Direct numerical simulation of a tip-leakage flow in a planar duct with a longitudinal slit

A planar duct flow configuration with a cross-flow injected from a longitudinal slit close to the upper wall of the duct is studied by using a direct numerical simulation approach to explore the underlying flow mechanism in relation to the tip-leakage vortex (TLV), which is one of the most important flow phenomena in turbomachinery. Major characteristics of TLV in a rotor of turbomachinery are identified in the current flow model. The analysis of mean and instantaneous flow fields reveals that the interaction between the main (axial) flow and jet (cross) flow is the primary source of the generation of the TLV. The evolution of the TLV is then investigated, and a vortex breakup phenomenon is identified. The evolution of TLV can be divided into three phases, i.e. the formation phase, the break-up phase, and the diffusion phase. Mean streamlines and turbulence kinetic energy (TKE) budgets are analysed, showing that the high TKE central spot in the formation phase is due to the interaction between highly swirling vortex filaments and mean velocity gradient. In the outer part of the TLV, the TKE is mainly produced in the shear-layer and transported towards the centre by the turbulence transport

A Novel High-performance Implementation of CRYSTALS-Kyber with AI Accelerator

Public-key cryptography, including conventional cryptosystems and post-quantum cryptography, involves computation-intensive workloads. With noticing the extraordinary computing power of AI accelerators, in this paper, we further explore the feasibility to introduce AI accelerators into high-performance cryptographic computing. Since AI accelerators are dedicated to machine learning or neural networks, the biggest challenge is how to transform cryptographic workloads into their operations, while ensuring the correctness of the results and bringing convincing performance gains. After investigating and analysing the workload of NVIDIA AI accelerator, Tensor Core, we choose to utilize it to accelerate the polynomial multiplication, usually the most time-consuming part in lattice-based cryptography. We take measures to accommodate the matrix-multiply-and-add mode of Tensor Core and make a trade-off between precision and performance, to leverage it as a high-performance NTT box performing NTT/INTT through CUDA C++ WMMA APIs. Meanwhile, we take CRYSTALS-Kyber, the candidate to be standardized by NIST, as a case study on RTX 3080 with the Ampere Tensor Core. The empirical results show that the customized NTT of polynomial vector ($n=256,k=4$) with our NTT box obtains a speedup around 6.47x that of the state-of-the-art implementation on the same GPU platform. Compared with the AVX2 implementation submitted to NIST, our Kyber-1024 can achieve a speedup of 26x, 36x, and 35x for each phase

Scour protection of submarine pipelines using rubber plates underneath the pipes

YesThis paper presents the results from laboratory experiments to investigate the protection of scour around submarine pipelines under unidirectional flow using a rubber plate placed underneath the pipes. The pressure difference on the two sides of the pipeline is the driving force to initiate the movement of sediment particles and can be obtained by force balance analysis. Experiments covering a wide range of incoming flow velocity, pipe diameter and plate length show that there exists a critical pressure difference over which the movement of sediment and, thus, scour takes place. Analysis of the experimental results demonstrates that this critical pressure difference is related to the pressure difference of the axial points between upstream and downstream of the pipe, which can be easily determined. This critical pressure difference is used to develop an empirical formula for estimating the critical length of the rubber plate, over which the sediment movement and scour will not take place. Good agreement between the experiments and calculated critical plate length using the proposed formula is obtained.National High-Tech Research and Development program of China (863 Program, Grant No.2008AA09Z309), National Nature Science Fund of China (Grant No.50879084, 51279071 and 51279189), the Open Funding from the State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University (SKLH-OF-1306

Investigation of low-dissipation monotonicity-preserving scheme for direct numerical simulation of compressible turbulent flows

© 2014 Elsevier Ltd. The influence of numerical dissipation on direct numerical simulation (DNS) of decaying isotropic turbulence and turbulent channel flow is investigated respectively by using the seventh-order low-dissipation monotonicity-preserving (MP7-LD) scheme with different levels of bandwidth dissipation. It is found that for both benchmark test cases, small-scale turbulence fluctuations can be largely suppressed by high level of scheme dissipation, while the appearance of numerical errors in terms of high-frequency oscillations could destabilize the computation if the dissipation is reduced to a very low level. Numerical studies show that reducing the bandwidth dissipation to 70% of the conventional seventh-order upwind scheme can maximize the efficiency of the MP7-LD scheme in resolving small-scale turbulence fluctuations and, in the meantime preventing the accumulation of non-physical numerical errors. By using the optimized MP7-LD scheme, DNS of an impinging oblique shock-wave interacting with a spatially-developing turbulent boundary layer is conducted at an incoming free-stream Mach number of 2.25 and the shock angle of 33.2°. Simulation results of mean velocity profiles, wall surface pressure, skin friction and Reynolds stresses are validated against available experimental data and other DNS predictions in both the undisturbed equilibrium boundary layer region and the interaction zone, and good agreements are achieved. The turbulence kinetic energy transport equation is also analyzed and the balance of the equation is well preserved in the interaction region. This study demonstrates the capability of the optimized MP7-LD scheme for DNS of complex flow problems of wall-bounded turbulent flow interacting with shock-waves

The Thermal Infrared Visual Object Tracking VOT-TIR2015 challenge results

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link - ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series

A Rapid Accurate Recognition System for Radar Emitter Signals

Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1−P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals

Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm

For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode