Abschlussarbeit: Machine Learning
vor 4 Monaten
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Abschlussarbeit: Machine Learning
Numerische Optimierungsalgorithmen werden heutzutage standardmäßig in der aerodynamischen Auslegung von Turbinen
- und Verdichter-Schaufeln eingesetzt. Da schon ein einzelner Simulationslauf einer Turbomaschinenkomponente viel Rechenzeit kostet und während einer Optimierung sehr viele Strömungssimulationen benötigt werden, nimmt die aerodynamische Optimierung großer Turbomaschinen beträchtliche Zeit in Anspruch. Es erscheint daher ein sehr lohnender Ansatz zu sein, den aufwendigen Strömungslöser durch ein schnelleres Machine-Learning-basiertes Modell zu ersetzen. Dadurch kann die Optimierung womöglich erheblich beschleunigt werden. Die Herausforderung besteht darin, die langsame, aber sehr genaue Strömungssimulation durch ein solches Rechenmodell zu ersetzen, das einerseits deutlich schneller, andererseits aber immer noch genau genug ist, damit der Optimierungsalgorithmus mit den approximativen Ergebnissen brauchbare Suchrichtungen generiert, die zum Optimum im hochdimensionalen Zielfunktionsraum führen.
STARKE AUFGABEN
- Weiterentwicklung eines datengetriebenen Modells in PyTorch als Proxy eines CFD Strömungslösers
- Entwicklung eines semi-parametrischen Modells
- Erstellung von Dokumentationen und Präsentationen
BESTE VORAUSSETZUNGEN
- Studium im Bereich Informatik, Statistik, Mathematik, Physik oder einer vergleichbaren Studienrichtung
- Programmiererfahrung mit Python und PyTorch und Kenntnisse in Deep Learning
- Strukturierte und eigenverantwortliche Arbeitsweise
AUSGEZEICHNETES UMFELD
- Praxiseinblicke in die innovative und zukunftsweisende Luftfahrtbranche
- Verantwortungsvolle und interessante Aufgaben sowie Teamspirit
- Persönliche:r Ansprechpartner:in und individuelle Betreuung
- Flexible Arbeitszeiten und Freizeitausgleich
- Netzwerkangebote
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