Computational fluid dynamics-derived correlations for power transformer thermal–hydraulic performance prediction

• Tipo: Artigo em Revista Científica Internacional
• Data: 2026-02

Autores

Sandra Couto (Autor) Outra; Diogo P. Gonçalves (Autor) Outra; Marta Santos (Autor) Outra; Soraia Neves (Autor) FEUP; M.C.F. Silva (Autor) FEUP

Revista

Título: Electric Power Systems Research

Vol. 112832 Nº 255

Páginas: 1-12

ISSN: 0378-7796

Editora: Elsevier

Projetos Associados

2024.05235.RESTART - Enhancing firefighters’ thermal protective clothing with integrated phase change materials: study of spatial effects on the occurrence of injuries

VirtualTextiLab - Desenvolvimento de um laboratório virtual para a otimização de produtos têxteis funcionais e sustentáveis em opensource

ALiCE - ALiCE - Laboratório Associado em Engenharia Química

CEFT - CEFT - Centro de Estudos de Fenómenos de Transporte

Outras Informações

DOI: 10.1016/j.epsr.2026.112832

Abstract (EN): The cooling performance of power transformer (PT) windings depends on both fluid properties and winding geometry. This study performed 108 steady-state CFD simulations on a core-type, oil-directed PT to assess different axial (6–8 mm) and radial (4–6 mm) channel dimensions with mineral oil and natural esters across multiple Reynolds numbers. Numerical predictions were validated against experimental data, confirming the accuracy of the simulations. Results showed that pressure drop trends were unaffected by heat transfer and correlated well with fluid properties under isothermal conditions. Transformer thermal performance was mainly influenced by Reynolds number and axial channel width; natural esters produced lower winding temperatures than mineral oil, but at a higher-pressure drop. Using a porous-bed flow analogy, new correlations were developed for pressure drop and winding-to-oil temperature gradients, achieving deviations below 0.7% and 1.8 ◦C, respectively. These correlations provide accurate and computationally efficient alternatives to CFD during design, supporting both new transformer development and retrofitting with alternative fluids.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12