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The aqueous waste generated during the treatment of Purex lean organic phase with alkaline carbonate solution contains washable degradation products and actinides in the form of carbonate complex. Management of such aqueous waste demands quantitative removal of the main degradation product, dibutyl phosphate (HDBP) and actinides from aqueous solution. In this context, batch studies have been carried out on the separation of HDBP from aqueous solution using n-dodecane (n-DD). For this purpose, the extraction behaviour of HDBP in n-DD was studied after acidification of alkaline carbonate solution with nitric acid. The studies with simulated waste containing HDBP and U(VI) nitrate showed the loss of U(VI) as precipitate during acidification as the uranium–DBP was poorly soluble in aqueous phase. However, the loss of U(VI) decreased with increase of aqueous phase acidity showing that the adjusted acidity of the carbonate waste plays an important role in the recovery of actinides.

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar , for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest resolution possible on such platforms. We show that optimizing memory operations and thread blocks results in 200x speedup of computationally intensive kernels compared with a CPU core. Using multiple GPUs and CUDA-aware MPI communication, we demonstrate both strong and weak scaling of our GPU-based HyPar implementation on the NVIDIA Volta V100 GPUs. We simulate the decay of homogeneous isotropic turbulence in a triply periodic box on grids with up to 102 4 3 points (5.3 billion degrees of freedom) and on up to 1,024 GPUs. We compare the wall times for CPU-only and CPU+GPU simulations. The results presented in the paper are obtained on the Summit and Lassen supercomputers at Oak Ridge and Lawrence Livermore National Laboratories, respectively. • Key strategies to accelerate CPU-based high-order finite-difference WENO scheme. • GPU-accelerated algorithm for DNS of isotropic turbulence decay in a periodic box. • Numerical justifications for our approaches and comparison to alternatives. • 200x speedup using a GPU compared with a single CPU core. • Scale-resolved simulations of turbulence on the Summit and Lassen supercomputers. • 100 time steps with 5 billion degrees of freedom in less than 30 s using 1024 GPUs. • Compare GPU and MPI simulations on the same number of nodes and show 10x speedup. [ABSTRACT FROM AUTHOR]