Patent Application
20250131152
The subject matter disclosed herein generally relates to corrosion modeling for metallic materials.
Corrosion is a complicated phenomenon that affects nearly every metallic material and engineered component. Corrosion of some commonly used materials, such as aluminum, nickel alloys and steel can be severely affected by environmental conditions, resulting in damage accumulation and subsequent compromised performance and durability. Localized attacks in the form of pitting, crevice corrosion, inter-granular attacks, and so forth are often unpredictable and detrimental to component reliability. The nucleation, growth, and/or re-passivation of this localized corrosion is stochastic due to the many variables involved in environmental conditions and the manufacturing processes and surface treatments of the components. Hence, it is desirable to model damage progression arising from localized corrosion in terms of an ensemble of localized corrosion events over a specific period of time as opposed to tracking individual attacks deterministically. The ability to model and predict corrosion behavior remains challenging due to the seemingly random nature of its onset and environment dependent progression. Current methods to study and analyze localized corrosion are often limited to destructive measurement of the samples or parts from field or lab exposure. The destructive testing and analysis results in limited data points that may not be representative of the corrosion kinetics.
A need remains for a non-destructive testing and measurement method for localized corrosion formation.
In an embodiment, a method for non-destructive testing and measurement of corrosion attacks comprises defining characteristic corrosion attack parameters. The first specimen is exposed to corrosive conditions to induce multiple corrosion attack sites. The time of exposure to corrosive conditions is measured. One or more spatially resolved corrosion attack characteristic parameters for the multiple corrosion attack sites is measured to provide a first corrosion data set. A first set of spatially resolved corrosion attack characteristic parameters are measured by a non-destructive technique. The probability of failure for the first specimen from the first corrosion data set is measured. The composition of the specimen may be changed based on results achieved.
In an embodiment, the first specimen is re-exposed to the same or different corrosive conditions to induce further corrosion growth. After re-exposing the first specimen to the same or different corrosive conditions to induce further corrosion growth, the time of exposure to the same or different corrosive conditions is measured. A second set of spatially resolved corrosion attack characteristic parameters for the corrosion attack sites is measured. The probability of failure is modelled from a second corrosion data set for the re-exposure.
In another embodiment, the first specimen is re-exposed to the same or different corrosive conditions for n iterations and generating n corrosion data sets for n sets of spatially resolved corrosion attack characteristic parameters, where n is an integer.
In yet another embodiment, the first specimen is re-exposed for n iterations to reach a stage that can eventually lead to failure.
In yet another embodiment, the method further comprises determining the stage where likelihood of corrosion driven failure increases substantially and exposing the first specimen to corrosive conditions to induce multiple corrosion attack sites and reach a stage that can lead to failure.
In yet another embodiment, the method further comprises determining corrosion driven failure modes and rate behavior; wherein the corrosion driven failure modes and the rate behavior are determined before defining the corrosion attack characteristic parameters.
In yet another embodiment, the method further comprises calibrating the method by exposing a second specimen to the same or different corrosive conditions to induce multiple corrosion attack sites. The time of exposure to the same or different corrosive conditions is measured and a second set of spatially resolved corrosion attack characteristic parameters is measured for the corrosion attack sites to provide a second corrosion data set. A probability of failure is modelled from the second corrosion data set.
In yet another embodiment, the method further comprises comparing the modeled probability of failure from a subset of the second corrosion data set for the second specimen and the modeled probability of failure from a subset of the first corrosion data set for the first specimen.
In an embodiment, the specimen is a metallic material.
In an embodiment, a difference between the first specimen and the second specimen is a type of metal alloy, a type of metal grade, or a combination thereof.
In an embodiment, the corrosive conditions to induce multiple corrosion attack sites for the second specimen are different from the corrosive conditions used for the first specimen.
In yet another embodiment, the different corrosive conditions include a difference in acid concentration, a difference in humidity, a difference in acid type, a difference in temperature, a difference in electrolyte concentration, a difference in electrolyte chemistry, a difference in time, or a combination thereof.
In yet another embodiment, a subset of the corrosion data corresponding to a subset of the corrosion attack sites is selected, wherein the modeling of the probability of failure is modeled from the selected subset of the corrosion data.
In yet another embodiment, the spatially resolved characteristic corrosion attack parameters are measured by micro-computed tomography or radiography.
The foregoing features and elements may be executed or utilized in various combinations without exclusivity, unless expressly indicated otherwise. These features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings. It should be understood, however, that the following description and drawings are intended to be illustrative and explanatory in nature and non-limiting.
The subject matter is particularly pointed out and distinctly claimed at the conclusion of the specification. The foregoing and other features, and advantages of the present disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
Disclosed herein is a non-destructive testing and measurement assembly and a corrosion modeling method for non-destructive testing and measurement of corrosion attacks. The method can be used to establish time serial data for predicting corrosion rates and associated likelihood of component failure. The method is particularly suited to test aggressive corrosion attacks related to component reliability. Unlike destructive testing methods, the method enables measurement of localized corrosion formation and growth where the same corrosion attack sites can be measured multiple times after exposure and re-exposure of a specimen to corrosive conditions. Accordingly, the method provides a non-destructive, high fidelity, large quantity dataset of corrosion attack sites that can be used in corrosion nucleation and growth modeling.
Potential applications of the assembly and method include corrosion prognostics of metallic components. The method is capable of rapidly generating large datasets to enable mechanistic, statistical, and/or machine-learning models to predict the formation and growth of localized corrosion.
“Non-destructive testing” and a “non-destructive technique,” as used herein, refer to non-damaging processes, where the same surface and surface features can be examined and re-examined over extended periods of time.
The assembly includes elements to provide the capability for testing and measuring the localized corrosion sites of a specimen. The assembly is adaptable to a wide variety of parts and components and can be used to measure localized corrosion sites for a small subset area of a specimen or for an entire specimen.
Referring to
The disclosed method quantifies probabilistic risk of failures stemming from localized corrosion attacks due to material properties and non-mechanical environmental conditions. Specifically, the method applies to the corrosion that directly causes component or system failures or that becomes direct precursors of subsequent failure such as structure fractures. The method does not apply to stress corrosion crack failure where stress accelerates corrosion that cannot be reliably modeled based on an ensemble of prior corrosion attacks alone. The method includes determining the corrosion evolution behavior in either the real deployment environment or simulated environment to identify a stage of corrosion that determines specimen or component failure. Statistical corrosion rates are determined by acquiring time-evolved corrosion measurement using the non-destructive methods described herein. In some embodiments, the time-evolved corrosion measurements are acquired until a specimen reaches a stage that may lead to failure.
The disclosed method can include utilizing the assembly 100 to test and measure localized corrosion sites on a specimen 102. The method can include exposing a specimen to corrosive conditions. Exposure to corrosive conditions induces multiple corrosion attack sites on the specimen. After completion of the exposure of the specimen, the time of exposure is measured and the parameters of the corrosion attack sites on the specimen are spatially resolved with a non-destructive technique to provide corrosion data. In some embodiments, a subset of the corrosion data is selected for failure modeling. After measurement of the parameters of the corrosion attack sites, the specimen is then re-exposed to the corrosive conditions. Following, re-exposure to the corrosive conditions, the time and the parameters of the corrosion attack sites on the specimen are again spatially resolved. The specimen is re-exposed to the corrosive conditions and non-destructively tested two or more times. After collection of the desired corrosion data from the corrosion attack sites on the specimen, modeling of the probability of failure is performed using the corrosion data or a subset of the corrosion data. In some embodiments, corrosion driven failure modes and rate behavior are determined before exposing the specimen to corrosive conditions.
Referring to
where β is the shape parameter and η is the scale parameter. These two parameters are determined using the measurement method described in this disclosure on laboratory or field exposure specimens. The values of β and η follow their own distributions, which are used to predict the probability of failure in an un-tested environment or for an extended time, or both.
Corrosion driven failure modes include general corrosion, pitting, crevice corrosion, stress corrosion cracking, corrosion fatigue, fretting, erosion corrosion, inter-granular corrosion, or a combination thereof. Rate behavior includes the induction, acceleration, and deceleration sequence of corrosion as shown in
Defining characteristic corrosion attack parameters (
During step 203, corrosion is induced on a specimen by exposing the specimen to corrosive conditions. The exposure to the corrosive conditions induces multiple corrosion attack sites. The conditions of the corrosive conditions can vary. In an embodiment, the corrosive conditions are tailored to replicate anticipated real-world conditions for a part or a component. In another embodiment, the corrosive conditions are the real-world use for a part or a component. The corrosive conditions can be static or varied. Examples of corrosive conditions properties include acidity, humidity, acid type, temperature, electrolyte concentration, electrolyte chemistry (e.g., salt type, mixtures), and combinations thereof. In some embodiments, the corrosive conditions can be different for a first specimen and a second specimen. Examples of different corrosive conditions include a difference in acid concentration, a difference in humidity, a difference in acid type, a difference in temperature, a difference in electrolyte concentration, a difference in electrolyte chemistry, and so forth.
Subsequent measurement of corrosion attacks can be performed during a stage where the likelihood of corrosion driven failures increases substantially, i.e., the labeled area 302 in
In some embodiments, after the spatially resolved parameters are measured on a corroded specimen, the corroded specimen can be re-exposed to the corrosive conditions to continue the corrosion growth to provide a “re-exposed, corroded specimen.” Subsequently, the spatially resolved parameters for the re-exposed, corroded specimen can be measured and compared to the initial corrosion data set. The re-exposure of the specimen to corrosive conditions can be repeated (step 108) multiple times as desired to provide a more accurate reflection of the progression of corrosion for the specimen.
One method of non-destructively examining and re-examining the specimen is conducted via computer aided tomography (hereinafter computer tomography or CT). The term “computed tomography,” or CT, refers to a computerized x-ray imaging procedure where a collection of angle-resolved x-ray radiographic projections of a specimen and subsequent computer-based transformation/back-projection of the lower dimensional x-ray radiographic projections to higher dimensions. In some embodiments, during a CT scan the narrow beam of x-rays is aimed at the specimen and is rotated around the specimen, producing signals that are processed by the machine's computer to generate cross-sectional images, or “slices.” In other embodiments, the specimen can be rotated while the x-ray source is held stationary to generate the cross-sectional images. These slices are called tomographic images and can provide more detailed information than conventional x-rays. Depending on the x-ray detector used, the x-ray radiographic projections can be one-dimensional or two-dimensional signals, and the back-projection transforms the x-ray radiographic projections to two-dimensional slices or three-dimensional slices, respectively. Once a number of successive slices are collected by the machine's computer, they can be digitally “stacked” together to form a three-dimensional (3D) image of the specimen that allows for easier identification of basic structures as well as internal features of the specimen.
Unlike a conventional x-ray-which uses a fixed x-ray tube-a CT scanner (not shown here) can use a motorized x-ray source that rotates around a specimen or can use a static x-ray source while the specimen is rotated. During a CT scan, the specimen is orientated respective to the CT scanner and the CT scanner and/or the specimen moves while the x-ray tube shoots narrow beams of x-rays through the bulk of the specimen. Instead of film, CT scanners use special digital x-ray detectors, which are located directly opposite the x-ray source. As the x-rays leave the specimen, they are picked up by the detectors and transmitted to a computer.
Each time the x-ray source completes a specimen rotation, the CT computer uses sophisticated mathematical techniques to construct a two-dimensional or three-dimensional image slice of the specimen. The thickness of the specimen represented in each image slice can vary depending on the CT machine used, but usually ranges from 1-10 millimeters. When a full slice is completed, the image is stored. In some embodiments, the x-ray scanning process is repeated to produce another image slice. This process continues until the desired number of slices is collected.
Image slices can be displayed individually in two or three dimensions. In some embodiments, multiple can be stacked together by the computer to generate a 3D image of the specimen that shows the microstructure of the specimen as well as any abnormalities (such as pits, nucleation sites, or the like, that the technician is trying to identify. This method has many advantages including the ability to rotate the 3D image in space or to view slices in succession, making it easier to find the exact place where a problem may be located.
For example, a CT scanner can be a two-dimensional image scanner and the specimen can be rotated a single time as the CT scanner shoots x-rays through the specimen. The data collected by the x-ray detector can undergo a computer-based transformation to provide a three-dimensional image slice-stack of the specimen. The three-dimensional image slice-stack can then be analyzed for spatially resolved parameters and to select a subset of corrosion data for modeling. Then the probability of failure can be modeled from the subset of corrosion data.
After compilation of the corrosion data from measurement of the spatially resolved parameters, the population of corrosion attack sites can be analyzed to generate the distribution of key parameters (e.g., the shape and scale parameters in Weibull analysis). The parameters, along with newly acquired corrosion attacks, can then be used to estimate the cumulative distribution function.
Accurate kinetics of individual corrosion attacks are influenced by many factors such as manufacturing history, defects, environmental variability, and the like. Therefore, examining a population of corrosion attacks, specifically a sub-population of all attacks, facilitates estimation of the corrosion rate. With the selected subset of corrosion data (step 205), models can be used to relate corrosion nucleation rate, corrosion pit depth growth rate and other corrosion attack parameters as a function of the base alloy of the specimen, the electrolyte chemistry of the corrosive conditions, temperature, and time of exposure. The models can be further applied to predict corrosion rates for engineering components to improve component life prediction for a given application environment. The models used to analyze the probability of failure based on the corrosion data can include statistical, regression, data-driven empirical or machine learning models, physics-based mechanistic models, and combinations thereof. For example, analytical equation models can be established to link known physics-based mechanisms with the high fidelity datasets from the established corrosion analysis method.
In an embodiment, the method 200 may be performed on a first specimen, repeating the steps of exposing the first specimen to corrosive conditions (203) and measurement (204) in repeated iterations. After the collection of the dataset, the data may undergo further analysis to select a subset of the data for modeling and performing the modeling to provide a probability of failure for a part or a component based on the subset of the corrosion data. In some embodiments, the method 200 can further comprise testing of a second specimen under the same or different corrosive conditions to provide another dataset. In another embodiment, the method 200 can further comprise testing of a second specimen, with a different specimen material type than the specimen material type as the first specimen, to provide another dataset. The dataset for the first specimen and the dataset for the second specimen can be compared to relate corrosion parameters and parameters of the corrosive conditions and/or parameters of the specimen material type. In other embodiments, datasets may be collected on multiple specimens under varied corrosive conditions or specimen material types.
In an embodiment, a single specimen (a first specimen) may be subjected to a first set of corrosive conditions and then examined and re-examined over uniform or non-uniform intervals of time (using the same first set of corrosive conditions) using one or more non-destructive tests to determine the progressive effects of corrosion. After each examination, the specimen is re-exposed to the same first set of corrosive conditions. The first set of corrosive conditions involve the use of defined characteristic corrosion attack parameters that the specimen may be exposed to during its life cycle. The exposure to the first set of corrosive conditions generally induces multiple corrosion attack sites. The time of exposure to the first set of corrosive conditions is measured.
In an embodiment, one or more spatially resolved corrosion attack characteristic parameters for the multiple corrosion attack sites is measured to provide a first corrosion data set. The measurement typically is conducted via one or more non-destructive techniques. The probability of failure is generally modeled from the first corrosion data set to provide a first model. In an embodiment, a subset of the corrosion data that corresponds to a subset of the corrosion attack sites is selected, examined and used for modeling. Models of the probability of failure may be generated from the selected subset of the first corrosion data set. Data subsets from any of the exposures of any of the specimens may thus be used to generate probability models for failure or correct existing models.
Multiple measurements such as, for example, a second measurement, a third measurement, and so on, up to “n” measurements may be made on the first specimen, resulting in the collection of second, third, and so on, up to “n” data sets. For each exposure, a set of spatially resolved corrosion attack characteristic parameters is measured. For example, a first exposure to a first set of corrosive conditions results in a first set of spatially resolved corrosion attack characteristic parameters being measured to generate a first corrosion data set, while a second exposure (at a later time) of the same specimen to the same first set of corrosive conditions results in a second set of spatially resolved corrosion attack characteristic parameters being measured to generate a second corrosion data set. The model arrived from the first corrosion data set may be tested against the results obtained for each subsequent measurement. The model may be corrected based on the collected “n” corrosion data sets obtained from the “n” different measurements on a single specimen under the same environmental conditions, thus providing for a statistical data-driven and data-tested, accurate predictive model. The number “n” above in each instance is an integer. For example, n can be 1, 2, 3, 4, and so on. In an embodiment, n can be greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, greater than or equal to 100, and so on.
In another embodiment, more than one specimen (e.g., a second specimen, a third specimen, and so on, up to “n” specimens) of the same material composition may be tested under “m” different conditions. For example, n and m can independently be 1, 2, 3, 4, and so on. N and m may be the same or different from one another. In an embodiment, n and m can independently be any integer up to 10, any integer up to 20, any integer up to 30, any integer up to 100, and so on. For example, a second specimen may be tested under the same conditions as the first specimen or may be tested under a second set of conditions. Models may be generated for each set of conditions. For example, when “n” different specimens each having the same composition are tested under the same conditions as those to which the first specimen is exposed to, the respective models generated may be correlated with the first model (detailed above). Alternatively, the first model may be corrected using the data generated from the “n” different specimens.
In another embodiment, when “n” different specimens are exposed to “m” different sets of corrosive conditions, a plurality of different models may be obtained for each of the different specimens under each of the different conditions. These different models may be aggregated to provide an “envelope” model that provides predictive capabilities for the life cycle of each different specimen under different conditions. Mathematical algorithms derived from the model may provide predictive information as to how certain specimen compositions would behave under certain environmental conditions. The composition of the specimen may be varied based on the results obtained. Alternatively, different materials may be selected for use in different conditions based on the predictions of the model.
The models may provide predictive capabilities for generating “new” specimens with different compositions. In other words, the composition of an article may be changed to combat certain harsh environmental conditions based on the predictive capabilities of the model. Alternatively, new surface coatings such as thermal barrier coatings may be designed or used to cover specimen surfaces that are aggressively attacked under certain environmental conditions. The model can also provide information pertaining to changing the corrosive conditions to mitigate the effects of long term corrosion. The described corrosion modeling method provides a non-destructive testing and measurement process to evaluate corrosion attacks. In comparison to destructive testing methods, the non-destructive corrosion modeling method is a more time- and cost-effective process that provides improved modeling accuracy. The method can provide large, accurate datasets, which are material, geometry, and environment specific. Data collected is both time and spatially resolved. The models provide increased accuracy and predictability of the life of engineered components subjected to corrosive conditions of use.
As used herein, the terms “about” and “substantially” are intended to include the degree of error associated with measurement of the particulate quantity based upon the equipment available at the time of filing the application. For example, the terms may include a range of ±8%, or 5%, or 2% of a given value or other percentage change as will be appreciated by those of skill in the art for the particulate measurement and/or dimensions referred to herein.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof. It should be appreciated that relative positional terms such as “forward,” “aft,” “upper,” “lower,” “above,” “below,” “radial,” “axial,” “circumferential,” and the like are with reference to normal operational attitude and should not be considered otherwise limiting.
While the present disclosure has been described in detail in connection with only a limited number of embodiments, it should be readily understood that the present disclosure is not limited to such disclosed embodiments. Rather, the present disclosure can be modified to incorporate any number of variations, alterations, substitutions, combinations, sub-combinations, or equivalent arrangements not heretofore described, but which are commensurate with the scope of the present disclosure. Additionally, while various embodiments of the present disclosure have been described, it is to be understood that aspects of the present disclosure may include only some of the described embodiments. Accordingly, the present disclosure is not to be seen as limited by the foregoing description but is only limited by the scope of the appended claims.
