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

Despite the progress in high performance computing, Computational FluidDynamics (CFD) simulations are still computationally expensive for manypractical engineering applications such as simulating large computationaldomains and highly turbulent flows. One of the major reasons of the highexpense of CFD is the need for a fine grid to resolve phenomena at the relevantscale, and obtain a grid-independent solution. The fine grid requirements oftendrive the computational time step size down, which makes long transientproblems prohibitively expensive. In the research presented, the feasibility ofa Coarse Grid CFD (CG-CFD) approach is investigated by utilizing MachineLearning (ML) algorithms. Relying on coarse grids increases the discretizationerror. Hence, a method is suggested to produce a surrogate model that predictsthe CG-CFD local errors to correct the variables of interest. Givenhigh-fidelity data, a surrogate model is trained to predict the CG-CFD localerrors as a function of the coarse grid local features. ML regressionalgorithms are utilized to construct a surrogate model that relates the localerror and the coarse grid features. This method is applied to athree-dimensional flow in a lid driven cubic cavity domain. The performance ofthe method was assessed by training the surrogate model on the flow full fieldspatial data and tested on new data (from flows of different Reynolds numberand/or computed by different grid sizes). The proposed method maximizes thebenefit of the available data and shows potential for a good predictivecapability.

Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning