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Improved designs for underwater manipulators are becoming increasingly important due to their utility in academic and industrial applications. In this work, an experiment was conducted in conjunction with a numerical simulation to investigate the hydrodynamic performance and structural reliability of the proposed foldable arm during the unfolding process at various movement velocities. A large-scale geometric model of the foldable arm with a single degree of freedom (DOF) was constructed. The distribution of the flow field, the movement stability and the equivalent stress of the foldable arm were quantitatively analyzed with a designed tank experiment and the Computational Fluid Dynamics (CFD) simulation. Simulation results show that the maximum deviation of the resistance and torque is 8.04% and 5.73%, respectively, compared with the experimental results of static postures. Comparison results prove the reliability of the numerical model. The results of transient simulation demonstrate that the optimal speed of the foldable arm is 3 Kn and the pressure distribution on the surface of foldable arm is relatively regular. Furthermore, a fluid–structure interaction (FSI) validation study of the foldable arm was presented. For the coupling between the fluid and structural mechanics domains, a nonmatching discretization approach was adopted. The results show that the directional deformation (Z axis) of the foldable arm is less than 0.50 mm. The proposed foldable arm has a large angle of rotation and high stability compared to the existing manipulators. [ABSTRACT FROM AUTHOR]

Over 766 million people have been infected by coronavirus disease 2019 (COVID-19) in the past 3 years, resulting in 7 million deaths. The virus is primarily transmitted through droplets or aerosols produced by coughing, sneezing, and talking. A full-scale isolation ward in Wuhan Pulmonary Hospital is modeled in this work, and water droplet diffusion is simulated using computational fluid dynamics (CFD). In an isolation ward, a local exhaust ventilation system is intended to avoid cross-infection. The existence of a local exhaust system increases turbulent movement, leading to a complete breakup of the droplet cluster and improved droplet dispersion inside the ward. When the outlet negative pressure is 4.5 Pa, the number of moving droplets in the ward decreases by approximately 30% compared to the original ward. The local exhaust system could minimize the number of droplets evaporated in the ward; however, the formation of aerosols cannot be avoided. Furthermore, 60.83%, 62.04%, 61.03%, 60.22%, 62.97%, and 61.52% of droplets produced through coughing reached patients in six different scenarios. However, the local exhaust ventilation system has no apparent influence on the control of surface contamination. In this study, several suggestions with regards to the optimization of ventilation in wards and scientific evidence are provided to ensure the air quality of hospital isolation wards. [ABSTRACT FROM AUTHOR]

Fibrous filters, which are the most commonly used means of particle filtration, are generally characterized by the air pressure drop and filtration efficiency. The nature of particle movement and interaction between the particle and fibre is of great importance for measuring the filtration efficiency of fibrous filters. Majority of previous studies investigated particle trajectory and deposition using the ideal trapping model, which assumed that particles will be trapped once contacted with a solid surface (fibre or deposited particle). This work investigates the dynamic performance of particle rebound and statistically analyses the deposition/accumulation of particles on a fibre surface. We use the computational fluid dynamics (CFD) method to calculate the flow field around a row of fibres. Then, we utilize a particle adherence and rebound criterion and simulate the particle trajectory and deposition using a self-developed solver in Fortran code. Effects of face velocity, particle diameter, and particle rebound characteristics on particle rebound and accumulation around one of the fibres are investigated. Additionally, the trajectories and accumulation of particles on the fibre surface are visually presented. Finally, the filtration efficiency of a single fibre is compared with published results. It is found that effects of particle rebound on the particle trajectory and deposition are significantly related to the face velocity and particle diameter. With considering the particle rebound, the filtration efficiency of a single fibre is obviously different from that of previous studies. [ABSTRACT FROM AUTHOR]

The presence of particles, which can intrude into the gas bearing, is one of the most common factors in the failure of hard disk drives (HDDs). Previous works investigated particle trajectories inside air-filled drives without considering temperature effects on the distribution of particles. Actually, especially for the submicron particle, particle trajectories and trapping status are affected by the temperature gradient since the thermophoretic force cannot be ignored. In this paper, considering major heat generation components such as the spindle motor and voice coil motor (VCM), trajectories and trapping status for AlO particles inside a 2.5 inch helium-filled drive are simulated by the commercial computational fluid dynamics solver FLUENT with user-defined functions (UDFs). The trapping criterion for AlO particles is used as boundary conditions for different colliding surfaces. The results reveal that particles in the air-filled drive will more likely degrade the head-disk interface (HDI) reliability. In addition, after considering the temperature, the particle trapping rate by the disk decreases both inside the air-filled drive and the helium-filled drive. And its reduction inside the air-filled drive is larger. Moreover, small particles will more likely degrade the HDI reliability since they can follow the rotatory flow well and have more chance to collide with the disk surface, and then easily attach onto the disk surface. [ABSTRACT FROM AUTHOR]

Previous works investigated particle trajectories in hard disk drives (HDDs) with the actuator arm and the head gimbals assembly at a fixed position. Actually, internal fluid field characteristics of HDDs can be greatly changed by the arm swing during track-seeking operations, which can affect particle trajectories and trapping status. In this paper, using the dynamic mesh method in a 2.5 in HDD for the first time considers the arm swing in the computational fluid dynamics (CFD) simulation. Al2O3 particle trajectories in this HDD are studied by the CFD solver FLUENT with user-defined functions. The trapping criterion for Al2O3 particles is used as a boundary condition for colliding surfaces. Simulation results reveal that the particle undergoes a larger number of collisions with the disk surface for the average seek time of 12 ms than for the average seek time of 6 ms. In addition, the faster the arm swings, the larger the number of particles that gather near the arm, and the shorter time it will take for particles to be trapped. Moreover, particle-surface collisions can successively take place many times in the helium flow. Furthermore, the particles are trapped slower in the helium-filled drive than in the air-filled drive. [ABSTRACT FROM AUTHOR]

The presence of particles, which can intrude into the air bearing, is one of the most common factors in the failure of hard disk drives (HDDs). Previous studies have investigated particles trajectories with the assumption of ideal trapping or reflecting boundary conditions in air-filled drives. However, only the colliding particle with insufficient energy to escape the potential well will be trapped by the surface. In this paper, considering the particle-surface energy during the collision, the trapping criterion of the incident normal critical velocity ( V\text {ni}^{\mathrm {\ast }}) for Al2O3 particles is developed as the boundary conditions for different colliding surfaces inside a 2.5 in drive. Then, trapping status for Al2O3 particles and particles trajectories inside the drive are simulated by using the commercial computational fluid dynamics solver FLUENT with user-defined functions. The results reveal that the particles will travel longer distances until trapped by HDD components when considering the trapping criterion. In addition, smaller particles will more likely degrade the head–disk interface reliability, since they easily stick on the disk surface. [ABSTRACT FROM PUBLISHER]

This article presents an improved Filtering Facepiece Respirator (FFR) designed to increase the comfort of wearers during low-moderate work. The improved FFR aims to lower the deadspace temperature and CO2 level by an active ventilation fan. The reversing modeling is used to build the 3D geometric model of this FFR; the Computational Fluid Dynamics (CFD) simulation is then introduced to investigate the flow field. Based on the simulation result, the ventilation fan of the improved FFR can fit the flow field well when placed in the proper blowing orientation; streamlines from this fan show a cup-shape distribution and are perfectly matched to the shape of the FFR and human face when the fan blowing inward. In the deadspace of the improved FFR, the CO2 volume fraction is controlled by the optimized flow field. In addition, an experimental prototype of the improved FFR has been tested to validate the simulation. A wireless temperature sensor is used to detect the temperature variation inside the prototype FFR, deadspace temperature is lowered by 2 K compared to the normal FFR without a fan. An infrared camera (IRC) method is used to elucidate the temperature distribution on the prototype FFR's outside surface and the wearer's face, surface temperature is lowered notably. Both inside and outside temperature results from the simulation are in agreement with experimental results. Therefore, adding an inward-blowing fan on the outer surface of an N95 FFR is a feasible approach to reducing the deadspace CO2 concentration and improve temperature comfort. [ABSTRACT FROM AUTHOR]

This article presents a reverse modeling of the headform when wearing a filtering facepiece respirator (FFR) and a computational fluid dynamics (CFD) simulation based on the modeling. The whole model containing the upper respiratory airway, headform, and FFR was directly recorded by computed tomography (CT) scanning, and a medical contrast medium was used to make the FFR “visible.” The FFR was normally worn by the subject during CT scanning so that the actual deformation of both the FFR and the face muscles during contact can be objectively conserved. The reverse modeling approach was introduced to rebuild the geometric model and convert it into a CFD solvable model. In this model, we conducted a transient numerical simulation of air flow containing carbon dioxide, thermal dynamics, and pressure and wall shear stress distribution in the respiratory system taking into consideration an individual wearing a FFR. The breathing cycle was described as a time-dependent profile of the air velocity through the respiratory airway. The result shows that wearing the N95 FFR results in CO2accumulation, an increase in temperature and pressure elevation inside the FFR cavity. The volume fraction of CO2reaches 1.2% after 7 breathing cycles and then is maintained at 3.04% on average. The wearers re-inhale excessive CO2in every breathing cycle from the FFR cavity. The air temperature in the FFR cavity increases rapidly at first and then stays close to the exhaled temperature. Compared to not wearing an FFR, wearers have to increase approximately 90 Pa more pressure to keep the same breathing flow rate of 30.54 L/min after wearing an FFR. The nasal vestibule bears more wall shear stress than any other area in the airway. [ABSTRACT FROM AUTHOR]

Fibrous media is the most common method of filtration, which is generally characterized by its pressure drop and filtration efficiency. In this work, the computational fluid dynamics (CFD) technology was applied to simulate the filtration performance of multi–fiber filters. The pressure drop and filtration efficiency with different fiber arrangements, fiber diameters, face velocities and particle sizes were studied. It was found that filtration efficiency changed with the face velocity for different particle sizes. The layered structures with the same fiber diameters and total solid volume fraction (SVF) were compared, indicating that the dense–sparse structure had the highest filtration efficiency for all the simulated particle sizes at the cost of high pressure drop. Then the dense–sparse structure was optimized to achieve a better filtration performance by using less tiny fibers in the front–row and removing some fibers in the back–row. [ABSTRACT FROM AUTHOR]

The efficient and precise acquisition of hydrodynamic results in complex interaction scenarios for unmanned underwater vehicles (UUVs) presents a significant challenge. This study proposes a time-sequenced hydrodynamics prediction system of underwater vehicle based artificial intelligence edge computing. Firstly, a SE-CausalNet framework is proposed and a hydrodynamic prediction system for underwater vehicles is developed based on a neuromorphic computing chip. Secondly, the computational fluid dynamics (CFD) results of the Remus UUV are validated with experimental data. Two UUV motion cases are proposed to further elucidate hydrodynamics characteristics under the complex interference of submarines. Finally, the hydrodynamics of the UUV for two motion cases is analyzed, and four datasets are constructed to fully demonstrate the predictive capability of the system. Results show that the pressure field for the free offset path case differs remarkably from that of the fixed path case, and noticeable deviations are observed in both horizontal and vertical planes from the original fixed trajectory of the UUV. The prediction aligns well with CFD results, with a sample inference time within 0.3 s. The mean squared error of the four datasets is all below 2.6 × 10−4, indicating a rapid and highly accurate assessment of disturbance moments and trajectories. • The hydrodynamics characteristics of UUV during interactions with a submarine are investigated. • A hydrodynamics prediction system based on AI edge computing platform is developed. • It provides valuable insights to guide the design and motion control of UUVs. [ABSTRACT FROM AUTHOR]

The accurate and rapid prediction of hydrodynamic characteristics greatly affects the monitoring of the manoeuvring performance of unidentified underwater vehicles. This work proposes a novel SEConv1D framework for the hydrodynamics prediction of the unidentified underwater vehicle. Firstly, a novel framework integrated with one-dimensional convolutional neural networks (Conv1D) and a squeeze-and-excitation network (SENet) is proposed. Secondly, a hydrodynamic dataset based on computational fluid dynamics (CFD) is constructed and verified by experiments. Finally, the proposed framework is applied to predict the total resistance coefficient (C d) of REMUS UUV and SUBOFF AFF-1. The predicted results agree well with experimental results, and the error of C d between the experimental data and predicted results for REMUS UUV and SUBOFF AFF-1 is less than 3.39% and 1.97%, respectively, which proved that the proposed framework is effective. Compared with those of the most popular networks (i.e., support vector machine, multilayer perceptron, artificial neural network and Conv1D), the mean error of the C d and friction resistance coefficient (C f) between the CFD and predicted results is small at only 0.19%, depicting reductions of 91.5%, 85.9%, 66.7% and 40.6%, respectively. The per inference time of the proposed framework is only 0.164 s form the real-time prediction. • A novel SEConv1D framework is proposed for the unidentified underwater vehicle resistance coefficient prediction. • A hydrodynamics dataset based on the Opt LHD algorithm, experimental and CFD simulation method is constructed. • It provides a powerful approach for the accurate and rapid prediction of the hydrodynamic characteristics. [ABSTRACT FROM AUTHOR]