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  • Energie

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Internship - modeling engineer - energy f/h

  • Energie
  • Expérience non précisée
  • CDI
  • Selon profil
  • Bac + 4 (Génie énergétique / Modélisation)
  • Paris 01 (75001)
  • ref : 2024-120712

L'essentiel du poste

Missions
  • Modéliser des turbines à gaz et leurs modes opérationnels
  • Explorer des approches de modélisation, contrôle logique et méthodes data-driven
  • Modifier des composants Modelica et tester sur un modèle complexe
  • Évaluer la valeur ajoutée et l'impact des modes de fonctionnement sur la détection de pannes
Compétences attendues
  • Modélisation de systèmes industriels
  • Contrôle et logique de commande
  • Méthodes data-driven
  • Analyse de données et statistiques
  • Modelica
  • Python
  • Data science
Qualités recherchées
  • Autonomie
  • Rigueur
  • Esprit d’équipe
À savoir avant de postuler
  • Conduite?
Mentionné dans l'annonce Déduit par l'IA
Vous correspondez si
Étudiant en école d’ingénieurs, recherche de stage de fin d’études de 6 mois, maîtrise de Modelica ou Python souhaitée.
Résumé généré par l'IA -
peut contenir des inexactitudes

Offre proposée par EDF, sur la plateforme Talents Handicap, pour l'emploi des personnes en situation de handicap (RQTH ou équivalent).

Le poste détaillé

To apply to this internship please complete the application form through the following link: https://www.welcometothejungle.com/fr/companies/metroscope/jobs/ccgt-modeling-intern_paris

Gas turbines, essential in global power generation due to their flexible operation, complement renewable energy sources by maintaining grid stability. Metroscope software supports the maintenance of these systems by diagnosing faults in combined cycle gas turbines (CCGTs), including their main components: gas turbines, heat recovery steam generators, steam turbines, and condensers. Metroscope’s process team has developed digital twins of CCGTs to enhance diagnostics for operators.

Two internships are proposed to improve these models and assess their operational benefits for CCGT operators.

Internship 1: Focuses on the detailed modeling of gas turbines, which is challenging due to their complexity and limited manufacturer information. The intern will explore a new modeling approach, emphasizing control logic and data-driven methods to rebuild compressor and turbine maps and identify control modes. Tasks include a literature review, methodology development for control mode extraction, modification of existing components in Metroscope's Modelica library, and testing on a complex gas turbine model. The intern will assess fault impact and evaluate the new method’s added value.

Internship 2: Aims to refine the CCGT model's operational mode simulations, particularly at full and partial loads, by adjusting the modeling causalities to align with real site operations. This involves a review of CCGT operational modes, identification of key performance indicators (KPIs) for each, and potential adjustments to Modelica components for causality integration. The intern will also test automatic causality switching to enhance fault detection and propose future software improvements for operator communication.

Supervisors Nabil Youssef and Lucile Danion will guide these internships, advancing Metroscope’s capability to enhance diagnostics and performance analysis for CCGT operators.

  • You are currently in an engineer school and are looking for a 6 month end-of-study internship
  • You have some prior experience with modeling, ideally with Modelica or Python
  • You are interested in the energy sector, and in studying in details industrial processes
  • You are dynamic, autonomous and want to work in a team
  • You have some basis in data science and statistics

  • You are currently in an engineer school and are looking for a 6 month end-of-study internship
  • You have some prior experience with modeling, ideally with Modelica or Python
  • You are interested in the energy sector, and in studying in details industrial processes
  • You are dynamic, autonomous and want to work in a team
  • You have some basis in data science and statistics