Search

Researchers and engineers are actively working on enhancing the efficiency of heat exchangers in engineering applications by developing novel designs, exploring new materials, and utilizing nanofluids. Three kinds of nanofluids with varying concentrations are investigated in this paper. The objective is to assess the performance of N‐shaped double‐pipe heat exchanger used in thermoelectric power plants. The performance has been evaluated using COMSOL Multiphysics software. The findings show that higher nanofluid concentrations resulted in elevated heat transfer coefficients and improved efficiency of N‐shape double pipe heat exchanger. The analysis revealed that a mere 1% rise in the volume fraction of nanofluids enhanced the efficiency of the heat exchanger on average 23% when compared to the base fluid (water). In comparison to the N‐shape double pipe (Inconel 625) heat exchangers, the N‐shape double pipe (copper) heat exchangers appear to be more efficient. The introduction of nanoparticles has a notable impact on the heat transfer coefficients. Specifically, within an N‐shaped double pipe (copper) heat exchanger, the inclusion of a 1% volume fraction results in a 2.09% enhancement in the heat transfer coefficient for Al2O3/water, a 1.3% improvement for Fe3O4/water, and a 1.15% increase for TiO2/water. It also exposed that adding 1% Al2O3/water led to a significant 0.623% increase in effectiveness, while TiO2/water showed a 0.259% rise, and Fe3O4/water exhibited a 0.375% improvement. Moreover, increasing the Reynolds number enhances the Nusselt number for Al2O3/water and Fe3O4/water nanofluids by 55.22%, and for TiO2/water by 54.60% at a 6% volume concentration, leading to additional increases in exchanger efficiency. Therefore, the augmentation in nanofluid concentration leads to a reduction in the temperature pinch points both at the intake and outflow. This observation suggests that nanofluids exhibit a superior ability compared to conventional fluids when it comes to effectively lowering temperatures. [ABSTRACT FROM AUTHOR]

Pakistan has been facing the threat of religious extremism and terrorism for the last two decades. This phenomenon has both national and transnational roots with the involvement of militant outfits like Tehreek-i-Taliban Pakistan (TTP), AL-Qaeda, Islamic Movement of Uzbekistan (IMU) and others. In this context, the presence of IMU in Pakistan and its lethal operational activities cannot be ignored. After the Operation Enduring Freedom (OEF) of the US in Afghanistan, IMU incubated itself in erstwhile FATA and carried out its ferocious activities by joining hands with local as well as international militant organizations. The current article argues that IMU felt the group’s presence by attacking important government installations and permeating its Jihadi militant doctrines in local organization in Pakistan. However, Pakistani forces through its tactical strategy not only managed to break the alliances of IMU with other organizations but also weed out IMU from Pakistani territory by launching successful military operation [Operation Zerb-eAzab] and others. Furthermore, the research also focuses tactical mapping of IMU, its genesis, activities in central Asia and Pakistan and its affiliation with other Jihadi organizations, its ideological grounding and threat perspectives. It contends that though IMU has no active presence in Pakistan and Pakistani military operations and strategies have been successful, the Pakistani state apparatus has to be cautious in future in this regard. [ABSTRACT FROM AUTHOR]

Graph convolutional neural networks (GCNN) have been widely used in graph learning and related applications. It has been identified that the filters in the state-of-the-art spectral graph convolutional networks (SGCNs) are essentially low-pass filters that enforce smoothness across the graph and use the functions of graph Laplacian as a tool that injects graph structure into the learning algorithm. There had been research findings that connect the smoothness functional in graphs, graph Laplacian, and regularization operators. We review the existing SGCNs in this context and propose a framework where the state-of-the-art filter designs can be deduced as special cases. We designed new filters that are associated with well-defined low-pass behavior and tested their performance on semisupervised node classification tasks. Their performance was found to be superior to that of the other state-of-the-art techniques. We further investigate the representation capability of low-pass features and make useful observations. In this context, we discuss a few points to further optimize the network, new strategies for designing SGCNs, their challenges, and some latest related developments. Based on our framework, we also deduce the connection between support vector kernels and SGCN filters.