English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Proper Orthogonal Decomposition (POD) analysis is widely used in processing large amounts of high-dimensional data in order to obtain low-dimension approximation, in which way we can capture the most typical modals of the phenomena. Based on this, POD is a powerful tool to analyze complex flow fields. However, there are generally only several modes that dominate unsteady factors, so it is wasteful to calculate all of the modes in analyzing large-scale unsteady calculation results. What’s more, the number of grid in simulation of gas turbine is generally huge, so a complete POD which needs full storage costs a lot in memory. Based on incremental singular value decomposition (SVD), a fast incremental POD (iPOD) approach is developed. To compute a full SVD by adding vectors, we iterate the update procedure with truncation based on the user-defined rank, in which way, the left and right singular vector matrices as well as the singular value matrix are updated incrementally, resulting in an online algorithm whose memory requirements reduced several levels. In this work, both 40 and 60 modes are adopted in analyzing the large eddy simulation results of shaped hole film cooling to capture the dominated flow structures and further understand the effect on cooling effectiveness. In comparison, the results of iPOD achieve a good agreement with that of exact method at a low computational cost, demonstrating that the iPOD method is appropriate in large scale high fidelity flow fields analysis. INTRODUCTION Due to the complexity of the flow field in the turbine, it is common that researchers obtain a large number of highdimensional data by experiment and computational fluid dynamics (CFD). Those data describe the flow fields in detail, however, the most typical character may be covered by other information as well. Therefore, it is necessary to find some way of processing the high-dimensional data, so that the dominant factor can be captured. In the analysis, the first step to extract physically important feature is often modal decomposition. There was an overview about modal analysis of fluid flows by Taira et al (Taira et al., 2017). They presented several well-established modal analysis techniques including POD, balanced POD, dynamic mode decomposition (DMD), Koopman analysis, global linear analysis, and resolvent analysis. The first three methods are data-based, and the rests are operator-based. Among these methods, POD can extract modes ranked by energy ratio, and the first mode occupies the most energy, which generally represents the time-averaged character of the fields. Furthermore, the flow field can be reconstructed by the first several modes that are modes with more energy.

Incremental Proper Orthogonal Decomposition Based Method for the Analyzation of Large Scale High Fidelity Simulation Results