Wuhan Ship Development & Design Institute

Compared to non-cavitating flow, cavitating flow is much complex owing to the numerical difficulties caused by cavity generation and collapse. In this paper, the cavitating flow around a NACA66 hydrofoil is studied numerically with particular emphasis on understanding the cavitation structures and the shedding dynamics. Large Eddy Simulation (LES) was coupled with a homogeneous cavitation model to calculate the pressure, velocity, vapor volume fraction and vorticity around the hydrofoil. The predicted cavitation shedding dynamics behavior, including the cavity growth, break-off and collapse downstream, agrees fairly well with experiment. Some fundamental issues such as the transition of a cavitating flow structure from 2D to 3D associated with cavitation–vortex interaction are discussed using the vorticity transport equation for variable density flow. A simplified one-dimensional model for the present configuration is adopted and calibrated against the LES results to better clarify the physical mechanism for the cavitation induced pressure fluctuations. The results verify the relationship between pressure fluctuations and the cavity shedding process (e.g. the variations of the flow rate and cavity volume) and demonstrate that the cavity volume acceleration is the main source of the pressure fluctuations around the cavitating hydrofoil. This research provides a better understanding of the mechanism driving the cavitation excited pressure pulsations, which will facilitate development of engineering designs to control these vibrations.

Ambient-pressure-dried (APD) preparation of transition metal carbide/nitrides (MXene) aerogels is highly desirable yet remains highly challenging. Here, ultrathin, high-strength-to-weight-ratio, renewable cellulose nanofibers (CNFs) are efficiently utilized to assist in the APD preparation of ultralight yet robust, highly conductive, large-area MXene-based aerogels via a facile, energy-efficient, eco-friendly, and scalable freezing-exchanging-drying approach. The strong interactions of large-aspect-ratio CNF and MXene as well as the biomimetic nacre-like microstructure induce high mechanical strength and stability to avoid the structure collapse of aerogels in the APD process. Abundant functional groups of CNFs facilitate the chemical crosslinking of MXene-based aerogels, significantly improving the hydrophobicity, water resistance, and even oxidation stability. The ultrathin, 1D nature of the CNF renders the minimal MXenes' interlayered gaps and numerous heterogeneous interfaces, yielding the excellent conductivity and electromagnetic interference (EMI) shielding performance of aerogels. The synergies of the MXene, CNF, and abundant pores efficiently improve the EMI shielding performance, photothermal conversion, and absorption of viscous crude oil. This work shows great promises of the APD, multifunctional MXene-based aerogels in electromagnetic protection or compatibility, thermal therapy, and oil-water separation applications.

Cavitating turbulent flow around hydrofoils was simulated using the Partially-Averaged Navier–Stokes (PANS) method and a mass transfer cavitation model with the maximum density ratio (ρl/ρv,clip) effect between the liquid and the vapor. The predicted cavity length and thickness of stable cavities as well as the pressure distribution along the suction surface of a NACA66(MOD) hydrofoil compare well with experimental data when using the actual maximum density ratio (ρl/ρv,clip = 43391) at room temperature. The unsteady cavitation patterns and their evolution around a Delft twisted hydrofoil were then simulated. The numerical results indicate that the cavity volume fluctuates dramatically as the cavitating flow develops with cavity growth, destabilization, and collapse. The predicted three dimensional cavity structures due to the variation of attack angle in the span-wise direction and the shedding cycle as well as its frequency agree fairly well with experimental observations. The distinct side-lobes of the attached cavity and the shedding U-shaped horse-shoe vortex are well captured. Furthermore, it is shown that the shedding horse-shoe vortex includes a primary U-shaped vapor cloud and two secondary U-shaped vapor clouds originating from the primary shedding at the cavity center and the secondary shedding at both cavity sides. The primary shedding is related to the collision of a radially-diverging re-entrant jet and the attached cavity surface, while the secondary shedding is due to the collision of side-entrant jets and the radially-diverging re-entrant jet. The local flow fields show that the interaction between the circulating flow and the shedding vapor cloud may be the main mechanism producing the cavitating horse-shoe vortex. Two side views described by iso-surfaces of the vapor volume fraction for a 10% vapor volume, and a non-dimensional Q-criterion equal to 200 are used to illustrate the formation, roll-up and transport of the shedding horse-shoe vortex. The predicted height of the shedding horse-shoe vortex increases as the vortex moves downstream. It is shown that the shape of the horse-shoe vortex for the non-dimensional Q-criterion is more complicated than that of the 10% vapor fraction iso-surface and is more consistent with the experiments. Further, though the time-averaged lift coefficient predicted by the PANS calculation is about 12% lower than the experimental value, it is better than other predictions based on RANS solvers.

As a new kind of artificial material developed in recent decades, metamaterials exhibit novel performance and the promising application potentials in the field of practical engineering compared with the natural materials. Acoustic metamaterials and phononic crystals have some extraordinary physical properties, effective negative parameters, band gaps, negative refraction, etc., extending the acoustic properties of existing materials. The special physical properties have attracted the attention of researchers, and great progress has been made in engineering applications. This article summarizes the research on acoustic metamaterials and phononic crystals in recent decades, briefly introduces some representative studies, including equivalent acoustic parameters and extraordinary characteristics of metamaterials, explains acoustic metamaterial design methods, and summarizes the technical bottlenecks and application prospects.

In differential evolution (DE) studies, there are many parameter adaptation methods, aiming at tuning the mutation factor F and the crossover probability CR . However, these methods still cannot resolve the issues of population premature convergence and population stagnation. To address these issues, in this paper, we investigate the population adaptation regarding population diversity at the dimensional level and propose a mechanism named auto-enhanced population diversity (AEPD) to automatically enhance population diversity. AEPD is able to identify the moments when a population becomes converging or stagnating by measuring the distribution of the population in each dimension. When convergence or stagnation is identified at a dimension, the population is diversified at that dimension to an appropriate level or to eliminate the stagnation issue. The AEPD mechanism was incorporated into a popular DE algorithm and it was tested on a set of 25 CEC2005 benchmark functions. The results showed that AEPD significantly improved the performance of the original algorithms. In addition, AEPD helped the algorithms become less sensitive to population size, a parameter widely considered problem dependent for many DE algorithms. The DE algorithm with AEPD also has a superior performance in comparison with several other peer algorithms.

Abstract Transition metal carbides/nitrides (MXenes) with metallic electrical conductivity and excellent processability attract increasing attention for assembling multifunctional macrostructures. However, the challenges, involving poor mechanical strength, inferior oxidation stability, and limited scalable manufacturing, impede their wide applications. Herein, the large‐area, high‐strength, ultra‐flexible hybrid films are developed through the multiple physical and chemical cross‐linking of MXene/cellulose films facilitated by graphene oxide. The MXene‐based films manifest significantly improved hydrophobicity, water/solvent resistance, and oxidation stability, and meanwhile, maintain excellent conductivity and electromagnetic interference shielding performance. The X‐band surface‐specific shielding effectiveness (SE) of 18,837.5 dB cm 2 g −1 and an SE over 60 dB in an ultra‐broadband frequency range are achieved, comparable to the best shields ever reported. Furthermore, the wearable films demonstrate excellent photothermal antibacterial and electrothermal deicing applications. Thus, such high‐performance MXene‐based films developed through a facile and scalable manufacturing method have substantial application prospects in flexible electronics, thermotherapy, electromagnetic compatibility, and aerospace.

When LCL-type converter is attached to weak grid, its current control and phase-locked loop (PLL) will interact with each other, via the point of common coupling voltage. Unfortunately, the conventional PLL controller design methods are mainly for PLL independent systems, regardless of the aforesaid interaction. As a consequence, PLL dynamic might deteriorate the grid current control and even leads to instability problem. For this issue, this article proposes an improved design of PLL controller parameters to mitigate the negative effect of PLL on the current control. First, the small-signal model of the current control system considering PLL effect is established, and the system stability with the conventional PLL controller design method is analyzed. Then, an improved design of PLL controller parameters is proposed, and the design guideline is given in detail. With the method, not only the dynamic and static response performance of PLL independent system can be maintained, but also the negative influence of PLL dynamic on the current control can be effectively reduced in weak grid. Moreover, the grid current control has a strong robustness to the grid impedance variation. Finally, the proposed method is validated by the simulation and experiment.

While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.

Abstract The dynamics of an elastic beam floating on the surface of disturbed water has formed the basis of a fairly comprehensive linear theory of hydroelastic behaviour of ships in waves. The existing theory cannot easily be extended to floating vehicles of more complicated shape (such as semi-submersibles), or to fixed offshore structures. A general method is presented, by which finite elements permit any three-dimensional elastic structure to be admitted in a linear hydroelastic theory. Sinusoidal waves provide the excitation of the structure and the fluid flow is three-dimensional. Some examples are given which illustrate the use of the theory and expose behaviour that has not been encountered hitherto.

This paper presents a forward approach to establish parametric scattering center models for known complex radar targets. In this approach, an automatic technique based on ray tracing and clustering is first developed to extract scattering centers directly from the computer-aided design (CAD) model of the targets. Following this, a set of forward methods is developed to determine the physically relevant parameters of two-dimension (2-D) attributed scatterers, such as type, amplitude, position and length. Finally, this approach is validated through the parametric model establishment of two complex targets and good agreement has been demonstrated between the reconstructed and actual radar characteristics. Different from the familiar inverse extraction approaches, the proposed approach provides a new forward way of constructing radar targets' feature database based on 2-D parametric scattering center model, which will ultimately facilitate the feature matching in the synthetic aperture radar (SAR) automatic target recognition (ATR) system.

The interest of human-robot collaboration (HRC) for intelligent manufacturing service system is gradually increasing. Fluent human-robot coexistence in manufacturing requires accurate estimation of the human motion intention so that the efficiency and safety of HRC can be guaranteed. Human motion is mainly defined as the sequential positions of the joints of human skeletons among traditional motion prediction solutions, which lead to a deficiency of tools or product components holding in hand. Context awareness based temporal processing is the key to evaluating human motion before the accomplishment of it, so as to save time as well as recognize the intention of the human. In this paper, a deep learning system combing convolutional neural network (CNN) and long short-term memory network (LSTM) towards vision signals is explored to predict human motion accurately. Creatively, this paper utilizes LSTM to extract temporal patterns of human motion automatically outputting the prediction result before motion takes place. Not only does it avoid complex feature extraction due to its end-to-end characteristic, but provide a natural interaction between human and robot without wearable devices or tags that may become a burden for the former. A case study of desktop computer product disassembly is executed to demonstrate the feasibility of the recommended method. Experimental performance proves that our method outperforms the other three optimization algorithms on the prediction accuracy.

BACKGROUND AND AIMS: Valve interstitial cells (VICs) undergo a transition to intermediate state cells before ultimately transforming into the osteogenic cell population, which is a pivotal cellular process in calcific aortic valve disease (CAVD). Herein, this study successfully delineated the stages of VIC osteogenic transformation and elucidated a novel key regulatory role of lumican (LUM) in this process. METHODS: Single-cell RNA-sequencing (scRNA-seq) from nine human aortic valves was used to characterize the pathological switch process and identify key regulatory factors. The in vitro, ex vivo, in vivo, and double knockout mice were constructed to further unravel the calcification-promoting effect of LUM. Moreover, the multi-omic approaches were employed to analyse the molecular mechanism of LUM in CAVD. RESULTS: ScRNA-seq successfully delineated the process of VIC pathological transformation and highlighted the significance of LUM as a novel molecule in this process. The pro-calcification role of LUM is confirmed on the in vitro, ex vivo, in vivo level, and ApoE-/-//LUM-/- double knockout mice. The LUM induces osteogenesis in VICs via activation of inflammatory pathways and augmentation of cellular glycolysis, resulting in the accumulation of lactate. Subsequent investigation has unveiled a novel LUM driving histone modification, lactylation, which plays a role in facilitating valve calcification. More importantly, this study has identified two specific sites of histone lactylation, namely, H3K14la and H3K9la, which have been found to facilitate the process of calcification. The confirmation of these modification sites' association with the expression of calcific genes Runx2 and BMP2 has been achieved through ChIP-PCR analysis. CONCLUSIONS: The study presents novel findings, being the first to establish the involvement of lumican in mediating H3 histone lactylation, thus facilitating the development of aortic valve calcification. Consequently, lumican would be a promising therapeutic target for intervention in the treatment of CAVD.

With the development of data plane programmable Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal. Currently, traditional attack detection methods are mainly used to detect whether a DDoS attack occurs and it is difficult to find the path that the attack flow traverses the network, which makes it difficult to accurately mitigate DDoS attacks. In this article, we propose a detection method based on Spatial-Temporal Graph Convolutional Network (ST-GCN) over the data plane programmable SDN, which maps the network into a graph. It senses the state of switches through In-band Network Telemetry (INT) with sampling, inputs the network state into the spatial-temporal graph convolutional network detection model, and finally finds out the switches through which DDoS attack flows pass. Based on this, we propose a defense method combined with an enhanced whitelist and a precise dropping strategy, which can effectively mitigate DDoS attacks and minimize the impact on legitimate network traffic. The evaluation results show that our detection method can accurately detect the path that the DDoS attack flows pass through, and can effectively mitigate the DDoS attack. Compared to classic methods, our method improves the detection accuracy by nearly 10%. At the same time, the southbound interface load and CPU overhead brought by our detection and defense process are much lower than the classic methods.

This paper proposes a double modulation wave carrier-based pulse width modulation (CBPWM) strategy for the three-level neutral-point-clamped (NPC) inverter. This modulation strategy not only overcomes one of the main problems of the NPC inverter, which is the lowfrequency voltage oscillation on the neutral point, but also reduces the common-mode voltage (CMV) generated by the inverter. Through the rigorous theoretical derivation, the unified duty cycles of each phase are derived, and the double modulation waves can be easily obtained according to different optimization objectives. However, in the previous double wave CBPWM work, the modulation strategy could not reduce the CMV, whose amplitude is still the same as that of the conventional CBPWM strategy. By rearranging the switching sequence, the vectors corresponding to the large CMV amplitude have been eliminated, and the amplitude of CMV can be reduced by half. Considering the occurrence of neutral-point voltage perturbations, a voltage controller is adopted to recover from the imbalance. Finally, the performance of the proposed modulation technique is verified by the experimental results.

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

Disassembly, which plays an essential role in remanufacturing, is the first step to extend the service life of end-of-life (EOL) products. Traditional disassembly is always accomplished by either humans or robots. Manual disassembly is a time-consuming process, and the high labour intensity will also pose a threat to human health, while robotic disassembly is difficult to flexibly handle complex parts. Continuous manual work leads to the accumulation of fatigue, which decreases the efficiency of manual work. In this paper, sequence planning considering human fatigue for human-robot collaboration in disassembly (HRCD) is proposed. This method involves assigning disassembly task to human and robot according to their respective characteristics, models for HRCD considering human fatigue is established. In the case of disassembling batches products with the same type, discrete Bees algorithm is used to obtain the optimal disassembly sequence to minimize the total disassembly time. Case studies based on gear pumps show that the proposed algorithm outperforms the other two optimization algorithms in solution quality.

In this article, in order to solve the trajectory tracking control problem with the drive saturation (thrust overrun) for the 4500-m human occupied vehicle named “Deep-sea Warrior,” a model predictive adaptive constraint control strategy is put forward. The proposed control strategy mainly consists of two controllers. The first part is a kinematics controller designed by quantum-behaved particle swarm optimization model predictive control method. The second part is a dynamic controller designed by an adaptive algorithm. In order to study the effect of the ocean current disturbance on tracking controller, the ocean current is incorporated into the kinematics and dynamics model of the 4500-m human occupied vehicle. The thrusts of four degrees of freedom under the ocean current are calculated from designed controllers. Then, the thrusts are assigned to six thrusters on the 4500-m human occupied vehicle according to its thruster arrangement. An ocean current observer based on artificial fish proportional-integral control is designed for unknown currents. The simulation results of tracking control in three-dimensional underwater environment are given, which illustrates that the proposed control strategy can not only meet the hardware requirements (drive saturation) but also achieve a stable and efficient tracking control performance because of its constraint to speed and speed increment, the effect of the ocean current on kinematics and dynamics models and the dual feedback mechanism.

Abstract Dynamical controls on terahertz (THz) wavefronts are crucial for many applications, but available mechanism requests tunable elements with sub‐micrometer sizes that are difficult to find in the THz regime. Here, different from the local‐tuning mechanism, we propose an alternative approach to construct wavefront‐control meta‐devices combining specifically designed metasurfaces and globally tuned graphene layers. Coupled‐mode‐theory (CMT) analyses reveal that graphene serves as a tunable loss to drive the whole meta‐device to transit from one functional phase to another passing through an intermediate regime, exhibiting distinct far‐field (FF) reflection wavefronts. As a proof of concept, we design/fabricate a graphene meta‐device and experimentally demonstrate that it can reflect normally incident THz wave to pre‐designed directions with different polarizations under appropriate gating voltages. We finally design a graphene meta‐device and numerically demonstrate that it can generate vectorial THz beams with continuously varying polarization distributions upon gating. These findings pave the road to realizing a wide range of THz applications, such as sensing, imaging, and wireless communications.

Abstract Transition metal carbides/nitrides (MXenes) show great potential for preparing wearable, flexible multifunctional e‐textiles due to the exceptional electrical and mechanical properties and easy processing in aqueous medium. At present, MXene‐based e‐textiles face challenges including high production costs, low utilization of precursor titanium aluminum carbide (MAX), poor durability in extreme environments, and the inability to achieve a balance between large‐scale fabrication and high performance. Here, this work proposes a “100%” utilization of MAX/MXene strategy to produce additive‐free conductive inks with controllable viscosity, subsequently enabling an accessible, scalable direct‐blade‐coating followed by chemical cross‐linking approach for creating wearable, high‐performance, multifunctional MXene‐based e‐textiles that perform in extreme conditions. The structural design provides integrated multifunctionality involving controllable and exceptional electromagnetic interference (EMI) shielding within an ultrabroadband frequency range, visual electrothermal conversion, electrothermal deicing, remarkable visual photothermal, and antibacterial performance. This work employs a fabrication process that is simple, cost‐effective, and scalable, presenting a novel “100% efficiency” and “waste‐to‐wealth” strategy to manufacture robust, durable, multifunctional e‐textiles. This approach provides exciting potential for the next generation of wearable electronics, EMI compatibility, visual heating, thermotherapy, antibacterial treatments, deicing, defense, and aerospace applications.

This study was a retrospective chart review performed to examine and describe physician practice patterns in managing attention deficit/hyperactivity disorder (ADHD) across Europe. Physicians treating ADHD in the UK, France, Germany, Italy, the Netherlands and Spain were recruited. Each physician abstracted medical records of five patients (aged 6-17 years at time of review) with a documented diagnosis of ADHD made between January 2004 and June 2007. Data provided by the physician via the abstraction included (a) physician characteristics, (b) patient characteristics, (c) ADHD diagnosis and (d) ADHD outcomes (adherence, symptom control and satisfaction). A total of 779 patients met study inclusion criteria. In the overall population, patients' mean (SD) age at time of diagnosis was 8.9 (2.6) years. The predominant treatment choice was long-acting methylphenidate, which was prescribed to more than 56 % of patients. According to physicians, only 30.8 % of patients showed 'complete symptom control' on current treatment and only 31.8 % of physicians reported being 'very satisfied' with their patients' current treatment. Physicians' assessments of complete symptom control and physician satisfaction with treatment were low, indicating unmet needs with current ADHD management in Europe.