@article{891265,
      recid = {891265},
      author = {Pirozzoli, Sergio, and Sengupta, Tapan Kumar,},
      title = {High-performance computing of big data for turbulence and  combustion /},
      pages = {1 online resource (257 pages) :},
      abstract = {This book provides state-of-art information on  high-accuracy scientific computing and its future  prospects, as applicable to the broad areas of fluid  mechanics and combustion, and across all speed regimes.  Beginning with the concepts of space-time discretization  and dispersion relation in numerical computing, the  foundations are laid for the efficient solution of the  Navier-Stokes equations, with special reference to  prominent approaches such as LES, DES and DNS. The basis of  high-accuracy computing is rooted in the concept of  stability, dispersion and phase errors, which require the  comprehensive analysis of discrete computing by rigorously  applying error dynamics. In this context, high-order  finite-difference and finite-volume methods are presented.  Naturally, the coverage also includes fundamental notions  of high-performance computing and advanced concepts on  parallel computing, including their implementation in  prospective hexascale computers. Moreover, the book seeks  to raise the bar beyond the pedagogical use of  high-accuracy computing by addressing more complex physical  scenarios, including turbulent combustion. Tools like  proper orthogonal decomposition (POD), proper generalized  decomposition (PGD), singular value decomposition (SVD),  recursive POD, and high-order SVD in multi-parameter spaces  are presented. Special attention is paid to bivariate and  multivariate datasets in connection with various canonical  flow and heat transfer cases. The book mainly addresses the  needs of researchers and doctoral students in mechanical  engineering, aerospace engineering, and all applied  disciplines including applied mathematics, offering these  readers a unique resource.},
      url = {http://library.usi.edu/record/891265},
      doi = {https://doi.org/10.1007/978-3-030-17},
}