Air data estimation in aircraft
An aircraft has an air data estimator 106 having a machine learning model trained to, in use, predict air data parameter values 120 from input values. The estimator is operable in real time on the input values, which is data, or derived data, from sensors internal to the aircraft only. Also claimed...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Patent |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Pierre Moinier Miles Ross Munro William Frank Ellison Ben George Watkinson |
| description | An aircraft has an air data estimator 106 having a machine learning model trained to, in use, predict air data parameter values 120 from input values. The estimator is operable in real time on the input values, which is data, or derived data, from sensors internal to the aircraft only. Also claimed is a computer implemented air data estimator where the input values are from internal aircraft avionics, so that the aircraft does not need air data sensors mounted on an external surface of the aircraft. Also claimed is a computer-implemented method of training an air data estimator to predict values of air data parameters of an aircraft, the method involving using supervised training of a neural network using data pairs, each with ground truth values of the air data parameters selected from a flight envelope of the aircraft, and corresponding simulated values from avionics in the aircraft, the simulated values computed using stored empirical data about aircraft engines and wind tunnel testing, and rules of computational fluid dynamics. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_GB2621843A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>GB2621843A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_GB2621843A3</originalsourceid><addsrcrecordid>eNrjZJB3zCxSSEksSVRILS7JzE0syczPU8jMU0jMLEouSkwr4WFgTUvMKU7lhdLcDPJuriHOHrqpBfnxqcUFicmpeakl8e5ORmZGhhYmxo7GhFUAAHmrI7w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Air data estimation in aircraft</title><source>esp@cenet</source><creator>Pierre Moinier ; Miles Ross Munro ; William Frank Ellison ; Ben George Watkinson</creator><creatorcontrib>Pierre Moinier ; Miles Ross Munro ; William Frank Ellison ; Ben George Watkinson</creatorcontrib><description>An aircraft has an air data estimator 106 having a machine learning model trained to, in use, predict air data parameter values 120 from input values. The estimator is operable in real time on the input values, which is data, or derived data, from sensors internal to the aircraft only. Also claimed is a computer implemented air data estimator where the input values are from internal aircraft avionics, so that the aircraft does not need air data sensors mounted on an external surface of the aircraft. Also claimed is a computer-implemented method of training an air data estimator to predict values of air data parameters of an aircraft, the method involving using supervised training of a neural network using data pairs, each with ground truth values of the air data parameters selected from a flight envelope of the aircraft, and corresponding simulated values from avionics in the aircraft, the simulated values computed using stored empirical data about aircraft engines and wind tunnel testing, and rules of computational fluid dynamics.</description><language>eng</language><subject>AIRCRAFT ; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT ; AVIATION ; COSMONAUTICS ; EQUIPMENT FOR FITTING IN OR TO AIRCRAFT ; FLYING SUITS ; GYROSCOPIC INSTRUMENTS ; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT ; MEASURING ; MEASURING DISTANCES, LEVELS OR BEARINGS ; MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK ; NAVIGATION ; PARACHUTES ; PERFORMING OPERATIONS ; PHOTOGRAMMETRY OR VIDEOGRAMMETRY ; PHYSICS ; SURVEYING ; TESTING ; TRANSPORTING</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240228&DB=EPODOC&CC=GB&NR=2621843A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240228&DB=EPODOC&CC=GB&NR=2621843A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Pierre Moinier</creatorcontrib><creatorcontrib>Miles Ross Munro</creatorcontrib><creatorcontrib>William Frank Ellison</creatorcontrib><creatorcontrib>Ben George Watkinson</creatorcontrib><title>Air data estimation in aircraft</title><description>An aircraft has an air data estimator 106 having a machine learning model trained to, in use, predict air data parameter values 120 from input values. The estimator is operable in real time on the input values, which is data, or derived data, from sensors internal to the aircraft only. Also claimed is a computer implemented air data estimator where the input values are from internal aircraft avionics, so that the aircraft does not need air data sensors mounted on an external surface of the aircraft. Also claimed is a computer-implemented method of training an air data estimator to predict values of air data parameters of an aircraft, the method involving using supervised training of a neural network using data pairs, each with ground truth values of the air data parameters selected from a flight envelope of the aircraft, and corresponding simulated values from avionics in the aircraft, the simulated values computed using stored empirical data about aircraft engines and wind tunnel testing, and rules of computational fluid dynamics.</description><subject>AIRCRAFT</subject><subject>ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT</subject><subject>AVIATION</subject><subject>COSMONAUTICS</subject><subject>EQUIPMENT FOR FITTING IN OR TO AIRCRAFT</subject><subject>FLYING SUITS</subject><subject>GYROSCOPIC INSTRUMENTS</subject><subject>INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT</subject><subject>MEASURING</subject><subject>MEASURING DISTANCES, LEVELS OR BEARINGS</subject><subject>MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK</subject><subject>NAVIGATION</subject><subject>PARACHUTES</subject><subject>PERFORMING OPERATIONS</subject><subject>PHOTOGRAMMETRY OR VIDEOGRAMMETRY</subject><subject>PHYSICS</subject><subject>SURVEYING</subject><subject>TESTING</subject><subject>TRANSPORTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZJB3zCxSSEksSVRILS7JzE0syczPU8jMU0jMLEouSkwr4WFgTUvMKU7lhdLcDPJuriHOHrqpBfnxqcUFicmpeakl8e5ORmZGhhYmxo7GhFUAAHmrI7w</recordid><startdate>20240228</startdate><enddate>20240228</enddate><creator>Pierre Moinier</creator><creator>Miles Ross Munro</creator><creator>William Frank Ellison</creator><creator>Ben George Watkinson</creator><scope>EVB</scope></search><sort><creationdate>20240228</creationdate><title>Air data estimation in aircraft</title><author>Pierre Moinier ; Miles Ross Munro ; William Frank Ellison ; Ben George Watkinson</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_GB2621843A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>AIRCRAFT</topic><topic>ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT</topic><topic>AVIATION</topic><topic>COSMONAUTICS</topic><topic>EQUIPMENT FOR FITTING IN OR TO AIRCRAFT</topic><topic>FLYING SUITS</topic><topic>GYROSCOPIC INSTRUMENTS</topic><topic>INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT</topic><topic>MEASURING</topic><topic>MEASURING DISTANCES, LEVELS OR BEARINGS</topic><topic>MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK</topic><topic>NAVIGATION</topic><topic>PARACHUTES</topic><topic>PERFORMING OPERATIONS</topic><topic>PHOTOGRAMMETRY OR VIDEOGRAMMETRY</topic><topic>PHYSICS</topic><topic>SURVEYING</topic><topic>TESTING</topic><topic>TRANSPORTING</topic><toplevel>online_resources</toplevel><creatorcontrib>Pierre Moinier</creatorcontrib><creatorcontrib>Miles Ross Munro</creatorcontrib><creatorcontrib>William Frank Ellison</creatorcontrib><creatorcontrib>Ben George Watkinson</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pierre Moinier</au><au>Miles Ross Munro</au><au>William Frank Ellison</au><au>Ben George Watkinson</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Air data estimation in aircraft</title><date>2024-02-28</date><risdate>2024</risdate><abstract>An aircraft has an air data estimator 106 having a machine learning model trained to, in use, predict air data parameter values 120 from input values. The estimator is operable in real time on the input values, which is data, or derived data, from sensors internal to the aircraft only. Also claimed is a computer implemented air data estimator where the input values are from internal aircraft avionics, so that the aircraft does not need air data sensors mounted on an external surface of the aircraft. Also claimed is a computer-implemented method of training an air data estimator to predict values of air data parameters of an aircraft, the method involving using supervised training of a neural network using data pairs, each with ground truth values of the air data parameters selected from a flight envelope of the aircraft, and corresponding simulated values from avionics in the aircraft, the simulated values computed using stored empirical data about aircraft engines and wind tunnel testing, and rules of computational fluid dynamics.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_GB2621843A |
| source | esp@cenet |
| subjects | AIRCRAFT ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT AVIATION COSMONAUTICS EQUIPMENT FOR FITTING IN OR TO AIRCRAFT FLYING SUITS GYROSCOPIC INSTRUMENTS INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT MEASURING MEASURING DISTANCES, LEVELS OR BEARINGS MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK NAVIGATION PARACHUTES PERFORMING OPERATIONS PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS SURVEYING TESTING TRANSPORTING |
| title | Air data estimation in aircraft |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T18%3A25%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Pierre%20Moinier&rft.date=2024-02-28&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EGB2621843A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |