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.

In case of wildfire, particles generated in combustion are in complex law of motion under the influence of flame temperature, airflow and lots of electrons and ions. They would distort the space electric field, and lead to gap discharge. This paper develops a multi-physics coupling calculation model of fluid, temperature, electric field and particle movement by combining the rod-plate gap experiment that simulates the wildfire condition. It analyzes the motion state of ash particles in flames, studies the charged particles of different polarity separately, and explores the impact of particle properties on the electric field of gap space by combining the distribution of particles. Results have shown that there are differences in the motion state of charged particles of different polarity, and the electrode will absorb some particles with different charges, while charged particles with the same polarity as the electrode will move away from the electrode in random motion. Particles of different properties (particle size, relative dielectric constant) have different impacts on the electric field of gap space, but they all promote the discharge propagation.

Mutriku wave farm is the first commercial plant all around the world. Since July 2011 it has been continuously selling electricity to the grid. It operates with the OWC technology and has 14 operating Wells-type turbines. In the plant there is a SCADA data recording system that collects the most important parameters of the turbines; among them, the pressure in the inlet chamber, the position of the security valve (from fully open to fully closed) and the generated power in the last 5 minutes. There is also an electricity meter which provides information about the amount of electric energy sold to the grid. The 2014 winter (January, February and March), and especially the first fortnight of February, was a stormy winter with rough sea state conditions. This was reflected both in the performance of the turbines (high pressure values, up to 9234.2 Pa; low opening degrees of the security valve, down to 49.4°; and high power generation of about 7681.6 W, all these data being average values) and in the calculated capacity factor (CF = 0.265 in winter and CF = 0.294 in February 2014). This capacity factor is a good tool for the comparison of different WEC technologies or different locations and shows an important seasonal behavior.

The relative permittivity (also known as dielectric constant) is one of the physical properties that characterize a substance. The measurement of its magnitude can be useful in the analysis of several fluids, playing an important role in many industrial processes. This paper presents a method for measuring the relative permittivity of fluids, with the possibility of real-time monitoring. The method comprises the immersion of a capacitive sensor inside a tank or duct, in order to have the inspected substance as its dielectric. An electronic circuit is responsible for exciting this sensor, which will have its capacitance measured through a quick analysis of two analog signals outputted by the circuit. The developed capacitance meter presents a novel topology derived from the well-known Howland current source. One of its main advantages is the capacitance-selective behavior, which allows the system to overcome the effects of parasitic resistive and inductive elements on its readings. In addition to an adjustable current output that suits different impedance magnitudes, it exhibits a steady oscillating behavior, thus allowing continuous operation without any form of external control. This paper presents experimental results obtained from the proposed system and compares them to measurements made with proven and calibrated equipment. Two initial capacitance measurements performed with the system for evaluating the sensor's characteristics exhibited relative errors of approximately 0.07% and 0.53% in comparison to an accurate workbench LCR meter.

The coupled thermohydroelastic processes of acoustic wave and heat propagation in weak viscous fluid and elastic bodies form the basis of dissipative acoustics. The problems of dissipative acoustics have many applications in engineering practice, in particular in the development of appropriate medical equipment. This paper presents mathematical models for time and frequency domain problems in terms of unknown displacements and temperatures in both the fluid and the elastic body. Formulated corresponding variational problems and constructed numerical schemes for their solution based on the Galerkin approximations. The method of proving the well-posedness of the considered variational problems is proposed.