The Boeing Company

Manuel Polaina Morales

Javier Lopez Leones

Stephen Solomon Altus

Pablo Costas Alvarez

Rosa Maria Rodriguez

Andrés Muñoz Hernández

Romani De Oliveira Ítalo

Ruben Vega Astorga

Javier López Leonés

Alejandro Güemes Jimenez

Maria Del Pozo Dominguez

Rubén Vega Astorga

Euclides Carlos Pinto Neto

Mevlut Uzun

Mustafa Umut Demirezen

Gokhan Inalhan

Javier LÓPEZ LEONÉS

A system and a method include a flight plan generating control unit configured to receive a flight plan request for an aircraft from a user interface, determine if the flight plan request matches a flight plan within a flight plan database, in response to the flight plan request matching the flight plan within the flight plan database, submit the flight plan to a validation sub-system, and in response to the flight plan request not matching the flight plan within the flight plan database, determine a new flight plan.

Flight planning systems and methods for aircraft

Aspects of the present disclosure provide systems and methods for evaluating a flight plan with respect to an executed route of an aircraft. An example method includes obtaining data associated with a route of a vehicle. The method further includes determining a first cost index associated with the route based at least in part on a segment of the data. The method further includes comparing the first cost index to a second cost index associated with a plan for the route. The method further includes determining a cost penalty associated with the route based at least in part on the comparison between the first cost index and the second cost index. The method further includes performing one or more actions associated with the vehicle in response to determining the cost penalty.

Flight plan evaluation

Apparatuses, methods, systems, and program products are disclosed for compression and distribution of meteorological data using machine learning. An apparatus includes a processor and a memory that stores code executable by the processor to receive a raw meteorological data set for a time frame, the raw meteorological data set comprising a plurality of dimensions. The code is executable by the processor to compress the raw meteorological data set using a machine learning encoding model to create an encoded meteorological data set that has a storage size that is smaller than a storage size of the raw meteorological data set, wherein the encoded meteorological data set can be decoded to create a decoded meteorological data set that is substantially similar to the raw meteorological data set. The code is executable by the processor to make the encoded meteorological data set accessible to one or more end users.

Compression and distribution of meteorological data using machine learning

A device includes a memory, a network interface, and a processor. The memory is configured to store an aircraft performance model. The aircraft performance model is based on historical flight data of one or more aircraft. The aircraft performance model includes a recurrent neural network layer. The network interface is configured to receive real-time time-series flight data from a data bus of a first aircraft. The processor is configured to receive, via the network interface, the real-time time-series flight data. The processor is also configured to generate, based on the real-time time-series flight data and the aircraft performance model, one or more aircraft performance parameters. The processor is further configured to provide the aircraft performance parameters to a display device.

Systems and methods for flight performance parameter computation

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Munich, Germany

Manuel Polaina Morales