# US 2025/0165005 A1 · Selecting altitude changing phase routes for aircraft

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- Number: US 2025/0165005 A1
- Document ID: US20250165005A1
- Status: Published application
- Kind: A1
- Application number: 18/671,612
- Filed: 2024-05-22
- Published: 2025-05-22
- Earliest claimed: 2023-11-20
- Assignees: The Boeing Company
- Applicants: The Boeing Company
- Inventors: Samet Ayhan
- Web page: https://patentgrep.com/patent/US20250165005A1

## CPC

- G08G 5/21: located onboard the aircraft
- G08G 5/32: for flight plan preparation
- B64D 45/00: Aircraft indicators or protectors not otherwise provided for
- G05D 1/48: Control of altitude or depth
- G05D 1/644: Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
- G06N 3/08: Learning methods
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G08G 5/52: for take-off
- G08G 5/54: for approach or landing
- G08G 5/55: for a single aircraft
- G05D 2101/15: using machine learning, e.g. neural networks
- G05D 2109/20: Aircraft, e.g. drones
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})
- B64D: EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for selecting an altitude changing phase route for an aircraft is presented. The method comprises receiving sequences of multivariate flight data from at least one prior flight, receiving a set of flight parameters for the aircraft including at least a total altitude change and a takeoff weight, and receiving a set of candidate altitude changing phase routes having candidate step profiles. For each candidate altitude changing phase route, a sequence of fuel burn quantities is predicted for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters. The fuel burn quantities are summed over the candidate altitude changing phase route to obtain an estimated fuel burn. A preferred candidate altitude changing phase route having a lowest estimated fuel burn is indicated.

## Claims

### Claim 1

A method for selecting an altitude changing phase route for an aircraft, comprising:
receiving sequences of multivariate flight data from at least one prior flight;
receiving a set of flight parameters for the aircraft including at least a total altitude change and a takeoff weight;
receiving a set of candidate altitude changing phase routes having candidate step profiles;
for each candidate altitude changing phase route,
predicting a sequence of fuel burn quantities for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters; and
summing the fuel burn quantities over the candidate altitude changing phase route to obtain an estimated fuel burn; and
indicating a preferred candidate altitude changing phase route having a lowest estimated fuel burn.

### Claim 2 (depends on 1)

The method of claim 1, further comprising fueling the aircraft based at least on the lowest estimated fuel burn.

### Claim 3 (depends on 1)

The method of claim 1, wherein the aircraft is at least partially autonomous, the method further comprising:
controlling the aircraft to follow the preferred candidate altitude changing phase route.

### Claim 4 (depends on 1)

The method of claim 1, wherein the altitude changing phase is a climb phase.

### Claim 5 (depends on 1)

The method of claim 1, wherein the altitude changing phase is a descent phase.

### Claim 6 (depends on 5)

The method of claim 5, wherein the flight parameters further include a top-of-descent weight of the aircraft.

### Claim 7 (depends on 5)

The method of claim 5, wherein the flight parameters further include a time to landing.

### Claim 8 (depends on 1)

The method of claim 1, wherein the multivariate flight data is discretized based on step profile.

### Claim 9 (depends on 1)

The method of claim 1, wherein the flight parameters further include a lateral distance.

### Claim 10 (depends on 9)

The method of claim 9, wherein a length of one or more altitude changing phases included in the multivariate flight data is adjusted to a common altitude changing phase length.

### Claim 11 (depends on 1)

The method of claim 1, wherein predicting the sequence of fuel burn quantities is further based on a hidden state of a trained machine, wherein for each of a pre-selected series of prior flights, a corresponding sequence of multivariate flight data recorded during the prior flight is processed in a trainable machine to develop the hidden state, which minimizes an overall residual for replicating the fuel burn quantities in each corresponding sequence.

### Claim 12

A machine trained to make fuel burn predictions for aircraft flight, comprising:
an input engine configured to:
receive sequences of multivariate flight data from at least one prior flight;
receive a set of candidate altitude changing phase routes having candidate step profiles; and
receive a set of flight parameters including at least a total altitude change and a takeoff weight;
a prediction engine configured to, for each candidate altitude changing phase route, predict a sequence of fuel burn quantities for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters;
a summation engine configured to sum the fuel burn quantities over each candidate altitude changing phase route to obtain an estimated fuel burn; and
an output engine configured to indicate a preferred candidate altitude changing phase route having a lowest estimated fuel burn.

### Claim 13 (depends on 12)

The machine of claim 12, wherein the candidate altitude changing phase is one of a climb phase and a descent phase.

### Claim 14 (depends on 12)

The machine of claim 12, wherein the multivariate flight data is discretized based on step profile.

### Claim 15 (depends on 12)

The machine of claim 12, further comprising an adjustment engine configured to adjust a length of one or more altitude changing phases included in the multivariate flight data to a common altitude changing phase length.

### Claim 16 (depends on 12)

The machine of claim 12, further comprising a trained encoder arranged logically upstream of a trained decoder, wherein the encoder is trained to emit a vector that featurizes an input sequence of multivariate flight data, and wherein the decoder is trained to replicate the fuel burn quantities of the input sequence based on the vector, thereby generating an output sequence.

### Claim 17 (depends on 16)

The machine of claim 16, wherein the encoder and the decoder are configured according to a long short-term memory (LSTM) architecture.

### Claim 18 (depends on 17)

The machine of claim 17, further comprising a fully connected layer configured to interpret the fuel burn quantities at each time step of the output sequence.

### Claim 19

A method of training a machine to predict fuel burn for an aircraft over an altitude changing phase of a flight, comprising:
receiving corresponding sequences of multivariate data for a plurality of previously occurring flights;
parsing the corresponding sequences into altitude changing phases of each of the plurality of previously occurring flights;
discretizing the multivariate data at least based on a step profile and an amount of fuel burn for each cruise phase;
processing each corresponding sequence to develop a hidden state, which minimizes an overall residual for replicating the amount of fuel burn in each corresponding sequence; and
exposing at least a portion of the hidden state.

### Claim 20 (depends on 19)

The method of claim 19, further comprising:
training two or more machines to predict fuel burn for the aircraft over each altitude changing phase;
evaluating training results for each of the two or more machines; and
adjusting hyperparameters for at least one machine based on the training results.