AMPP Store - Individual Conference Papers

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Precision Navigation For Hull Crawling Robots - Efficient Robots For Automated Grooming

Product Number: 51322-17660-SG

Author: Karl Lander

Publication Date: 2022

$20.00

The impacts of marine biofouling to the maritime and naval communities, as well as the planet as a whole are well documented. Whether its increased fuel consumption and carbon emissions, transport of invasive species, or that it just plain looks bad, marine biofouling needs to be addressed more aggressively, be that timely removal of growth, or increased monitoring of hull condition to know when it’s time for removal. Current methods of removing growth are costly both in time and money, potentially environmentally unfriendly and risk impacting the health of the ship’s coating system.

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Precursor Evolution and SCC Initiation of Cold-Worked Alloy 690 in Simulated PWR Primary Water

Product Number: 51317--9475-SG

ISBN: 9475 2017 CP

Author: Ziqing Zhai

Publication Date: 2017

$20.00

Stress corrosion crack initiation of two thermally-treated, cold-worked UNS N06690 materials was investigated in 360 ºC simulated primary water using constant load tensile tests and blunt notch compact tension tests equipped with direct current potential drop for in-situ detection of cracking.

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Predictability of Computational Fluid Dynamics for Solid Particle Erosion of 90° Stainless-Steel Elbows in Various Erosive Environments

Product Number: 51324-20886-SG

Author: K. Alanazi; R. Mohan; S. S. Kolla; O. Shoham; A. Nassef

Publication Date: 2024

$40.00

Many applications, including elbows of piping systems in pressurized water reactors, require ductile materials. However, the impingement of solid particles entrained in turbulent flows causes these materials to erode. To mitigate erosion failures, this paper proposes a low-cost method for predicting erosion in elbows carrying gas-dominated and liquid-dominated flows. The method solely relies on Computational Fluid Dynamics (CFD), drawing inspiration from experimental data of 90-degree stainless-steel elbows found in the literature. Utilizing the standard k-e turbulence model and standard wall function, penetration data of elbows operating in three different flow directions were predicted. In each flow direction, three models- DNV (2007), Oka et al. (2005), and Finnie (1960)- were evaluated. The CFD evaluation was based upon empirical data obtained in millimeters per day from four particle sizes (100, 300, 350, 450 µm), three radius-of-curvature-to-diameter ratios (1.50, 1.53, 3.25), three gas superficial velocities (25.24, 47.00, 72.00 m/s), and two liquid superficial velocities (4.00, 6.48 m/s). The DNV model exhibited a high degree of agreement with experimental data when applied to low liquid (4.00 m/s) and gas (25.24 m/s) superficial velocities. In contrast, experimental data obtained from high superficial gas (47.00, 72.00 m/s) and liquid (6.48 m/s) velocities were well correlated with the model proposed by Oka et al. At high superficial velocities, the DNV model underpredicted the data. Saffman force inclusion in particle tracking has remarkably increased the erosion prediction of all models for gas-dominated flows but resulted in less prediction for liquid-dominated flows.

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Predicting Accumulated Galvanic Corrosion Damage Using A Service Life Model

Product Number: 51319-13292-SG

Author: Robert Adey

Publication Date: 2019

$20.00

Aircraft and similar structures are exposed to many different types of environmental conditions over their operational life and this very much determines the type of damage they experience. For example sea based aircraft experience a much more aggressive environment than land based aircraft. Therefore it is critical to know the environmental exposure of an aircraft over the course of its lifetime to make meaningful predictions of accumulated corrosion damage.Computer simulation models have been demonstrated to be able to predict the rate of corrosion and the incidence of pitting but they require information on the electrolyte thickness conductivity/corrosivity and the polarization properties of the material. Therefore to use a computational model to predict the corrosion rate for an aircraft at a particular point of time during its service we would have to define the model parameters at that time as well as the exposure history.In this paper a service life model concept is developed based on environmental data collected using sensors combined with a galvanic corrosion simulation model. The galvanic simulation model input data is identified from the sensor data to predict the accumulation of corrosion damage. Applications of the model are presented and discussed.The approach is applicable not only to aircraft but to a wide range of structures.

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Predicting Atmospheric Galvanic Corrosion Of Aluminum Alloy By Combining Electrochemical Techniques And Accelerated Laboratory Corrosion Tests

Product Number: 51322-18163-SG

Author: Raghu Srinivasan, Toomas Kollo, Matt Cullin

Publication Date: 2022

$20.00

Aluminum (Al) alloys are the most common non-ferrous metals used (approximately 25 million tons per year) and the second most commonly used metal alloy after steel¹. Some of the properties of Al alloys that attribute to their worldwide use include lightness, thermal conductivity, electrical conductivity, suitability for surface treatments, and corrosion resistance. Al alloys are also combined with other metals/materials to achieve desired properties for specific applications. Al alloys can be joined to other materials with ease to enhance their combined properties with the following techniques: welding, bolting, riveting, clinching, adhesive bonding, and brazing¹.

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Predicting Corrosion of Successive Feeds in Distilling Units - An Experimental Approach

Product Number: 51324-20668-SG

Author: Gheorghe Bota; Ishan Patel; Peng Jin; David Young

Publication Date: 2024

$40.00

Cheap heavy crudes become attractive for oil refineries to increase their benefit margins but the corrosivity of heavy crudes compels the refinery engineers to blend them with the more expensive light sweet crudes. Crude oil blends with complex composition including organo-sulfur compounds, fatty acids, nitrogen and chlorine compounds become corrosive when processed at high temperatures due to these reactive species. Therefore, maintaining corrosion control is a constant effort in oil refineries, and it involves the use of dedicated corrosion models combined with specific experimental lab procedures and methods. This work is presenting the practical application of a lab testing procedure used for predicting the high temperature corrosivity of different crude fractions that were run successively for different time periods in a specific “flow-through” apparatus. The testing procedure consists of two distinct phases performed in the same apparatus, at the same temperature, and for different time durations. During the first phase of the test, scales are formed using a distilling fraction on metal samples and further, in the second test phase, these preformed scales on samples are exposed without interruption to a different distilling fraction. Thus, the two successive test phases, each using a different distilling fraction, are associated with the “changing feeds” in the distilling tower. Corrosive effects are evaluated by sample weight loss measured in successive fraction tests and in separate tests performed with each of the selected fractions. Experimental results are compared to predictions of a corrosion model for sulfidation and naphthenic acid corrosion.

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Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning

Product Number: 51324-20803-SG

Author: Emily Seto; Meifeng Li; Jing Liu

Publication Date: 2024

$40.00

The importance of effective corrosion management in carbon capture, utilization, and storage (CCUS) networks has significantly increased. Captured CO2 is often transported in the supercritical state (s-CO2) and can contain impurities like H2O, O2, SOx, or NOx. While repurposing existing oil and gas pipelines for s-CO2 transport has been suggested, further testing and risk assessment is required to validate this strategy and its associated risks. Given the substantial amount of corrosion data available from recent corrosion studies, machine learning (ML) has emerged as a promising tool for corrosion prediction and management. This study aims to utilize supervised ML techniques to predict the corrosion severity of pipeline steels operating in s-CO2 systems. The selected algorithms, random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) were trained on a comprehensive data set of X-series pipeline steels which includes corrosion rates, impurity levels, temperatures, pressures, and exposure times. Additional testing data set and error and accuracy scores were used to determine the most accurate algorithm. An additional experimental testing was performed to verify the predictions of the model. It was found that the RF model had the best accuracy of 65.3% out of the three tested models and KNN had the worst accuracy of 59.2%. In multiple impurity environments the RF model was able to accurately predict corrosion severity but overestimated corrosion severity in environments with short exposure times.

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Predicting Hot Oil Corrosion: A Framework for Quantifying Reactive Organic Sulfur Compounds in Crude Unit Process Streams

Product Number: 51323-19105-SG

Author: Winston Robbins, Sridhar Srinivasan, Gerrit Buchheim

Publication Date: 2023

$20.00

In Corrosion/2021, the authors introduced a molecular mechanistic model that quantifies and predicts SNAPS corrosion rates. During Corrosion/2022, we presented the mechanistic corrosion prediction framework describing the molecular basis of the model’s reactions, kinetics, and mass transport of ROSC to vessel walls. In this molecular model, sulfidation corrosion is calculated for direct heterolytic reaction of ROSC with solid surfaces.

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Predicting Long-Term Exposure Performance of Galvanized Rebar Based on Artificial Intelligence and Electrochemical Methods

Product Number: 51324-21166-SG

Author: Deeparekha Narayanan; Yi Lu; Victor Ponce; Homero Castaneda

Publication Date: 2024

$40.00

In this work, we carried out electrochemical studies on ASTM A615 (bare steel rebar), ASTM A767 (steel rebar with hot dip galvanized zinc coating), and ASTM A1094 (steel rebar with continuously galvanized zinc coating) rebars exposed to two different environments. In one condition, the samples were exposed to a simulated concrete pore solution (SCPS) containing 3.5 wt.% NaCl. Over a period of 12 months, the electrochemical properties of the samples were regularly assessed through open circuit potential (OCP), linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) on a weekly basis. In the other condition, steel rebars were embedded in concrete with water-to-cement ratio of 0.53. A controlled surface area of the cast concrete block was exposed to a 3.5 wt% NaCl solution using a dam mounted on it. This method allowed for the introduction of chlorides into the reinforced concrete while maintaining control over the exposure process. Under these conditions, the rebars were continuously monitored by carrying out OCP and EIS tests for a period of up to three years since curing. Based on the experimental results obtained, we developed a mathematical framework that combines mechanistic and machine-learning concepts for analyzing the behavior of the rebars in both conditions. EIS analysis was utilized to quantify the transports processes, activation, and interface interaction of the rebars with the corrosive environments in each condition. EIS served as tool to quantify the transports processes, activation mechanisms, and interface interaction of the rebars within corrosive environments across diverse conditions. We conducted this analysis using a Time Series Prediction (TPS) approach of several phase angle plots along 300 days of rebars in pore solution and 900 days of rebars in reinforced concrete, which leveraged recurrent neural networks techniques to predict corrosion mechanisms. This approach allowed us to learn dynamically from real-time measurements, eliminating the sole reliance on domain expertise for parameter optimization. Finally, we utilized our comprehensive experimental-theoretical framework, which integrated Electrochemical Impedance Spectroscopy testing, to make long-term predictions for the performance of the rebars using neural networks techniques. These predictions spanned several years and were based on rigorous analysis. To validate the accuracy and reliability of our framework, we compared the predictions with the experimental results, thereby confirming the accuracy and reliability of our predictions.

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Predicting the Enhanced Effects of Coating Deterioration in Marine Environments

Product Number: 51323-19341-SG

Author: Michael Kibler, James Ellor

Publication Date: 2023

$20.00

Organic coatings are the most used method of corrosion prevention and protection of metallic substrates
in many industries. Owners in both the public and private sectors will invest significant resources into
testing coating options to provide the best protection for new and existing products or infrastructure.
Often, this testing defaults to some variation of accelerated salt spray testing or outdoor marine exposure
with results being based on aspects such as visual measurements of rust through, corrosion
creepage/undercutting, and blistering.

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Predicting the Impact of Metallic Coating and Paint Degradation on the Corrosion of F-18 Airframes

Product Number: 51315-5956-SG

ISBN: 5956 2015 CP

Author: Alan Rose

Publication Date: 2015

$20.00

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Prediction and Assessment of Ammonium Bisulfide Corrosion Under Refinery Service Conditions - Part 3

Product Number: 51317--8929-SG

ISBN: 8929 2017 CP

Author: Richard Horvath

Publication Date: 2017

$20.00

Summarizes results from Phase III of the joint industry program (JIP) on refinery alkaline sour water (ammonium bisulfide) corrosion. Phase III included a comprehensive engineering analysis of data from all three phases of the Sour Water JIP.

Individual Conference Papers

Association for Materials Protection and Performance