| dc.description.abstract | Pitting corrosion poses a significant threat to deepwater oil and gas production facilities, yet our current understanding of its impact on specific systems such as steel catenary riser (SCR), tensioned leg platforms (TLP), and deep draft caisson vessels (DDCV) is limited. Existing research primarily focuses on pitting corrosion behavior in controlled laboratory environments and standard numerical methodologies, which fail to capture the complex and harsh conditions in deepwater facilities. This research gap hampers our ability to fully comprehend the extent and consequences of pitting corrosion on these systems. Moreover, there is a lack of comprehensive modeling methodologies to assess the effectiveness of corrosion mitigation strategies designed explicitly for SCR, TLP, and DDCV systems. While corrosion mitigation measures are commonly employed in the oil and gas industry, their efficacy in protecting these systems from pitting corrosion remains largely unexplored. Addressing these research gaps is crucial to improve our understanding of pitting corrosion behavior in deepwater facilities and to develop targeted and efficient mitigation strategies that ensure these critical assets' integrity, safety, and long-term viability.
In the field of corrosion fatigue, several research gaps exist, including understanding the mechanisms of pit initiation, quantifying the influence of stress and temperature on pit growth, characterizing the pit-to-crack transition, investigating the interaction between mechanical and electrochemical factors, exploring the influence of multiple pits cluster formations, studying corrosion fatigue short crack growth, and developing comprehensive models for corrosion fatigue. These gaps hinder our understanding of corrosion fatigue mechanisms and limit the development of effective strategies for mitigating its damaging effects. By addressing these research gaps, we can enhance our understanding of corrosion fatigue, improve predictive models, and develop strategies to mitigate corrosion-induced damage.
This thesis focuses on the development of advanced computational methods to analyze and understand the mechanisms of localized pitting corrosion damage, pit-to-crack transition, and thermal diffusion in evolving discontinuities in metallic structures. The primary objective is to devise integrated multi-stage modeling methodologies that encompass various stages of corrosion-related processes, including pit initiation, pit growth, pit-to-crack transition, and stable crack growth. Furthermore, the research aims to investigate corrosion fatigue and stress-assisted pitting corrosion by considering relevant factors. This work also seeks to characterize the pit-to-crack transition and quantify the effects of stress, strain, and temperature on pit growth in corrosive environments. Additionally, the thesis aims to develop computationally efficient models for non-local thermal diffusion in evolving discontinuities, leveraging the power of machine learning algorithms.
To achieve these aims, the thesis will pursue specific objectives, including the development of an advanced model for corrosion fatigue, validation of the model through experimental data, investigation of the pit-to-crack transition, analysis of factors influencing the pit-to-crack transition, quantification of stress levels and pit growth rates, exploration of temperature's influence on pit growth, development of a predictive model incorporating temperature influence, validation of the temperature influence model, incorporation of corrosion fatigue short crack growth into the advanced computational model, and develop computationally efficient model for nonlocal thermal diffusion in evolving discontinuities.
By accomplishing these aims and objectives, this thesis will expand our knowledge of localized corrosion mechanisms, enhance the accuracy of numerical models, and contribute to the development of effective strategies for managing pitting corrosion and its associated challenges. The thesis is organized into four parts, each focusing on different aspects of the problem. These parts employ various computational approaches such as Probabilistic Cellular Automata (PCA), the eXtended Finite Element Method (XFEM), and hybrid Peridynamics-based Machine Learning (PD-ML) models to simulate damage mechanisms accurately. These computational techniques are employed in a multi-stage sequential coupling to simulate damage mechanisms using various combinations. The chapters within each part provide detailed explanations and discussions of the specific approaches and their application to the research problem.
The first part of the work introduces a novel approach to understanding localized corrosion and cracking damage mechanisms in pipeline steel subjected to fatigue loading. The methodology consists of a sequential coupling of PCA and the XFEM to simulate the initiation of pitting corrosion, pit-to-crack transition, and stable cracking. PCA describes the stochastic nature of localized corrosion damage, whereas XFEM is used to model arbitrary inhomogeneities, such as cracks, voids, and material interfaces. Level Set Methods (LSM) are implemented to track the crack propagation location accurately. Additionally, XFEM is enriched by additional degrees of freedom using the partition of unity concept, which enables efficient solutions without requiring mesh conformity to internal boundaries or re-meshing. A localized strain criterion initiates cracks from the pit surface when the pit reaches a critical strain value. The study demonstrates that the proposed coupling of PCA and XFEM provides a reliable approximation of experimental data and may be applied to predict the service life and integrity of pipelines subjected to similar loading conditions.
The second part of the work presents a hybrid formulation of a PD-ML model for thermal diffusion analysis in one-dimensional and two-dimensional problems with evolving discontinuities. Here, we use a multivariate linear regression approach to establish the relationship between the temperature values of material points, their neighboring points, and the applied external heat fluxes. The thermal modal analysis method uses the finite element method to generate training and testing data. An efficient numerical procedure is developed to couple the peridynamics and PD-ML models. The model is analyzed under multiple configurations of micro-thermal conductivity functions for one-dimensional thermal bar problems under both steady-state and transient loading conditions to identify the configuration that exhibits superior convergence behavior towards the local solution. The hybrid model effectively captures intricate discontinuities and boundaries while being computationally efficient, indicating its potential for thermal diffusion analysis in one- and two-dimensional problems with evolving discontinuities.
The third part of the work explores the potential of theory-based data science in the field of material science by introducing a hybrid formulation of a PD-ML model for pitting corrosion in one- and two-dimensional problems. Using a multivariate linear regression model, we establish relationships between the concentration value of material points, its family members' concentration, and the corresponding applied external mass fluxes within the linear regime for isotropic materials. Training and testing data are generated using the model analysis for mass diffusion using the finite element method for the machine learning model. An efficient numerical algorithm is developed to couple the peridynamics and PD-ML models. The model is analyzed under multiple configurations of micro-diffusivity functions for one-dimensional bar problems under transient loading conditions to identify the configuration that exhibits superior convergence behavior towards the local solution. Furthermore, an integral aspect of this study entailed the integration of a cutting-edge predictive model that effectively incorporates the intricate influence of temperature, followed by rigorous experimental and computational validation.
The last part of the work presents a novel approach to modeling localized corrosion and cracking damage mechanisms in pipeline steel subjected to mechanical loading. Here, we employ a sequential coupling of PCA and a PD-ML model to simulate the initiation of pitting corrosion and pit-to-crack transition. To maintain consistency, we are utilizing the PCA model that was previously developed in part one of this thesis. PD-ML is a hybrid approach that couples machine learning and ordinary state-based peridynamics models for fracture prediction of linear elastic 2D structures. We assume that a material point's displacements have a linear relationship with the displacement of its neighbors and its applied external forces. Weighted coefficients for the PDML model are obtained using multivariate linear regression analysis. The training and testing data are generated from structural modal analysis using the finite element method for a square plate. The study finds that coupling PCA and PD-ML provides a good approximation of experimental data of stress-assisted pitting corrosion and may be used to predict the integrity of pipelines subject to mechanical loading.
Overall, the thesis showcases novel approaches to modeling and simulating the behavior of materials and structures under various loading conditions, focusing on pitting corrosion, pit-to-crack transition, and thermal diffusion and aiming to contribute to developing improved numerical methods for their analysis. Combining the above works, we present a comprehensive approach for predicting the localized corrosion and cracking damage mechanisms in pipeline steels under fatigue and mechanical loading. We also provide thermal diffusion analysis with evolving discontinuities in one- and two-dimensional problems. The approaches combine PCA, the XFEM, and the hybrid PD-ML models to simulate the damage mechanisms accurately. The approaches presented in the thesis can potentially improve computational efficiency and the accuracy of predicting pipelines' service life and integrity subjected to similar loading conditions and provide insight into the thermal diffusion process in materials with evolving discontinuities. The thesis also strives to advance our understanding of localized corrosion damage mechanisms, improve numerical modeling techniques, and provide valuable tools for predicting and mitigating corrosion-induced damage in metallic structures. The findings of this research work have practical implications for various industries and contribute to ensuring the integrity and long-term viability of critical assets. | en_US |
