Fr. 135.00

FluSHELL - A Tool for Thermal Modelling and Simulation of Windings for Large Shell-Type Power Transformers

Englisch · Taschenbuch

Versand in der Regel in 6 bis 7 Wochen

Beschreibung

Mehr lesen

This thesis addresses a novel application of network modelling methodologies to power transformers. It develops a novel thermal model and compares its performance against that of a commercial computational fluid dynamics (CFD) code, as well as in experiments conducted in a dedicated setup built exclusively for this purpose. Hence, the thesis cross-links three of the most important aspects in high-quality research: model development, simulation and experimental validation.
Network modelling is used to develop a tool to simulate the thermal performance of power transformers, widely acknowledged to be critical assets in electrical networks. After the strong de-regulation of electricity markets and de-carbonization of worldwide economies, electrical networks have been changing fast. Both asset owners and equipment manufacturers are being driven to develop increasingly accurate modelling capabilities in order to optimize either their operation or their design. Temperature is a critical parameter in every electric machine and power transformers are no exception.

As such, the thesis is relevant for a wide range of stakeholders, from utilities to power transformer manufacturers, as well as researchers interested in the energy industry. It is written in straightforward language and employs a highly pedagogic approach, making it also suitable for non-experts.

Inhaltsverzeichnis

Introduction.- Scale Model.- CFD Sclae Model.- The FluSHELL Tool.- FluSHELL Validation.- Conclusions and Future Work.

Über den Autor / die Autorin

Hugo Campelo completed his PhD in Chemical Engineering at the University of Porto (Portugal) in 2016, but his career as a Computational Fluid Dynamics (CFD) consultant actually started ten years ago. Since 2010 he has been working for a large electromechanical group that manufactures power transformers, in which he currently coordinates a team of R&D experts focused on addressing the thermal-hydraulic challenges associated with power transformers. A CFD expert, his main interests lie in numerical fluid flow and heat transfer.

Zusammenfassung

This thesis addresses a novel application of network modelling methodologies to power transformers. It develops a novel thermal model and compares its performance against that of a commercial computational fluid dynamics (CFD) code, as well as in experiments conducted in a dedicated setup built exclusively for this purpose. Hence, the thesis cross-links three of the most important aspects in high-quality research: model development, simulation and experimental validation.
Network modelling is used to develop a tool to simulate the thermal performance of power transformers, widely acknowledged to be critical assets in electrical networks. After the strong de-regulation of electricity markets and de-carbonization of worldwide economies, electrical networks have been changing fast. Both asset owners and equipment manufacturers are being driven to develop increasingly accurate modelling capabilities in order to optimize either their operation or their design. Temperature is a critical parameter in every electric machine and power transformers are no exception.


As such, the thesis is relevant for a wide range of stakeholders, from utilities to power transformer manufacturers, as well as researchers interested in the energy industry. It is written in straightforward language and employs a highly pedagogic approach, making it also suitable for non-experts.

Kundenrezensionen

Zu diesem Artikel wurden noch keine Rezensionen verfasst. Schreibe die erste Bewertung und sei anderen Benutzern bei der Kaufentscheidung behilflich.

Schreibe eine Rezension

Top oder Flop? Schreibe deine eigene Rezension.

Für Mitteilungen an CeDe.ch kannst du das Kontaktformular benutzen.

Die mit * markierten Eingabefelder müssen zwingend ausgefüllt werden.

Mit dem Absenden dieses Formulars erklärst du dich mit unseren Datenschutzbestimmungen einverstanden.