Aircraft Flight Control for Ice Crystal Risk Avoidance
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Solution Overview
Problem
Existing aircraft flight planning systems fail to accurately predict and avoid atmospheric contaminants like ice crystals, leading to potential engine blockage and instrument failure, with current methods relying on outdated ice crystal icing envelopes and lacking real-time data for precise trajectory adjustments.
Innovation Solution
An aircraft flight control system utilizing machine learning and statistical models to assess atmospheric contamination risk, incorporating spatial and temporal uncertainty calculations, provides real-time recommendations for adjusting flight trajectories to avoid hazardous conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aircraft use traditional weather avoidance methods based on basic weather parameters, then aircraft can avoid storms and dangerous weather phenomena, but aircraft cannot avoid ice crystal accumulation and other atmospheric contaminants that clog engines and instruments
Solution Approach 1:
The system transitions from monitoring basic weather parameters (temperature, precipitation, wind) to measuring specific atmospheric contaminant parameters (ice water content, particle size distribution, contaminant concentration). This parameter change enables detection of ice crystals that traditional systems miss, directly resolving the contradiction between avoiding storms and avoiding ice crystal accumulation.
Solution Approach 2:
The patent replaces traditional radar-based detection systems with advanced sensing technologies capable of detecting non-precipitating ice crystals and atmospheric contaminants. This substitution enables detection of contaminants that do not reflect radar waves, thereby resolving the limitation of traditional weather avoidance methods.
2Reliability
If aircraft re-route to avoid hazardous weather phenomena, then aircraft can avoid storms and dangerous conditions, but flight costs increase and flight time extends
Solution Approach 1:
The system performs preliminary detection and assessment of atmospheric contaminants along planned flight paths before departure and during flight. By identifying safe corridors and predicting contaminant movement, the system enables more direct routing decisions that avoid unnecessary detours while maintaining safety, thus reducing flight time losses.
Solution Approach 2:
The system continuously monitors atmospheric conditions and provides real-time feedback to update flight paths dynamically. This feedback mechanism allows aircraft to maintain safer, more direct routes by reacting to actual contaminant positions rather than relying on static weather forecasts, thereby reducing unnecessary flight time extensions.
3Reliability
If aircraft activate anti-icing systems continuously to prevent ice accumulation, then engine and instrument protection is improved, but energy consumption increases
Solution Approach 1:
The system transitions from static, continuous anti-icing operation to dynamic, condition-based operation. By detecting actual atmospheric contaminant levels and predicting ice accumulation risk in real-time, the system activates anti-icing only when necessary, thereby maintaining engine protection while significantly reducing unnecessary energy consumption during clear conditions.
Solution Approach 2:
The system provides advance warning of approaching contaminant regions, allowing pilots to activate anti-icing systems before ice accumulation becomes critical. This preliminary action prevents ice buildup during high-risk periods while avoiding continuous operation during safe periods, optimizing the balance between protection and energy use.
Data Source
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AI summary
There is provided an aircraft flight control system (100), comprising: a computing arrangement including an input interface and an output interface; wherein in operation the computing arrangement executes instructions to provide indications related to an estimated atmospheric contamination risk to at least one aircraft at selected locations and altitudes or pressures, by: (i) receiving at least one aircraft flight plan data (802, 804, 806, 808) from the input interface; wherein at least one aircraft flight plan data includes at least one of time, a pressure or an altitude, a trajectory and a location representing at least one aircraft flight; (ii) determining the estimated atmospheric contamination risk using a measure of the at least one atmospheric contaminant for the at least one aircraft flight based upon a location, an attitude or pressure, a trajectory and a time information extracted from the at least one aircraft flight plan data, wherein the estimated atmospheric contamination risk is calculated by using a statistical, a regression model or a machine learning model utilising at least one of historical or live atmospheric contaminant data, and wherein the statistical or machine learning model is calibrated, trained or optimized using at least one of historical atmospheric contaminant data, live atmospheric contaminant data or simulations of the atmospheric contaminant risk; (iii) providing, via the output interface, a resultant indication related to the estimated atmospheric contamination risk of the least one aircraft; and (iv) automatically adopting a modified flight plan having lower atmospheric contamination risk based on the resultant indication.