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This paper presents the design of capacitive wireless power transfer systems based on a Class-E inverter approach. The main reason for adopting the Class-E inverter approach is because of its high efficiency, theoretically 100%. However, the operation of a Class-E inverter is highly sensitive to its circuit’s parameters. In a typical capacitive wireless power transfer application, the capacitive coupling distance between plates is subject to changes, and hence its power transfer efficiency is greatly affected if the Class-E inverter is properly tuned. This drawback motivates us to develop an auto frequency tuning algorithm for a Class-E inverter which maintains its power transfer efficiency in spite of the variations of capacitive coupling distances between plates and circuit’s parameters. Finally, simulation and experiment are carried out to verify the effectiveness of the auto frequency tuning algorithm. [ABSTRACT FROM AUTHOR]

Exascale computers are expected to have highly hierarchical architectures with nodes composed by multiple core processors (CPU; central processing unit) and accelerators (GPU; graphics processing unit). The different programming levels generate new difficult algorithm issues. In particular when solving extremely large linear systems, new programming paradigms of Krylov methods should be defined and evaluated with respect to modern state of the art of scientific methods. Iterative Krylov methods involve linear algebra operations such as dot product, norm, addition of vectors and sparse matrix–vector multiplication. These operations are computationally expensive for large size matrices. In this paper, we aim to focus on the best way to perform effectively these operations, in double precision, on GPU in order to make iterative Krylov methods more robust and therefore reduce the computing time. The performance of our algorithms is evaluated on several matrices arising from engineering problems. Numerical experiments illustrate the robustness and accuracy of our implementation compared to the existing libraries. We deal with different preconditioned Krylov methods: Conjugate Gradient for symmetric positive-definite matrices, and Generalized Conjugate Residual, Bi-Conjugate Gradient Conjugate Residual, transpose-free Quasi Minimal Residual, Stabilized BiConjugate Gradient and Stabilized BiConjugate Gradient (L) for the solution of sparse linear systems with non symmetric matrices. We consider and compare several sparse compressed formats, and propose a way to implement effectively Krylov methods on GPU and on multicore CPU. Finally, we give strategies to faster algorithms by auto-tuning the threading design, upon the problem characteristics and the hardware changes. As a conclusion, we propose and analyse hybrid sub-structuring methods that should pave the way to exascale hybrid methods. [ABSTRACT FROM PUBLISHER]