Method for determining corrosion of object
Patent Information
- Application Number
- JP2024094091
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-07-14
- Filing Date
- 2024-06-11
- Publication Date
- 2025-03-05
Smart Images
Figure 2025028814000001 
Figure 2025028814000002 
Figure 2025028814000003
Abstract
Description
[Technical field]
[0001] FIELD OF THE DISCLOSURE
[0001] The present disclosure relates generally to the field of corrosion identification, and more particularly, to the field of corrosion identification based on physical and environmental factors. [Background technology]
[0002]
[0002] Vehicles (e.g., aircraft, ships, trucks, automobiles, trains and other rail-based transportation, spacecraft, submarines, and trailers), equipment, and buildings, as well as other stationary or non-stationary objects or structures (such vehicles, equipment, buildings, objects, and structures collectively referred to hereinafter as "objects") are constructed from materials that are susceptible to corrosion. This applies to entire objects as well as smaller components or portions thereof. Corrosion can be particularly problematic for objects that are used or deployed under harsh environmental conditions.
[0003]
[0003] Materials from which objects are constructed can experience various types of corrosion. One example involves galvanic corrosion, which occurs when two dissimilar materials come into contact with a corrosive electrolyte and a small current flows due to the electrochemical potential difference separating them. Another example of corrosion is pitting corrosion, which results from environmental influences.
[0004]
[0004] Regardless of how corrosion occurs, it is a major factor in determining an object's overall lifespan and maintenance schedule, and in most cases, such lifespan is shortened compared to if corrosion could be somehow prevented or its effects reduced. As an example of the adverse effects of corrosion, corrosion problems on military equipment alone are estimated to cost the U.S. Department of Defense nearly $20 billion per year.
[0005]
[0005] Accurately identifying the effects of corrosion when designing an object is difficult, and in reality, doing so generally requires design experience, subjective decision-making, and knowledgeable, skilled designers.
[0006]
[0006] Prior solutions to identify the effects of corrosion on objects attempt to identify physics-based computational models of material interactions. However, such models are notoriously difficult to predict, and data on which they can be relied upon with precision is often scarce. In addition, the complex physical phenomena involved often require computationally expensive first-principles modeling, which can take a long time to obtain a solution. Empirical models of corrosion have also been developed, but they require extensive and expensive testing to build, and because of the typical time scales for corrosion damage to occur, they are very long to compare and validate. Furthermore, empirical models often do not include time- and / or position-dependent factors in corrosion damage, instead providing only a gross estimate under specific environmental conditions.
[0007]
[0007] Furthermore, many design processes do not consider corrosion until a complete object design exists. This may occur after the object has been designed, manufactured, and is in use in the field. In some instances, corrosion analysis is not performed until after damage has been observed on the object. One current procedure is to perform a root cause analysis (RCA) to identify the cause of corrosion. RCA does not represent an ideal solution because it is not used to design the object, but rather to repair the problem after corrosion has already occurred. Summary of the Invention
[0008]
[0008] One aspect is directed to a method for identifying corrosion of an object occurring over a period of time, the method including identifying a physical factor of the object, identifying an environmental factor occurring during the period of time, identifying a plurality of nodal points on the object, identifying corrosion of the object at the plurality of nodal points at a plurality of times during the period of time based on the physical factor and the environmental factor, and identifying cumulative corrosion of the object based on the identified corrosion at each of the plurality of nodal points at each of the plurality of times.
[0009] In another aspect, the method further includes identifying the environmental factors based on historical weather data for one or more geographic locations in which the object is located during a period of time.
[0010]
[0010] In another aspect, identifying the physical factors includes identifying a shape of the object and a material of the object.
[0011]
[0011] In another aspect, the method further includes determining whether a wet condition occurs for each of the multiple times, and determining that the corrosion is zero when the wet condition does not occur.
[0012]
[0012] In another aspect, the method further includes determining an external force applied to the object over a period of time.
[0013]
[0013] In another aspect, the method further includes identifying a mesh extending across the object and identifying a plurality of node points based on the mesh.
[0014]
[0014] In another aspect, the method further includes identifying a physical factor of the object based on the date range and geographic location received from the user.
[0015]
[0015] One aspect is directed to a method of identifying corrosion of an object occurring over a period of time, the method including identifying physical and environmental factors of a geographic location of the object for the period of time, identifying corrosion at a plurality of spaced nodal points on the object at each of a plurality of times occurring during the period of time, and accumulating the corrosion at the plurality of nodal points for the plurality of times to identify accumulated corrosion of the object.
[0016]
[0016] In another aspect, the method further includes receiving input including a date range of a period of time, a geographic location, and an identity of the object.
[0017]
[0017] In another aspect, the method further includes receiving a geometric model of the object and determining a positioning of a plurality of node points on the object based on the geometric model.
[0018] In another aspect, the environmental factors include two or more of relative humidity, precipitation, temperature, and dew point.
[0019]
[0019] In another aspect, the method further includes determining whether a wet condition occurs at a plurality of node points at each of a plurality of times, and determining that there is no corrosion at the plurality of node points for the plurality of times when a wet condition does not occur.
[0020]
[0020] In another aspect, the period of time is a past period that has already occurred, and the environmental factors are based on weather conditions that occurred at the geographic location during the period of time.
[0021]
[0021] In another aspect, the physical and environmental factors include predictive factors based on conditions the object is expected to encounter during use.
[0022]
[0022] In another aspect, the object is an aircraft and the geographic location includes multiple locations to which the aircraft has traveled over a period of time.
[0023]
[0023] One aspect is directed to a computing device configured to identify corrosion on an object. The computing device comprises a memory circuit that stores program instructions and a processing circuit configured to execute the program instructions. The program instructions, when executed, cause the computing device to receive a date range, a geographic location of the object, and an identity of the object, identify a plurality of node points on the object based on the identity of the object, identify an external force applied to the plurality of node points for a plurality of times during a period of time, identify a film thickness at the plurality of node points, identify an environmental condition at the plurality of node points, and identify corrosion at the plurality of node points based on the external force, the film thickness, and the environmental condition.
[0024]
[0024] In another aspect, the processing circuitry is further configured to determine cumulative corrosion of the object based on corrosion at multiple node points for multiple times.
[0025]
[0025] In another aspect, the processing circuitry is further configured to determine wetting conditions at the multiple node points at each of the multiple times and to determine film thicknesses at the multiple node points based on the wetting conditions.
[0026]
[0026] In another aspect, the processing circuitry is further configured to determine that there is no film thickness at the multiple node points when a wet condition does not occur.
[0027]
[0027] In another aspect, the processing circuitry is further configured to determine from a remote source environmental factors acting on the object during a period of time that includes multiple times.
[0028]
[0028] The above-described features, functions, and advantages can be realized alone in various aspects or can be combined in further multiple aspects, details of which can be confirmed by referring to the following description and the attached drawings. [Brief description of the drawings]
[0029] [Figure 1]
[0029] FIG. [Diagram 2]
[0030] 1 is a flow chart of a method for identifying corrosion on an object. [Diagram 3]
[0031] 1 is a flow chart of another method for identifying corrosion on an object. [Figure 4]
[0032] FIG. 1 is a schematic diagram of a computer design of an object having a mesh including a number of node points. [Diagram 5]
[0033] FIG. 1 is a schematic diagram of a computing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0030]
[0034] This application is directed to a method for identifying corrosion of an object. The method considers the development of continuous or discontinuous films that may change over time and that differ across the surface shape of the object. The method can be used to predict the corrosion of an existing object that has been exposed to environmental conditions that may cause corrosion over a date range. The method can also be used to predict the corrosion of an object that will be exposed to a specific environmental condition.
[0031]
[0035] 1 illustrates examples of objects 90 associated with an aircraft. In some examples, the object 90A is the entire aircraft. In other examples, the object 90 is one or more limited sections of the vehicle, such as a fuselage 90B, a wing 90C, a flight control member 90D, and an engine cover 90E. Other examples in the aircraft context include, but are not limited to, ribs, spars, stringers, and engine components.
[0032]
[0036] The method for identifying corrosion uses one or more factors, examples of which include the physical factors of the object 90 and environmental factors in which the object 90 is located or through which the object 90 is moved, visited, or transported, and the method avoids the need for computationally expensive high-fidelity simulation techniques while still retaining sufficient fidelity to identify time-dependent, position-dependent corrosion damage locations on the object 90 in a variety of environmental conditions.
[0033]
[0037] FIG. 2 illustrates a method for identifying corrosion of an object. The method includes identifying one or more physical factors of the object (Block 100). The physical factors may include, but are not limited to, the material from which the object is constructed, and may include, but are not limited to, one or more of the substrate and finish of the material. Another physical factor is the thickness of the material, including, but are not limited to, the thickness of the substrate and finish. The physical factors also include, but are not limited to, the shape of the object. Examples of shapes include surface features such as curvatures, corners, edges, openings, punch-outs, coupons, and seams. The physical factors may also include connections between two or more different sections, such as, but are not limited to, rivets, bolts, seams, welds, joints, and bonds.
[0034]
[0038] The method also includes identifying environmental factors (block 102). Examples include one or more weather conditions, which may include, but are not limited to, one or more of relative humidity, climate, precipitation, air temperature, dew point, dry or wet bulb temperature, wind speed, wind direction, and / or solar irradiance. Another weather condition is distance from the ocean, which is a major factor in the amount of salinity in the air (i.e., air along the coast is more salty than air away from the ocean). Air pollution, smoke (e.g., from wildfires), and smog are also non-limiting examples that may be included as environmental factors.
[0035]
[0039] The method uses these factors to identify corrosion of the object at one or more times during a given time period (block 104). The time period for testing and the times for testing within the time period may be user defined or may be default settings. In one non-limiting example, the time period is six months and testing is performed once a week.
[0036]
[0040] In some embodiments, the determination of corrosion at one or more times is a final output. In other embodiments, the method also includes determining cumulative corrosion of the object based on the determination of corrosion from the various times (block 106).
[0037]
[0041] In some examples, identifying corrosion on an object is performed on an existing object, such as identifying corrosion on a wing assembly that is part of an aircraft and has been in continuous or active use during a given period of time (e.g., corrosion on a wing that has been in service for 9 months), the physical factors are defined from the existing object, and the environmental conditions are based on known weather and / or climate conditions that have occurred during a period of time.
[0038]
[0042] In some embodiments, the corrosion identification uses the proposed design and / or artificial weather conditions. In some embodiments, the proposed design uses engineering specifications for the object. The weather conditions can be based on various values, such as average values for a given geographic location, or record highs or lows for a given geographic location. The test essentially identifies the corrosion that would have occurred if the object had been exposed to the weather conditions.
[0039]
[0043] 3 illustrates a method for identifying corrosion on an object. This example uses an existing object that has been exposed to weather conditions over a given date range. The method for identifying corrosion is generally divided into an input section 20, a calculation section 30, and an output section 50.
[0040]
[0044] The input section 20 defines one or more factors for identifying corrosion on an object. These factors may be entered by a user or may be part of an automated process that analyzes corrosion. The factors include a date range and a geographic location (block 21). The geographic location may include various levels of detail. Examples include, but are not limited to, a country, a region, a city, a specific address (e.g., an airport address, a building address), and a latitude / longitude. For an object that moves during a date range (e.g., an aircraft), the geographic location may include multiple locations. In one example for an object that is an aircraft, the geographic location includes each of the destinations where the aircraft landed during a given period of time.
[0041]
[0045] Another input is the time schedule (block 22). The time schedule provides a frequency for the corrosion calculations. Some examples include, but are not limited to, monthly, weekly, and hourly.
[0042]
[0046] The input section 20 also includes physical factors (block 23). Examples of physical factors include, but are not limited to, material, thickness, shape, surface geometry, and connections. In some embodiments, the physical factors are a particular make / model of the object (e.g., a 1997 BMW 328i sedan). In another embodiment, a user inputs an engineering drawing of the object, including shape and thickness. In some embodiments, one or more remote sources 70 are accessed to obtain relevant data (e.g., the surface geometry of a Boeing 737 aircraft).
[0043]
[0047] The calculation section 30 uses factors from the input section 20 to calculate the erosion of the object. Environmental factors are identified for the input date range and geographic location (block 31). In one embodiment, the environmental factors are accessed from one or more remote sources 70, including environmental data such as historical weather data, climate data, and sensor measurements. The remote sources 70 may include, but are not limited to, various databases, sites available over a network (e.g., the Internet), and various model sources. In some embodiments, the databases may be either external (e.g., publicly available APIs) or internal (e.g., files or internal server collections of measurements). Examples of remote sources 70 available over a network include, but are not limited to, the National Oceanic and Atmospheric Administration (NOAA), Integrated Land Surface Database (ISD), Meteorological Assimilation Data Collection System (MADIS), and Parameter Elevation Regression on Independent Slope Model (PRISM).
[0044]
[0048] In some embodiments, one or more of the environmental factors are based on sensor readings. The object includes one or more sensors that monitor one or more environmental conditions. The readings from the one or more sensors are stored and accessed as needed to provide the environmental data. In one particular embodiment, the object is an aircraft that includes multiple sensors that monitor environmental conditions.
[0045]
[0049] In cases where environmental factors are not available for the date range and / or geographic location, data is obtained by interpolation using known data (block 32). A location transformation and date transformation identifies the closest available environmental and geographic data. In one particular embodiment, a location transformation converts the input latitude / longitude to the closest weather collection station for which environmental data is available. In some embodiments, this step (block 32) includes supplemental calculations to identify further environmental data (e.g., identifying dew point based on other known parameters).
[0046]
[0050] One or more of the physical factors are used to identify a mesh 80 of the object (block 33). The mesh 80 corresponds to the surface shape of the object. In one embodiment shown in FIG. 4, the mesh 80 includes an interlaced network of node points 81 extending along the surface of the object. The number and positioning of the node points 81 on the object can vary. In some embodiments, the node points 81 are evenly spaced and positioned around the object, as shown in FIG. 4. Other embodiments include node points 81 positioned on one or more limited sections of the object, such as, but not limited to, a particular surface or exposed edge. In some embodiments, the mesh 80 is a geometry transformation from a digital file, such as a CAD geometry file. Other embodiments include a mesh 80 based on a spline geometry and a mesh 80 based on user-provided data, such as position coordinates for the node points 81.
[0047]
[0051] The calculation assigns initial conditions to node points 81 (block 34). In some embodiments, the initial conditions are based on user input. In other embodiments, the initial conditions include physical and / or environmental factors identified for the geographic location at the start of date range 20.
[0048]
[0052] The process starts the corrosion calculation for the first time during the time schedule. Environmental factors for that time are obtained (block 35). In some embodiments, physical factors are also obtained. The method identifies external forces to be applied to the node points 81 (block 36). Examples of external forces include, but are not limited to, gravity based on the direction of gravity relative to the mesh 80 (resulting in a static, non-moving force distribution), force schedules over the time schedule caused by objects as they move (e.g., forces applied to an aircraft in flight), wind forces, thermal forces (e.g., radiative heating due to reflected solar radiation), convective forces (e.g., forces due to wind moving across the surface of the mesh at a prescribed speed and direction), washing of the object, rinsing of the object, and changes in concentration of species in the surrounding environment. The force schedule 74 may be input by a user, stored in memory, and / or obtained from a remote source 70.
[0049]
[0053] The process identifies the presence of an electrolyte film at the node point 81 (block 37). The presence of a film is calculated by an associated wetness condition 75 based on one or more of environmental and physical factors. In some embodiments, a wetness condition occurs due to the following conditions: the relative humidity (RH) at the node point 81 exceeds a predetermined level, the temperature at the node point 81 is below a dew point temperature, and a proximate environmental condition at the node point 81, such as a wall temperature, is below the dew point. If a wetness condition is identified, the process identifies the film thickness. If a wetness condition does not exist, the process determines that there is no corrosion for the given time. In some embodiments, the process assumes that the wetness condition is met and automatically proceeds to identify the film thickness.
[0050]
[0054] The film thickness is calculated via a film thickness model 71. The film thickness model 71 may be accessed from a remote source 70 and / or held in a memory circuit. Film thickness calculations may include a variety of methods. Examples include, but are not limited to, analytical formulas (e.g., Nusselt film model formulas), heuristics based on empirically measured experimental values, model fitting, machine learning models, film models (e.g., for situations such as ice formation), and more advanced physical simulations such as full computational fluid dynamics simulations of the film.
[0051]
[0055] Next, environmental factors are identified for the nodal points 81 (block 38). In some embodiments, one or more of the environmental factors (e.g., relative humidity, dew point) are the same for each nodal point 81. In some embodiments, the process identifies proximate environmental factors for the nodal points 81. The term "proximate" refers to the conditions closest to the nodal point 81. In some embodiments, the proximate calculation uses a physical model 72, such as a heat transfer model, to identify a proximate temperature for the film temperature. In some embodiments, the heat transfer model calculates multiple layers. In one particular embodiment, the calculation uses an ambient temperature. For this ambient temperature, an internal film temperature is identified from a convective heat transfer model, and from this internal film temperature, a proximate temperature is calculated from a conductive heat transfer model.
[0052]
[0056] Once the physical and environmental factors are known, the method calculates the corrosion damage at the nodal point 81 (block 39). In some embodiments, the process uses a corrosion model 73. The corrosion model 73 can take a variety of forms, examples of which include, but are not limited to, analytical expressions (e.g., mathematical functions of proximate environmental conditions), empirically determined model fits, machine learning models, and more advanced calculations such as macromolecular, chemical, or Monte Carlo simulations. The resulting corrosion damage at the nodal point 81 is stored for a given time in a time schedule (block 40).
[0053]
[0057] The process then determines whether there are any additional times in the time schedule (block 41). At each additional time, corrosion damage is identified for the nodal point 81. Once corrosion has been identified for each of the times, data is analyzed for the object 90. In some embodiments, this concludes the process.
[0054]
[0058] In other embodiments, one or more post-processing steps calculate one or more additional desired properties (block 42). In some embodiments, the post-processing includes calculating the total mass loss across the complete exposed surface of the feature. This may include summing all corrosion rates across the nodal points 81 for multiple times in the time schedule. In some embodiments, the post-processing includes calculating a maximum corrosion rate. This may include calculating the supremum of the corrosion rates across the nodal points 81 across the times in the time schedule. In some embodiments, the post-processing includes calculating the total time of wetting (TOW), the presence of moisture across the object 90, the total / average / distributed film thickness, and / or the corrosion current density. In some embodiments, the post-processing includes integrating the corrosion current density to determine the total accumulated charge due to corrosion activity.
[0055]
[0059] The output section 50 outputs various corrosion data (block 51). Examples of outputs include corrosion data for the node points 81, accumulated corrosion data for the object 90, and various other data calculated during post-processing steps. The output may include corrosion data for one or more of a number of times from a time schedule, or an accumulated amount of corrosion taking into account corrosion over various input times.
[0056]
[0060] In some examples, the process is performed during the design phase using laboratory and / or virtual testing to identify corrosion predictions. The physical factors used for corrosion calculations may be the proposed design details of the proposed object 90. This allows the proposed design details of the object 90 to be tested and any necessary changes due to corrosion risks to be made early in the design process and before actual manufacturing. Additionally and / or alternatively, the environmental factors may be data other than historical recorded data. In some examples, average environmental conditions for a given geographic location may be used for corrosion testing. In other examples, higher level conditions may be used to ensure a safety margin (e.g., 95th percentile relative humidity, dew point temperature during testing).
[0057]
[0061] In another aspect, one or more of the inputs 20 are predicted values based on conditions the object is expected to encounter during use. For example, during the design stage of the object, a designer / engineer assigns values to one or more of the environmental and / or physical factors expected to be experienced by the object.
[0058]
[0062] 5 illustrates a computing device 60 configured to perform corrosion calculations. The computing device 60 may include a variety of different forms, including, but not limited to, a server computer, a workstation computer, a desktop computer, a smart phone, a tablet computer, and a laptop computer. The computing device 60 may include one or more of each of several components, such as, for example, a processing circuit 61 (e.g., a processor unit) connected to a memory circuit 62 (e.g., a storage device).
[0059]
[0063] The processing circuitry 61 may consist of one or more processors alone or in combination with one or more memories. The processing circuitry 61 is generally any computer hardware capable of processing information, such as, for example, data, computer programs, and / or other suitable electronic information. The processing circuitry 61 is comprised of a collection of electronic circuits, some of which may be packaged as an integrated circuit or multiple interconnected integrated circuits (integrated circuits are sometimes more commonly referred to as "chips"). The processing circuitry 61 may be configured to execute program instructions 79, such as a film thickness model 71, a physics model 72, and an corrosion model 73. The program instructions 79 may be stored on-board the processing circuitry 61 or otherwise in a memory circuitry 62 (of the same or another device).
[0060]
[0064] The processing circuitry 61 may be several processors, a multi-processor core, or some other type of processor, depending on a particular implementation. Furthermore, the processing circuitry 61 may be implemented using several heterogeneous processor systems (wherein a main processor is on a single chip with one or more secondary processors). As another illustrative example, the processing circuitry 61 may be a symmetric multi-processor system that includes multiple homogeneous processors. In yet another example, the processing circuitry 61 may be embodied as or otherwise include one or more ASICs, FPGAs, and the like. Thus, the processing circuitry 61 may be capable of executing a computer program to perform one or more functions, although the processing circuitry 61 of various embodiments may be capable of performing one or more functions without the aid of a computer program. In any case, the processing circuitry 61 may be appropriately programmed to perform functions or operations according to the exemplary implementations of the present disclosure.
[0061]
[0065] The memory circuit 62 is generally computer hardware capable of storing information, such as, for example, data, computer programs (e.g., computer-readable program code), and / or other suitable information, either temporarily and / or permanently. The memory circuit 62 may include volatile and / or non-volatile memory and may be fixed or removable. Examples of suitable memory circuits 62 include random access memory (RAM), read-only memory (ROM), hard drives, flash memory, thumb drives, removable computer diskettes, optical disks, magnetic tapes, or any combination thereof. Optical disks may include read-only compact disks (CD-ROMs), read / write compact disks (CD-R / WCs), DVDs, and the like. In various instances, the memory circuit 62 may be referred to as a computer-readable storage medium. A computer-readable storage medium is a non-transitory device capable of storing information and may be distinguished from a computer-readable transmission medium, such as a transitory electronic signal capable of conveying information from one place to another. Computer-readable media as described herein may generally refer to computer-readable storage media or computer-readable transmission media.
[0062]
[0066] The computing device 60 also includes a communications interface circuit 63 configured to transmit and receive information to and from a remote source 70, a user device, other device(s), network(s), etc. The communications interface 63 may be configured to transmit and / or receive information over physical (wired) and / or wireless communications links. Examples of suitable communications interfaces include a network interface controller (NIC), a wireless NIC (WNIC), etc. The communications interface circuit 63 may include one or more transmitters and / or receivers.
[0063]
[0067] The user interface 64 allows a user to control one or more aspects of the corrosion test. The user interface 64 includes one or more input devices 65, such as, but not limited to, a keypad, a touchpad, a roller ball, and a joystick. The user interface 64 also includes one or more displays 66 for displaying information about the test and / or for the operator to input commands to the processing circuitry 61.
[0064]
[0068] As will be recognized by those skilled in the art without undue experimentation, program instructions 79 may be loaded from a computer-readable storage medium into a computer or other programmable device to create a particular machine, such that the particular machine is a means for implementing the functions specified herein. These program instructions 79 may also be stored on a computer-readable storage medium and instruct a computer, processing circuit, or other programmable device to function in a particular manner, thereby creating a particular machine or a particular product. Program instructions 79 may be retrieved from a computer-readable storage medium and loaded into a computer, processing circuit, or other programmable device to configure the computer, processing circuit, or other programmable device to perform the operations performed on or by computing device 60, processing circuit 61, or other programmable device.
[0065]
[0069] The reading, loading, and execution of the program instructions 79 may be performed serially, such that one instruction is read, loaded, and executed at a time. In some example implementations, the reading, loading, and / or execution may be performed in parallel, such that multiple program instructions 79 are read, loaded, and / or executed in unison. Execution of the program instructions 79 may produce a computer-implemented process, whereby the program instructions 79, when executed by a computer, processing circuit, or other programmable device, provide operations for performing the functions described herein.
[0066]
[0070] The present invention allows prediction of corrosion damage behavior over time in changing environments where an object may be stored or used. The application considers the development of continuous or discontinuous films that may change over time and differ across the surface geometry of the vehicle platform. This can be used to predict corrosion damage or potential moisture damage issues in objects 90 stored for long periods of time. The present invention can also be used to study and predict the effect of environmental changes on damage caused by corrosion during storage or use.
[0067]
[0071] The present application may be practiced in other ways than those specifically described herein without departing from the essential characteristics of the invention. The present embodiments are to be considered in all respects as illustrative and not restrictive, and all changes that come within the meaning and range of equivalency of the appended claims are intended to be embraced therein.
Claims
1. 1. A method for identifying corrosion of an object (90) occurring over a period of time, comprising: identifying a physical attribute of the object (90); identifying environmental factors occurring during said period of time; Identifying a plurality of nodal points (81) on the object (90); identifying the corrosion of the object (90) at the plurality of nodal points (81) based on the physical and environmental factors at a plurality of times during the period of time; and determining cumulative erosion of the object (90) based on the erosion determined at each of the plurality of nodal points (81) at each of the plurality of times.
2. The method of claim 1 , further comprising identifying the environmental factors based on historical weather data for one or more geographic locations in which the object (90) is located during the period of time.
3. The method of claim 1 , wherein identifying the physical factors includes identifying a shape of the object (90) and a material of the object (90).
4. The method of claim 1 , further comprising: for each of the plurality of times, determining whether a wet condition occurs; and determining that the corrosion is zero when the wet condition does not occur.
5. The method of claim 1 , further comprising determining an external force applied to the object (90) over the period of time.
6. The method of claim 1, further comprising identifying a mesh (80) extending across the object (90) and identifying the plurality of nodal points (81) based on the mesh (80).
7. The method of claim 1 , further comprising identifying the physical factor of the object (90) based on a date range and a geographic location received from a user.
8. 1. A method for identifying corrosion of an object (90) occurring over a period of time, comprising: determining physical and environmental factors of the geographic location of the object (90) for a period of time; identifying corrosion at a plurality of spaced nodal points (81) on the object (90) at each of a plurality of times occurring during the period of time; and accumulating the erosion at the plurality of nodal points (81) for the plurality of times to determine an accumulated erosion of the object (90).
9. The method of claim 8, further comprising receiving input including a date range of the period of time, the geographic location, and an identity of the object (90).
10. 9. The method of claim 8, further comprising receiving a geometric model of the object (90) and determining a positioning of the plurality of nodal points (81) on the object (90) based on the geometric model.
11. The method of claim 8 , wherein the environmental factors include two or more of relative humidity, precipitation, temperature, and dew point.
12. determining whether a wet condition occurs at the plurality of nodal points (81) at each of the plurality of times; and 9. The method of claim 8, further comprising determining that there is no corrosion at the plurality of nodal points (81) for the plurality of times when the wet condition does not occur.
13. The method of claim 8 , wherein the period of time is a past period that has already occurred, and the environmental factors are based on weather conditions that occurred at the geographic location during the period of time.
14. The method of claim 8 , wherein the physical and environmental factors include predictive factors based on conditions the object (90) is expected to encounter during use.
15. 9. The method of claim 8, wherein the object (90) is an aircraft (99) and the geographic locations include a plurality of locations to which the aircraft (99) has traveled during the period of time.
16. 1. A computing device configured to identify corrosion on an object (90), comprising: a memory circuit (62) for storing program instructions (79); a processing circuit (61) configured to execute the program instructions, which, when executed, cause the computing device to: receiving a date range, a geographic location of the object (90), and an identity of the object (90); identifying a plurality of node points (81) on the object (90) based on the identity of the object (90); For multiple times during a period, identifying an external force applied to said plurality of nodal points (81); determining a film thickness at said plurality of nodal points (81); identifying environmental conditions at said plurality of nodal points (81); and identifying corrosion at the plurality of nodal points (81) based on the external force, the film thickness, and the environmental condition.
17. 17. The computing device of claim 16, wherein the processing circuitry (61) is further configured to determine cumulative erosion of the object (90) based on the erosion at the plurality of node points (81) for the plurality of times.
18. 17. The computing device of claim 16, wherein the processing circuitry is further configured to determine a wetting condition for the plurality of nodal points at each of the plurality of times and to determine the film thickness at the plurality of nodal points based on the wetting condition.
19. 20. The computing device of claim 18, wherein the processing circuitry is further configured to determine that the plurality of nodal points are absent of film thickness when the wet condition does not occur.
20. 17. The computing device of claim 16, wherein the processing circuitry (61) is further configured to determine from a remote source environmental factors acting on the object (90) during a period that includes the plurality of times.