Abstract: This paper presents a study for estimating the size of a tidal turbine array for the Faro-Olhão Inlet (Potugal) using a surrogate optimisation approach. The method compromises problem formulation, hydro-morphodynamic modelling, surrogate construction and validation, and constraint optimisation. A total of 26 surrogates were built using linear RBFs as a function of two design variables: number of array rows and number of Tidal Energy Converters (TECs) per row. Surrogates describe array performance and environmental effects associated with hydrodynamic and morphological aspects of the multi inlet lagoon. Validated surrogates were employed to formulate a constraint optimisation model. Results evidence that the largest array size that satisfies performance and environmental constraints is made of 3 rows and 10 TECs per row.

Abstract: Large-scale dynamic traffic simulations generate a sizeable amount of raw data that needs to be managed for analysis. Typically, big data reduction techniques are used to decrease redundant, inconsistent and noisy data as these are perceived to be more useful than the raw data itself. However, these methods are normally performed independently so it wouldn’t compete with the simulation’s computational and memory resources. In this paper, we are motivated in developing a data reduction technique that will be integrated into a simulation process and executed numerous times. Specifically, we are interested in reducing the size of each link’s time-dependent travel time data in a large-scale dynamic traffic simulation. The objective is to approximate the time-dependent link travel times along the y−axis to reduce memory consumption while insignificantly affecting the simulation results. An important aspect of the algorithm is its capability to restrict the maximum absolute error bound which avoids theoretically inconsistent results not accounted for by the dynamic traffic simulation model. One major advantage of the algorithm is its efficiency’s independence from input complexity such as the number of sampled data points, sampled data’s shape and irregularity of sampling intervals. Using a 10x10 grid network with variable time-dependent link travel time data complexities and absolute error bounds, the dynamic traffic simulation results show that the algorithm achieves around 80%−99% of link travel time data reduction using a small amount of computational resource.

Abstract: The ability to perform large-scale ultrasound simulations has generated significant interest in medical ultrasonics, including treatment planning in therapeutic ultrasound, and image reconstruction in photoacoustic tomography. However, routine execution of such simulations using modern pseudospectral methods is computationally very challenging. To enable fast simulation, a cluster of computers is typically used. Nowadays, the trend in parallel computing is towards the use of accelerated nodes where the hard work is offloaded from processors to accelerators. During last five years, Intel has released two generations of accelerators called Intel Xeon Phi. The goal of this paper is to investigate the parameters on both architectures with respect to current processors, and evaluate the suitability of accelerated clusters for the distributed simulation of ultrasound propagation in medical applications. The paper reveals that the former version of Xeon Phis, the Knight's Corner architecture, suffers from several flaws that reduces the performance far below the Haswell processors. On the other hand, the second generation called Knight's Landing shows a very promising performance comparable with current processors.