Corrosion Behaviour of Cupronickel 90/10 Alloys in Arabian Sea Conditions and its Effect on Maintenance of Marine Structures

The composition of seawater plays a very significant role in determining the severity of corrosion process in marine assets. The influential contributors to the general and pitting corrosions in marine structures include temperature, dissolved oxygen (DO), salinity, PH, chlorides, pollutants, nutrients, and microbiological activities in seawater. The Cu-Ni (90/10) alloy is increasingly used in marine applications such as heat exchangers and marine pipelines because of its excellent corrosion resistant properties. Despite the significant advancements in corrosion shielding procedures, complete stoppage of corrosion induced metal loss, especially under rugged marine environments, is practically impossible. The selection of appropriate metal thickness is merely a multifaceted decision because of the high variability in operating conditions and associated corrosion rate in various seawater bodies across the globe. The present research study aims to analyze the early phase of corrosion behavior of Cu-Ni (90/10) alloy in open-sea conditions as well as in pollutant-rich coastal waters of the Arabian Sea. Test samples were placed under natural climatic conditions of selected sites, followed by the mass loss and corrosion rate evaluation. The corrosion rate in the pollutant-rich coastal waters was around five times higher than in the natural seawater. A case study on marine condenser (fitted with of Cu-Ni 90/10 alloy tubes) is presented, and a risk-based inspection (RBI) plan is developed to facilitate equipment designers, operators, and maintainers to consider the implications of warm and polluted seawater on equipment reliability, service life, and subsequent health inspection/ maintenance

Computational based automated pipeline corrosion data assessment

Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data. Unfortunately, available corrosion data are still under-utilized. Complicated assessment code, and manual analysis which is tedious and error prone has overburdened pipeline operators. Moreover, the current practices produce a negative corrosion growth data defying the nature of corrosion progress, and consuming a lot of computational time during the reliability assessment. Therefore, this research proposes a computational based automated pipeline corrosion data assessment that provides complete assessment in terms of statistical and computational. The purpose is to improve the quality of corrosion data as well as performance of reliability simulation. To accomplish this, .Net framework and Hypertext Preprocessor (PHP) language is used for an automated matching procedure. The alleviation of deterministic value in corrosion data is gained by using statistical analysis. The corrosion growth rate prediction and comparison is utilized using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) model. Artificial Chemical Reaction Optimization Algorithm (ACROA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) model is used to improve the reliability simulation based on the matched and predicted corrosion data. A computational based automated pipeline corrosion data assessment is successfully experimented using multiple In-Line Inspection (ILI) data from the same pipeline structure. The corrosion data sampling produced by the automated matching is consistent compared to manual sampling with the advantage of timeliness and elimination of tedious process. The computational corrosion growth prediction manages to reduce uncertainties and negative rate in corrosion data with SVM prediction is superior compared to A ^N . The performance value of reliability simulation by ACROA outperformed the PSO and DE models which show an applicability of computational optimization models in pipeline reliability assessment. Contributions from this research are a step forward in the realization of computational structural reliability assessment

Prediction and assessment of corrosion-fatigue in offshore wind turbines

Offshore wind energy has seen substantial growth globally as part of a shift towards net zero. Offshore wind turbines with predominant fixed-bottom monopile support structures are near their end of service life, necessitating assessments of their remaining life. The sector confronts fatigue challenges, environmental concerns such as corrosion from harsh conditions as they expand into deep seas, damage assessment challenges, and design optimisation challenges. This study aimed to develop a novel corrosion-fatigue damage theory for predicting corrosion-fatigue damage and remaining life in monopile-supported horizontal-axis offshore wind turbines. The research methodology involved experimental investigations, analytical estimations, and computational modelling. The experimental work involved the fabrication of structural steel plates, mechanical tests, fatigue tests, and corrosion characterisation analyses. Analytical methods were applied using the beam theory, linear wave theory, and blade element momentum approach. Computational modelling involved applying finite element analysis in stress analysis for uniform corrosion and soil-structure interaction and assessing residual stress. The results showed that the splash zone may accumulate the most damage over time owing to monopile thickness reduction and local pits. The fatigue life of materials appeared less influenced by thickness effects and more by a combination of stress concentration, residual stress, axial misalignment, and angular distortion. An S-N curve for corrosion-fatigue assessment applying a corrosion-based fatigue prediction model was developed. These results were integrated into a corrosion-fatigue damage theory applied to operational loads for predicting the remaining life of offshore wind turbines. In conclusion, the developed theory showed great promise for assisting engineers and stakeholders with vital information for corrosion-fatigue assessment of offshore wind turbines necessary for effective maintenance, decommissioning, and life extension

Investigation of corrosion of carbon steel under insulation

Corrosion of metals under insulation is a serious concern for industries due to the fact that the insulation hides the metal from view which increases the likelihood of sudden failure. Carbon steel is one of the metal alloys frequently used in industries due to economic and technical reasons. However, it is quite susceptible to corrosion under insulation (CUI). The factors affecting corrosion of carbon steel under mineral wool insulation such as temperature, effectiveness of inhibitor, quantity and distribution of electrolyte in the insulation have not been extensively studied in the literature. In fact, studies on corrosion of metals under insulation are quite sparse compared to immersion (uninsulated) conditions. Therefore, the objectives of this study were to assess the effect of temperature (60 oC to 130 oC) on corrosion of carbon steel under insulation, effectiveness of a new commercial inhibitor (VpCI 619) in mitigating CUI of carbon steel, quantity and distribution of electrolyte (1wt. % NaCl) in mineral wool insulation as well as investigation of the drying times of the insulation using galvanic current and electrochemical impedance measurements. In addition, the prediction of CUI rate using Artificial Neural Network (ANN) was carried out with the aim of assessing the accuracy of prediction of different network parameters such as number of hidden layers, number of input parameters and choice of activation function. Prior to CUI studies, the water absorption capacity of mineral wool insulation was determined using standard procedures (ASTM C1511). This was carried out to assess the time it will take for the insulation to be saturated with water, the variability of repeated measurements as well as the total water content in the insulation. The CUI studies were carried out using a test rig that was based on ASTM G189-07 standard. The corrosion rates were estimated using weight loss technique and the effects of temperature, vapour phase inhibitor consisting primarily of sodium molybdate, quantity of electrolyte in insulation were investigated. The drying out profile of the insulation was assessed using galvanic current and electrochemical impedance measurements. Furthermore, the prediction of CUI rate was carried out using Artificial Neural Network and the effect of single and double hidden layers, sigmoid and hyperbolic tangent activation functions, as well as number of input parameters on accuracy of prediction of CUI rate were assessed. The results of the water absorption studies indicated continuous absorption of water even after immersion for 22 days. The water absorption capacity was greater for thermally treated insulation compared to untreated insulation samples due to thermal degradation of the oily additives and polymeric binders. The effect of temperature on CUI indicated an increase in corrosion rate from 60 oC to 80 oC. Further increase in temperature up to 130 oC resulted in a decrease in corrosion rate. The existence of a maximum point in the curve was attributed to the competing effects of two factors which include increased diffusivity of oxygen which dominates at low temperature and decreasing solubility of oxygen and insulation dry-out which dominates at temperatures exceeding 80 oC. The new commercial inhibitor was observed to mitigate the corrosion rate at the temperatures investigated in this study. The inhibition efficiency indicated an average of 89% when a dosage of 5.2 g/m2 of the inhibitor was used. The effectiveness was also observed to be dosage dependent with lower doses having less inhibition efficiency. The drying times of the insulation assessed using galvanic current and impedance methods were observed to decrease as temperature increased. The galvanic current was observed to decrease to zero while the impedance increased to high values as the insulation dries out. However, the drying times obtained from galvanic current method showed a higher variability compared to impedance method. The result of prediction of CUI rate using Artificial Neural Network indicated an increase in accuracy as the number of input parameters increased. Surprisingly, the accuracy of the predicted output from the four input parameters (temperature, dosage of inhibitor, quantity of electrolyte in insulation and sample position) was higher than the accuracy of the most influential parameters (temperature and dosage of inhibitor). This suggests that incorporation of more input parameters having some relationship with the output is more important in achieving a higher accuracy compared to using the most influential parameters only. In conclusion, this study indicated that mineral wool insulation absorbs water for a long period without saturation which increases the risk of CUI. Also, CUI rate increased with temperature up to 80 oC but decreased on further increase up to 130 oC. The new commercial inhibitor was effective in mitigating CUI at the temperatures investigated. Also, more test solution was observed at the lower part of the insulation compared to the upper part when installed on the CUI test rig which increases the risk of severe corrosion at the lower section of the insulation. The prediction of CUI rate using ANN indicated that inclusion of more input parameters could improve prediction accuracy. Moreover, the choice of activation functions also has effect on the accuracy of the predicted output.Corrosion of metals under insulation is a serious concern for industries due to the fact that the insulation hides the metal from view which increases the likelihood of sudden failure. Carbon steel is one of the metal alloys frequently used in industries due to economic and technical reasons. However, it is quite susceptible to corrosion under insulation (CUI). The factors affecting corrosion of carbon steel under mineral wool insulation such as temperature, effectiveness of inhibitor, quantity and distribution of electrolyte in the insulation have not been extensively studied in the literature. In fact, studies on corrosion of metals under insulation are quite sparse compared to immersion (uninsulated) conditions. Therefore, the objectives of this study were to assess the effect of temperature (60 oC to 130 oC) on corrosion of carbon steel under insulation, effectiveness of a new commercial inhibitor (VpCI 619) in mitigating CUI of carbon steel, quantity and distribution of electrolyte (1wt. % NaCl) in mineral wool insulation as well as investigation of the drying times of the insulation using galvanic current and electrochemical impedance measurements. In addition, the prediction of CUI rate using Artificial Neural Network (ANN) was carried out with the aim of assessing the accuracy of prediction of different network parameters such as number of hidden layers, number of input parameters and choice of activation function. Prior to CUI studies, the water absorption capacity of mineral wool insulation was determined using standard procedures (ASTM C1511). This was carried out to assess the time it will take for the insulation to be saturated with water, the variability of repeated measurements as well as the total water content in the insulation. The CUI studies were carried out using a test rig that was based on ASTM G189-07 standard. The corrosion rates were estimated using weight loss technique and the effects of temperature, vapour phase inhibitor consisting primarily of sodium molybdate, quantity of electrolyte in insulation were investigated. The drying out profile of the insulation was assessed using galvanic current and electrochemical impedance measurements. Furthermore, the prediction of CUI rate was carried out using Artificial Neural Network and the effect of single and double hidden layers, sigmoid and hyperbolic tangent activation functions, as well as number of input parameters on accuracy of prediction of CUI rate were assessed. The results of the water absorption studies indicated continuous absorption of water even after immersion for 22 days. The water absorption capacity was greater for thermally treated insulation compared to untreated insulation samples due to thermal degradation of the oily additives and polymeric binders. The effect of temperature on CUI indicated an increase in corrosion rate from 60 oC to 80 oC. Further increase in temperature up to 130 oC resulted in a decrease in corrosion rate. The existence of a maximum point in the curve was attributed to the competing effects of two factors which include increased diffusivity of oxygen which dominates at low temperature and decreasing solubility of oxygen and insulation dry-out which dominates at temperatures exceeding 80 oC. The new commercial inhibitor was observed to mitigate the corrosion rate at the temperatures investigated in this study. The inhibition efficiency indicated an average of 89% when a dosage of 5.2 g/m2 of the inhibitor was used. The effectiveness was also observed to be dosage dependent with lower doses having less inhibition efficiency. The drying times of the insulation assessed using galvanic current and impedance methods were observed to decrease as temperature increased. The galvanic current was observed to decrease to zero while the impedance increased to high values as the insulation dries out. However, the drying times obtained from galvanic current method showed a higher variability compared to impedance method. The result of prediction of CUI rate using Artificial Neural Network indicated an increase in accuracy as the number of input parameters increased. Surprisingly, the accuracy of the predicted output from the four input parameters (temperature, dosage of inhibitor, quantity of electrolyte in insulation and sample position) was higher than the accuracy of the most influential parameters (temperature and dosage of inhibitor). This suggests that incorporation of more input parameters having some relationship with the output is more important in achieving a higher accuracy compared to using the most influential parameters only. In conclusion, this study indicated that mineral wool insulation absorbs water for a long period without saturation which increases the risk of CUI. Also, CUI rate increased with temperature up to 80 oC but decreased on further increase up to 130 oC. The new commercial inhibitor was effective in mitigating CUI at the temperatures investigated. Also, more test solution was observed at the lower part of the insulation compared to the upper part when installed on the CUI test rig which increases the risk of severe corrosion at the lower section of the insulation. The prediction of CUI rate using ANN indicated that inclusion of more input parameters could improve prediction accuracy. Moreover, the choice of activation functions also has effect on the accuracy of the predicted output

Complex Mechanical Properties of Steel

Whereas considerable progress has been reported on the quantitative estimation of the microstructure of steels as a function of most of the important determining variables, it remains the case that it is impossible to calculate all but the simplest of mechanical properties given a comprehensive description of the structure at all conceivable scales. Properties which are important but fall into this category are impact toughness, fatigue, creep and combinations of these phenomena. The work presented in this thesis is an attempt to progress in this area of complex mechanical properties in the context of steels, although the outcomes may be more widely applied. The approach used relies on the creation of physically meaningful models based on the neural network and genetic programming techniques. It appears that the hot–strength, of ferritic steels used in the power plant industry, diminishes in concert with the dependence of solid solution strengthening on temperature, until a critical temperature is reached where it is believed that climb processes begin to contribute. It is demonstrated that in this latter regime, the slope of the hot–strength versus temperature plot is identical to that of creep rupture–strength versus temperature. This significant outcome can help dramatically reduce the requirement for expensive creep testing. Similarly, a model created to estimate the fatigue crack growth rates for a wide range of ferritic and austenitic steels on the basis of static mechanical data has the remarkable outcome that it applies without modification to nickel based superalloys and titanium alloys. It has therefore been possible to estimate blindly the fatigue performance of alloys whose chemical composition is not known. Residual stress is a very complex phenomenon especially in bearings due to the Hertzian contact which takes place. A model has been developed that is able to quantify the residual stress distribution, under the raceway of martensitic ball bearings, using the running conditions. It is evident that a well–formulated neural network model can not only be extrapolated even beyond material type, but can reveal physical relationships which are found to be informative and useful in practice

A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines

Digital tools such as machine learning and the digital twins are emerging in asset management of offshore wind structures to address their structural integrity and cost challenges due to manual inspections and remote sites of offshore wind farms. The corrosive offshore environments and salt-water effects further increase the risk of fatigue failures in offshore wind turbines. This paper presents a review of corrosion fatigue research in horizontal-axis offshore wind turbines (HAOWT) support structures, including the current trends in using digital tools that address the current state of asset integrity monitoring. Based on the conducted review, it has been found that digital twins incorporating finite element analysis, material characterisation and modelling, machine learning using artificial neural networks, data analytics, and internet of things (IoT) using smart sensor technologies, can be enablers for tackling the challenges in corrosion fatigue (CF) assessment of offshore wind turbines in shallow and deep waters

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens

Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches

As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models

An Overview of Maintenance Management Strategies for Corroded Steel Structures in Extreme Marine Environments

Maintenance is playing an important role in integrity management of marine assets such as ship structures, offshore renewable energy platforms and subsea oil and gas facilities. The service life of marine assets is heavily influenced by the involvement of numerous material degradation processes (such as fatigue cracking, corrosion and pitting) as well as environmental stresses that vary with geographic locations and climatic factors. The composition of seawater constituents (e.g. dissolved oxygen, salinity, temperature content, etc.) is one of the major influencing factors in degradation of marine assets. Improving the efficiency and effectiveness of maintenance management strategies can have a significant impact on operational availability and reliability of marine assets. Many research studies have been conducted over the past few decades to predict the degradation behaviour of marine structures operating under different environmental conditions. The utilisation of structural degradation data – particularly on marine corrosion – can be very useful in developing a reliable, risk-free and cost-effective maintenance strategy. This paper presents an overview of the state-of-the-art and future trends in asset maintenance management strategies applied to corroded steel structures in extreme marine environments. The corrosion prediction models as well as industry best practices on maintenance of marine steel structures are extensively reviewed and analysed. Furthermore, some applications of advanced technologies such as computerized maintenance management system (CMMS), artificial intelligence (AI) and Bayesian network (BN) are discussed. Our review reveals that there are significant variations in corrosion behaviour of marine steel structures and their industrial maintenance practices from one climatic condition to another. This has been found to be largely attributed to variation in seawater composition/characteristics and their complex mutual relationships