DON26TZ01-NV012 TITLE: Sensing to Measure and Validate Corrosion in Naval Systems
OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)
COMPONENT TECHNOLOGY PRIORITY AREA(S): Advanced Materials;Integrated Sensing and Cyber;Sustainment
PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)
OBJECTIVE: Develop and deliver a sensory tool that can be used to monitor and assess several modes of corrosion activity as a function of time within Navy ship systems and subsystems. The sensory tool will incorporate artificial intelligence (AI) identify and estimate component life in a given platform/system for a given material selection, CAD geometry, and environment during the ship operations. AI can incorporate a set of mathematical models that will detect when the error happens and when to do maintenance. The main objectives of AI are to reduce maintenance time, production downtime, and the cost of component supplies.
DESCRIPTION: It is increasingly important for corrosion rate analysis to be performed on steel structures such as ships, offshore platforms and bridges to determine their safe operating life and for the development of effective and efficient maintenance practices. Optimal timeframes for asset availability and for planned redundancy also demand information about corrosion rates. Corrosion loss affects the effective load capacity of steel plating through causing plating thickness loss. The design of steel ships typically incorporates a corrosion allowance, i.e., an amount of corrosion loss that can be tolerated before the structural system is considered compromised. Corrosion protection measures include paint coatings and sacrificial anode systems for immersed areas. However, these methods are not always wholly effective, and continual maintenance usually is required but not always applied. In extreme cases, repair and replacement of structural details may be necessary, incurring very considerable cost penalties due to direct repair costs. It follows that the estimates of the expected rate of deterioration are important inputs for optimal maintenance and repair decisions for ships.
Naval ships are exposed to a range of corrosive environments and as a result the patterns of corrosion vary widely. The structural details and the orientation and position within the space within a given environment also will cause different corrosion patterns and rates. For immersion environments, influences on corrosion include chemical factors such as salinity, oxygen content, pH, and presence of pollutants; physical factors such as temperature and pressure; and biological factors such as bacteria and biomass. For ballast tanks the immersion environment usually is considered the most critical but in modelling the corrosion process attention might also need to be given to the occurrence of repeated wet/dry cycles as a result of the tanks being filled and emptied to adjust the freeboard trim of the ship. In addition, the presence of sacrificial anodes may have some influence, although they are effective only under immersed conditions and for uncoated areas. Thus, a de-ballasted tank will not be protected. It follows that the amount of corrosion in a ballast tank is a function of the environment, the type of corrosion protection, and the tank status. Apart from corrosion protection and operational practices, the main influence on the environmental parameters is the result of the conditions encountered during operations – what might be called the trading route, including geographical influences.
The number of hours a ship is generally in an operating or training status have decreased. Navy corrosion maintenance costs continue to escalate, reaching upwards to nearly $10B/year. Roughly 40% of those costs are caused by corrective maintenance that can be attributed to the improper selection of materials, usually from design process decisions that addressed system requirements without considering materials corrosion behavior in environments for which they are planned.
The application of a resistant coating on ships, offshore structures, and pipelines is the primary prevention method of corrosion wastage in the marine industries. To guarantee coating integrity and to be able to thoroughly survey for corrosion wastage on marine structures, new advanced nondestructive methods are being sought. The requirements of convenient and rapid determination of corrosion wastage on coated structures, even in the difficult spatial positions of the structure, will require advanced technologies which are being developed for other industries that also require very high structural integrity. Corrosion detection and monitoring are essential diagnostic and prognostic means for preserving material "health" and reducing life-cycle cost of industrial infrastructures, weapon systems, ships, aircraft, ground vehicles, pipelines, etc.
Sensor system attributes of small size, low weight, open plug-and-play interface architecture, self-diagnostics and validation make this a valuable interface and controller platform for other industrial and military monitoring applications. The system simplicity and low cost allows for wide area coverage by monitoring multiple sites on an individual structure and for fleet-wide vehicle condition monitoring. Other than Military vehicles, the smart sensor system has market potential in stationary structures, industrial processes, and civil and commercial transportation. By collecting and consolidating datasets into a fleet management system, DOW can better allocate maintenance resources and increase availability and service life objectives for these platforms. The collected data drive sustainment analytics and fleet management by increasing the accuracy of predictive maintenance schedules and decreasing inspection intervals and unnecessary preventative maintenance.
Artificial Intelligence (AI) plays a pivotal role in interpreting the vast amounts of data collected by drones. Machine learning (ML) algorithms analyze the images to identify patterns of corrosion, thereby enabling more accurate and timely maintenance decisions. This level of automation reduces human error and ensures that Navy vessels remain in optimal condition. AI is a machine’s capability to impersonate human behavior, respond perceptively, solve problems, and make decisions automatically without human interference or with less human interference. The main objective of AI research involves general intelligence, automated planning, perception, natural language processing, knowledge representation, and robotics.
PHASE I: Explore the various non-destructive and electrochemical technologies through a literature search and downselect to the two or three most promising evaluation options that are capable of sensing the most corrosion degradation mechanisms. Non-contact technologies are preferred if degradation sensitivities are not lost. There is a critical need for the development of a real-time monitoring capability for U.S. Navy assets that has the potential to identify the onset of various corrosion modes like pitting as well as actively characterize stress corrosion (SCC) initiation and progression.
Optimization of quasi- and fully- distributed fiber optic sensing hardware for corrosion and SCC monitoring in Navy-relevant environments, including ultrasonic acoustics. Employ laboratory corrosion and SCC experiments on instrumented structural alloy coupons to develop a correlation between acoustic emission and corrosion/SCC signatures. Create physics-based modeling of both ultrasonic guided wave non-destructive examination (NDE) and acoustic emission to develop a training set for the AI-classification framework. Improve correlations by using training and validation of AI-classification framework and application for identification, localization, and classification of various corrosion modes and SCC in relevant alloys.
The NDE or electrochemical methods should be assessed as to the quality and accuracy of the objective measurements. The speed at which the requisite information can be obtained (ft2/minute) will also be an evaluation parameter. Offerors must show at least one technology that can reliably characterize the quality of the materials interface and bulk interior, assess the spatial resolution of the technique, and assess the substrate surface conditions such as corrosion including sites with significant surface roughness.
PHASE II: The technology(ies) selected in Phase I should be further tested using larger uncoated and coated coupons of various alloys to better gauge what the speed (ft2/minute) of detection of decohesive sites, coating defects, and substrate corrosion, if present. Work with a Navy laboratory for collaborations in assisting the offeror in maturing and transitioning the technology(ies). Further modeling validation in select field Navy environments. This will be required to assert the reliability and sensitivity of the selected technology will be needed. Other acceptance testing as dictated by the Navy Laboratory should also be done and the evaluation/monitoring technology must be assessed as to its compatibility.
PHASE III DUAL USE APPLICATIONS: The ability of some NDE and electrochemical methods to penetrate most non-metallic materials allows non-contact examination of materials. The properties of interest across the industries may be broadly categorized into three areas—layer thickness, defects and contamination, susceptibility to SCC, and material characterization. Commercial and military ships both operate in a marine environment, and although they operate in different duty cycles, both are exposed to the aggressiveness of seawater and associated micro-marine environments. Both commercial and military ships suffer from similar corrosion failures.
REFERENCES:
KEYWORDS: Electrochemical; monitoring; marine; corrosion; ship; mechanical failures; artificial intelligence, performance; protection
TPOC 1
David Shifler
david.a.shifler.civ@us.navy.milTPOC 2
Nicole Tailleart
nicole.r.tailleart.civ@us.navy.mil
** TOPIC NOTICE ** |
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