Patent Application
20210158287
The present specification generally relates to systems and methods for selecting components for an engine based on operating environment characteristics and manufacturing data.
Engines are assembled according to a specification. The specification for an engine generally includes a range of tolerances (e.g., relating to sizes, performance values, and the like) which are acceptable for the particular engine model. A specification may be further detailed into a bill of materials (“BOM”), which identifies components for assembly, for example, by serial number, quantity, and the like.
In a first aspect A1, a system for selecting components for an engine includes an electronic control unit including a processor and a memory component and a machine-readable instruction set stored in the memory component of the electronic control unit. The machine-readable instruction set causes the system to perform at least the following when executed by the processor: receive a catalog of a plurality of components, where the plurality of components include a measured manufacturing characteristic, receive at least one operating environment characteristic of a plurality of operating environment characteristics, and select one or more components from the catalog of the plurality of components for a bill of materials based on the measured manufacturing characteristic and the at least one operating environment characteristic.
A second aspect A2 includes the system of A1 wherein the measured manufacturing characteristic belongs to a predefined grouping of components within a distribution of variability for a predefined component type, and the grouping corresponds to at least one of the operating environment characteristics of the plurality of operating environment characteristics.
A third aspect A3 includes the system of any of the first-second aspects A1-A2, wherein the machine-readable instruction set further causes the system to determine a group for a component within one of a predefined plurality of groupings of components based on the measured manufacturing characteristic.
A fourth aspect A4 includes the system of the third aspect A3, wherein the predefined plurality of groupings of components define a distribution of variability for a specific component type.
A fifth aspect A5 includes the system of any of the third-fourth aspects A3-A4, wherein the determination of the group for the component is further based on at least one of: a cumulative damage model, a fleet model, or inspection data of the engine.
A sixth aspect A6 includes the system of any of the first-fifth aspects A1-A5, wherein the selected one or more components improve a performance asset of the engine.
A seventh aspect A7 includes the system of any of the first-sixth aspects A1-A6, wherein the selected one or more components improve a life cycle of the one or more components within the engine.
An eighth aspect A8 includes the system of any of the first-seventh aspects A1-A7, wherein the selected one or more components improve a service interval of the engine.
A ninth aspect A9 includes the system of any of the first-eighth aspects A1-A8, wherein the plurality of operating environment characteristics includes at least one of: a length of flight, a region take-off or landing temperature, an approach or landing trajectory, a take-off trajectory, a landing braking protocol, a take-off acceleration profile, or a particulate density or size in the air.
In an tenth aspect A10 a computer implemented method includes receiving, by an electronic control unit of a system, a catalog of a plurality of components, where the plurality of components include a measured manufacturing characteristic, receiving, by the electronic control unit, at least one operating environment characteristic from a plurality of operating environment characteristics, and selecting, by the electronic control unit, one or more components from the catalog of the plurality of components for a bill of materials based on the measured manufacturing characteristic and the at least one operating environment characteristic.
An eleventh aspect A11 includes the computer implemented method of the tenth aspect A10, wherein the measured manufacturing characteristic belongs to a predefined grouping of components within a distribution of variability for a predefined component type, and the grouping corresponds to at least one of the operating environment characteristics of the plurality of operating environment characteristics.
A twelfth aspect A12 includes the computer implemented method of any of the tenth-eleventh A10-A11, wherein the method further comprises determining, by the electronic control unit, a group for a component within one of a predefined plurality of groupings of components based on the measured manufacturing characteristic, wherein the predefined plurality of groupings of components define a distribution of variability for a specific component type.
A thirteenth aspect A13 includes the computer implemented method of the twelfth aspect A12, wherein the determination of the group for the component is further based on at least one of: a cumulative damage model, a fleet model, or inspection data of the engine.
A fourteenth aspect A14 includes the computer implemented method of any of the tenth-thirteenth aspect A10-A13, wherein the plurality of operating environment characteristics includes at least one of a length of flight, a region take-off or landing temperature, an approach or landing trajectory, a take-off trajectory, a landing braking protocol, a take-off acceleration profile, or a particulate density or size in the air.
In a fifteenth aspect A15, a computer program product for selecting components for an engine includes a computer readable storage medium having programing instructions embodied therewith. The programing instructions are executable by a processor to cause the processor to: receive a catalog of a plurality of components, where the plurality of components include a measured manufacturing characteristic, receive at least one operating environment characteristic from a plurality of operating environment characteristics, and select one or more components from the catalog of the plurality of components for a bill of materials based on the measured manufacturing characteristic and the at least one operating environment characteristic.
A sixteenth aspect A16 includes a computer program product of the fifteenth aspect A15, wherein the measured manufacturing characteristic belongs to a predefined grouping of components within a distribution of variability for a predefined component type, and the grouping corresponds to at least one of the operating environment characteristics of the plurality of operating environment characteristics.
A seventeenth aspect A17 includes a computer program product of any of the fifteenth-sixteenth aspect A15-A16, wherein the programing instructions are further executable to cause the processor to determine group for a component within one of a predefined plurality of groupings of components based on the measured manufacturing characteristic, wherein the predefined plurality of groupings of components define a distribution of variability for a specific component type.
An eighteenth aspect Alb includes a computer program product of the seventeenth aspect A17, wherein the determination of the group for the component is further based on at least one of: a cumulative damage model, a fleet model, or inspection data of the engine.
A nineteenth aspect A19 includes a computer program product of any of the fifteenth-eighteenth aspect A15-A18, wherein the selected one or more components improve at least one of a performance assets, a life cycle of the selected one or more components, or a service interval of the engine.
A twentieth aspect A20 includes a computer program product of any of the fifteenth-nineteenth aspect A15-A19, wherein the plurality of operating environment characteristics includes at least one of a length of flight, a region take-off or landing temperature, an approach or landing trajectory, a take-off trajectory, a landing braking protocol, a take-off acceleration profile, or a particulate density or size in the air.
These and additional features provided by the embodiments described herein will be more fully understood in view of the following detailed description, in conjunction with the drawings.
The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which:
It has been discovered that by selecting one or more components for assembly into an engine from a particular group of a distribution of acceptable components a particular engine model may be improved. For example, an engine may be improved such that maintenance is more predictable, a performance asset is improved, a life cycle of the engine is improved, and/or a service interval or on wing time is improved. The weakest thermal performer in a set of part (e.g., blades, nozzles, shrouds, liner segments, or the like) generally limits commercial engines. Embodiments of the present disclosure relate to systems and methods for selecting components for an engine based on operating environment characteristics and manufacturing data. Embodiments described herein will be described with reference to aviation engines. However, it should be understood that the systems and methods described herein are also applicable to marine engines, automobile engines, locomotive engines, stationary engines, or the like.
One or more of the embodiments described herein implement one or more algorithms that may be performed by an electronic control unit that collects, analyzes, and utilizes operating environment characteristics to determine how an engine can be improved for a particular operating environment. As used herein, “improved” may refer to but is not limited by achieving a target operating value for an engine or a performance level of at least one engine characteristic that is optimized or almost optimized for the engine operating in an environment. As used herein, “operating environment characteristics” may be used interchangeable with and/or refer to route structure characteristics, take-off and/or landing characteristics, operating locations, and/or the like. The operating environment characteristics may include characteristics such as a length of flight, duration of operation, interval and/or intensity of operation during each duration of operation, environment temperature profiles (e.g., average, minimum, and/or maximum temperatures at ground elevation, cruising altitude, or the like), acceleration profiles, which may include one or more acceleration intervals and/or jerks (i.e., rate of change of acceleration”), deceleration profiles (e.g., engine braking or reverse thrust operation; which may include one or more acceleration intervals, and/or jerks), approach and/or landing trajectories, particulate characteristics (e.g., particulate densities and/or particulate sizes), and/or the like.
The electronic control unit may determine and generate a classification for a group of operating environment characteristics. For example, a classification may be determined and/or generated for a region or route that is harsher than normal. That is, a harsher than normal region or route may include an operating environment where takeoff and landing temperatures are higher than normal, where high particulate densities or sizes are present, and/or the like which may affect the performance, life cycle, or service interval of an engine. In some embodiments, a classification may be determined and/or generated for a route that includes or requires higher than normal performance functionality and assets of an engine. For example, a flight route, which includes short runways, thus requiring more intense acceleration profiles and/or deceleration profiles, may be defined as a first classification. A second classification may be defined by routes having approach and/or landing trajectories that result in greater than normal stresses on the engine. These are only a few illustrative examples. It is understood that combinations of operating environment characteristics based on data collected from actual route analytics, cumulative damage modeling, fleet modeling, inspection data of an engine and/or the like may be utilized to define a classification.
The electronic control unit may utilize a classification to determine and select one or more components having one or more measured manufacturing characteristics that results in an engine and the components thereof where the performance, life cycle, service interval, and/or the like is improved. As used herein, the term “components” and “parts” may be used interchangeably to mean components or parts of an engine. Bill of materials may be used to identify components that are assembled into engines to fulfill production orders. The bill of materials may also be used to identify components for maintenance, repair, and/or overhaul (MRO) of an engine. Embodiments described herein may configure a bill of materials by selecting one or more components from a catalog of a plurality of components based on measured manufacturing characteristics. Measured manufacturing characteristics may include, but are not limited to, thermal barrier coating thickness, blade airflow (e.g., airflow), metal thickness, wall thickness, and/or the like.
These measured manufacturing characteristics can affect the thermal performance of the blades or the thermal life expectancy of the blades. Therefore, the thermal performance of the blades can be measured relative to the mean life or lifespan (i.e., life cycle) of the blades. The mean life of the blades can be quantified in terms of number of cycles (e.g., takeoff and landing) that a blade has before it reaches end use. For example, the blades with a robust thermal performance has on average a number of cycles more than the average blades.
The thermal barrier coating can insulate the blades from thermal fatigue thereby extending the mean life of the blades. For example, a blade with a thicker thermal barrier coating has better insulation from thermal fatigue than does a blade with a thinner thermal barrier coating. Additionally, many turbine blades are hollow airfoil that can channel airflow for internal cooling. Internal cooling can be attained by injecting a coolant inside the blades. Airflow measurements can be performed by measuring the mass rate of airflow. Airflow measurements can be performed to verify that the mass rate of airflow is within minimum and maximum limits for total blade airflow. If the minimum amount of coolant is not present, the blades can have a shorter mean life than intended. Similarly, the wall thickness of the blades can also be a determinant of mean life. The wall thickness of a blade can vary between blades during production.
Within a set of conforming blades, some blades can be more robust than others in terms of thermal performance while others may be more capable of delivering on performance assets such as fuel efficiency. As used herein, a “performance asset” of an engine may include but is not limited to fuel efficiency, fuel consumption, emissions, power, torque, operating temperature, thrust, and/or the like. For example, one blade can be flown a number of cycles while another blade can be flown a greater number of cycles. The blades can be evaluated based on thermal performance and selected for a particular engine's bill of materials based on a grouping within a standard deviation. The blades that have a similar thermal performance can be selected for a particular engine's bill of materials together so that they degrade at the same time. This selection algorithm (e.g., implemented by selection logic referred to herein) can be used to reduce downtime and/or provide for a more predictable maintenance. For example, if the blades degrade at different times, the engine would need to be taken apart to repair one blade, then another, then another and so on. In order to avoid grouping sets of blades that are too similar and cause unforeseen downstream effects, the selection algorithm can also introduce variation into the sets of blades. For example, a bill of materials for an engine can trade a component or blade with another bill of materials that is one standard deviation away about 68% of the time and two standard deviations away about 28% of the time.
Optimizing the performance, life cycle, and/or service interval of an engine to have a known distribution of performance can affect the Weibull distribution or probability distribution of the engine life cycle or performance. Instead of the component set or set of blades demanding removal because of an outlier, the proposed concept tailors the Weibull distribution to a known distribution of component performance, component life cycle, and/or component maintenance schedule where the components will degrade or perform more similarly as a set. The Weibull distributions for a gas turbine hot section parts such as high-pressure turbine stage 1 blades (HPT SIBS) are dictated by the weakest performer in the set. For example, if 61 of the 62 HPT SIBS are performing well but one has poor thermal performance, it will drive the engine off wing earlier than expected. However, embodiments described herein enable more predictive maintenance of the engine and optimization of fleet-wide performance for a particular set of operating environment characteristics.
Simulations can be employed using the cumulative damage model (CDM) to approximate the fleet impact of grouping components. In some embodiments, simulations can employ fleet models and/or inspection data of engines to approximate the impact of grouping components for a particular classification that defines one or more operating environment characteristics. A CDM can determine the condition driving distress and the number of exposures to that condition that led to accelerated distress. For example, exposures to dusty environments (e.g., high particulate densities) resulting in dust collecting on a part in turn can lead that part to become hotter in operation. An analysis can predict the number of remaining exposures the part can withstand before it should be serviced or scheduled for maintenance. Engines that are assembled with components selected utilizing the systems and methods of intelligent selection of components described herein should require service or maintenance at about the same time using the CDM simulation, fleet modeling, and/or inspection data modeling.
The detailed description disclosed herein is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.
One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.
Referring now to the drawings,
The computer processing systems, computer-implemented methods, apparatus and/or computer program products described herein can employ hardware and/or software to generate models that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. For example, the one or more embodiments can perform the lengthy and complex interpretation and analysis on a copious amount of data including operating environment characteristics and measured manufacturing characteristics to generate engine configurations that improve performance assets, life cycle, and/or a service interval or on wing time. In another example, the one or more embodiments can perform predictive analytics on a large amount of data to facilitate generating one or more models of the subset of components to automatically generate a bill of materials for assembly or maintenance, repair, or overall of an engine.
The computing device 102 may include a display 102a, a processing unit 102b and an input device 102c, each of which may be communicatively coupled together and/or to the network 100. The computing device 102 may be used to interface with a front-end application, which may utilize the system and method for selecting components for an engine. In some embodiments, one or more computing devices may be implemented to collect and/or transmit operating environment characteristics and/or measured manufacturing characteristics for components by carrying out one or more specific steps described herein. In some embodiments, the computing device 102 may represent a source of operating environment characteristics such as route characteristics for an airline. Additionally, included in
It should be understood that while the computing device 102 and the administrator computing device 104 are depicted as personal computers and the electronic control unit 103 for selecting components for an engine is depicted as a single server, these are merely examples. More specifically, in some embodiments, any type of computing device (e.g., mobile computing device, personal computer, server, and the like) may be utilized for any of these components. Additionally, while each of these computing devices is illustrated in
As also illustrated in
The processor 230 may include any processing component(s) configured to receive and execute programming instructions (e.g., instructions stored in the data storage component 236 and/or the memory component 240). The instructions may be in the form of a machine-readable instruction set stored in the data storage component 236 and/or the memory component 240 (e.g., one or more programming instructions). The input/output hardware 232 may include a monitor, keyboard, mouse, printer, camera, microphone, speaker, and/or other device for receiving, sending, and/or presenting data. The network interface hardware 234 may include any wired or wireless networking hardware, such as a modem, LAN port, Wi-Fi card, WiMAX card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices.
It should be understood that the data storage component 236 may reside local to and/or remote from the electronic control unit 103 and may be configured to store one or more pieces of data for access by the electronic control unit 103 and/or other components. The data storage component 236, may include, but is not limited to, data defining a catalog of components 238a, operating environment characteristics 238b, cumulative damage model(s) 238c, fleet models 238d, inspection data 238e, and/or bill of materials 238f. The catalog of components 238a defines a plurality of components identified by unique serial numbers that are in compliance with manufacturing specifications. The components of the catalog of components 238a further include measured manufacturing characteristics, which may be utilized when the electronic control unit 103 determines which of a set of like parts may be selected for assembly into an engine such that performance, life cycle, service intervals and/or on wing time are improved. The operating environment characteristics 238b, as described herein, may be data collected from customers, airlines, and/or modeled parameters, which may be utilized by the logic of the system to select components for an engine to improve the engine. The cumulative damage model(s) 238c, fleet models 238d, and inspection data 238e may utilize engine parameters, performance results, and actual operation details and/or maintenance histories for engines. As described herein, the system analyzes engine performance and maintenance data, optionally through simulations implementing CDMs, fleet models 238d or the like to identify more precise component specifications which can improve an engine for operation in a given operating environment characteristic or more general classifications (e.g., defined by one or more operating environment characteristics 238b). The bill of materials 238f may include a set of serial numbers, quantities, and the like defining the components for assembly or replacement in an engine.
The memory component 240 may be machine-readable memory (which may also be referred to as a non-transitory processor readable memory). The memory component 240 may be configured as volatile and/or nonvolatile memory and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components. Additionally, the memory component 240 may be configured to store operating logic 242, selection logic 244a, analysis logic 244b, and/or characterization logic 244c, each of which may be embodied as collective or individual computer programs, firmware, or hardware, as an example, and will be described in more detail herein. In some embodiments, the computer programs may be stored on a computer program product medium. Furthermore, the local interface 246, also included in
Included in the memory component 240 is the operating logic 242, selection logic 244a, analysis logic 244b, and/or characterization logic 244c. The operating logic 242 may include an operating system and/or other software for managing components of the electronic control unit 103. The selection logic 244a, when executed, can select (e.g., group, kit, organize, etc.) the components for aviation engines based on the classification of the one or more operating environment characteristics 238b by the characterization logic 244c from the measured manufacturing characteristics. For example, the components or blades can be grouped together based on measured manufacturing characteristics relevant to their thermal life expectancy (e.g., thermal performance). For example, selecting a particular group of blades having a predetermined coating thickness or other measured manufacturing characteristic can help avoid having one weak blade that fails before the others. Aviation engines can be driven off wing and placed in downtime repair by a minimally thermal protected blade. The remaining blades that still have life are then forced to be driven off wing, removed or scrapped, and extra cycles are wasted. For example, one blade can have a number of cycles while another blade can have a greater number of cycles. Randomly selecting blades can ensure that the blades do not fail at similar times; however, optimizing performance, life cycle, and/or a service internal may suffer when components are randomly selected from a distribution of acceptable components. That is, by selecting components within a particular grouping of a distribution for a characteristic that results in an improved engine where performance is improved, engine and/or component life cycle is improved, and/or a more predictable service interval when also accounting for one or more operating environment characteristics 238b.
Intelligent selection of components with similar and/or small variance can save time spent on maintenance so that the engine does not have to be taken apart because one weak blade needs to be repaired. Intelligent selection of components based on one or more operating environment characteristics 238b results in improved fleet durability performance, reduced shop visits and reduced premature scrapping of components.
The analysis logic 244b, when executed, can analyze one or more operating environment characteristics 238b to determine a combination of components that will result in an improved engine for the particular set of operating environment characteristics 238b. For example, an engine operating in a hotter than normal environment may be improved for performance if components with better air flow characteristics are installed. By way of another example, engines that operate where the operating environment characteristics 238b such as route characteristics demand higher performance (e.g., have intense acceleration or deceleration profiles), then components which fall into a distribution having a greater than average metal thickness may be optimal for such engines to increase performance assets and reduce the number of maintenance intervals for the engine. The analysis logic 244b may be configured to determine component attributes and trade-offs between the operating environment characteristics 238b and the measured manufacturing characteristics to deliver an improved engine. In some embodiments, simulations may be implemented to perform or support operations of the analysis logic 244b.
The characterization logic 244c, when executed, can receive measured manufacturing characteristics and determine groupings of components within a distribution that may be selected to meet the requirements for improved engine operation based on the one or more operating environment characteristics 238b. For example, as shown in
Referring now to
At block 320, the computer-implemented method 300 may include receiving (e.g., via the input/output hardware 232 of the electronic control unit 103) one or more operating environment characteristics 238b. The one or more operating characteristics may be received from a computing device 102, an administrator computing device 104, or another source. The one or more operating environment characteristics 238b may be generated and/or input into the system by a third party such as a customer from historical route data, a design team, a simulation, and/or the like.
At block 330, the computer-implemented method 300 may include selecting one or more components form the catalog of the plurality of components for a bill of materials 238f. The selection, for example implementing selection logic 244a as described herein, is based on the measured manufacturing characteristics and at least one operating environment characteristic. The selection is configured to select components which improve an engine for operation based on the at least one operating environment characteristic.
At block 340, the computer-implemented method 300 may include adding the one or more components to a bill of materials 238f. At block 350, the computer-implemented method 300 may include determining whether the bill of materials 238f for the engine is complete. When the bill of materials 238f is complete, for example, once each component for an engine to be assembled or repaired is identified by its serial number, then at block 360, the computer-implemented method 300 may store the bill of materials 238f in the data storage component 236. In some embodiments, the computer-implemented method 300 may transmit the bill of materials 238f to the assembly system or team and/or the repair shop or system. The engine is then repaired or assembled according to the bill of materials 238f. When the bill of materials 238f is determined to be incomplete (no at block 350), the computer-implemented method 300 may cause the system to return to block 330 or another block and continue selecting components for the bill of materials 238f.
Referring now to
This may improve the maintenance and/or service scheduling for an engine thus optimizing on wing time.
At block 440, the computer-implemented method 400 may include determining a group for a component within a plurality of groupings of components based on one or more measured manufacturing characteristics and/or one or more operating environment characteristics 238b. That is, the system at block 440 may execute the characterization logic 244c as described herein. The characterization logic 244c may operate to define the groupings within a characteristic distribution.
At block 450, the computer-implemented method 400 may include selecting one or more components from the catalog of the plurality of components for a bill of materials 238f, which is similar to the functionality depicted by block 330 with reference to the computer-implemented method 300 in
At block 470, the computer-implemented method 400 may include determining whether the bill of materials 238f for the engine is complete. When the bill of materials 238f is complete, for example, once each component for an engine to be assembled or repaired is identified by its serial number, then at block 480, the computer-implemented method 400 may end (or conclude an iteration of the method) with the storage of the bill of materials 238f in the data storage component 236. In some embodiments, at block 480, the computer-implemented method 400 may end (or conclude an iteration of the method) with the transmission of the bill of materials 238f to the assembly system or team and/or the repair shop or system. The engine is then repair or assembled according to the bill of materials 238f. When the bill of materials 238f is determined to be incomplete (NO at block 470), the computer-implemented method 400 may cause the system to return to block 450 or another block and continue selecting components for the bill of materials 238f.
It should now be understood that engines assembled or maintained utilizing the systems and methods described herein allow for better prediction of engine service, and optimization of engine placement based on sophisticated inspection and models (i.e., placement within regions or environment where performance of the engine, life cycle of the components, and/or maintenance schedules for the engine is improved).
Turning now to
The HPT S1Bs of the turbine 510 are in the hot section of the aviation engine 500 where thermal fatigue can degrade the blades. The temperature of the turbine 510 can reach over 2,000° F. (1,093° C.). This is may be especially true for aviation engines 500 that operate in higher than average temperatures, for example. As a result, the engines generally may require more frequent maintenance. However, by assembling and/or maintaining an aviation engine 500 utilizing the systems and methods described herein the components of such an engine may be improved to handle higher than average operating temperatures and thus improve the frequency of maintenance and/or the components life cycle. By selecting components for an engine based on the one or more operating environment characteristics 238b, one or more performance assets, a life cycle and/or service intervals of the engine may be improved.
Similarly, the systems and methods described herein with respect to an aviation engine 500 may also be applied to the assembly and maintenance of an automobile engine 600.
Referring now to
For example, the distribution curve 702 includes three groupings (i.e., a first group A, a second group B, and a third group C). By way of a non-limiting example, the distribution curve 702 may represent the variation in a coating thickness of a component after manufacturing. The first group A may include those components with a thin coating thickness but a coating thickness within an acceptable manufacturing tolerance. The second group B may include those components with a coating thickness that is closest to the target coating thickness and the third group C may include those components that include a coating thickness that is thicker but within the acceptable manufacturing tolerance. A component with a coating thickness that is thicker than the target manufacturing thickness may be advantageous to use in an engine that operates in an environment having a higher than normal particulate density and/or size in the air. That is, a component such as a blade may take more wear with a thicker coating before needing to be replaced or serviced, thereby optimizing a service interval (e.g., on wing time) for the engine.
It should now be understood that engines assembled or maintained utilizing the systems and methods described herein provide for better prediction of engine service, optimization of engine placement based on analysis of one or more operating environment characteristics through inspection and modeling. As such, engines manufactured with a select set of components may be allocated to regions of operation where they are improved (i.e., performance of the engine, life cycle of the components, and/or maintenance schedules for the engine is improved).
Moreover, it is understood that systems and methods describe herein relate to selecting components for an aviation engine based on operating environment characteristics and measured manufacturing characteristics. Systems may include an electronic control unit having a processor and a memory component and a machine-readable instruction set stored in the memory component of the electronic control unit. The machine-readable instruction set, when executed by the electronic control unit, may cause the system to receive a catalog of a plurality of components, where the plurality of components include a measured manufacturing characteristic, receive at least one operating environment characteristic of a plurality of operating environment characteristics, and select one or more components from the catalog of the plurality of components for a bill of materials based on the measured manufacturing characteristic and the at least one operating environment characteristic, where the selection of the one or more components improves the aviation engine.
It is noted that the term “about” may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. This and other terms are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.
While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.
