A hybrid stabilization technique for simulating water wave - Structure interaction by incompressible Smoothed Particle Hydrodynamics (ISPH) method

The Smoothed Particle Hydrodynamics (SPH) method is emerging as a potential tool for studying water wave related problems, especially for violent free surface flow and large deformation problems. The incompressible SPH (ISPH) computations have been found not to be able to maintain the stability in certain situations and there exist some spurious oscillations in the pressure time history, which is similar to the weakly compressible SPH (WCSPH). One main cause of this problem is related to the non-uniform and clustered distribution of the moving particles. In order to improve the model performance, the paper proposed an efficient hybrid numerical technique aiming to correct the ill particle distributions. The correction approach is realized through the combination of particle shifting and pressure gradient improvement. The advantages of the proposed hybrid technique in improving ISPH calculations are demonstrated through several applications that include solitary wave impact on a slope or overtopping a seawall, and regular wave slamming on the subface of open-piled structure

An improved solid boundary treatment for wave-float interactions using ISPH method

The Smoothed Particle Hydrodynamics (SPH) method has proved to have great potentials in dealing with the wave-structure interactions. Compared with the Weakly Compressible SPH (WCSPH) method, the ISPH approach solves the pressure by using the pressure Poisson equation rather than the equation of state. This could provide a more stable and accurate pressure field that is important in the study of wave-structure interactions. This paper improves the solid boundary treatment of ISPH by using a high accuracy Simplified Finite Difference Interpolation (SFDI) scheme for the 2D wave-structure coupling problems, especially for free-moving structure. The proposed method is referred as the ISPH_BS. The model improvement is demonstrated by the documented benchmark tests and laboratory experiment covering various wave-structure interaction applications

Thermodynamic analysis of BN, AlN AND TiN Precipitation in boron-bearing steel

In this paper, the precipitation behavior of BN, AlN and TiN particles in boron-bearing steel was studied based on thermodynamic calculation. During solidification process, precipitation amount of BN has a positive relationship with boron content, while has negative relationship with temperature. The binding capacity of boron and nitrogen is greater than that of aluminum and nitrogen, BN is preferentially precipitated as boron added to steel. BN particle reduces the free nitrogen content in steel and then prevents the formation of AlN particle. Combination of titanium and nitrogen element is more precedence than of boron and nitrogen element. Formation of TiN particle precedes BN particle, and the precipitation amount of BN is significantly reduced by adding titanium element to boronbearing

Thermodynamic analysis of BN, AlN AND TiN Precipitation in boron-bearing steel

In this paper, the precipitation behavior of BN, AlN and TiN particles in boron-bearing steel was studied based on thermodynamic calculation. During solidification process, precipitation amount of BN has a positive relationship with boron content, while has negative relationship with temperature. The binding capacity of boron and nitrogen is greater than that of aluminum and nitrogen, BN is preferentially precipitated as boron added to steel. BN particle reduces the free nitrogen content in steel and then prevents the formation of AlN particle. Combination of titanium and nitrogen element is more precedence than of boron and nitrogen element. Formation of TiN particle precedes BN particle, and the precipitation amount of BN is significantly reduced by adding titanium element to boronbearing

Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DF Relaying Systems

Intelligent reflecting surface (IRS) has emerged as a promising and low-cost technology for improving wireless communications by collecting dispersed radio waves and redirecting them to the intended receivers. In this letter, we characterize the achievable rate when multiple IRSs are utilized in the manner of decode-and-forward (DF) relaying. Our performance analysis is based on the Nakagami-m fading model with perfect channel state information (CSI). Tight upper bound expressions for the ergodic rate are derived. Moreover, we compare the performance of the multi-IRS DF relaying system with that of the one with a single IRS and confirm the gain. We then optimize the IRS configuration considering the numbers of IRSs and IRS reflecting elements, which provides useful insights for practical design

Mobility-aware multi-user offloading optimization for Mobile Edge Computing

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and resource allocation among multiple users served by one base station to achieve the optimal system-wide user utility, which is defined as a trade-off between task latency and energy consumption. Mobility in the process of task offloading is considered in the optimization. We prove that the problem is NP-hard and propose a heuristic mobility-aware offloading algorithm (HMAOA) to obtain the approximate optimal offloading scheme. The original global optimization problem is converted into multiple local optimization problems. Each local optimization problem is then decomposed into two subproblems: a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading users, and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision. The proposed HMAOA is with polynomial complexity. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate its excellent performance

Genetic variation of Pit-1 gene in Chinese indigenous and Western goose populations

Pituitary-specific transcription factor (Pit-1, or GHF1, or POU1F1) is expressed in the pituitary gland; it regulates pituitary development and expression of the growth hormone, prolactin and thyrotropin -submit genes. Pit-1 gene has been regarded as a candidate gene for production performance. The genetic variation of Pit-1 gene was investigated in five Chinese indigenous goose populations and oneWestern goose population by PCR-SSCP. In this study, the sequences of goose Pit-1 gene were identified with duck sequence; three SNPs detected were A57G in the intron, G161A and T282G were in the exon, and T282G changed the amino acid from Cys to Trp. A57G and G161A appeared only in the Western population Landoise goose. The genotypes distribution showed significant differences between different types of population

Deep Reinforcement Learning-Based Offloading Scheduling for Vehicular Edge Computing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordVehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VT) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this paper, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a trade-off between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.European Commissio

Alfvenic Ion Temperature Gradient Activities in a Weak Magnetic Shear Plasma

We report the first experimental evidence of Alfvenic ion temperature gradient (AITG) modes in HL-2A Ohmic plasmas. A group of oscillations with $f=15-40$ kHz and $n=3-6$ is detected by various diagnostics in high-density Ohmic regimes. They appear in the plasmas with peaked density profiles and weak magnetic shear, which indicates that corresponding instabilities are excited by pressure gradients. The time trace of the fluctuation spectrogram can be either a frequency staircase, with different modes excited at different times or multiple modes may simultaneously coexist. Theoretical analyses by the extended generalized fishbone-like dispersion relation (GFLDR-E) reveal that mode frequencies scale with ion diamagnetic drift frequency and $\eta_i$, and they lie in KBM-AITG-BAE frequency ranges. AITG modes are most unstable when the magnetic shear is small in low pressure gradient regions. Numerical solutions of the AITG/KBM equation also illuminate why AITG modes can be unstable for weak shear and low pressure gradients. It is worth emphasizing that these instabilities may be linked to the internal transport barrier (ITB) and H-mode pedestal physics for weak magnetic shear.Comment: 9 pages, 7 figure