Parameter optimisation of the Eolic Cell to augment wind power density through the Metamodel of Optimal Prognosis

ABSTRACT

The present work advances a methodology to optimise variables involved in fluid dynamic phenomena for augmented wind turbines. Particularly, the study focuses on improving the performance of a convergent-divergent augmented wind turbine based on eolic cells designed to increase wind speed at the throat section, where a peripherally supported magnetic levitation rotor will be installed as part of a novel wind energy system for distributed generation. Previous studies focused on maximising average wind velocity as the target variable. In contrast, this study shifted its focus to power density, resulting in more effective and consistent results. Numerical axisymmetric computational fluid dynamics simulations were conducted to determine the impact of these improvements. Response surfaces were created for parametric analysis, and the metamodel of optimal prognosis was implemented to provide accuracy. The results indicate a significant improvement in available power, with an average increase of up to 12.5 times compared to non-augmented conditions.

KEYWORDS:

• Wind energy
• CFD simulation
• metamodel of optimal prognosis
• augmentation effect
• distributed generation

Acknowledgment

The Universidad Nacional de Ingeniería (UNI) and the Universidad Nacional del Santa (UNS), research parties, also supported the present study.

Disclosure statement

The authors disclose the corresponding financial interest or personal relationship, which may be considered as potential competing interests: Alfredo R. Calle holds a positive patentability report from WIPO #WO2021034203 with a publication date of 25.02.2021.

Data availability statement

The data that support the findings of this study are available from the corresponding author, ARC, upon reasonable request.

CRediT author statement

Alfredo R. Calle: Conceptualisation, Methodology, Writing – Original Draft, Formal analysis, Investigation, Visualisation, Project administration. Giusep A. Baca: Methodology, Software, Formal analysis, Investigation. Salome Gonzales: Supervision, Methodology, Resources, Writing – Reviewing and Editing, Funding acquisition. Andrés Diaz: Writing – Reviewing and Editing. Hugo Calderon: Writing – Reviewing and Editing. José López: Writing – Reviewing and Editing.

Additional information

Funding

This work was supported by ProCiencia under [Grant number PE501078852-2022-PROCIENCIA].