important to maximize their beneficial use of public facilities. Direct measurements of the weight change and corrosion rate would be time consuming and expensive. This study aims to develop new empirical models based on the experimental data of testing 25 specimens immersed in five different environments for predicting the weight change and corrosion rate of the low-carbon steel. Using the equation developed based on the correlation between corrosion rate and chloride ion concentration is able to predict the corrosion rate of low-carbon steel at the limited chloride ion concentration. An increase in the trend lines of plotting the modeled and measured weight change of low-carbon steel versus immersion time is very similar to each other and progressively increase with increasing of the NaCl concentration. The corrosion rate of low-carbon steel increases from 0.202 to 0.286 mm/y with increasing of the NaCl concentration from 0 to 5% (w/w) in aqueous solution. The weight change and corrosion rate of the steel material are predicted using the new empirical models to contribute to the most reliable applications of low-carbon steel building materials in the future.

A numerical external pitting damage prediction method of buried pipelines

Abstract The work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

The Study of Sedimentation at Jongkong Reservoir in District Central of Bangka through Erosion of Catchment Area

Jongkong reservoir creates a reservoir of water that is used for the purposes of raw water as fundamental media for improvement of drinking water production especially at dry season for the District of Koba. The advantage of being able to hold water in wet season and releasing it in dry season will be detained if the life storage of the reservoir can be utilized effectively, but sedimentation problem will decrease its volume which can influence the life time of reservoir. Finally the reservoir becomes less effective, so it is very important to study. The method which is used to predict an average erosion rate in the catchment area considers factors of rain erosivity, soil erodibility, ground inclination of slope or slope length, management of crop and land, soil conservation. Calculation was done by using the USLE equation (Universal Soil Loss Equation) which was developed by Wischmeier and Smith (1965, 1978). From this research result, the prediction of surface erosion rate in the basin catchment area in the year 2019 is equal to 53.20 mm/year or equivalent with 62,491.92 m3/year, and the value of Sediment Delivery Ratio (SDR) is equal to 32,28 %. The number of material which eroded is equal to 957.59 Ton/Ha/year so it is classified into the medium erosion class category. The dead storage of 5% of the the volume of the reservoir cage will be fully charged in the year 2022 or for 3 The next year and the total volume of the reservoir Ridge will be full of sediment in the year 2062 or 43 years.

Study on Failure Behavior of Gas Well Tubing Under CO2 Corrosion After Erosion

The volcanic gas reservoir in Xushen gas field in the Northeast of China has the characteristic of low single-well production. Before gas wells are put into production, most of wells require fracturing transformation of the large sand amount and large displacement. Hence, an integrated fracturing and production string was developed. In the process of natural gas production, the integrated fracturing and production string is faced with the erosion caused by fracturing and CO2 corrosion. In order to ensure the safety of production, analysis and assessment of the dual effects of erosion and corrosion on the pipe string are required. The influence of the erosion angle, liquid velocity and sand ratio on the erosion rate of the integrated fracturing and production string was studied by using a self-made erosion experimental device and finally established the erosion rate prediction model; the samples of the tubing material after the erosion test were used in a high-temperature autoclave to carry out corrosion evaluation experiments to study the CO2 corrosion resistance of the tubing after erosion. The test results showed that severe corrosion occurred in the sample of P110 tubing after erosion, while slight corrosion occurred in the Super 13Cr tubing. On the basis of theoretical analysis results, the design and field construction of the integrated fracturing and production string are guided and ensured the safety of string in the whole life cycle.

A fully coupled model of hydrodynamic-chemical-electrochemical processes for CO2 uniform corrosion in multi-physics environment

Carbon dioxide uniform corrosion occurs widely in the petroleum industry and threatens the safe production and transportation of petroleum resources. It is a complex issue involving a variety of physical, chemical and electrochemical processes in multiple temporal and spatial scales. A fully coupled model, taking into account all involved processes in a coupled fashion, such as fluid flow, multi-ion transport, multi-component chemical/electrochemical reactions, is established to comprehensively investigate the mechanism of CO2 corrosion in multi-physics environment. The reliability of this model is validated by the good consistency between the predicted corrosion rates and the experimental results in the literature, and this model performs better in corrosion rate prediction than the previous models due to the explicit solution for the convective mass transfer. Based on this fully coupled model, the corrosion mechanism in the multi-physics coupled environments is revealed in terms of the time-dependent ion concentration field, mass transfer and electrode reaction kinetics. The interrelationship between the corrosion kinetics and the flow effect is explored. The results indicate that the time-dependent corrosion behavior is determined by the combined effect of the solution chemistry and electrical field. By accelerating the mass transport of corrosive species and the involved multi-component reactions, the fluid flow can significantly promote the corrosion process, while the promotion effect weakens gradually at higher flow velocities. Additionally, the flow effect at lower pH values is much more pronounced due to the transformation of the predominant controlling factors for electrode kinetics.

Geometric and Material Variability Influences Stress States Relevant to Coastal Permafrost Bluff Failure

Scientific knowledge and engineering tools for predicting coastal erosion are largely confined to temperate climate zones that are dominated by non-cohesive sediments. The pattern of erosion exhibited by the ice-bonded permafrost bluffs in Arctic Alaska, however, is not well explained by these tools. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is needed. We conducted physics-based numerical simulations of mechanical response that focus on the impact of geometric and material variability on permafrost bluff stress states for a coastal setting in Arctic Alaska that is prone to toppling mode block failure. Our three-dimensional geomechanical boundary-value problems output static realizations of compressive and tensile stresses. We use these results to quantify variability in the loci of potential instability. We observe that niche dimension affects the location and magnitude of the simulated maximum tensile stress more strongly than the bluff height, ice wedge polygon size, ice wedge geometry, bulk density, Young’s Modulus, and Poisson’s Ratio. Our simulations indicate that variations in niche dimension can produce radically different potential failure areas and that even relatively shallow vertical cracks can concentrate displacement within ice-bonded permafrost bluffs. These findings suggest that stability assessment approaches, for which the geometry of the failure plane is delineated a priori, may not be ideal for coastlines similar to our study area and could hamper predictions of erosion rates and nearshore sediment/biogeochemical loading.

Impact factor analysis, prediction, and mapping of soil corrosion of carbon steel across China based on MIV-BP artificial neural network and GIS

Q235 carbon steel is one of the most widely used carbon steels, and soil corrosion and failures of it caused accidents, casualties, and great financial losses. Corrosion of Q235 carbon steel differed in spatial because of spatial variation in soil environmental factors. However, the national scale spatial pattern of soil corrosion of Q235 carbon steel across China has not been explored. The values of 12 impact factors, corrosion rate, and pitting corrosion rate at 25 sites covered all soil types in China were collected. Mean impact value (MIV) algorithm and back propagation artificial neural network (BP ANN) were combined and applied in the impact factor analysis. Prediction models for corrosion rate and pitting corrosion were developed based on BP ANN. The proposed prediction models and information about soil properties with high resolution (1 km × 1 km) were used in the prediction of corrosion rate. Based on geographical information system (GIS), the national scale spatial pattern of soil corrosion of Q235 carbon steel across China were analyzed. The water content and pH were of the largest influence (|MIV| > 0.522) on both corrosion rate and pitting corrosion rate. For prediction models of corrosion rate and pitting corrosion rate, the predicted values were close to the measured values. Corrosion rates were of higher spatial differences, ranged from 0.632 to 5.181 g/(dm2·a). Pitting corrosion rates in the northeast were higher than other areas, which might be caused by higher values of total salt content, organic matter, and total voidage. Average corrosion rate in soil type 1 (1.161 mm/a) was nearly two times of that in soil type 2 (0.610 mm/a) and over three times of that in other types, indicating that corrosion rate varied largely among different soil types. The importance of 12 impact factors of corrosion of Q235 carbon steel were evaluated, and the national scale corrosion rate and pitting corrosion rate in China were predicted and mapped for the first time. Both pitting corrosion rates and corrosion rates were of strong spatial variation.

Numerical Simulation of Solid Particle Erosion of Epoxy by Overlapping Angular Particle Impacts

The solid particle erosion of polymers occurs in a wide variety of industries and has been extensively studied experimentally. However, numerical models capable of accurately simulating the associated material removal mechanisms and predicting erosion rate do not yet exist. In this paper, a coupled smoothed particle hydrodynamics (SPH)/finite element (FE) model was developed to simulate the erosion of an epoxy by successive overlapping impacts of angular 22 µm, 97 µm and 152 µm silicon carbide particles at various angles of attack. The epoxy was modeled using a strain-rate-dependent elastic–plastic material model that fails at a critical plastic strain. It was found that, once the critical plastic strain was calibrated using a single experiment, the numerical model could predict both the length of the incubation period and the steady-state erosion rate to within 6% and 11%, respectively, of the measured values. It was found that fundamental material removal mechanisms such as cutting, ploughing and the accumulation of plastic deformation due to multiple overlapping impacts were all successfully simulated. Overall, it has been demonstrated that numerical models can be used to investigate the effect of influential parameters on the solid particle erosion of a polymer. This may have important implications for the development of effective methods to improve the erosion resistance of polymers.

Empirical models of corrosion rate prediction of steel in reinforced concrete structures

Corrosion rate is one of the most important input parameters in corrosion-induced damage prediction models as well as in calculation of service-life for reinforced concrete structures. In most cases, instantaneous measurements or constant corrosion rate values used in damage prediction models is irrelevant. The new factors appearing such as corrosion-induced cover cracking, concrete quality to change the corrosion rate should be taken into consideration. This study shows several empirical models to predict the corrosion rate and their limits of application. The predicted values of steel corrosion rate using four empirical models are compared with the measured values of a series of 55 experimental samples collected from the literature. The results show that the empirical models overestimated the experimental corrosion rate. Using model proposed by Liu and Weyers provided the best agreement with the experimental data.
 Keywords:
 corrosion rate; prediction model; reinforced concrete; chloride ions; reinforcement corrosion.

Investigation of Corrosion Rate of Mild Steel in Fruit Juice Environment Using Factorial Experimental Design

Although little attention is paid to corrosion study in food processing and production industry, it is highly important to take a critical look at what transpires at the production stages and how these can affect human health due to contamination of the final product. This research employs the design of experiment module of a Minitab software to analyze the corrosion effect of mild steel in fruit juice environment. Coupon method was adopted to calculate the rate of corrosion in orange, pineapple, and cashew fluid for a duration of 25 days with measurement taken at 5-day intervals. The highest corrosion rate of 0.71 mmpy was observed in cashew fluid followed by pineapple fluid, and the least rate of 0.08 mmpy was observed in orange fluid. The generalized model equation was obtained to predict corrosion rate of mild steel in a similar environment. In general, design of experiment offers a better way to analyze corrosion experiment result, and this innovative idea is shown in this work.

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

ABSTRACT The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R2 values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.

A probability prediction model of erosion rate for Ti-6Al-4V on high-speed sand erosion

When a helicopter flies in sand and dust environment or encounters sandy weather, the sand entering the engine compressor can cause severe erosion damage to the blades. This paper aims to deduce an erosion rate prediction model for titanium alloy blade material which can take into account the probability factors such as particle size, velocity and sand fracture. Methods to judge whether sand fractured and quantify the effect of sand fragmentation on erosion damage were obtained. The probability relationship between particle size and mass ratio, impact kinetic energy and fracture rate, particle size range and impact velocity were studied. It is verified that the model can not only adapt to the high-speed erosion conditions, but also adapt to the probability characteristics of many impact factors. The angle of maximum erosion rate under relative high-speed erosion condition is significantly larger than that of under relatively low-speed erosion condition.

Computational fluid dynamic analysis for investigating the influence of pipe curvature on erosion rate prediction during crude oil production

The flow dynamics in pipes is a very complex system because it is largely affected by flow conditions. The transport of crude oil in pipelines within unconsolidated petroleum reservoirs is associated with presence of solid particles. These particles are often transported as dispersed phases during crude oil production and are therefore detrimental to the pipe surface integrity. This could lead to the occurrences of crevice corrosion due to pipe erosion. In relation to the above discussion, this paper is aimed at analyzing crude oil dynamics during flow through pipeline and identifying erosion hotspot for different pipe elbow curvatures. Reynolds Averaging Navier-Stokes (RANS) and Particle Tracing Modeling (PTM) approach were used. The focus is to simulate fluid dynamics and particle tracing, respectively. Post-processed results revealed that the fluid velocity magnitude was relatively high at the region with minimum curvature radius. The maximum static pressure and turbulence dissipation rate were experienced in areas with low-velocity magnitude. Also, the rate of erosive wear was relatively high at the elbow and the hotspot varied with pipe curvature. The particle flow rate, mass, and size were varied and it was found that erosion rate increased with an increase in particle properties.

Requirement and Calculation of Corrosion Allowance for Piping and Valves in the Oil and Gas Industry

“Acid Gases” refer to two of the undesirable by-products of oil and gas, namely CO2 and H2S$$.$$ The reaction of H2S and other sulphur compounds with water increases the degree of “sour corrosion” by forming sulphuric acid. CO2 corrosion of carbon and alloy steels is designated as “Sweet Corrosion” which is defined as the deterioration of metal components resulting from contact with gas or solutions including both CO2 and water. CO2 corrosion is an important problem in the oil and gas industry due to metal loss and its severe effects in terms of localized corrosion. Accurate prediction and modelling of CO2 corrosion rates for carbon and alloy steel pipes are vital tasks at the basic design phase of oil, gas and petrochemical projects in order to determine whether to consider additional wall thickness for the pipes and valves defined as “Corrosion Allowance (CA)” or to change the pipes’ or valves’ base materials to “Corrosion Resistance Alloys (CRA)”. Other corrosion mitigation approaches such as injecting the corrosion inhibitors and glycol or methanol can reduce the metal loss and CO2 corrosion. This paper proposes a practical model to calculate and select the corrosion allowance for piping and valves in oil and gas industry. Different process parameters such as scaling, fugacity, pH, glycol and corrosion inhibitor injection, water cut, operating pressure and temperature as well as CO2 partial pressure have been applied in the model.

A study to analysis of corrosion rate of uncoated and coated WC- Co- Cr naval brass materials

In this work, use of high velocity oxy-fuel (HVOF) spraying technique was explored as a way of producing coatings with a resistance to corrosion and erosion. Response surface methodology (RSM) was employed to develop the empirical relationships to predict corrosion rate of uncoated and coated WC-Co-Cr naval brass materials. Dissolution of naval brass and formation of naval hydroxide are the key factors for immersion corrosion. Atomic microscope analysis shows that the corroded surface of uncoated naval brass shows higher Ra value is owing to the surface damage. Coated naval brass show less undulations on the analyzed region.