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    <id>22230</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>3</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Component-specific Engine Design Taking into Account Holistic Design Aspects</title>
    <parentTitle language="eng">International Journal of Turbomachinery, Propulsion and Power</parentTitle>
    <identifier type="doi">10.3390/ijtpp3020012</identifier>
    <identifier type="issn">2504-186X</identifier>
    <note>This article belongs to the Special Issue Selected Papers from the 17th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery</note>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Marco</firstName>
      <lastName>Hendler</lastName>
    </author>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <author>
      <firstName>Peter Michael</firstName>
      <lastName>Flassig</lastName>
    </author>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
  <doc>
    <id>22233</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>513</pageFirst>
    <pageLast>527</pageLast>
    <pageNumber/>
    <edition/>
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    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Coupled Subsystem Optimiziation for Preliminary Core Engine Design</title>
    <parentTitle language="eng">Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems</parentTitle>
    <identifier type="doi">10.1007/978-3-319-89890-2_33</identifier>
    <identifier type="isbn">978-3-319-89889-6</identifier>
    <identifier type="isbn">978-3-319-89890-2</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="UBICOseries">Computational Methods in Applied Sciences ; 49</enrichment>
    <author>
      <firstName>Simon</firstName>
      <lastName>Extra</lastName>
    </author>
    <editor>
      <firstName>Esther</firstName>
      <lastName>Andrès-Pèrez</lastName>
    </editor>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Michael</firstName>
      <lastName>Lockan</lastName>
    </author>
    <editor>
      <firstName>Leo M.</firstName>
      <lastName>González</lastName>
    </editor>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <editor>
      <firstName>Jacques</firstName>
      <lastName>Periaux</lastName>
    </editor>
    <author>
      <firstName>Peter Michael</firstName>
      <lastName>Flassig</lastName>
    </author>
    <editor>
      <firstName>Nicolas R.</firstName>
      <lastName>Gauger</lastName>
    </editor>
    <editor>
      <firstName>Domenico</firstName>
      <lastName>Quagliarella</lastName>
    </editor>
    <editor>
      <firstName>Kyriakos C.</firstName>
      <lastName>Giannakoglou</lastName>
    </editor>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
  <doc>
    <id>22794</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-12-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Verbesserte Vorauslegung des Kerntriebwerks mithilfe eines kaskadierten Optimierungsprozesses</title>
    <parentTitle language="deu">Luft- und Raumfahrt - Digitalisierung und Vernetzung, Deutscher Luft- und Raumfahrtkongress 2018, 04.-06. September 2018, Friedrichshafen, Graf-Zeppelin-Haus</parentTitle>
    <identifier type="doi">10.25967/480163</identifier>
    <identifier type="urn">urn:nbn:de:101:1-2020011711475557798029</identifier>
    <identifier type="url">https://www.dglr.de/publikationen/2020/480163.pdf</identifier>
    <note>DLRK2018-480163</note>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Simon</firstName>
      <lastName>Extra</lastName>
    </author>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Marco</firstName>
      <lastName>Hendler</lastName>
    </author>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <author>
      <firstName>Peter Michael</firstName>
      <lastName>Flassig</lastName>
    </author>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
  <doc>
    <id>25148</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>American Society of Mechanical Engineers</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-01-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Low-Order Representation of Manufacturing Variations Based on B-Spline Morphing</title>
    <parentTitle language="eng">ASME 2019 International Mechanical Engineering Congress and Exposition,  November 11–14, 2019&#13;
Salt Lake City, Utah, USA</parentTitle>
    <identifier type="isbn">978-0-7918-5938-4</identifier>
    <identifier type="doi">10.1115/IMECE2019-10294</identifier>
    <identifier type="url">https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2019/59384/V02BT02A053/1072900</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Josè</firstName>
      <lastName>Urbano</lastName>
    </author>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <author>
      <firstName>Ulf</firstName>
      <lastName>Gerstberger</lastName>
    </author>
    <author>
      <firstName>Peter Michael</firstName>
      <lastName>Flassig</lastName>
    </author>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
  <doc>
    <id>36784</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>25</pageLast>
    <pageNumber>25</pageNumber>
    <edition/>
    <issue/>
    <volume>2025</volume>
    <type>articler</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace>Philadelphia, Pa.</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-11-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated design of a four-bar mechanism starting from hand drawings of desired coupler trajectories and velocity profiles</title>
    <abstract language="eng">When a specific, relatively simple motion must be executed with high precision and high speed, simple mechanisms like the four-bar linkage are often the first choice. Combined with control of the crank speed, these systems are able to move the end-effector with any changing speed. Designing a certain mechanism for a pre-defined trajectory, however, usually requires an expert, which is why automatic design by a design assistant system is desirable. Modern AI methods may support such an automatic design, which shall function only based on hand drawings of the desired path and a corresponding velocity profile. For a planar four-bar mechanism, which serves only as an example, this article proposes a general data-driven procedure where first the geometry of a mechanism is synthesized by machine learning such that the track point follows approximately the desired trajectory. Then, the kinematic relation between the track point’s velocity profile and the crank’s angular velocity is learned. This learned inverse kinematics model may be applied to an arbitrary user-defined velocity profile of the track point. Finally, a controller governing the crank torque is designed purely based on data to track the resulting angular crank velocity. Not every drawn motion can necessarily be realized with a mechanism; however, the goal is to identify a mechanism that approximates the drawn curve as accurately as possible. While various design toolboxes already exist, especially for the design of four-bar linkages, this article presents a design pipeline using data-driven approaches that solely learn from forward simulation data. This nurtures the hope that presented approach is also applicable to more complex systems where analytic formulations cannot be easily derived.</abstract>
    <parentTitle language="eng">Mechanics based design of structures and machines</parentTitle>
    <identifier type="doi">10.1080/15397734.2025.2543559</identifier>
    <identifier type="issn">1539-7742</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>
      <firstName>Benedict</firstName>
      <lastName>Röder</lastName>
    </author>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Sanam</firstName>
      <lastName>Hajipour</lastName>
    </author>
    <author>
      <firstName>Henrik</firstName>
      <lastName>Ebel</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Eberhard</lastName>
    </author>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Design assistant</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mechanism design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Four-bar linkage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse kinematics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tracking control</value>
    </subject>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
  <doc>
    <id>36785</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>Global Power and Propulsion Society (GPPS)</publisherName>
    <publisherPlace>Zug, Schweiz</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-11-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Consideration of balancing within the fan-blisk design process</title>
    <abstract language="eng">The design of fan-blisks is a multi-criterion optimisation challenge primarily involving the aerodynamic shape optimisation of blade profiles and subsequent blade balancing, i.e., shifting profiles in axial and circumferential directions to avoid stress hotspots. Although it is already known that blade balancing affects aerodynamic properties, this correlation is often not taken into account, which is why it is usually performed as an independent step after solving the aerodynamic design problem. However, this assumption is questionable because such shifts should be used to control both secondary flow effects and resulting stress. Therefore, the paper proposes problem formulations combining both aspects. Since optimisation requires costly numerical evaluations, machine learning methods are investigated to predict aerodynamic performance and stress constraints more efficiently. This enables a global optimisation process by reducing computational costs. Various different surrogate types are investigated, where stress constraints are formulated either as regression task predicting stress maxima, or as classification problem directly assessing design feasibility.</abstract>
    <parentTitle language="eng">GPPS Shanghai25 Technical Paper Proceedings</parentTitle>
    <identifier type="doi">10.33737/gpps25-tc-017</identifier>
    <identifier type="url">https://gpps.global/gpps-shanghai25-proceedings/</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>
      <firstName>Clara</firstName>
      <lastName>Henkel</lastName>
    </author>
    <submitter>
      <firstName>Peggy</firstName>
      <lastName>Fobo</lastName>
    </submitter>
    <author>
      <firstName>Dieter</firstName>
      <lastName>Bestle</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Flassig</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Janke</lastName>
    </author>
    <author>
      <firstName>Michael</firstName>
      <lastName>Slaby</lastName>
    </author>
    <collection role="institutes" number="3507">FG Technische Mechanik und Fahrzeugdynamik</collection>
  </doc>
</export-example>
