Efficient analysis and storage of data is an integral but often challenging task when working with computation fluid dynamics mainly due to the amount of data it can output. Methods centered around the proper orthogonal decomposition were used to analyze, compress, and model various simulation cases. Two different high-fidelity, time-accurate turbomachinery simulations were investigated to show various applications of the analysis techniques. The first turbomachinery example was used to illustrate the extraction of turbulent coherent structures such as traversing shocks, vortex shedding, and wake variation from deswirler and rotor blade passages. Using only the most dominant modes, flow fields were reconstructed and analyzed for error. The reconstructions reproduced the general dynamics within the flow well, but failed to fully resolve shock fronts and smaller vortices. By decomposing the domain into smaller, independent pieces, reconstruction error was reduced by up to 63 percent. A new method of data compression that combined an image compression algorithm and the proper orthogonal decomposition was used to store the reconstructions of the flow field, increasing data compression ratios by a factor of 40.The second turbomachinery simulation studied was a three-stage fan with inlet total pressure distortion. Both the snapshot and repeating geometry methods were used to characterize structures of static pressure fluctuation within the blade passages of the third rotor blade row. Modal coefficients filtered by frequencies relating to the inlet distortion pattern were used to produce reconstructions of the pressure field solely dependent on the inlet boundary condition. A hybrid proper orthogonal decomposition method was proposed to limit burdens on computational resources while providing high temporal resolution analysis.Parametric reduced order models were created from large databases of transient and steady conjugate heat transfer and airfoil simulations. Performance of the models were found to depend heavily on the range of the parameters varied as well as the number of simulations used to traverse that range. The heat transfer models gave excellent predictions for temperature profiles in heated solids for ambitious parameter ranges. Model development for the airfoil case showed that accuracy was highly dependent on modal truncation. The flow fields were predicted very well, especially outside the boundary layer region of the flow.
College and Department
Ira A. Fulton College of Engineering and Technology; Mechanical Engineering
BYU ScholarsArchive Citation
Blanc, Trevor Jon, "Analysis and Compression of Large CFD Data Sets Using Proper Orthogonal Decomposition" (2014). Theses and Dissertations. 5303.
proper orthogonal decomposition, reduced order models, reduced order reconstruction, data compression, computational fluid dynamics, post-processing, domain decomposition, computational fluid dynamics, coherent structures, turbomachinery, pressure distortion, conjugate heat transfer