System and method for providing information about particulate matter within an aircraft engine

Abstract

A tangible computer-readable non-transitory storage medium storing instructions that, when executed by a processor of an aircraft, cause the processor to perform a method is provided. The method includes receiving, from an engine particulate sensor of the aircraft, a measurement of particulate matter in a gas path of an engine of the aircraft during a flight of the aircraft. The method also includes presenting a visualization of the particulate matter measurement to a pilot of the aircraft, wherein the visualization supports navigation of the aircraft in response to a presence of the particulate matter.

Description

Technical Field

[0001] The embodiments relate to the field of aero-engine performance evaluation, and more specifically, to systems and methods for monitoring particulate matter within aircraft engines. Background Technology

[0002] Aircraft turbine engines typically include a core that has a compressor section, a combustion section, a turbine section, and an exhaust section in a sequential flow order. One or more shafts can be arranged to drive the turbine section to the compressor section, and optionally, to drive the turbine section to a load. When such an aircraft engine is incorporated into a rotorcraft (such as a helicopter), one or more shafts of the aircraft engine can be mechanically coupled to the rotorcraft's main rotor. This arrangement allows the main rotor to provide lift to the rotorcraft.

[0003] As background, aircraft engines draw air from the environment through an inlet, which then typically travels the length of the engine to the exhaust port. The inlet air, which may include oxygen, nitrogen, and other gaseous elements, can also include particulate matter (PAM). For example, a low-flying helicopter may kick up PAM (i.e., dirt, sand, and other debris from the ground), some of which may be absorbed into the engine itself. In some environments, the air may also contain significant amounts of dust or debris from other environmental sources, such as smoky urban environments or explosions in combat environments.

[0004] For aircraft operating at low altitudes (such as helicopters, which may be in environments with high concentrations of sand, dust, or debris), even short-term ingestion of large amounts of dust, sand, or other PAMs can reduce the efficiency of the aircraft's engines, which in turn can jeopardize the aircraft's maneuverability and prevent or limit mission completion. Attached Figure Description

[0005] Advantageous designs of the embodiments derive from the independent and dependent claims, the specification, and the drawings. Various examples of embodiments of this disclosure are described in detail below with reference to the accompanying drawings:

[0006] Figure 1 An exemplary aircraft engine according to this system and method is shown.

[0007] Figure 2 An exemplary placement of an exemplary particle sensor is shown in an exemplary module of an exemplary aircraft engine.

[0008] Figure 3 An exemplary controller that can be deployed in an aircraft engine according to an embodiment is shown.

[0009] Figure 4 Elements of an exemplary engine power, health, and maintenance system according to an embodiment are shown.

[0010] Figure 5 A flowchart of an exemplary method for engine power modeling is presented, which is based at least in part on PAM data.

[0011] Figure 6A A flowchart is presented of an exemplary method for providing aircraft navigation support based at least in part on the detection of PAM in the gas flow path of an aircraft engine.

[0012] Figure 6B A flowchart is presented of an exemplary method for providing aircraft navigation support based at least in part on the detection of PAM in the gas flow path of an aircraft engine.

[0013] Figure 6C A flowchart is presented of an exemplary method for providing aircraft navigation support based at least in part on the detection of PAM in the gas flow path of an aircraft engine.

[0014] Figure 6D A flowchart of an exemplary method for determining a PAM security threshold is presented, and a system-level representation of an exemplary suitable processing module for making such a determination is also presented.

[0015] Figure 7 A flowchart is presented of an exemplary method for providing aircraft maintenance recommendations based at least in part on the detection of PAM in the gas flow path of an aircraft engine.

[0016] Figure 8 An exemplary calculated engine maintenance diagram according to an embodiment is presented.

[0017] Figure 9 An exemplary calculation of engine maintenance is presented according to an embodiment of the indicated task requiring maintenance. Detailed Implementation

[0018] The following detailed description is exemplary in nature only and is not intended to limit this disclosure, its elements, or its application. Furthermore, the scope of the invention is not intended to be construed as being limited by any theory set forth in the foregoing background or summary or any theory in the following detailed description.

[0019] As used herein, the term PAM generally refers to small particulate matter that is substantially suspended in the air or briefly present in the air due to sudden air movement and may be carried into the engine. For example, PAM can include dust, debris, sand, aerosol particles, etc., and can range in size from 0.1 micrometers (μm) to 2 millimeters (mm).

[0020] Particles with airborne particles (PAMs) are typically large enough to jeopardize engine operation over time, yet small enough to avoid immediately affecting engine operation or damaging engine components. However, larger particles (such as particulate matter, gravel, etc.) pose a greater risk of direct damage to engine operation. These larger particles may, for example, have a particularly strong negative impact on compressor performance, preventing the compressor from compressing air (i.e., reducing the compressor's flow rate).

[0021] Smaller particles can have a greater impact on high-pressure turbines because they may melt and combine with the turbine blades. High-pressure turbines can use air cooling to prevent blade melting, but the airflow cooling channels can become clogged with certain substances. This reduces the turbine cooling air scalar, or secondary flow rate. Particles of various sizes can also contribute to typically higher downstream engine temperatures, thus reducing engine efficiency scalars.

[0022] Degradation is also an important consideration. For example, a prudent approach is to ensure that the aircraft engine is functioning properly before and / or during flight, and to maintain the aircraft engine until degradation exceeds a threshold. One measure of degradation is the engine torque factor (ETF): the dimensionless ratio of the current maximum corrected torque to the nominal maximum corrected torque available to the engine.

[0023] When the ETF degrades below a threshold, the engine may be put on hold for maintenance. Ingestion of dust or other PAMs can cause engine degradation and may be sufficient to reduce maximum available vertical lift.

[0024] One method for determining the ETF is by utilizing a baseline engine power model that provides a correlation between corrected engine temperature and corrected engine torque. Specific conventional techniques are used to determine the ETF specific to a particular aircraft engine. These techniques also include determining the ETF that can take into account certain operating conditions (e.g., internal gas pressure) and / or environmental conditions of the aircraft engine.

[0025] One drawback of the conventional methods described above is the impact of PAM intake on engine performance. At higher altitudes, the amount of PAM inhaled may be minimal. However, for engines operating continuously at low altitudes for extended periods (e.g., military aircraft), the accumulation of PAM from the air can significantly reduce engine power. The embodiments of this disclosure address the aforementioned drawbacks of the conventional art.

[0026] This example stems from the observation that slightly repositioning an aircraft flying in a sand-dense environment can significantly reduce the amount of sand ingested by its engines. For example, changing the aircraft's position by a few feet or tilting it a few degrees during flight can reduce the amount of sand ingested by its engines by two times or more. The real-time availability of this information can help pilots determine whether the current mission (or a set of missions) can be successfully completed or whether the mission should be aborted.

[0027] Figure 1 This is a schematic cross-sectional view of one embodiment of a gas turbine engine 100 according to an embodiment, which can be used as an aircraft engine. The gas turbine engine 100 has an axial longitudinal centerline axis 12 passing through it for reference. The engine 100 includes a turbine core 14 and a fan section 16 positioned upstream therefrom. The turbine core 14 generally includes a generally tubular casing 18 defining an annular inlet 20. The casing 18 further surrounds and supports a supercharger 22 for increasing the pressure of air entering the turbine core 14 to a first pressure level.

[0028] A high-pressure compressor 24 (e.g., a high-pressure multistage axial compressor) receives pressurized air from a booster 22 and further increases the pressure of that air. The high-pressure compressor 24 includes rotating blades and stationary impellers within the engine 100 that guide the compressed air. The pressurized air flows to a combustor 26, where fuel is injected into the pressurized air stream and ignited to increase the temperature and energy level of the pressurized air.

[0029] High-energy combustion products flow from combustor 26 to first (high-pressure) turbine 28, which drives high-pressure compressor 24 via first (high-pressure) drive shaft 30. The high-energy combustion products then flow to second (low-pressure) turbine 32, which drives turbocharger 22 and fan section 16 via second (low-pressure) drive shaft 34, coaxial with first drive shaft 30. After driving each of the respective first turbine 28 and second turbine 32, combustion products exit turbine core 14 through exhaust nozzle 36 to provide at least a portion of the jet propulsion thrust of engine 100.

[0030] Fan section 16 includes a fan rotor 38 (e.g., a rotatable axial fan rotor) surrounded by an annular fan housing 46. The annular fan housing 46 is supported from the turbine core 14 by a plurality of substantially radially extending outlet guide vanes 42. Thus, the fan housing 40 surrounds the fan rotor 38 and the fan rotor blades 44. A downstream section of the annular fan housing 46 extends over the outer portion of the turbine core 14, thereby defining a secondary or bypass airflow duct 48 that provides additional jet propulsion thrust.

[0031] An initial airflow 50 enters the gas turbine engine 100 through inlet 52 of the fan housing 40. The initial airflow 50 may include PAM 6. The initial airflow 50 passes through the fan rotor blades 44 and forms a second airflow 54 that moves through the airflow duct 48 and a second compressed airflow 56 that enters the supercharger 22.

[0032] The pressure of the second compressed airflow 56 increases and enters the high-pressure compressor 24 (arrow 58). After mixing with fuel and burning in the combustor 26, the combustion products 60 leave the combustor 26 and flow through the first turbine 28. The combustion products 60 then flow through the second turbine 32 and exit the exhaust nozzle 36, providing a portion of the thrust to the engine 100.

[0033] Combustor 26 includes an annular combustion chamber 62 coaxial with the longitudinal centerline axis 12, and an inlet 64 and an outlet 66. Combustor 26 receives an annular pressurized airflow from the high-pressure compressor discharge outlet 69. A portion of the compressor discharge air flows into a mixer (not shown). Fuel is injected from fuel nozzle 80 to mix with air and form a fuel-air mixture, which is supplied to the annular combustion chamber 62 for combustion. Ignition of the fuel-air mixture is accomplished by a suitable igniter, and the resulting combustion products 60 flow axially toward and into the annular first-stage turbine nozzle.

[0034] The nozzles are defined by an annular flow channel comprising a plurality of radially extending, circumferentially spaced nozzle blades 74 that deflect the gas, causing it to flow at an angle and impinge on the first-stage turbine blades of the first turbine 28. The first turbine 28 rotates the high-pressure compressor 24 via a first drive shaft 30. The second turbine 32 drives the supercharger 22 and the fan rotor 38 via a second drive shaft 34.

[0035] An annular combustion chamber 62 is housed within a housing 18, and fuel is supplied to the annular combustion chamber 62 through one or more fuel nozzles 80. Liquid fuel is delivered through one or more passages or conduits within the rod of the fuel nozzle 80.

[0036] The gas path is the entire front-to-back path of the initial airflow 50 (and PAM 6) through the engine 100, and may include multiple channels that are generally parallel to and coaxial with the centerline axis 12. Therefore, the gas path may include the initial airflow 50, inlet 52, duct 48, and turbocharger 22, high-pressure compressor 24, exhaust outlet 69, combustor 26, annular combustion chamber 62, first-stage turbine nozzle 75, first turbine 28, second turbine 32, and engine exhaust nozzle 36.

[0037] During operation, PAM 6 is ingested by the gas turbine engine 100, typically suspended in or mixed with the initial airflow 50 entering inlet 52. PAM accumulation is a critical input for engine analysis. The levels and effects of these accumulations are important for assessing engine service life, wear, and / or other maintenance programs.

[0038] Since PAM 6 is generally harmful to engine operation, one objective of this embodiment is to assist the aircraft operator (or drone navigation system) in navigation to minimize PAM 6 ingested by engine 100. Therefore, the embodiment includes an environmental PAM sensor 73 (e.g., an electrostatic sensor) and techniques for detecting PAM 6 in engine 100.

[0039] exist Figure 2 In this example, the engine sensor system 200 may include one or more gas path sensors 71 (e.g., air pressure sensor 71.1, temperature sensor 71.2) for the turbine blades of the shaft and engine performance sensors 72 (e.g., rotational speed sensor). Multiple engine environment sensors (e.g., gas path sensors 71 and performance sensors 72, respectively) may be mounted at suitable points within the engine 100, in conjunction with the processing system or controller 300. Figure 3 The gas path sensor 71, performance sensor 72, and PAM sensor 73 are positioned exemplary and for illustrative purposes only, as these sensors may be placed in other suitable locations within the engine 100.

[0040] To measure PAM 6 in the initial airflow 50, PAM sensors 73 (73.1, 73.2, ..., 73.n) can be electrostatic and are communicatively connected to the controller 300. Multiple PAM sensors 73 can be installed at appropriate points within the engine 100. Figure 2 Two PAM sensors 73 are shown located in the front section of the turbocharger 22 and the high-pressure compressor 24.

[0041] The exemplary PAM sensor 73 is an electrostatic sensor. That is, the PAM sensor 73 detects the presence of charge attached to the PAM particles. The amount of charge can be measured and correlated with the amount or density of PAM particles.

[0042] Each PAM sensor 73 is configured to be mounted to the engine 100 in an area where PAM 6 is readily present in the initial airflow 50. The sensing surfaces of one or more PAM sensors 73 are exposed to the initial airflow 50 and are configured to detect PAM 6. The PAM sensor 73 may also include internal electrodes and an amplifier constructed within the sensing portion. PAM 6 in the form of charged dust particles can flow over the sensing surfaces of one or more PAM sensors 73. The charged dust particles induce electron movement therein, which is easily detected by the amplifier to indicate the charge level associated with the PAM 6 flowing over the sensing surface.

[0043] Although the exemplary PAM sensor 73 is an electrostatic sensor, other types of sensors may be used and will be within the spirit and scope of this disclosure. For example, other types of sensors may include optical and acoustic sensors. Using techniques known to those skilled in the art, optical and acoustic sensors can respectively detect the opacity level and acoustic characteristics of sand or PAM particles in relation to sand or PAM density. Optical sensors can also identify discoloration of metallic surfaces and / or changes in reflectivity of internal engine surfaces. Discoloration indicates that PAM 6 has melted or otherwise adhered to components within engine 100.

[0044] Other forms of PAM or particulate sensors can be used, which transmit signals indicating one or more of the density, flow rate, mass, velocity, and / or volume of PAM6 detected in engine 100. Alternative sensors can be used to identify static accumulation of PAM6 on components within engine 100. As an example, PAM sensor 73 can also be spectroscopic to detect specific types (i.e., atomic or molecular composition) of PAM6.

[0045] In other examples, PAM sensor 73 can detect PAM 6 in the form of airborne aerosol particles, ice crystals, contaminants, and / or volcanic ash within the engine flow path. PAM sensor 73 can be configured to alarm engine controller 300 in the event of such detection (see below). Figure 3 The PAM sensor 73 can also provide a continuous real-time data stream 403 for PAM 6 that is not detected (see below). Figure 4 Furthermore, sensor 73 can be configured to detect internally generated PAM and alarm controller 300.

[0046] For example, refer to Figure 4The received real-time data 403 can be categorized for computational purposes. A first subset of the sensor data may refer to the environmental data {EnvDt} 403.1. This first subset will typically reflect the internal environment of engine 100 and the gas path (e.g., temperature), but may also include data related to the performance of specific engine components, such as the current bleed air coefficient. EnvDt 403.1 may also include the overall aircraft environment, such as external temperature, aircraft speed, aircraft altitude, icing conditions, external wind speed, and similar data.

[0047] The second subset of sensor data can be referred to as performance data {PrfDt}403.2. PrfDt 403.2 typically indicates the raw performance of various engine components or modules measured.

[0048] The categories of sensor data can differ in different embodiments. For example, gas flow rate or pressure may be considered as real-time gas path sensor data 403.1 in some embodiments, or as real-time performance data 403.2 in others. EnvDt 403.1 can be used as input data for an engine power model, while PrfDt 403.2 can be used for comparison with expected or modeled engine performance.

[0049] Figure 3 This is a system-level diagram of an exemplary engine controller 300 (e.g., a digital computer). The engine controller 300 can execute computer code that enables the aircraft to sense PAM 6, perform engine performance checks, navigation guidance techniques, and maintenance assessments. For example, the engine controller 300 could be a dedicated controller that implements digital control and operation of engine 100.

[0050] Alternatively, controller 300 may be a remote computer that receives engine data from engine 100, such as a ground-based computer or a satellite-based computer. In other embodiments, the techniques disclosed herein may be implemented via off-board analysis (ground station or remote monitoring) software.

[0051] The controller 300 includes a printed circuit board (PCB) 305 or motherboard interconnected with other controller components. The PCB 305 includes a central processing unit (CPU) 315. The CPU 315 may include signal processing capabilities to support fine-grained temporal / spatial evaluation of PAM distributions.

[0052] As an example, such an assessment could include a fine-grained distribution of PAM 6 inside the engine, or a detailed temporal / spatial mapping of the type, density, or distribution of PAM encountered in the environment in which the aircraft with engine 100 is traveling.

[0053] This mapping can be used to identify or predict future patterns in PAM distribution, whether inside or outside the engine, and can be used to correlate PAM distribution with various internal parts or structures of the engine 100, or with external terrain, location, etc. In a non-limiting example, such signal processing can establish a correlation between the percentage of PAM 6 and aircraft speed, wind speed, engine torque, engine temperature, or other factors. This analysis can help predict future maintenance needs and ultimately improve engine design.

[0054] The controller 300 also includes static memory / firmware 320, control circuitry 325, dynamic memory 330, and / or a non-volatile data storage device 335. The control circuitry 325 can perform various tasks, including data and control exchange, input / output (I / O) tasks, access to the system data bus 312, network connection operations, etc. The control circuitry 325 can also control or interface with the non-volatile data storage device 335, and interface with the PAM sensor 73, the gas path sensor 71, and the performance sensor 72.

[0055] System data bus 312 provides data communication between CPU 315, static memory 320, dynamic memory 330, and non-volatile data storage device 335. A display 360, which may be a visual display and / or an audio display (e.g., a cockpit information system), may be communicatively coupled to controller 300 to present flight data to the aircraft operator. The flight data displayed on display 360 may include indications of engine power performance and / or indications of aircraft engine maintenance requirements. Voice, mechanical, or tactile input devices may also be communicatively coupled to controller 300 to enable operator control.

[0056] During flight, when engine 100 receives the initial airflow 50, embedded PAM 6 may significantly reduce engine efficiency, potentially causing the aircraft to lose lift or flight. Furthermore, regardless of its size, a sufficient density of PAM 6 can pose a hazard to flight operations. For example, the longer engine 100 ingests PAM-rich air, the greater the risk to flight operations. The accumulation of PAM 6 over extended periods (e.g., hours, days, weeks) can degrade engine performance over a prolonged period and require frequent maintenance.

[0057] Techniques used to determine the impact of PAM 6 on engine performance may take into account particle size distribution or density. PAM sensor 73 senses engine inlet 52 and, in conjunction with engine health and module health technologies (e.g., engine performance module 405 and condition assessment module 27), uses this information to identify which gas path modules may require immediate or near-term maintenance or repair.

[0058] Figure 4 An exemplary combined software-processing-module diagram and data flow elements for the Engine Power, Health, and Maintenance System (EPHMS) 400 are shown. The EPHMS 400 evaluates the aircraft engine hardware health calculation module 430 and provides engine power assurance calculations for a specific aircraft gas turbine engine 100 (e.g., via the engine power assurance calculation module 435). A data bus 470 (implemented in hardware or virtually in software) can transfer data between the outputs of sensors 71, 72, 73, various models 410, 425, and the engine performance module 405. A sensor regulator 490 calibrates the operation of sensors 71, 72, and 73.

[0059] In the exemplary EPHMS 400, the engine performance module 405 receives real-time data 403 from sensors 71, 72, and 73 during aircraft flight. The engine performance module 405 may be implemented in part as an engine power model 410. In various embodiments, the engine power model 410 may be based on a mathematical model utilizing suitable simulation techniques 412 (and / or simulation lookup tables), where simulation parameters 414 are used to model the expected engine behavior.

[0060] The engine power model 410 generates a set of modeled sensor responses (MSRs) 415 in real time based on real-time data 403. MSRs 415 are also referred to as engine power capabilities. The real-time data 403 and the output from the engine performance module 405 are merely examples. For instance, the real-time data 403 received as input as described above could represent various other engine parameters.

[0061] Real-time data 403 is obtained in real time from some or all of sensors 71, 72, and 73. Engine power model 410 simulates the expected engine performance based on simulation techniques 412 and modeling simulation parameters 414. Simulation techniques 412 will typically be parameterized using suitable engine performance / operating parameters known in the art.

[0062] Engine performance / operating parameters may include parameters indicating specific physical characteristics of engine 100 components (e.g., engine part diameter, length, volume, part mass, etc.). Additional exemplary parameters include the compression capacity, thrust, and efficiency factor of engine components.

[0063] Those skilled in the art will recognize that if simulation parameter 414 is changed (e.g., modified during flight), this will alter MSR 415. Engine power model 410 produces one or more data streams as output for MSR 415.

[0064] Engine performance module 405 is partially implemented as engine power model 410 and is based on physics and engineering techniques, along with one or more parameterized models of aircraft engine components and their interactions. Engine performance model 405 is constructed to model aircraft engine performance and engine power generation.

[0065] The engine performance module 405 can also be implemented in a physics-based and engine component-based simulation model using other forms of simulation tools. For example, the engine power model 410 can be implemented via a suitable neural network model 680 (see...). Figure 6D ), statistical regression model 685 ( Figure 6D (or implemented through other forms of predictive modeling.)

[0066] The neural network model 680 can be trained using known methods to simulate aircraft engine performance under various conditions without particulate factors. The neural network model 680 can be further enhanced to identify additional degradation as a function of various intake levels of PAM 6. The neural network model 680 can then provide real-time predictions of engine performance degradation and forecast reductions in flight time without requiring specific modeling of engine components.

[0067] Considering the PAM 6 intake, a neural network model 680 or a statistical regression model 685 of engine performance can be developed based on historical field data of aircraft performance. These models can also be used by engine performance model 405, engine power model 410, and tracking comparison module 420 to predict engine performance degradation.

[0068] Engine power model 410 can output MSR 415. MSR 415 represents a summary view of the power that engine 100 can deliver. MSR 415 can be formulated in different ways. Generally, gas path sensor data 403.1 and performance sensor data 403.2 can provide exemplary real-time data states as the thrust generated by engine 100, such as fuel consumption, torque coefficient, and engine pressure at various points along the engine. The current engine power capability can be evaluated by simulating any one operating / environmental state (as above) as a function of another state. For example, engine power capability can be determined as the thrust generated by engine 100 for a given engine fuel consumption.

[0069] Engine performance module 405 includes a tracking / comparison module 420. The tracking / comparison module 420 tracks real-time data 403 from sensors 71, 72, and 73, as well as MSR 415. MSR 415 differs from real-time data 403, and particularly from real-time performance sensor data 403.2, indicating that some components of the aircraft engine 100 are not performing at the expected efficiency. In response to this determination, the tracking / comparison module 420 determines and outputs the component efficiency 425 for the appropriate engine components.

[0070] Component efficiency 425 can be defined as the efficiency adjustment of a specific engine hardware component of engine 100. For example, the engine compressor may currently be operating at only 94% of its expected efficiency or at 96% of its expected efficiency.

[0071] Component efficiency 425 can be identified by adjusting various simulation parameters 414. Any parameters to be adjusted will be specific to the engine power of the engine power model 410, which employs a particular simulation technique. For example, the engine power model 410 may include one or more techniques (e.g., linear or linear mathematical expressions) that indicate the expected engine turbine speed as a function of engine temperature. Adjustments to engine component efficiency 425 can be reflected in this reduction of turbine speed / engine line, indicating that the turbine blades rotate at a lower angular velocity at any given engine temperature.

[0072] The engine performance module 405 continuously adjusts the simulation parameters 414 of the engine power model 410 so that the MSR 415 converges to the real-time sensor performance data 403.2. In this way, the MSR 415, which essentially reflects the current component efficiency of the engine 100, takes into account the decline in engine power and performance over time. Based on the continuously updated and accurate engine power model 410, the engine performance module 405 can accurately predict the near-term engine health and engine performance.

[0073] exist Figure 4 In this module, the engine condition assessment module 427 receives output data from the engine performance module 405, including the MSR 415 and the determined component efficiency 425. The engine condition assessment module 427 outputs an engine health factor (EHF) 434 indicating the overall health of the engine 100. For example, the EHF 434 may include engine health metrics such as engine torque coefficient, engine temperature, engine thrust, engine pressure, etc.

[0074] The engine hardware health calculation module 430 generates an engine health report 433. The engine health report 433 provides a measurement of the health status of one or more engine components or the engine 100 as a whole. The engine health report 433 can indicate component efficiency 425 for various engine health metrics, including the rate of decrease in engine power over time. Other indicators of engine health status can also be determined. The engine health report 433 can include summary values ​​of engine health status determined based on various combinations of EHF 434.

[0075] The overall engine health value can be determined based on multiple factors, such as the weighted average efficiency of different engine components and engine power capability. The overall engine health value can also be based on the past or projected rate of decline in engine power capability to determine the expected date when engine maintenance is required.

[0076] The engine power assurance calculation module 435 generates an engine power assurance report 437 based on PAM intake and the corresponding reduction in engine power if PAM intake continues. The assurance report indicates whether the engine 100 can maintain sufficient flight power under various aircraft stress conditions caused by PAM intake.

[0077] EPHMS 400 can be configured to output data from Engine Condition Assessment Module 427 and present that data to the aircraft operator via Display 360. The data can also be stored in Dynamic Memory 330 and / or Data Storage Device 335. In this way, all engine health assessments of EPHMS 400 (e.g., Engine Health Report 433, Engine Power Assurance Report 437) enable the aircraft operator to modify current aircraft operations and allow aircraft maintenance personnel to predict future maintenance needs.

[0078] As an example, PAM data can be mathematically filtered and summarized by the amount of PAM 6 entering engine 100 on average over a short period of time. This data can help aircraft operators maneuver the aircraft to reduce the current intake of PAM 6. The mathematical processing can be performed by the particulate intake analysis module 460 of engine performance module 405.

[0079] In an alternative embodiment, mathematical processing may be performed by the particulate filter module 440 or the engine condition assessment module 427. A local PAM database 445 may support short-term retention of PAM inhalation (volume, density, composition) to support real-time PAM data analysis. Modification of the aircraft's flight path can be facilitated by presenting a visual display of the inhaled PAM 6 to the aircraft operator via a display 360.

[0080] PAM 6 ingested by an aircraft engine during flight can reduce engine efficiency and operation, while also increasing the required engine maintenance frequency. Therefore, EPHMS 400 may include measures for measuring PAM 6 and assessing its impact on engine maintenance and efficiency.

[0081] PAM sensor 73 can detect various types of real-time PAM sensor data 403.3 related to the intake and flow of PAM through engine 100. Real-time PAM sensor data 403.3 may include particle density (or density distribution), mass, and accumulation on components of engine 100.

[0082] PAM sensor 73 can be configured to detect particle size and / or particle composition. The data detected by PAM sensor 73 can be encoded in raw, compressed, and / or summary form to generate real-time PAM sensor data 403.3, which can be a continuous data stream during flight.

[0083] Following path (A) of the PAM sensor data 403.3, the raw particle data can be directly received by the engine power model 410. Simulation technique 412 determines the reduction in compressor efficiency as a function of particle density, particle size, particle mass or particle composition (PDSMC), and possibly other PAM sensor data 403.3 from PAM sensor 73. Simulation technique 412 also determines (i) the increased load on the turbine blades, (ii) the reduction in combustion chamber efficiency, (iii) the reduction in gas flow rate, and (iv) other effects on the performance of engine 100 as functions of PDSMC.

[0084] The engine performance module 405 can use the tracking / comparison module 420 to compare the MSR 415 with real-time sensor performance data 403.2. Based on the comparison, the efficiencies of various engine components 425 can be used to modify the simulation parameters 414.

[0085] For the real-time PAM sensor data 403.3, an alternative data path (B) is adopted. The engine power model 410 processes real-time data 403.1 and 403.2 from the gas path sensor 71 and performance sensor 72, respectively. The real-time PAM sensor data 403.3 can be received by the particle intake analysis module 460. The particle intake analysis module 460 can maintain an operating log of the sensed PAM flow, which may include particle density, particle volume, mass, composition, and other data. Based on the real-time PAM sensor data 403.3, the particle intake analysis module 460 determines the total efficiency particle debit 465 of the engine 100 as a whole, or the corresponding total particle efficiency debit 465.n of its components.

[0086] For example, the particle efficiency debit 465 can be expressed as a percentage, indicating that the selected engine component (e.g., the compressor) will operate at only 93% efficiency due to PAM 6, compared to the performance of a compressor without PAM 6 intake. The total efficiency particle debit 465 can then be modified to MSR 415 and component efficiency 425.

[0087] The engine performance module 405 continuously adjusts the simulation parameters 414 of the engine power model 410 so that its output converges to the real-time performance sensor data 403.2. Therefore, the embodiment provides real-time simulation data based on the intake of PAM 6 that substantially reflects the current component efficiency 425.

[0088] Based on the engine power model 410, the engine performance module 405 can identify the maximum power (lift, thrust) that the engine 100 can provide under assumed, non-current mission conditions. For example, based on the engine power model 410, the engine performance module 405 can identify the maximum power, lift, and thrust that the engine 100 can provide under worst-case mission conditions (e.g., maximum expected aircraft load, maximum or minimum external temperature, maximum external wind, etc.).

[0089] By incorporating real-time PAM sensor data 403.3 as a component into the simulation technology 412 and simulation parameters 414, the engine power model 410 provides a more reliable simulation of engine performance. Therefore, measuring PAM 6 helps to more reliably extrapolate engine power to various potential or assumed adverse environments.

[0090] The engine power assurance calculation module 435 can generate an engine power assurance report 437 as a function of the most recent PAM intake and the corresponding decrease in engine power if the PAM intake continues over time. In the short term (i.e., minutes, hours), the engine power assurance report 437 can indicate whether the engine 100 can maintain sufficient flight power under various aircraft stress conditions.

[0091] The EPHMS 400 can receive output data from the engine condition assessment module 427 and present it to the aircraft operator as an alert via a display 360, or it can store the data in the dynamic memory 330 and the data storage device 335. Thus, the engine condition assessments from the EPHMS 400 (e.g., engine health report 433, engine power assurance report 437) can be used to enable the aircraft operator to modify current aircraft operations and to enable maintenance personnel to predict maintenance needs.

[0092] During the operation of the aircraft engine 100, the tracking / comparison module 420 adjusts the simulation parameters 414 so that the MSR 415 matches the real-time performance sensor data 403.2. This optimizes the engine power model 410. The updated engine power model 410 can then be run to its limits—meaning that the model can be run under maximum flight stress conditions (e.g., maximum flight speed) to determine whether the engine 100 can sustain flight under these maximum conditions.

[0093] Engine performance module 405 includes a PAM experience database (PAMEDB) 463 for storing data related to PAM type / size and the impact of different types of PAM on components of engine 100. For example, PAMEDB 463 may be an element of simulation parameter 414 or particulate intake analysis module 460. Data stored in PAMEDB 463 can indicate that a certain type of PAM is more likely to damage a first component X (e.g., compressor) of engine 100 and less likely to damage a component Y (e.g., engine blades).

[0094] The data stored in PAMEDB 463 can be determined based on historical evidence collected from the current aircraft's flight and / or PAM data from past and present flights of multiple aircraft. The stored data can also support the biasing of component efficiency parameters for various other components of engine 100. PAMEDB 463 may include known particle levels for certain geographical or environmental regions (e.g., cities, deserts, etc.). Such data can be added to (or supplemented with) the real-time PAM sensor data 403.3 input to engine power model 410.

[0095] In this embodiment, the power guarantee and / or health status of engine 100 is determined at least in part based on a feedback loop that uses sensed engine data to modify simulation parameters 414 to reflect the actual performance of an aging engine. Engine power model 410 can be continuously updated throughout the lifespan of engine 100. Engine power model 410 and simulation parameters 414 can be reset to the initial state of a new engine, a nominal engine, or a conventional engine.

[0096] Engine power model 410 simulates the engine compressor, turbine, combustion chamber, and other components of engine 100 based on thermodynamic and mechanical models expressed in simulation techniques 412 and simulation parameters 414. Engine power model 410 is calibrated in real time by comparing MSR 415 with real-time performance sensor data 403.2.

[0097] Figure 5This is a flowchart of an exemplary method 500 for assessing engine power, health, and maintenance requirements based on the level and composition of PAM 6 ingested by the aircraft engine 100 during flight. This method can be performed by the controller 300 based on real-time data 403 from sensors 71, 72, and 73. Method 500 begins at block 505, where an engine power model 410 is stored in the controller 300's memory during aircraft software updates, power-on cycles, etc.

[0098] In some embodiments, simulation technique 412 and simulation parameters 414 may take into account the expected or default aging model of engine 100. For example, simulation parameter 414 may be defined as a function of time or the cumulative mileage traveled by the aircraft. Alternatively, simulation parameter 414 may be listed in a table, where time / cumulative mileage is the parameter. Thus, engine power model 410 autonomously changes over time according to the expected decline in engine performance. Such aging expressions or tables may be based on a variety of sources, including historical information about similar engines.

[0099] The simulation parameter 414 can be represented as a variable in the simulation technique 412, where a specific simulation parameter 414 is loaded for execution. In this way, the simulation parameter can vary during the life of the engine 100, thereby changing the value of the variable in the simulation technique 412.

[0100] In box 510, during flight, controller 300 receives real-time gas path sensor data 403.1, performance sensor data 403.2, and PAM sensor data 403.3 from sensors 71, 72, and 73, respectively. For example, gas path sensor data 403.1 may include gas flow rate / pressure, temperature, speed, altitude, aircraft environmental data, etc. Performance sensor data 403.2 may include turbine and engine shaft rotation speeds, etc.

[0101] In optional box 512, method 500 can classify the types of particles flowing into and along the gas path based on real-time PAM sensor data 403.3. For example, real-time PAM sensor data 403.3 can be classified according to the size, mass, composition, etc., of the detected particles. Real-time PAM sensor data 403.3 can also represent the amount of PAM 6 entering and leaving engine 100 or its components. Therefore, method 500 determines the volume or mass of particle data captured in engine 100.

[0102] The memory within controller 300 may contain stored data indicating the affected engine components and the extent of the impact based on the type of particles (e.g., size, composition, density, etc.). If such data is stored, optional box 512 can determine the engine components (and the extent) affected by PAM 6 in the gas path of engine 100.

[0103] In block 515, a subset of the received real-time data 403 is input to engine simulation technology 412 (or engine simulation table). In some embodiments, real-time performance sensor data 403.2 (e.g., compressor / turbine shaft speed, etc.) may also be considered as input data. Based on this input data, simulation technology 412 is used to generate the performance of engine power model 410 for various components of engine 100.

[0104] In block 520, method 500 generates an MSR 415 for engine operating performance. MSR 415 is the value of real-time data 403 predicted by engine power model 410 for the engine environment sensors 71, 72, and 73 based on simulation technology 412 and current simulation parameters 414. In block 525, method 500 compares the MSR 415 with the real-time data 403 and / or the expected engine component efficiency 425.

[0105] In box 530, method 500 determines whether MSR 415 is equal to or sufficiently close to real-time gas path performance data 403.1, performance sensor data 403.2, and PAM sensor data 403.3. A sufficiently close match is defined by a threshold equality parameter or threshold equality range stored in memory associated with the tracking / comparison module 420. These ranges are determined by experienced systems engineers, engine history, technical and legal requirements, organizational standards, and other specifications.

[0106] If MSR 415 matches the received real-time data 403, method 500 continues by looping back to box 510. If the difference between MSR 415 and real-time data 403 does not match, method 500 proceeds to box 535.

[0107] In block 535, method 500 determines the current efficiency levels of various components of engine 100. In block 540, method 500 modifies simulation parameters 414 such that MSR 415 reflects the current, actual, real-time efficiency determined in block 535. This results in an update to the engine power model 410.

[0108] Box 540 modifies the simulation parameters 414 based on various stored expressions and converts the real-time data 403 and component efficiency 425 into suitable simulation parameters 414. In box 540, determining the suitable simulation parameters 414 can be an iterative process (i.e., box 537), where box 535 loops back to box 515 to repeat the simulation and comparison operations of boxes 515, 520, 425, 530, 535, and 540.

[0109] In box 545, the modified analog parameter 414 is stored and used in boxes 515 and 520. After box 545, method 500 can return to box 510, receive sensor data, and continue with subsequent operations.

[0110] Method 500 can further proceed to blocks 550 and 555. In block 550, method 500 generates an engine health report 433 from component efficiency 425. In block 555, method 500 generates an engine power guarantee calculation based on one or more of the real-time performance sensor data 403.2, the engine health report 433, and the real-time prediction of the updated engine power model 410.

[0111] In one embodiment, sensors and a real-time performance model can combine recent engine power losses with the near future to identify whether the anticipated power loss due to PAM 6 ingestion is relevant to mission success (i.e., the anticipated power loss is sufficient to cause the aircraft to lose flight power, thereby triggering the need to exit the mission or modify mission operations). Upon detecting such a relevant power loss, method 500 provides an appropriate real-time alert to the aircraft operator via display 360 or another audio interface.

[0112] Figure 6A This is a flowchart illustrating a first exemplary method 600.1, which is used to present a visualization 625.1 of the PAM intake level detected by the aircraft engine 100 during flight to an aircraft operator. Method 600.1 can be performed, for example, via software executed in the engine controller 300. In block 605, method 600.1 receives the current level of PAM intake from one or more engines of the aircraft. The current PAM level can be in the form of detailed PAM levels from PAM sensor 73, or in the form of analyzed detailed aggregated PAM data 464 from PAM intake analysis module 460.

[0113] Method 600.1 proceeds to box 610, where the aggregated PAM data 464 is filtered to generate PAM data 612. Method 600.1 presents PAM 6 inhalation data relevant to immediate short-term task requirements. PAM sensor 73 distinguishes incoming PAM 6 particles by particle size. For example, the average volume of PAM 6 can be determined by filter 610 to generate PAM data 612, possibly averaged over a period of time.

[0114] In another embodiment, the average density of PAM 6 in engine 100 can be determined by filter 610 to generate filtered PAM data 612. Filter 610 may also apply moving averages, low-pass filtering (to remove transient changes in PAM volume / density), and other forms of processing to produce a signal suitable for visual presentation.

[0115] In frame 615, the filtered PAM data 612 is displayed to the aircraft operator via display 360. Figure 6A An exemplary visualization 625.1 of PAM data 612 is shown. In visualization 625.1, display 360 presents the current PAM intake level. The current PAM levels are shown for engines 1 and 2 respectively. For each engine, a PAM intake level range bar or bar 635 may indicate the range of PAM from the lowest level to the highest level. For each engine, a level indicator 640 depicts the current level (e.g., measurement) of PAM 6 in each engine.

[0116] For aircraft operators, this visual information can aid in aircraft navigation. For example, a helicopter pilot may be approaching a destination or attempting to hover in position. Through visualization 625.1 on the PAM ingestion level display 360, the pilot can observe when PAM ingestion exceeds the PAM safety threshold level 642. The pilot can then maneuver the helicopter to different horizontal or vertical positions and check the display to determine which locations to observe to reduce PAM ingestion 6.

[0117] Figure 6B This is a flowchart illustration of the second exemplary method 600.2, which provides a visualization 625 to the aircraft operator of the PAM level detected by the aircraft engine 100 during flight. The visualization 625 supports the pilot in navigating the aircraft in response to the presence of PAM.

[0118] Method 600.2 is similar to Method 600.1. In block 605, the current PAM intake level of engine 100 is acquired by sensor 73 and analyzed by PAM intake analysis module 460. In block 608, the PAM levels from multiple engines are summed together, and in block 610, the summed data is filtered. The filtering (and biasing) of the real-time PAM sensor data 403.3 differs from Method 600.1 in that, compared to Method 600.1, a certain degree of increased weighting can be given to the smaller-sized PAM data reflecting PAM 6.

[0119] In box 615, the current filter output 612 (e.g., PAM date) is displayed as a moving scale line (e.g., via a horizontal indicator 640) on the edge of the historical PAM data visualization 625.2. Simultaneously, in box 613, a scroll buffer 330.1 (e.g., stored in dynamic memory 330) is updated with the output of the most recently filtered PAM data 612. Buffer 330.1 maintains a constructible time-cycle output of real-time PAM sensor data 403.3. The most recent S-second data (e.g., 120 seconds) is displayed on a historical data trajectory graph 650 on display 360. The historical visualization 625.2 shows the moving historical trajectory graph 650 on the Y-axis relative to time on the X-axis. A single bar or column 635 serves as the background for the displayed PAM data against the time.

[0120] For aircraft operators, this extended visual information can further aid aircraft navigation. For example, a helicopter pilot might notice their helicopter's position where PAM inhalation is at its lowest over time. If this aligns with other mission objectives and requirements, the pilot can then identify the point in time when PAM inhalation is minimal, and thus pinpoint the location and maneuver the helicopter accordingly.

[0121] Method 600.2 displays PAM inhalation as a function of the most recent time. PAM data 612 can be displayed as a function of the most recent location, for example, indicated by a local ground map having appropriate numerical or color symbols representing different levels of PAM inhalation along the travel path. For example, green can be used for low levels of PAM 6, blue for higher levels, and progressively yellow, orange, and red for even higher levels of PAM inhalation.

[0122] Other display formats can also be used. For example, historical PAM visualization 625.2 can include appropriate location coordinates L1, L2, ..., Ln at the peaks or troughs of the data trajectory graph 650. L1, L2, etc. can represent Global Positioning System (GPS) coordinates, street intersections, place names, or other markers for specific locations.

[0123] Figure 6CThis is a flowchart illustration of a third exemplary method 600.3, used to present a visualization 625.3 to the aircraft operator of the PAM levels detected by the two aircraft engines 100 during flight. Method 600.3 is similar to exemplary methods 600.1 and 600.2, but with some modifications, including taking into account the detection relationship between PAM inhalation 6 and aircraft altitude.

[0124] In method 600.3, the current PAM intake levels of engines 1 and 2 are acquired by sensor 72 (box 605.1), summed (box 609), and then filtered / smoothed (box 610). User-programmable time values ​​are stored in a rolling buffer (e.g., dynamic memory 330). In substantially simultaneous box 605.2, radar altimeter data 655 is collected from the aircraft's radar altimeter. The altitude data history 657 for the most recent two minutes is also maintained in a rolling data buffer 330.2 (box 613.2).

[0125] In box 615, historical PAM data 330.1 and historical altitude data 330.2 / 690 are displayed on visualization 625.3, which can be PAM data and altitude map 625.3 on display 360, where historical PAM ingress levels are on the Y-axis and historical radar altimeter levels are on the X-axis.

[0126] For example, because a helicopter may hover at different altitudes within a historical timeframe, and because PAM intake may vary over time at a given altitude, a scatter plot (e.g., a mapping) 650 of altitude-related PAM can be generated. A single bar or column 635 serves as the background for the displayed PAM over time. Furthermore, current PAM intake data 652 is labeled on the same display, plotted relative to the current radar altitude on the X-axis.

[0127] For aircraft operators, this extended visual information about altitude-related PAM inhalation can help pilots select an optimal altitude, balancing mission requirements with reduced PAM inhalation.

[0128] Figure 6D This is a flowchart illustrating an exemplary method 660 for determining a PAM safety threshold level 642. Box 665 identifies a suitable short-term aircraft power threshold, which may depend on mission requirements (see below). Figure 8 (To be discussed). The power threshold indicates the minimum power an aircraft may require to maintain flight. Secondary thresholds can be used to determine when an aircraft can maintain flight but is at risk of losing its ability to fly.

[0129] In block 670, method 660 identifies one or more PAM threshold levels 642 associated with a short-term power threshold. Specifically, in block 670, engine performance module 405 can identify (over time) the threshold level 642 of PAM ingestion associated with the short-term power threshold. Engine performance module 405 may be pre-configured with an initial PAM ingestion threshold level 642, which can be modified over time as aircraft engine performance declines.

[0130] In box 675, an appropriate PAM threshold level 642 is presented on the particle data visualization 625. Box 675 can occur simultaneously with box 615, which needs to display the real-time PAM level for operators to assess in the aircraft cockpit.

[0131] While the aircraft can continue flying and at least land without engine power, helicopters use engine power to stay airborne and land safely. Helicopters must reach and maintain a vertical lift threshold of 830 (see below). Figure 8 This is used to lift the helicopter. The vertical lift threshold of 830 is a function of the aircraft and engine design. However, aircraft engineers can establish various power thresholds to indicate the power requirements of different aircraft missions.

[0132] For example, some missions requiring the transport of lighter-weight aircraft require less power than missions specified for maximum aircraft load. Similarly, other types of missions in mild or moderate weather conditions typically require less power than those in severe storm conditions.

[0133] Figure 7 This is a flowchart illustrating an exemplary method 700 for recommending engine maintenance and / or aircraft maintenance, in part based on real-time PAM sensor data 403.3 of PAM 6 entering the aircraft engine 100.

[0134] In block 710, method 700 determines engine power or performance without considering PAM intake information. This can be performed, for example, via engine power model 410. Meanwhile, in block 715, method 700 receives real-time PAM sensor data 403.3 from PAM sensor 73 and determines a PAM intake metric (e.g., via PAM intake analysis module 460 or tracking / comparison module 420). In block 725, method 700 determines component efficiency 425 due to variations in PAM 6.

[0135] In block 730, the engine power model 410 is modified based on the newly determined component efficiency 425 (e.g., via the tracking / comparison module 420). As described above, these engine power model changes can be accomplished in various ways. Block 710 continues to determine engine performance and engine power based on the updated engine power model 410.

[0136] Based on changes in engine efficiency, box 735 determines the projected rate of change in engine efficiency and / or the projected rate of change in engine power based on the historical changes in engine power / efficiency. Changes in power or efficiency due to PAM 6 are reflected in future updated projections of engine power or efficiency.

[0137] In box 740, method 700 determines the expected time when engine power drops to a threshold 830, below which engine performance is unsatisfactory. This threshold efficiency determines the threshold 830 at which the aircraft requires maintenance. The output is based on engine threshold 752.

[0138] In box 745, based on thresholds 752 to vertical lift threshold 830, method 700 proposes engine / aircraft maintenance recommendations. These recommendations may take the form of suggested future maintenance schedule dates. However, if PAM 6 ingestion causes a rapid drop in engine power, recommendations may take the form of an emergency alert to issue an emergency maintenance request to the aircraft operator.

[0139] Figure 8 This is an exemplary illustration of a calculated engine maintenance curve 800 according to an embodiment. In curve 800, the vertical axis indicates engine power / efficiency, which can be determined via real-time data or through engine power model 410. In the exemplary curve 800, the Y-axis 805 represents the maximum vertical lift the aircraft can withstand.

[0140] Exemplary plot line 810 represents engine power, engine efficiency, maximum vertical lift, or other characteristics of engine capability or power. First plot line 810.1 represents the maximum vertical lift based on real-time engine readings or simulation and PAM 6 intake. The first portion 810.1(H) of plot line 810.1 represents the historical (H) maximum vertical lift, which may gradually decrease over time.

[0141] The first extrapolation (P) portion 810.1.P of plotting line 810.1 can represent a linear extrapolation decrease in engine vertical lift over time. When the extrapolated plotting line 810.1.P crosses the vertical lift threshold 830 at the extrapolated future time 840.1, engine maintenance may be recommended.

[0142] The improved extrapolation plotting portion 810.1.P* of the vertical lift plotting line 810.1 can take into account a more complex simulation of future engine performance as a function of various measured engine performance factors and environmental data. The improved extrapolation portion 810.1.P* can result in an earlier future time 840.2 or a later time (not shown) for maintaining the recommended future performance. In contrast to the vertical lift plotting line 810.1, the second exemplary maximum vertical lift plotting line 810.2 can represent a simulated calculation of engine performance as a function of the intake of PAM 6.

[0143] Therefore, by extrapolating the power or efficiency to the future based on the past history of PAM intake according to the vertical lift plot line 810.2, the maximum available vertical lift line 810.2.P with extrapolated efficiency is obtained. Compared to times 840.2 or 840.1, the vertical lift plot line 810.2.P intersects the vertical lift threshold 830 at an earlier maintenance time 840.3. This indicates that maintenance should be performed much earlier than maintenance estimates that do not consider PAM 6 intake.

[0144] However, by using real-time PAM sensor data 403.3, extrapolated performance vertical lift plot line 810.2.P, and historical PAM inhalation data from the engine, the extrapolated vertical lift plot line 810.2.P can reflect that PAM 6 inhalation is not as severe as in the worst-case scenario. Therefore, the recommended maintenance time 840.3 allows more time before maintenance is needed, thus eliminating the need for unnecessary or premature maintenance.

[0145] Engine Health Report 433 and Engine Health Factors 434 may include raw or summary data on PAM 6 intake during a single flight. Engine Health Report 433 may include summary data on PAM 6 intake over multiple flights. This data can help maintenance personnel further identify overall engine health and maintenance requirements.

[0146] Engine Health Report 433 can also identify PAM 6 as summarized or categorized in various ways, such as by terrain, altitude, mission type, or atmospheric conditions that lead to greater PAM accumulation. Such reports can be helpful for future mission planning.

[0147] The engine hardware health calculation module 430 can generate an engine health report 433, which indicates the health of component modules or the health of the engine 100. The engine health report 433 can indicate the component efficiency 425 of various engine components, and can indicate the maximum available engine power or the rate of decrease in engine power.

[0148] EHF 434 may also include engine torque coefficient, temperature, thrust, pressure, and PAM accumulation. Engine hardware health calculation module 430 may also assist EPHMS 400 and exemplary method 700 in providing recommendations for engine maintenance and recommended maintenance dates 840.

[0149] Figure 9 An exemplary calculated engine maintenance curve 900 is shown. Specifically, Figure 9 An additional exemplary maximum vertical lift plot line 910 is shown, which reflects a simulated extrapolation calculation of the engine performance vertical lift plot line 910.P generated in response to a sudden high-level intake of PAM 6 by the engine 100. The vertical lift plot line 910 also includes a history section 910.H.

[0150] In one embodiment, the extrapolated vertical lift plot line 910.P is based on the anticipated reduction in engine power due to PAM inhalation. Alternatively, the extrapolated vertical lift plot line 910.P assumes that high levels of PAM inhalation can continue into the future. In alternative embodiments, various heuristic methods can be employed to estimate how much higher levels of PAM inhalation can be expected in the near future.

[0151] The exemplary maximum vertical lift plotting line 910 intersects the vertical lift threshold 830 at an estimated time point 940 (mission power loss) relative to the current time. As a result, the embodiment can determine the condition of engine 100 and its correlation with mission success over the expected duration of the mission.

[0152] In this embodiment, the temporal meaning of engine PAM intake is determined by actively modeling the operation and / or performance of the aircraft engine. This modeling can be based on mathematical models of engine components and parts, as well as mathematical-physical models of engine mechanics, aircraft performance, aerodynamics, and other environmental factors.

[0153] In an alternative embodiment, a processor-based learning system can be trained based on historical engine data, including a neural network utilizing neural network model 680, to predict engine performance degradation consistent with past declines due to PAM. In other embodiments, a processor-based statistical analysis system can identify statistical data and models that associate PAM inhalation levels with engine performance degradation. These models can be extrapolated to new environmental conditions and then further used to predict PAM inhalation levels that may reduce engine performance in various mission environments.

[0154] Those skilled in the art will also understand that various adaptations and modifications can be made to the preferred and alternative embodiments described above without departing from the scope and spirit of this disclosure. Therefore, it should be understood that this disclosure can be practiced in ways other than those specifically described herein, within the scope of the appended claims.

[0155] A tangible computer-readable non-transitory storage medium stores instructions that, when executed by a hardware processor of an aircraft, cause the hardware processor to perform a method. The method includes receiving measurements of the amount of particulate matter in the gas path of the engine from an engine PAM sensor of the aircraft's engine during flight. The method also includes presenting a visualization of the particulate matter measurements to the aircraft's pilot, wherein the visualization supports navigation of the aircraft in response to the presence of particulate matter.

[0156] An exemplary system includes a hardware processor associated with an aircraft engine, a particle sensor configured to measure particulate matter levels in the engine during flight, and a display for the aircraft. The hardware processor is configured to receive measurements of the amount of particulate matter in the engine's gas path from the particle sensor during flight and to present a visualization to the aircraft's pilot via the display to guide the aircraft's navigation in response to the presence of particulate matter.

[0157] A tangible computer-readable non-transitory storage medium storing instructions that, when executed by a hardware processor of an aircraft, cause the hardware processor to perform a method. The method includes: receiving measurements of particulate matter quantity in the engine's gas path from a particulate sensor of the aircraft's engine; receiving the engine's environmental state from an environmental sensor of the engine; and determining engine efficiency based on the environmental state and the measured particulate matter quantity, the engine efficiency being determined according to an engine power model included in the stored instructions. The method further includes: determining the engine's current power capability based on the engine efficiency; determining the engine's power capability over time based on the current power capability and historical power capabilities; and determining engine maintenance requirements based on the change in engine power capability.

[0158] A system including an engine controller associated with an aircraft. The engine controller includes a hardware processor in the aircraft engine and multiple sensors. The multiple sensors include (i) a particle sensor configured to measure at least one of a measured value of particulate matter in the engine's gas path and a measured value of accumulated particulate matter in the engine; and (ii) an environmental sensor configured to detect the environmental conditions of the engine; and a memory configured to store an engine power model for simulating engine power. The hardware processor is configured to: determine engine efficiency based on the engine power model and engine data from the environmental sensors; determine changes in engine efficiency based on particle data from the particle sensor; determine the current power capability of the engine based on engine efficiency; determine changes in engine power capability over time based on the current power capability and historical power capability; and determine engine maintenance requirements based on changes in power capability.

[0159] Further aspects of the invention are provided by way of the subject matter of the following clauses:

[0160] 1. A tangible computer-readable non-transitory storage medium storing instructions, which, when executed by a hardware processor of an aircraft, cause the hardware processor to perform a method, the method comprising: receiving, during flight of the aircraft, a measurement of the amount of particulate matter in the gas path of the engine from an engine particulate sensor of the aircraft's engine; and presenting a visualization of the particulate matter measurement to a pilot of the aircraft, wherein the visualization supports navigation of the aircraft in response to the presence of particulate matter.

[0161] 2. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein presenting the visualization to the pilot of the aircraft includes a visual indication presenting a current measurement of the amount of particulate matter in the gas path of the engine.

[0162] 3. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein presenting the visualization to the pilot of the aircraft includes a visual indication presenting a plurality of time-series continuous measurements of the amount of particulate matter in the gas path of the engine within the most recent time interval.

[0163] 4. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein presenting the visualization to the pilot of the aircraft includes a visual indication of multiple measurements of the amount of particulate matter detected in the engine at different flight altitudes.

[0164] 5. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein presenting the visualization to the pilot of the aircraft includes a visual indication of presenting multiple measurements of the amount of particulate matter in the engine at multiple different geographic ground coordinates.

[0165] 6. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein the method further comprises: receiving a raw particulate sensor data stream from the engine particulate sensor; and filtering the raw particulate sensor data stream to extract measurements of selected particulate data for display to the pilot.

[0166] 7. A tangible computer-readable non-transitory storage medium according to any of the preceding claims, wherein the method further comprises: receiving a raw particulate sensor data stream from the engine particulate sensor; and processing the raw particulate sensor data stream to classify the particulate matter according to one or more particulate matter characteristics, the one or more particulate matter characteristics including at least one of density, mass, particle size, and composition.

[0167] 8. A tangible computer-readable non-transitory storage medium according to any of the preceding clauses, wherein the method further comprises: presenting to the pilot an indication of engine risk associated with the amount of particulate matter in the gas path.

[0168] 9. A system comprising: a processor associated with an engine of an aircraft; a particle sensor configured to measure particulate matter levels in the engine during flight; and a display of the aircraft; wherein: the processor is configured to: receive, during flight, a measurement of the amount of particulate matter in the gas path of the engine from the particle sensor; and present a visualization to a pilot of the aircraft via the display to guide the navigation of the aircraft in response to the presence of particulate matter.

[0169] 10. The system according to any one of the preceding clauses, wherein the processor is further configured to present the visualization as at least one of: a visual indication of the particulate matter level in the gas path of the engine; a visual indication of multiple time-series continuous measurements of the particulate matter level in the gas path of the engine within the most recent time interval; a visual indication of multiple measurements of the amount of particulate matter in the engine detected at different flight altitudes; and a visual indication of multiple measurements of the amount of particulate matter in the engine at multiple different geographic ground coordinates.

[0170] 11. The system according to any one of the preceding clauses, further comprising: (i) an operation sensor configured to detect the operating state of the engine, and (ii) an engine power model stored in the memory of the processor for simulating the power of the engine; wherein the processor is configured to: determine the efficiency of the engine based on the engine power model and engine data from the operation sensor; determine the change in the efficiency of the engine based on particle data from the particle sensor; determine the current power capability of the engine based on the determined efficiency; determine the change in the power capability of the engine over time based on the current power capability and historical power capability; and determine the maintenance requirements of the engine based on the change in the power capability.

[0171] 12. The system according to any of the preceding clauses, wherein the processor is further configured to: update an initial engine power model based on the expected performance of a new or nominal engine, the update including updating the engine power model over time in response to the measured value of the particulate matter amount; and model the engine power based on (i) the updated engine power model, (ii) past performance data of the engine and (iii) past engine particulate data, and determine the change of the engine's efficiency over time by determining the expected future efficiency.

[0172] 13. The system according to any of the preceding clauses, wherein the processor is further configured to: determine, based on the change in the power capability of the engine, a real-time task perception of engine power loss due to inhaled particulate matter.

[0173] 14. A method comprising: receiving a measurement of particulate matter quantity in a gas path of an engine from a particulate sensor of an aircraft engine; receiving an environmental state of the engine from an environmental sensor of the engine; determining an engine efficiency based on the environmental state and the measurement of particulate matter quantity via a processor associated with the aircraft, the engine efficiency being determined according to an engine power model included in instructions stored in a memory of the processor; determining a current power capability of the engine based on the engine efficiency via the processor; determining a change in the engine's power capability over time via the processor based on the current power capability and historical power capabilities; and determining maintenance requirements for the engine via the processor based on the change in the engine's power capability.

[0174] 15. The method according to any of the preceding clauses, wherein the engine power model is initially based on the expected performance of a new or nominal engine; and wherein the engine power model is updated over time in response to the measured value of the particulate matter content.

[0175] 16. The method according to any of the preceding clauses, wherein determining the change in the power capability of the engine over time comprises determining the expected future efficiency based on modeling the engine power according to (i) an updated engine power model, (ii) past engine environment data of the engine, and (iii) past engine particle data.

[0176] 17. The method according to any of the preceding clauses, wherein determining the engine maintenance requirement based on the change in the engine's power capability over time includes determining the future time or date when the engine's expected future power reaches a permissible engine power threshold.

[0177] 18. The method according to any one of the preceding clauses, wherein receiving the environmental state of the engine from the engine's environmental sensors includes receiving two or more of the following: (i) thrust generated by the engine; (ii) fuel consumption of the engine; (iii) engine torque coefficient; (iv) engine pressure; and (v) engine temperature; and determining the expected future efficiency of the engine includes evaluating any one of the environmental states (i)-(v) based on another of the environmental states (i)-(v).

[0178] 19. The method according to any one of the preceding clauses, wherein receiving the environmental state of the engine from the engine's environmental sensors includes receiving two or more of the following: (i) thrust generated by the engine, (ii) fuel consumption of the engine, (iii) engine torque coefficient, (iv) engine pressure, and (v) engine temperature; and determining the current power capability of the engine includes evaluating any one of the environmental states (i)-(v) based on another of the environmental states (i)-(v).

[0179] 20. The method according to any of the preceding clauses, wherein the method further comprises determining real-time task awareness of engine power loss due to particulate matter inhalation based on the change in the engine's power capability.

Claims

1. A tangible computer-readable non-transitory storage medium storing instructions, the method comprising: The instructions, when executed by a hardware processor of an aircraft, cause the hardware processor to perform a method comprising: during a flight of the aircraft, receiving, from an engine particle sensor of an engine of the aircraft, measurements of an amount of particulate matter in a gas path of the engine; presenting, to a pilot of the aircraft, a visualization of the measurements of the particulate matter, wherein the visualization supports navigation of the aircraft in response to presence of particulate matter; determining, based on an engine power model for modeling power of the engine and engine data from the engine particle sensor, an efficiency of the engine; determining, based on particulate data from the engine particle sensor, a change in the efficiency of the engine over time; determining, based on the determined efficiency, a current power capability of the engine; and determining, based on current power capability and historical power capability, a change in the power capability of the engine over time.

2. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, presenting the visualization to the pilot of the aircraft comprises presenting a visual indication of a current measurement of the amount of particulate matter in the gas path of the engine.

3. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, presenting the visualization to the pilot of the aircraft comprises presenting a visual indication of a plurality of time-sequential measurements of the amount of particulate matter in the gas path of the engine over a recent time interval.

4. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, presenting the visualization to the pilot of the aircraft comprises presenting a visual indication of a plurality of measurements of the amount of particulate matter in the engine detected at different flight altitudes.

5. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, presenting the visualization to the pilot of the aircraft comprises presenting a visual indication of a plurality of measurements of the amount of particulate matter in the engine at a plurality of different geographical ground coordinates.

6. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, the method further comprises: receiving a raw particulate sensor data stream from the engine particle sensor; and filtering the raw particulate sensor data stream to extract measurements of selected particulate data for display to the pilot.

7. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, the method further comprises: receiving a raw particulate sensor data stream from the engine particle sensor; and processing the raw particulate sensor data stream to classify the particulate matter according to one or more particulate matter characteristics, the one or more particulate matter characteristics comprising at least one of density, mass, particle size, and composition.

8. The tangible computer-readable non-transitory storage medium of claim 1, wherein, wherein, the method further comprises: presenting, to the pilot, an indication of engine risk associated with the amount of particulate matter in the gas path.

9. A system for providing information about particulate matter within an aircraft engine, characterized by, comprising: a processor associated with an engine of an aircraft; an operating sensor configured to detect an operating state of the engine; a particulate sensor configured to measure a particulate matter level in the engine during a flight; and a display of the aircraft; wherein: the processor is configured to: during the flight, receive, from the particulate sensor, measurements of an amount of particulate matter in a gas path of the engine; present, via the display, a visualization to a pilot of the aircraft to guide navigation of the aircraft in response to presence of particulate matter; determining an efficiency of the engine based on an engine power model for simulating power of the engine and engine data from the operational sensors; determining a change in the efficiency of the engine over time based on particulate data from the particulate sensors; determining a current power capability of the engine based on the determined efficiency; and determining a change in the power capability of the engine over time based on current power capability and historical power capability.

10. The system of claim 9, wherein, wherein, the processor is further configured to present the visualization as at least one of: a visual indication of the particulate matter level in the gas path of the engine; a visual indication of a plurality of time-sequential measurements of the particulate matter level in the gas path of the engine over a recent time interval; a visual indication of a plurality of measurements of the amount of particulate matter in the engine detected at different flight altitudes; and a visual indication of a plurality of measurements of the amount of particulate matter in the engine at a plurality of different geographical ground coordinates. wherein, 11. The system of claim 9, wherein, the processor is configured to: determine a maintenance requirement of the engine from the change in the power capability over time. wherein, 12. The system of claim 11, wherein, the processor is further configured to: update an initial engine power model based on expected performance of a new or nominal engine, the updating including updating the engine power model in response to the measurements of the amount of particulate matter over time; and determine the change in the efficiency of the engine over time by determining a projected future efficiency based on modeling engine power from (i) the updated engine power model, (ii) past performance data of the engine, and (iii) past engine particulate data. wherein, 13. The system of claim 11, wherein, the processor is further configured to: determine a real-time task awareness of engine power loss due to ingested particulate matter from the change in the power capability of the engine over time. including:

14. A method for providing information about particulate matter within an aircraft engine, characterized by, receiving, from a particulate sensor of an engine of an aircraft, a measurement of an amount of particulate matter in a gas path of the engine; receiving, from an environmental sensor of the engine, an environmental state of the engine; determining, via a processor associated with the aircraft, an engine efficiency based on the environmental state and the measurement of the amount of particulate matter, the engine efficiency determined from an engine power model included in instructions stored in a memory of the processor; determining, via the processor, a current power capability of the engine based on the engine efficiency; determining, via the processor, a change in power capability of the engine over time based on the current power capability and historical power capability; and determining, via the processor, a maintenance requirement of the engine from the change in the power capability of the engine over time. wherein, 15. The method of claim 14, wherein, the engine power model is initially based on expected performance of a new or nominal engine; and wherein, the engine power model is updated over time in response to the measurements of the amount of particulate matter. wherein, 16. The method of claim 15, wherein, ​ determining the change in the power capability of the engine over time includes determining a projected future efficiency based on modeling engine power according to (i) an updated engine power model, (ii) past engine environmental data of the engine, and (iii) past engine particulate data.

17. The method of claim 16, wherein, wherein, determining the engine maintenance requirement from the change in the power capability of the engine over time includes determining a future time or date at which a projected future power of the engine reaches an allowable engine power threshold.

18. The method of claim 16, wherein, wherein, receiving environmental states of the engine from environmental sensors of the engine includes receiving two or more of: (i) thrust generated by the engine, (ii) fuel consumption of the engine, (iii) engine torque coefficient, (iv) engine pressure, and (v) engine temperature; and determining the projected future efficiency of the engine includes evaluating any one of the environmental states (i)-(v) according to another one of the environmental states (i)-(v).

19. The method of claim 16, wherein, wherein, receiving environmental states of the engine from environmental sensors of the engine includes receiving two or more of: (i) thrust generated by the engine, (ii) fuel consumption of the engine, (iii) engine torque coefficient, (iv) engine pressure, and (v) engine temperature; and determining the current power capability of the engine includes evaluating any one of the environmental states (i)-(v) according to another one of the environmental states (i)-(v).

20. The method of claim 15, wherein, wherein, the method further includes determining a real-time task awareness of engine power loss due to particulate ingestion from the change in the power capability of the engine over time.

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