Biblio

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.

We address secure resource allocation for an OFDMA cooperative cognitive radio network (CRN) with energy harvesting (EH) capability. In the network, one primary user (PU) cooperates with several untrusted secondary users (SUs) with one SU transmitter and several SU receivers, where the SU transmitter and all SU receivers may overhear the PU transmitter's information while all SU receivers may eavesdrop on each other's signals. We consider the scenario when SUs are wireless devices with small physical sizes; therefore to improve system performance we suppose that SUs are equipped with co-located orthogonally dual-polarized antennas (ODPAs). With ODPAs, on one hand, the SU transmitter can first harvest energy from radio frequency (RF) signals emitted by the PU transmitter, and then utilize the harvested energy to simultaneously serve the PU and all SU receivers. On the other hand, by exploiting polarization-based signal processing techniques, both the PU's and SUs' physical-layer security can be enhanced. In particular, to ensure the PU's communication security, the PU receiver also sends jamming signals to degrade the reception performance of SUs, and meanwhile the jamming signals can also become new sources of energy powering the SU transmitter. For the considered scenario, we investigate the joint allocation of subcarriers, powers, and power splitting ratios to maximize the total secrecy rate of all SUs while ensuring the PU's minimum secrecy rate requirement. Finally, we evaluate the performance of our resource allocation scheme through numerical analyses.