Wind identification along a flight trajectory, part 1: 3D-kinematic approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1992-10

AUTHORS

A. Miele, T. Wang, W. W. Melvin

ABSTRACT

This paper deals with the identification of the wind profile along a flight trajectory by means of a three-dimensional kinematic approach. The approach is then applied to a recent aircraft accident, that of Flight Delta 191, which took place at Dallas-Fort Worth International Airport on August 2, 1985.In the 3D-kinematic approach, the wind velocity components are computed as the difference between the inertial velocity components and the airspeed components. The airspeed profile is obtained from flight measurements. The inertial velocity profile is obtained by integration of the measured inertial acceleration. The accelerometer biases and the impact values of the inertial velocity components are determined by matching the computed flight trajectory with the measured flight trajectory, available from the digital flight data recorder (DFDR) and air traffic control radar (ATCR). This leads to a least-square problem, which is solved analytically.Key to the precision of the identified wind profile is the correct identification of the accelerometer biases and the impact velocity components. In turn, this depends on the proper selection of the integration time. Because the measured data are noise-corrupted, unstable identification occurs if the integration time is too short. On the other hand, stable identification takes place if the integration time is properly chosen.Application of the method developed to the case of Flight Delta 191 shows that the identification problem has a stable solution if the integration time is larger than 180 sec. Numerical computation shows that, for Flight Delta 191, the maximum wind velocity difference determined with the 3D-kinematic approach was ΔWx=124 fps in the longitudinal direction, ΔWy=66 fps in the lateral direction, and ΔWh=71 fps in the vertical direction. More... »

PAGES

1-32

References to SciGraph publications

  • 1993-01. Wind identification along a flight trajectory, part 2: 2D-kinematic approach in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 1978. A Practical Guide to Splines in NONE
  • 1993-04. Wind identification along a flight trajectory, part 3: 2D-dynamic approach in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00939903

    DOI

    http://dx.doi.org/10.1007/bf00939903

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018311680


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0906", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Electrical and Electronic Engineering", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
              "id": "http://www.grid.ac/institutes/grid.21940.3e", 
              "name": [
                "Aero-Astronautics Group, Rice University, Houston, Texas"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Miele", 
            "givenName": "A.", 
            "id": "sg:person.015552732657.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
              "id": "http://www.grid.ac/institutes/grid.21940.3e", 
              "name": [
                "Aero-Astronautics Group, Rice University, Houston, Texas"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "T.", 
            "id": "sg:person.014414570607.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Airworthiness and Performance Committee, Air Line Pilots Association, Washington, DC", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Delta Airlines, Atlanta, Georgia", 
                "Airworthiness and Performance Committee, Air Line Pilots Association, Washington, DC"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Melvin", 
            "givenName": "W. W.", 
            "id": "sg:person.011027201155.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011027201155.13"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/bf00940777", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023491243", 
              "https://doi.org/10.1007/bf00940777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4612-6333-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109705102", 
              "https://doi.org/10.1007/978-1-4612-6333-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00952821", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030043609", 
              "https://doi.org/10.1007/bf00952821"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1992-10", 
        "datePublishedReg": "1992-10-01", 
        "description": "This paper deals with the identification of the wind profile along a flight trajectory by means of a three-dimensional kinematic approach. The approach is then applied to a recent aircraft accident, that of Flight Delta 191, which took place at Dallas-Fort Worth International Airport on August 2, 1985.In the 3D-kinematic approach, the wind velocity components are computed as the difference between the inertial velocity components and the airspeed components. The airspeed profile is obtained from flight measurements. The inertial velocity profile is obtained by integration of the measured inertial acceleration. The accelerometer biases and the impact values of the inertial velocity components are determined by matching the computed flight trajectory with the measured flight trajectory, available from the digital flight data recorder (DFDR) and air traffic control radar (ATCR). This leads to a least-square problem, which is solved analytically.Key to the precision of the identified wind profile is the correct identification of the accelerometer biases and the impact velocity components. In turn, this depends on the proper selection of the integration time. Because the measured data are noise-corrupted, unstable identification occurs if the integration time is too short. On the other hand, stable identification takes place if the integration time is properly chosen.Application of the method developed to the case of Flight Delta 191 shows that the identification problem has a stable solution if the integration time is larger than 180 sec. Numerical computation shows that, for Flight Delta 191, the maximum wind velocity difference determined with the 3D-kinematic approach was \u0394Wx=124 fps in the longitudinal direction, \u0394Wy=66 fps in the lateral direction, and \u0394Wh=71 fps in the vertical direction.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/bf00939903", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1044187", 
            "issn": [
              "0022-3239", 
              "1573-2878"
            ], 
            "name": "Journal of Optimization Theory and Applications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "75"
          }
        ], 
        "keywords": [
          "Flight Delta 191", 
          "air traffic control radar", 
          "velocity components", 
          "accelerometer biases", 
          "digital flight data recorder", 
          "wind profiles", 
          "flight trajectory", 
          "wind velocity components", 
          "Dallas-Fort Worth International Airport", 
          "integration time", 
          "wind velocity difference", 
          "airspeed components", 
          "wind identification", 
          "flight data recorder", 
          "velocity profiles", 
          "airspeed profile", 
          "inertial acceleration", 
          "vertical direction", 
          "longitudinal direction", 
          "kinematic approach", 
          "impact value", 
          "lateral direction", 
          "velocity difference", 
          "flight measurements", 
          "numerical computations", 
          "proper selection", 
          "data recorder", 
          "aircraft accidents", 
          "flight", 
          "International Airport", 
          "radar", 
          "Part 1", 
          "stable identification", 
          "identification problem", 
          "stable solutions", 
          "direction", 
          "least squares problem", 
          "recent aircraft accidents", 
          "components", 
          "acceleration", 
          "applications", 
          "profile", 
          "approach", 
          "measurements", 
          "trajectories", 
          "time", 
          "solution", 
          "problem", 
          "airports", 
          "accidents", 
          "precision", 
          "method", 
          "integration", 
          "computation", 
          "FP", 
          "recorder", 
          "values", 
          "sec", 
          "means", 
          "place", 
          "turn", 
          "identification", 
          "biases", 
          "hand", 
          "selection", 
          "data", 
          "cases", 
          "differences", 
          "correct identification", 
          "paper"
        ], 
        "name": "Wind identification along a flight trajectory, part 1: 3D-kinematic approach", 
        "pagination": "1-32", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1018311680"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/bf00939903"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/bf00939903", 
          "https://app.dimensions.ai/details/publication/pub.1018311680"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:20", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_221.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/bf00939903"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bf00939903'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf00939903'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00939903'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00939903'


     

    This table displays all metadata directly associated to this object as RDF triples.

    157 TRIPLES      21 PREDICATES      98 URIs      87 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/bf00939903 schema:about anzsrc-for:09
    2 anzsrc-for:0906
    3 schema:author Nb6c214bd1a7049739770f81a0520ed04
    4 schema:citation sg:pub.10.1007/978-1-4612-6333-3
    5 sg:pub.10.1007/bf00940777
    6 sg:pub.10.1007/bf00952821
    7 schema:datePublished 1992-10
    8 schema:datePublishedReg 1992-10-01
    9 schema:description This paper deals with the identification of the wind profile along a flight trajectory by means of a three-dimensional kinematic approach. The approach is then applied to a recent aircraft accident, that of Flight Delta 191, which took place at Dallas-Fort Worth International Airport on August 2, 1985.In the 3D-kinematic approach, the wind velocity components are computed as the difference between the inertial velocity components and the airspeed components. The airspeed profile is obtained from flight measurements. The inertial velocity profile is obtained by integration of the measured inertial acceleration. The accelerometer biases and the impact values of the inertial velocity components are determined by matching the computed flight trajectory with the measured flight trajectory, available from the digital flight data recorder (DFDR) and air traffic control radar (ATCR). This leads to a least-square problem, which is solved analytically.Key to the precision of the identified wind profile is the correct identification of the accelerometer biases and the impact velocity components. In turn, this depends on the proper selection of the integration time. Because the measured data are noise-corrupted, unstable identification occurs if the integration time is too short. On the other hand, stable identification takes place if the integration time is properly chosen.Application of the method developed to the case of Flight Delta 191 shows that the identification problem has a stable solution if the integration time is larger than 180 sec. Numerical computation shows that, for Flight Delta 191, the maximum wind velocity difference determined with the 3D-kinematic approach was ΔWx=124 fps in the longitudinal direction, ΔWy=66 fps in the lateral direction, and ΔWh=71 fps in the vertical direction.
    10 schema:genre article
    11 schema:isAccessibleForFree false
    12 schema:isPartOf N2797dfb453e14ef3b879439a3cee1205
    13 N57fa6189ebfc40f284ed2b8fba54600e
    14 sg:journal.1044187
    15 schema:keywords Dallas-Fort Worth International Airport
    16 FP
    17 Flight Delta 191
    18 International Airport
    19 Part 1
    20 acceleration
    21 accelerometer biases
    22 accidents
    23 air traffic control radar
    24 aircraft accidents
    25 airports
    26 airspeed components
    27 airspeed profile
    28 applications
    29 approach
    30 biases
    31 cases
    32 components
    33 computation
    34 correct identification
    35 data
    36 data recorder
    37 differences
    38 digital flight data recorder
    39 direction
    40 flight
    41 flight data recorder
    42 flight measurements
    43 flight trajectory
    44 hand
    45 identification
    46 identification problem
    47 impact value
    48 inertial acceleration
    49 integration
    50 integration time
    51 kinematic approach
    52 lateral direction
    53 least squares problem
    54 longitudinal direction
    55 means
    56 measurements
    57 method
    58 numerical computations
    59 paper
    60 place
    61 precision
    62 problem
    63 profile
    64 proper selection
    65 radar
    66 recent aircraft accidents
    67 recorder
    68 sec
    69 selection
    70 solution
    71 stable identification
    72 stable solutions
    73 time
    74 trajectories
    75 turn
    76 values
    77 velocity components
    78 velocity difference
    79 velocity profiles
    80 vertical direction
    81 wind identification
    82 wind profiles
    83 wind velocity components
    84 wind velocity difference
    85 schema:name Wind identification along a flight trajectory, part 1: 3D-kinematic approach
    86 schema:pagination 1-32
    87 schema:productId N940df72c98e544afa8e1d8f983aead19
    88 Nb07272f4707f416ca8e7a8e53349f05c
    89 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018311680
    90 https://doi.org/10.1007/bf00939903
    91 schema:sdDatePublished 2022-12-01T06:20
    92 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    93 schema:sdPublisher N884e9adada66433c818b276aa189fc3b
    94 schema:url https://doi.org/10.1007/bf00939903
    95 sgo:license sg:explorer/license/
    96 sgo:sdDataset articles
    97 rdf:type schema:ScholarlyArticle
    98 N2797dfb453e14ef3b879439a3cee1205 schema:volumeNumber 75
    99 rdf:type schema:PublicationVolume
    100 N449a75173d22468da48702746ce9a455 rdf:first sg:person.011027201155.13
    101 rdf:rest rdf:nil
    102 N57fa6189ebfc40f284ed2b8fba54600e schema:issueNumber 1
    103 rdf:type schema:PublicationIssue
    104 N607d62a1c48f424d9be862fe992c1d56 rdf:first sg:person.014414570607.44
    105 rdf:rest N449a75173d22468da48702746ce9a455
    106 N884e9adada66433c818b276aa189fc3b schema:name Springer Nature - SN SciGraph project
    107 rdf:type schema:Organization
    108 N940df72c98e544afa8e1d8f983aead19 schema:name dimensions_id
    109 schema:value pub.1018311680
    110 rdf:type schema:PropertyValue
    111 Nb07272f4707f416ca8e7a8e53349f05c schema:name doi
    112 schema:value 10.1007/bf00939903
    113 rdf:type schema:PropertyValue
    114 Nb6c214bd1a7049739770f81a0520ed04 rdf:first sg:person.015552732657.49
    115 rdf:rest N607d62a1c48f424d9be862fe992c1d56
    116 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    117 schema:name Engineering
    118 rdf:type schema:DefinedTerm
    119 anzsrc-for:0906 schema:inDefinedTermSet anzsrc-for:
    120 schema:name Electrical and Electronic Engineering
    121 rdf:type schema:DefinedTerm
    122 sg:journal.1044187 schema:issn 0022-3239
    123 1573-2878
    124 schema:name Journal of Optimization Theory and Applications
    125 schema:publisher Springer Nature
    126 rdf:type schema:Periodical
    127 sg:person.011027201155.13 schema:affiliation grid-institutes:None
    128 schema:familyName Melvin
    129 schema:givenName W. W.
    130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011027201155.13
    131 rdf:type schema:Person
    132 sg:person.014414570607.44 schema:affiliation grid-institutes:grid.21940.3e
    133 schema:familyName Wang
    134 schema:givenName T.
    135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44
    136 rdf:type schema:Person
    137 sg:person.015552732657.49 schema:affiliation grid-institutes:grid.21940.3e
    138 schema:familyName Miele
    139 schema:givenName A.
    140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49
    141 rdf:type schema:Person
    142 sg:pub.10.1007/978-1-4612-6333-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109705102
    143 https://doi.org/10.1007/978-1-4612-6333-3
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/bf00940777 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023491243
    146 https://doi.org/10.1007/bf00940777
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/bf00952821 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030043609
    149 https://doi.org/10.1007/bf00952821
    150 rdf:type schema:CreativeWork
    151 grid-institutes:None schema:alternateName Airworthiness and Performance Committee, Air Line Pilots Association, Washington, DC
    152 schema:name Airworthiness and Performance Committee, Air Line Pilots Association, Washington, DC
    153 Delta Airlines, Atlanta, Georgia
    154 rdf:type schema:Organization
    155 grid-institutes:grid.21940.3e schema:alternateName Aero-Astronautics Group, Rice University, Houston, Texas
    156 schema:name Aero-Astronautics Group, Rice University, Houston, Texas
    157 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...