Engine Air Filter Flow Predictor Using Power-Flow Correlation
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Solution Overview
Problem
Current systems fail to accurately predict the performance of a degraded engine air filter across varying engine power levels, leading to potential engine damage and safety risks during one-engine-inoperative flight conditions due to inaccurate assessments of air filter health.
Innovation Solution
A measurement system that learns the unique performance characteristics of a specific engine by collecting volumetric flow data and using it to assess air filter health, predicting pressure changes across a range of engine power settings, including maximum power, through a method involving engine data repositories, flow measurement units, and sensor data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current air filter performance assessment systems are used, then the system complexity remains low, but the measurement precision of air filter health across varying engine power levels deteriorates
Solution Approach 1:
The system performs preliminary characterization of the engine-air filter system during a test bench phase before actual operation. Flow vs. power data is collected and stored in advance, creating a reference model that enables accurate predictions during field operation without requiring complex real-time measurements. This preliminary action transfers the complexity from operational measurement to initial system characterization.
Solution Approach 2:
The patent introduces an intermediary computational model that relates engine power to volumetric flow rate through the air filter. Instead of directly measuring flow at multiple power levels during operation, the system uses the intermediary relationship (Q = f(P)) established during testing to predict performance across the power range. This intermediary approach simplifies operational measurement requirements while maintaining prediction accuracy.
2Measurement precision
If maximum power level testing is performed to assess air filter health, then the measurement precision improves, but the loss of time and operational disruption increases
Solution Approach 1:
All necessary flow vs. power data is collected during an initial test bench characterization phase before the air filter is installed in service. This preliminary action completes the data collection requirement offline, allowing the operational system to use stored data for predictions without requiring time-consuming in-service testing. The time investment is made once during manufacturing/testing rather than repeatedly during operation.
Solution Approach 2:
The system uses the engine's own operational data (power levels, operating conditions) combined with the pre-stored flow characteristics to self-determine air filter performance. No external testing equipment or additional measurement infrastructure is needed during operation—the system serves itself by leveraging existing operational parameters and previously collected characterization data.
3Measurement precision
If in-service flow measurement infrastructure is installed to monitor air filter performance, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent extracts the flow measurement function from the operational aircraft system and relocates it to the test bench environment during initial characterization. By taking out the complex flow measurement requirement from in-service operation and completing it during manufacturing/testing, the operational system only needs to store and process simple engine power data, which is already available from existing engine control systems. This extraction eliminates the need for installing flow meters or other complex measurement infrastructure in the aircraft.
Solution Approach 2:
Instead of physically measuring flow during operation, the system creates a copy of the flow-power relationship through computational modeling based on test bench data. The stored flow vs. power curves serve as a digital replica of the actual flow characteristics, allowing the system to predict performance at any power level without physical flow measurement equipment. This copying approach replaces complex physical measurement infrastructure with simple data storage and computation.
Data Source
AI summary
In one example embodiment, a filter condition measurement device features an engine data repository, one or more sensor units, and a measurement unit. The measurement system is configured to identify a first flow value corresponding to a sensed engine power value from the engine data repository, determine a filter coefficient for the filter as a function of the first flow value and the sensed delta-pressure value; identify a second engine power value from the plurality of stored engine power values and a second flow value corresponding to the second engine power value; and determine a second delta-pressure value for the air filter as a function of the filter coefficient and the second flow value.