New method to capture traveling waves in flow passing a wind turbine

Clainche, Soledad Le; Mao, Xuerui; Vega, José M.

doi:10.1002/we.2331

Cited by 7 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present methods only identify deterministic motion, which is related to the large-scale coherent structures present in the flow, while the small-scale flow structures, generally understood as low-energy structures, are omitted. Nevertheless, understanding the flow dynamics driving the large size, most energetic, coherent structures present in the flow, would shed light on the physical mechanisms in charge of the pollutant dispersion (as it was tested in other complex problems in [164][165][166]). Hence, the new future trends should focus on continuing to apply and develop highly efficient data-driven tools to identify the main flow patterns in urban flows, not only focusing on the aforementioned classical tools, but also exploring new algorithms of machine learning-for instance, those based on nonlinear functions.…”

Section: Discussionmentioning

confidence: 99%

“…A novel Koopman operator defines a new eigenvalue problem, the eigenvalues of which represent the spatial growth rates and wavenumbers in the previous expansion. This method has been successfully used to identify the main flow structures in complex flows [163,166] and to identify the flow instabilities leading the flow transition in the wake of a wind turbine [165].…”

Section: Appendix A2 Spectral Proper Orthogonal Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

2021

Self Cite

View full text Add to dashboard Cite

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Appendix A2 Spectral Proper Orthogonal Decompositionmentioning

confidence: 99%

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…turbine operation [48,49], aircraft flight flutter testing [50,51], basic fluid dynamics [52], and pattern-forming systems [53]. See also [26] for a reader-friendly description of the HODMD and STKD methods and some of their applications, as well as for specific MATLAB implementations of their algorithms.…”

Section: Discussionmentioning

confidence: 99%

Uncovering spatio-temporal patterns in semiconductor superlattices by efficient data processing tools

Terragni,

Bonilla,

Vega

2021

Preprint

View full text Add to dashboard Cite

Time periodic patterns in a semiconductor superlattice, relevant to microwave generation, are obtained upon numerical integration of a known set of drift-diffusion equations. The associated spatio-temporal transport mechanisms are uncovered by applying (to the computed data) two recent data processing tools, known as the higher order dynamic mode decomposition and the spatio-temporal Koopman decomposition. Outcomes include a clear identification of the asymptotic self-sustained oscillations of the current density (isolated from the transient dynamics) and an accurate description of the electric field traveling pulse in terms of its dispersion diagram. In addition, a preliminary version of a novel data-driven reduced order model is constructed, which allows for extremely fast online simulations of the system response over a range of different configurations.

show abstract

“…A novel Koopman operator defines a new eigenvalue problem, the eigenvalues of which represent the spatial growth rates and wavenumbers in the previous expansion. This method has been successfully used to identify the main flow structures in complex flows [157,172] and to identify the flow instabilities leading the flow transition in the wake of a wind turbine [178].…”

Section: Discussionmentioning

confidence: 99%