Um algoritmo paralelo eficiente de migração reversa no tempo (rtm) 3d com granularidade fina

The reverse time migration algorithm (RTM) has been widely used in the seismic industry to generate images of the underground and thus reduce the risk of oil and gas exploration. Its widespread use is due to its high quality in underground imaging. The RTM is also known for its high computational cost. Therefore, parallel computing techniques have been used in their implementations. In general, parallel approaches for RTM use a coarse granularity by distributing the processing of a subset of seismic shots among nodes of distributed systems. Parallel approaches with coarse granularity for RTM have been shown to be very efficient since the processing of each seismic shot can be performed independently. For this reason, RTM algorithm performance can be considerably improved by using a parallel approach with finer granularity for the processing assigned to each node. This work presents an efficient parallel algorithm for 3D reverse time migration with fine granularity using OpenMP. The propagation algorithm of 3D acoustic wave makes up much of the RTM. Different load balancing were analyzed in order to minimize possible losses parallel performance at this stage. The results served as a basis for the implementation of other phases RTM: backpropagation and imaging condition. The proposed algorithm was tested with synthetic data representing some of the possible underground structures. Metrics such as speedup and efficiency were used to analyze its parallel performance. The migrated sections show that the algorithm obtained satisfactory performance in identifying subsurface structures. As for the parallel performance, the analysis clearly demonstrate the scalability of the algorithm achieving a speedup of 22.46 for the propagation of the wave and 16.95 for the RTM, both with 24 threads.

Remoção de corantes utilizando tensoativos: extração por ponto de nuvem e floculação iônica

The textile sector is one of the main contributors to the generation of industrial wastewaters due to the use of large volumes of water, which has a high organic load content. In these, it is observed to the presence of dyes, surfactants, starch, alcohols, acetic acid and other constituents, from the various processing steps of the textiles. Hence, the treatment of textile wastewater becomes fundamental before releasing it into water bodies, where they can cause disastrous physical-chemical changes for the environment. Surfactants are substances widely used in separation processes and their use for treating textile wastewaters was evaluated in this research by applying the cloud point extraction and the ionic flocculation. In the cloud point extraction was used as surfactant nonylphenol with 9.5 ethoxylation degree to remove reactive dye. The process evaluation was performed in terms of temperature, surfactant and dye concentrations. The dye removal reached 91%. The ionic flocculation occurs due to the presence of calcium, which reacts with anionic surfactant to form insoluble surfactants capable of attracting the organic matter by adsorption. In this work the ionic flocculation using base soap was applied to the treatment of synthetic wastewater containing dyes belonging to three classes: direct, reactive, and disperse. It was evaluated by the influence of the following parameters: surfactant and electrolyte concentrations, stirring speed, equilibrium time, temperature, and pH. The flocculation of the surfactant was carried out in two ways: forming the floc in the effluent itself and forming the floc before mixing it to the effluent. Removal of reactive and direct dye, when the floc is formed into textile effluent was 97% and 87%, respectively. In the case where the floc is formed prior to adding it to the effluent, the removal to direct and disperse dye reached 92% and 87%, respectively. These results show the efficience of the evaluated processes for dye removal from textile wastewaters.

Escalonando uma aplicação paralela com demanda variável em ambiente de nuvem

This study proposes a solution responsible for scheduling data processing with variable demand in cloud environments. The system built check specific variables to the business context of a company incubated at Digital Metropole Institute of UFRN. Such a system generates an identification strategy machinery designs available in a cloud environment, focusing on processing performance, using data load balancing strategies and activities of parallelism in the software execution flow. The goal is to meet the seasonal demand within a standard time limit set by the company, controlling operating costs by using cloud services in the IaaS layer.