System and method for determining engine operation during taxi of a multi-engine aircraft

Abstract

The present application relates to systems and methods for determining engine operation during taxi of a multi-engine aircraft. Systems and methods of determining taxi of an aircraft having a plurality of engines. The system can include a database operable to store collected historical data regarding conditions that affect one or more engine characteristics, where the one or more engine characteristics include fuel usage, engine wear, noise, emissions, and effects on related aircraft systems. A computing device is in communication with the database and is operable to: train a machine learning model using the historical data; determine an expected outcome of an engine characteristic for each engine; and determine which of the one or more engines not to use during taxi of the aircraft.

Description

Technical Field

[0001] This application relates to the taxiing of an aircraft, and more specifically, to a system and method for evaluating the aircraft's engines and determining which of one or more engines should not be used during taxiing. Background Technology

[0002] Many aircraft include multiple engines. Engines can be positioned at various locations along the aircraft (including along the wings, fuselage, and tail section). Engines generate thrust, which propels the aircraft during flight. Engines also provide thrust for propelling the aircraft during taxiing on the ground. Taxiing occurs before flight to move the aircraft from the terminal to the runway, and also after flight to move the aircraft from the runway to the terminal.

[0003] The flight crew determines how to operate the engines during taxiing. The manner of operation affects one or more characteristics, including but not limited to fuel consumption, engine wear, the operation of other systems on the aircraft, engine emissions, and the overall noise generated by the aircraft during taxiing. Flight crew may operate the engines in various ways during taxiing. In some examples, flight crew rely on a default pattern used during each taxiing event. For example, some flight crew use all engines to propel the aircraft during taxiing. In other examples, flight crew operate the engines based on their experience or for arbitrary reasons. For example, a flight crew always uses only one engine (e.g., the starboard engine) during taxiing.

[0004] Current systems and methods do not rely on engine analysis to determine engine operation during coasting. This can lead to inefficient engine operation, failing to optimize one or more engine characteristics. Therefore, engine analysis is needed to determine how the engine should operate during coasting. Summary of the Invention

[0005] One aspect relates to a method for taxiing an aircraft having multiple engines. The method includes: collecting historical data on conditions affecting one or more engine characteristics during taxiing; determining current conditions affecting the one or more engine characteristics during taxiing; generating expected results for one or more engine characteristics for each engine and based on the historical data and current conditions; and determining, based on the expected results of the one or more engine characteristics, to taxi without using one or more engines and with a limited number of engines.

[0006] On the other hand, one or more engine characteristics include fuel use, engine wear, noise, emissions, and the impact on related aircraft systems.

[0007] In another aspect, the method also includes operating one of the aircraft's engines during taxiing and shutting down the remaining engines during taxiing.

[0008] In another aspect, the method also includes: training a machine learning model using historical data; and using the machine learning model and current conditions to generate expected results for one or more engine characteristics for each engine.

[0009] In another aspect, the method also includes receiving current conditions from one or more sensors mounted on the aircraft.

[0010] In another aspect, the method also includes: collecting historical data from the engine manufacturer, wherein the historical data includes theoretical values; and generating expected results for one or more engine characteristics for each engine based on the theoretical values.

[0011] On the other hand, historical data includes weather conditions at the airport where the aircraft is taxiing, and current conditions include the current weather conditions at the airport.

[0012] On the other hand, historical data includes data from only the time when the aircraft is gliding.

[0013] One aspect relates to a method for taxiing an aircraft having multiple engines. The method includes: storing historical data for each engine that affects engine characteristics, wherein engine characteristics include one or more of fuel usage, engine wear, noise, emissions, and related aircraft system effects; training a computer machine learning model using the historical data; determining current data on conditions affecting the engine characteristics; determining, based on the machine learning model and the current conditions, which of the one or more engines is not used for taxiing; and using a limited number of engines to taxi the aircraft.

[0014] In another aspect, the method also includes: generating expected results for engine characteristics of each engine based on a machine learning model and current conditions; and determining which of the one or more engines should not be used based on the expected results for engine characteristics of each engine.

[0015] In another aspect, the method also includes using an engine with the highest expected results in terms of engine characteristics to propel the aircraft, and not using one or more other engines with lower expected results in terms of engine characteristics.

[0016] In another aspect, the method also includes sending a signal to the aircraft's cockpit to shut down which of one or more engines.

[0017] In another aspect, the method also includes collecting historical data about each engine and storing the historical data in a database.

[0018] In another aspect, the method also includes collecting historical data from engine manufacturer data, which is based on theoretical data about the engine's engineering aspects.

[0019] In another aspect, the method also includes collecting historical data from engines on other aircraft that share one or more characteristics with the engine.

[0020] On the other hand, historical data includes weather conditions at the airport where the aircraft is taxiing, and current data includes current weather conditions at the airport.

[0021] In another aspect, the method also includes taking into account the characteristics of each engine and determining which of one or more engines should not be used.

[0022] One aspect relates to a system for determining the taxiing of an aircraft having multiple engines. The system includes a database operable to store historical data collected regarding conditions affecting characteristics of one or more engines, including fuel usage, engine wear, noise, emissions, and their impact on associated aircraft systems. A computing device communicates with the database and is operable to: train a machine learning model using the historical data; determine expected outcomes for the engine characteristics of each engine; and determine which of the one or more engines should not be used during the aircraft's taxiing.

[0023] In another aspect, historical data includes one or more of the following: the manufacturer of each engine; the model of each engine; the installation date of each engine on the aircraft; the maintenance history of each engine; the fuel consumption of each engine; and the emissions of each engine.

[0024] On the other hand, computing devices are integrated inside the aircraft.

[0025] The features, functions and advantages already discussed can be realized independently in each aspect or in combination in other aspects, and further details can be seen in the following description and figures. Attached Figure Description

[0026] Figure 1 It is an isometric view of the aircraft.

[0027] Figure 2 This is a schematic diagram of an airport, which includes taxiways extending between the terminal and the runway.

[0028] Figure 3 It is a diagram of the data, which includes historical data and current conditions used to determine which of one or more engines to shut down during the aircraft's taxiing.

[0029] Figure 4 This is a flowchart of a method for gliding an aircraft that includes multiple engines.

[0030] Figure 5 This is a schematic diagram of a computing system configured to receive data from one or more sources.

[0031] Figure 6 This is a flowchart of a method for gliding an aircraft that includes multiple engines.

[0032] Figure 7 This is a schematic diagram of a computing device. Detailed Implementation

[0033] Figure 1 An example of an aircraft 20 is shown, which includes a fuselage 21 configured to transport personnel and / or cargo. A cockpit 22 is located at the front of the fuselage 21 and includes controls for operating the aircraft 20. Wings 23 extend outward from the fuselage 21, with engines 30 mounted underneath. Figure 1 Examples include an aircraft 20 having two engines 30 mounted on wings 23 on opposite sides of a fuselage 21. Other examples include more than two engines 30. Other examples include engines 30 mounted at different locations on the aircraft 20. Various types of engines 30 can be used with the aircraft 20, with examples including, but not limited to, turbofan engines, turbojet engines, and turboprop engines.

[0034] During flight and while still gliding on the ground, engine 30 propels aircraft 20. Figure 2 An example of an airport 100 is shown, which includes a terminal 101 that accommodates passengers and / or cargo transported by aircraft 20. Terminal 101 includes one or more boarding gates 102, at which aircraft 20 is positioned during passenger / cargo loading and unloading. Taxiway 103 extends between boarding gates 102 and runway 104. Taxiway 103 includes various sections and turns to allow aircraft 20 to move between boarding gates 102 and runway 104.

[0035] The computing system 50 monitors the operation of the engines 30 of the aircraft 20. The computing system 50 includes a computing device 51 and a database 52. The computing device 51 is configured to determine which of one or more engines 30 should be shut down during taxiing. The database 52 stores collected data 60 about the engines 30, which is used to determine shutdown. In some examples, the database 52 is integrated with the computing device 51. In other examples, these components are physically separate. The computing device 51 and the database 52 can be located in various locations, including but not limited to on the aircraft 20, at airport 100 (e.g., control center 105 or terminal 101), and at remote locations far from the aircraft 20 and airport 100 (e.g., a remote server monitoring the engine use of an airline). Figure 2 An example is shown in which computing system 50 includes a computing device 51 and a database 52. In other examples, computing system 50 includes two or more computing devices 51 and / or databases 52.

[0036] Data 60 is collected and used to determine one or more engine characteristics that occur during taxiing of the aircraft 20. Engine characteristics include, but are not limited to, fuel usage, engine wear, noise, engine emissions, and the impact of shutting down engine 30 on other systems on the aircraft 20. Data 60 is collected from a variety of different sources. One source is from the aircraft 20, such as, but not limited to, sensor readings and data input from flight personnel. Another source is from the airport 100, such as, but not limited to, sensors detecting weather and input from personnel at control center 105. Various remote sources 70 may also provide data, including the original equipment manufacturer (OEM) of engine 30, which provides baseline data on one or more engine characteristics. In some examples, OEM data is theoretical data based on engineering aspects of engine 30. Other examples of remote sources 70 include weather sources (e.g., the National Weather Service) that provide weather conditions to airport 100, and airlines that operate the aircraft 20 and provide maintenance history, fuel usage, and other data on aircraft operation.

[0037] The collected data 60 is specific to each engine 30. For example, an aircraft 20 with three engines 30 has corresponding data 60 for each engine 30. Based on the collected data 60, the computing device 51 determines one or more engines 30 of the aircraft 20 to be shut down during taxiing. The data is provided to flight personnel capable of shutting down the engines 30. This provides various advantages, including reduced fuel consumption, reduced engine wear, maintenance of one or more aircraft systems during taxiing, reduced emissions, and reduced noise generated by the aircraft 20.

[0038] like Figure 3As illustrated, data 60 includes historical data 61 and current data 62. Historical data 61 includes various aspects of the construction of engine 30, including but not limited to manufacturer, model, and year of manufacture. Historical data 61 also includes the installation date when engine 30 was installed on aircraft 20. Engine 30 may be original to aircraft 20 or may have been installed at some point during the lifespan of aircraft 20. Historical data 61 also includes the total flight hours and maintenance history of engine 30.

[0039] Historical data 60 includes aspects of fuel consumption. Fuel consumption is determined in various ways, including based on readings from sensors 24 on aircraft 20. Sensors 24 can be mounted in various locations, including but not limited to at engine 30, along fuel lines, and at fuel tank 25. Fuel consumption may include fuel usage during previous taxiing of aircraft 20. Fuel consumption may also include total fuel consumption of engine 30, including taxiing and flight operations. In some examples, fuel consumption data includes projected fuel consumption provided by the OEM.

[0040] Historical data 61 includes system constraints imposed on other systems on the aircraft powered by engine 30. Operation of engine 30 affects the operation of the corresponding systems. Examples of such systems include air management systems, environmental control systems, and lighting systems. In some examples, shutting down engine 30 causes the associated system to shut down. In other examples, when engine 30, which supplies power to the associated system, shuts down, the associated system draws power from another source (e.g., from auxiliary power unit (APU) 27).

[0041] Historical data 61 includes emissions from engine 30. This can be based on various inputs, such as sensor readings on aircraft 20 and data from the OEM.

[0042] In some examples, historical data 61 is accumulated throughout all operations of engine 30. Additionally or alternatively, historical data 61 is collected only at predetermined times (e.g., when engine 30 is operating during taxiing). For example, fuel consumption data includes sensor readings acquired only while aircraft 20 is taxiing. In specific examples, fuel consumption data is acquired only during taxiing at a substantially constant speed. In some examples, using historical data collected only during certain events (e.g., only during taxiing) is more accurate than using historical data from all engine operations. Furthermore, this prevents inaccurate sensor readings, such as data collected during engine start-up, engine shutdown, and acceleration or deceleration during taxiing, which have been determined to be less accurate.

[0043] Data 60 also includes current data 62. Current data 62 describes the conditions that aircraft 20 is currently experiencing. Examples include the total weight of cargo / passengers, the current taxiing speed of aircraft 20 along taxiway 103, and the altitude of airport 100. Current data 62 includes weather aspects such as temperature, humidity, wind direction, and wind speed. Current data also includes the operation of engine 30, such as fuel consumption and emissions.

[0044] The quantity and type of historical data 61 and current data 62 can vary. In some examples, data 60 has been collected and / or is available for each of the various aspects. In other examples, no data is available for one or more aspects. The computing system 50 uses the available data 60 to determine which of one or more engines 30 should be shut down during coasting.

[0045] Figure 4 A method for taxiing an aircraft 20 having multiple engines 30 is illustrated. The method includes collecting historical data 61 (block 200) regarding conditions affecting engine characteristics during taxiing of the aircraft 20. Furthermore, current conditions 62 affecting engine characteristics during taxiing are determined (block 202). Based on the historical data 61 and the current conditions 62, expected results for engine characteristics are generated for each engine 30 (block 204). Based on the expected results for engine characteristics of each engine 30, one or more engines 30 are shut down during taxiing (block 206).

[0046] Historical data 61 provides an indication of the impact of shutting down engine 30 on engine characteristics. This information is then used to determine the impact of shutting down engine 30 under current conditions. For example, historical data 61 includes fuel consumption figures for a specific engine model with usage history and weather conditions. The current conditions of engine 30 are compared with historical data 61 to provide expected outcomes for the engine relative to one or more engine characteristics. In some examples, the accuracy of the expected outcomes depends on the degree of correlation between current data 62 and historical data 61.

[0047] In some examples, the expected outcome for each engine 30 is calculated as a score. This score is a combination of the effects of shutting down engine 30 on engine characteristics. For example, shutting down the first engine has score components for fuel consumption, noise, emissions, and related aircraft system characteristics. Shutting down the second engine has different score components for these same engine characteristics. The different components are summed for each engine 30 to determine the total score. Based on the comparison of scores, one or more engines 30 are shut down.

[0048] Figure 5An example of a computing system 50 is shown, which includes a computing device 51 and an associated database 52. The computing system 50 is configured to communicate with various data sources, including aircraft 20, terminal 101, control center 105, and remote source 70, to determine current conditions 62. In some examples, the computing system 50 is configured to monitor a single aircraft 20 (e.g., but not limited to when the computing system 50 is integrated with aircraft 20). In other examples, the computing system 50 is configured to monitor multiple aircraft 20 and communicate with various numbers of different aircraft 20.

[0049] Computing device 51 includes a machine learning (ML) model 53. Computing device 51 is configured to train machine learning model 53 using historical data 61 (e.g., training data). This training enables machine learning model 53 to then apply current data 62 to determine which of one or more engines 30 of the aircraft 20, currently taxiing or to be taxiing in the near future, should be shut down.

[0050] Figure 6 A method for taxiing an aircraft 20 having multiple engines 30 is illustrated. The method includes storing historical data 61 (block 220) affecting engine characteristics for each engine 30. The historical data 61 is then used to train a computer machine learning model 53 (block 222). Current conditions 62 affecting the engine characteristics of the engines 30 are determined (block 224). The trained machine learning model 53 uses the current conditions 62 to determine which of one or more engines 30 to shut down (block 226). This determination then results in the shutdown of the applicable one or more engines 30 during taxiing (block 228).

[0051] In some examples, machine learning model 53 determines the expected outcome for each engine 30. The expected outcome is the anticipated effect on engine characteristics when engine 30 is shut down during coasting. The expected outcomes for different engines 30 are determined and compared to determine which engines 30 are shut down during coasting.

[0052] In various examples, the number of engines 30 that remain active during taxiing to provide thrust for the aircraft 20 can vary. In some examples, a single engine 30 remains active. In other examples, two or more engines 30 remain active.

[0053] In some examples, the determined result is signaled to the flight crew on aircraft 20. The flight crew then shuts down one or more engines 30 during taxiing.

[0054] Figure 7A computing device 51 is shown that determines to shut down one or more engines 30 during taxiing of the aircraft 20. In some examples, the computing device 51 is a stand-alone component. In some examples, the computing device 51 is integrated into one or more other computing devices 51. In some examples where the computing device 51 is located on the aircraft 20, the computing device is integrated into one or more other computing devices (e.g., but not limited to, a fuel control computing device and a flight control computing device).

[0055] Computing device 51 may include one or more of each of a plurality of components, such as, for example, a processing circuitry system 54 (e.g., a processor unit) connected to a memory circuitry system 55 (e.g., a storage device). The processing circuitry system 54 may consist of one or more processors alone, or one or more processors combined with one or more memories. The processing circuitry system 54 is typically computer hardware capable of processing information such as, for example, data, computer programs, and / or other suitable electronic information. The processing circuitry system 54 consists of a series of electronic circuits, some of which may be packaged as integrated circuits or multiple interconnected integrated circuits (sometimes more commonly referred to as "chips"). The processing circuitry system 54 may be configured to execute a computer program 59 having programming instructions, which may be stored on the processing circuitry system 54 or otherwise stored in a memory circuitry system 55 (of the same or another device).

[0056] According to a particular implementation, the processing circuitry system 54 may be multiple processors, a multi-core processor, or some other type of processor. Furthermore, the processing circuitry system 54 may be implemented using multiple heterogeneous processor systems, where a main processor and one or more secondary processors reside on a single chip. As another illustrative example, the processing circuitry system 54 may be a symmetric multiprocessor system comprising multiple processors of the same type. In yet another example, the processing circuitry system 54 may be embodied as or otherwise include one or more ASICs, FPGAs, etc. Therefore, while the processing circuitry system 54 may be able to execute computer programs to perform one or more functions, various examples of the processing circuitry system 54 may be able to perform one or more functions without the assistance of a computer program. In any case, the processing circuitry system 54 may be suitably programmed to perform functions or operations according to exemplary embodiments of this disclosure.

[0057] Memory circuitry 55 is typically computer hardware capable of storing information (such as data, computer programs (e.g., computer-readable program code), and / or other suitable information) on a temporary and / or permanent basis. Memory circuitry 55 may include volatile and / or non-volatile memory and may be fixed or removable. Examples of suitable memory circuitry 55 include random access memory (RAM), read-only memory (ROM), hard disk drive, flash memory, thumb drive, removable computer disk, optical disk, magnetic tape, or some combination thereof. Optical disk may include optical disc read-only memory (CD-ROM), optical disc read / write (CD-R / W), DVD, etc. In various cases, memory circuitry 55 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 is distinguished from a computer-readable transmission medium (such as an electronic transient signal) capable of carrying information from one location to another. As used herein, computer-readable medium may generally refer to either a computer-readable storage medium or a computer-readable transmission medium.

[0058] In some examples, the memory circuitry 55 is configured to store one or more of historical data 61, current conditions 62, and machine learning model 63. In some examples, historical data 61 and machine learning model 63 are stored remotely to reduce the processing requirements for training machine learning model 63.

[0059] The computing device 51 also includes a communication circuitry 56 configured to transmit and / or receive information from various data sources (e.g., airports, remote nodes) and sensors 24 mounted on the aircraft 20. The communication circuitry 56 can be configured to transmit and / or receive information via physical (wired) and / or wireless communication links. Examples of suitable communication interfaces include network interface controllers (NICs), wireless NICs (WNICs), etc. The communication circuitry 56 may have one or more transmitters and / or receivers. In some examples, a user interface is included to allow input from flight personnel and others monitoring the aircraft 20. The user interface may include one or more input devices, such as, but not limited to, keypads, touchpads, ball bearings, and joysticks. The user interface also includes one or more displays for displaying information about fuel jettison.

[0060] As will be understood by one of ordinary skill in the art without excessive experimentation, program code instructions 59 can be loaded from a computer-readable storage medium onto a computing device 51 or other programmable means to produce a particular machine, such that particular machine is an apparatus for performing the functions specified herein. These program code instructions 59 can also be stored in a computer-readable storage medium that can instruct a computer, processing circuitry system, or other programmable means to function in a particular manner to produce a particular machine or a particular article of manufacture. Program code instructions 59 can be retrieved from a computer-readable storage medium and loaded into a computer, processing circuitry system, or other programmable means to configure the computing device 51, processing circuitry system 54, or other programmable means to perform operations to be performed on or by the computer, processing circuitry system, or other programmable means.

[0061] In some examples, data 60 is stored in a database 52 separate from computing device 51. In other examples, data 60 is stored in the memory circuitry 55 of computing device 51. In still other examples, a portion of data 60 is stored in both database 52 and memory circuitry 55.

[0062] The system and method provide for the use of a limited number of engines 30 during taxiing. In some examples, this involves shutting down one or more engines 30 during taxiing. For example, after landing on runway 104 with all engines 30 operational, one or more engines 30 are shut down for taxiing. In another example, the engines 30 are started and operated to prepare for flight (e.g., such as after being pulled back from gate 102). After this initial preparation, one or more engines 30 are shut down for taxiing. Once taxiing is complete, one or more engines 30 are started, and each engine 30 on aircraft 20 is operated for flight. In other examples, one or more engines 30 are not started until after taxiing (e.g., while taxiing from gate 102 to the runway before flight). Once taxiing is complete, one or more engines 30 are started, and each engine 30 on aircraft 20 is prepared for flight.

[0063] This application also includes the following examples: Example 1. A method for gliding an aircraft (20) having multiple engines (30), the method comprising: For each of the engines (30), historical data (61) affecting engine characteristics of the engine (30) are stored, including one or more of the following: fuel use, engine wear, noise, emissions and the effects of related aircraft (20) systems; The historical data (61) is used to train the computer machine learning model (53). Determine the current conditions (62) that affect the engine characteristics of the engine (30); Based on the machine learning model (53) and the current conditions (62), determine which of the one or more engines (30) is not used for coasting; and The aircraft (20) is glided using a limited number of the engines (30).

[0064] Example 2. The method described in Example 1 further includes: Based on the machine learning model (53) and the current conditions (62), the expected results of the engine characteristics of each engine (30) are generated; and The expected results of the engine characteristics of each of the engines (30) are used to determine which of the one or more engines (30) will not be used.

[0065] Example 3. The method according to Example 2 further includes using the engine 30 with the highest expected results of engine characteristics to propel the aircraft 20, and not using one or more other engines 30 with lower expected results of engine characteristics.

[0066] Example 4. The method according to Example 1 further includes signaling to the cockpit 22 of the aircraft 20 which one of the one or more engines 30 will be shut down.

[0067] Example 5. The method according to Example 1 further includes collecting the historical data 61 about each of the engines 30 and storing the historical data 61 in a database 52.

[0068] Example 6. The method according to Example 1 further includes collecting the historical data 61 from manufacturer data about the engine 30, the manufacturer data being theoretical data based on engineering aspects of the engine 30.

[0069] Example 7. The method according to Example 1 further includes collecting the historical data 61 from the engine 30 of another aircraft 20 that shares one or more characteristics with the engine 30.

[0070] Example 8. The method according to Example 1, wherein the historical data 61 includes the weather conditions of the airport 100 on which the aircraft 20 is taxiing, and the current conditions include the current weather conditions of the airport 100.

[0071] Example 9. The method according to Example 1 further includes considering each of the engine characteristics and determining which of the one or more engines 30 should not be used.

[0072] Example 10. A system for determining the taxiing of an aircraft 20 having a plurality of engines 30, the system comprising: Database 52, operable to store historical data 61 collected about conditions affecting one or more engine characteristics, including fuel use, engine wear, noise, emissions, and impact on associated aircraft systems; Computing device 51, which communicates with the database 52 and is operable to: The historical data 61 was used to train the machine learning model 53; Determine the expected results of the engine characteristics of each of the engines 30; and Determine which of the one or more engines 30 is not used for gliding the aircraft 20.

[0073] Example 11. According to the system described in Example 10, the historical data 61 includes one or more of the following: The manufacturer of each of the said engines 30; Model number of each of the aforementioned engines 30; The installation date of each of the engines 30 on the aircraft 20; Maintenance history of each of the aforementioned engines 30; Fuel consumption of each of the aforementioned engines 30; and Emissions from each of the aforementioned engines 30.

[0074] Example 12. The system according to Example 10, wherein the computing device 51 is integrated within the aircraft 20.

[0075] Regarding quantities or measurements, the term "substantially" means that the characteristic, parameter, or value does not need to be precisely achieved. Instead, deviations or variations (including, for example, tolerances, measurement errors, measurement accuracy limitations, and other factors known to those skilled in the art) may occur in quantities that do not exclude the effects that the characteristic is intended to provide.

[0076] For ease of description, spatial relative terms (such as "under," "below," "lower," "over," "upper," etc.) are used to explain the positioning of one element relative to a second element. These terms are intended to include different orientations of the device, in addition to those different from those depicted in the accompanying drawings. Furthermore, terms such as "first," "second," etc., are also used to describe various elements, regions, segments, etc., and are not intended to be limiting. Throughout this description, similar terms refer to similar elements.

[0077] Of course, the invention may be practiced in ways other than those specifically set forth herein without departing from its essential characteristics. This embodiment should be considered illustrative and non-limiting in all respects, and all variations within the meaning and equivalence of the appended claims are intended to be included therein.

Claims

1. A method for gliding an aircraft (20) having multiple engines (30), the method comprising: Collect historical data (61) on conditions affecting one or more engine characteristics of the engine (30) during taxiing of the aircraft (20). Determine the current conditions (62) affecting the characteristics of the one or more engines during the taxiing of the aircraft (20); For each of the engines (30) and based on the historical data (61) and the current conditions (62), the expected results of the characteristics of the one or more engines are generated; and Based on the expected results of the characteristics of the one or more engines of the engine (30), it is determined that one or more of the engines (30) will not be used and that coasting will be performed with a limited number of the engines (30).

2. The method of claim 1, wherein the one or more engine characteristics include fuel use, engine wear, noise, emissions, and impact on associated aircraft systems.

3. The method of claim 1, further comprising operating one of the engines 30 of the aircraft (20) during taxiing and shutting down the remaining engines (30) during taxiing.

4. The method according to claim 1, further comprising: The historical data (61) is used to train the machine learning model (53); and Using the machine learning model (53) and the current conditions (62), the expected results of one or more engine characteristics are generated for each engine (30).

5. The method of claim 1, further comprising receiving the current conditions (62) from one or more sensors (24) mounted on the aircraft (20).

6. The method according to claim 1, further comprising: The historical data (61) is collected from the manufacturer of the engine (30), wherein the historical data (61) includes theoretical values; and Based on the theoretical values, the expected results of the one or more engine characteristics are generated for each of the engines (30).

7. The method according to claim 1, wherein, The historical data (61) includes the weather conditions of the airport (100) where the aircraft (20) is taxiing, and the current conditions (62) include the current weather conditions of the airport (100).

8. The method according to claim 1, wherein, The historical data (61) includes data from only the time when the aircraft (20) is gliding.

9. A method for gliding an aircraft (20) having multiple engines (30), the method comprising: For each of the engines (30), historical data (61) affecting engine characteristics of the engine (30) are stored, including one or more of the following: fuel use, engine wear, noise, emissions and the effects of related aircraft (20) systems; The historical data (61) is used to train the computer machine learning model (53). Determine the current conditions (62) that affect the engine characteristics of the engine (30); Based on the machine learning model (53) and the current conditions (62), determine which of the one or more engines (30) is not used for coasting; and The aircraft (20) is glided using a limited number of the engines (30).

10. The method of claim 9, further comprising: Based on the machine learning model (53) and the current conditions (62), the expected results of the engine characteristics of each engine (30) are generated; and The expected results of the engine characteristics of each of the engines (30) are used to determine which of the one or more engines (30) will not be used.

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