Search

Recent years have witnessed the boom of the NBA, so there are increasingly more number of people watching and following it, no matter where they come from. And everyone has their own favourite team. Through access to information, it is widely known that the Los Angeles Clippers and the Brooklyn Nets are two favorites to win the game. Due to some reasons such as geographical reasons, “superstar effect” and others, a host of fans recognize that Clippers and Nets will meet in the finals. Additionally, they even believe that the brilliance of the game between the two teams can be beyond expectation. This work aimed to analyze and predict the winner. But there is an old saying, “There are a thousand Hamlets in a thousand people's eyes.” Thus, viewers are subjective to some degree. And most current papers use some machine learning algorithms to judge and predict the trend of the game. However, basketball game changes rapidly. Especially for such popular teams, the performance of the superstars largely determines whether the game can be won or not. This article presents some simpler and clearer aspects based on data visualization to analyze and predict the result. Finally, after analyzing the strength of the overall teams and the superstars, I draw a conclusion that Nets have a relatively large win rate.

The asymmetry of flow separation inside the nozzle with large expansion ratio is the main factor for the generation of side-load in the engine, so the accurate prediction of the flow separation is of great significance. The turbulence model is the key to the prediction, but in different scenarios, the performance of the turbulence model can introduce huge uncertainties. In this paper, a typical thrust-optimized nozzle (VOLVO-S1) is simulated and analyzed for three widely used turbulence models: SST k - ω 、 RNG k - ε and Spalart-Allmaras model. The results show that all these models can effectively predict the mode of shock separation phenomenon. The comprehensive performance of SST k - ω is the best; The Spalart-Allmaras model has better prediction performance under low pressure ratio, and the RNG k - ε model has better prediction effect under high pressure ratio.

With its strong floating-point operation capability and high memory bandwidth in data parallelism, the graphics processing unit (GPU) has been widely used in general-purpose computing. GPU-based computations have been extensively applied in the field of computational fluid dynamics (CFD). This paper aims to design an extremely efficient double-precision GPU-accelerated parallel algorithm for supersonic flow computations on hybrid grids. Compute unified device architecture (CUDA) is used as a general-purpose parallel computing platform and programming model to perform parallel computing codes on GPUs. The cell-centered finite volume method based on unstructured grids is used in the spatial discretization of governing equations, whereas the three-stage explicit Runge-Kutta scheme with second-order accuracy is used for temporal discretization. The turbulence is solved by using the $K-\omega$ SST two-equation model. Three test cases are studied to validate the computational accuracy of the proposed algorithm. The numerical results agree well with the experiment data, thereby suggesting that the GPU-accelerated parallel algorithm has good accuracy.

The main purpose of this paper is to develop a double-precision parallel algorithm implemented on graphics processing units (GPUs) for quick and accurate numerical simulations of large-scale supersonic transverse jet interaction problems. The finite volume method based on structured grid is considered; the AUSM + UP upwind scheme and the explicit multistage Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by K–ω SST two-equation model. Numerical investigation is performed for a supersonic missile body. Numerical results show that performing calculations on GPU can accurately capture the complex wave structures and vortex structures in the supersonic transverse jet flowfield. For single-GPU implementation, parallel computing can achieve an acceleration ratio of 90 times or more, and four GPU parallel computing can achieve an acceleration ratio of 106–245 times. Thus, GPU parallel computing can achieve a large-scale and efficient solution to supersonic transverse jet interaction problems.

Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the K−ω SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

The HLLEM approximate Riemann solver can capture discontinuities sharply, maintain positive definiteness, and satisfy the entropy condition automatically. These attractive properties make the HLLEM scheme widely used in the simulations of many compressible fluid problems. However, in the simulations of low Mach incompressible flow, the accuracy of HLLEM solver cannot be guaranteed. In the current study, a detailed discrete asymptotic analysis is conducted on the HLLEM scheme and the responsible terms for the loss of accuracy are identified. This allows us to develop two modified methods to solve this low Mach number problem. The first method is to add a low Mach number correction term on the basis of the original HLLEM scheme. The second is to simply rescale the responsible terms with a Mach number-based function. The asymptotic analysis of these two low Mach correction methods shows that the difference between the continuous system and the discrete system disappears, which means the resulting LM-HLLEM and LM-HLLEM2 schemes are both capable of obtaining physically correct solutions in low Mach limit. The results obtained from various test cases demonstrate that both these two HLLEM-type schemes can simulate incompressible and compressible fluid problems accurately and robustly.

With the advent of the era of big data, the number of global data centers increases rapidly. But at the same time, it also brings some conflicts of resources. Because electronics use a lot of energy to dissipate heat. In this paper, In order to select the most suitable materials for Seabed Data Center, we established a material screening model based on weight and entropy weight method, and finally obtained the optimal design scheme.

In this paper, continuous research is undertaken to explore the underlying mechanism of numerical shock instabilities of Godunov-type schemes for strong shocks. By conducting dissipation analysis of Godunov-type schemes and a sequence of numerical experiments, we are able to clarify that the instability may be attributed to insufficient entropy production inside the numerical shock structure. As a result, a general entropy-control technique for improving the robustness of various Godunov-type schemes at strong shocks is developed. It plays a part in guaranteeing that enough entropy is produced inside the numerical shock structure. Furthermore, such a modified approach does not introduce any additional numerical dissipation on linear degenerate waves to suppress the shock instability. Numerical results that are obtained for various test cases indicate that the proposed methods have a good performance in terms of accuracy and robustness.

Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization. Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for GPUs, which reduces the complexity of programming. The main purpose of this paper is to design an extremely efficient multi-GPU parallel algorithm based on MPI+CUDA to study the hypersonic flow characteristics. Solutions of hypersonic flow over an aerospace plane model are provided at different Mach numbers. The agreement between numerical computations and experimental measurements is favourable. Acceleration performance of the parallel platform is studied with single GPU, two GPUs, and four GPUs. For single GPU implementation, the speedup reaches 63 for the coarser mesh and 78 for the finest mesh. GPUs are better suited for compute-intensive tasks than traditional CPUs. For multi-GPU parallelization, the speedup of four GPUs reaches 77 for the coarser mesh and 147 for the finest mesh; this is far greater than the acceleration achieved by single GPU and two GPUs. It is prospective to apply the multi-GPU parallel algorithm to hypersonic flow computations.

Graphics Processing Units (GPU) has been widely used in the area of general computing. Nowadays, CFD has dramatically benefited from the strong abilities of floating-point operation and memory bandwidth of GPU architecture. Lots of GPU-based CFD solvers have already shown that GPU has capacities in accelerating numerical simulations. Compute unified device architecture (CUDA), a general parallel computing platform and programming model, reduces program complication, brings the great opportunities to CFD, and has been successfully used to the parallel solution of compressible Navier-Stokes equations. In this paper, we present a CPU/GPU heterogeneous parallel CFD solver which established on NVIDIA GTX 1070 GPU, and the serial code executes on the host, the parallel code performs on the device. Three optimization methods are discussed: maximize utilization, reducing global memory access and minimize data transfer between host and device, which further improve the performance of the solver. Two cases, including flow over sphere at Reynolds number 118 and flow over double ellipsoid, are presented to demonstrate the solver's capacities for compressible flow. Numerical results agree well with experimental data, which illustrate that the solver has a high computational precision for compressible flow.

A low diffusion flux splitting method capable of capturing crisp shock profile and exact contact surface is presented. Here, the flux vector of the Euler equations is split into convective and pressure parts following Toro–Vazquez formulation. The low diffusive property of the present scheme is brought about by adding an anti-diffusion term to the pressure parts. This numerical method can be regarded as an improved version of Toro and Vazquez’s flux splitting schemes (i.e. TV and TV-AWS) which are found to produce shock instabilities and carbuncle phenomena. Numerical results for several carefully chosen one- and two-dimensional test problems are investigated to demonstrate the accuracy and robustness of the proposed scheme.

The phenomenon "double-wave interference" appears when supersonic missile passing through shock wave. Here, a kind of supersonic missile’s aerodynamic characteristics was studied in the process of missile crossing carrier’s body shock wave. Specially, the characteristics of overload’s change was simulated and discussed. The results shows that, from being launched to fully crossing the carrier’s shock wave, the missile’s overload changes from maximum to minimum in three steps. Axial overload appears the largest value at the beginning of the missile crossing the shock. Normal overload’s biggest value appears when the carrier’s shock swept missile’s body.

Aeroheating of scramjet inlet cowl lip and sidewall leading edge is computed and analyzed. Three cases depending cowl lip position relative to forebody shock are investigated. The results are validated with experiments in reference, which shows that the inlet leading edge is always bearing more serious aerothermal challage than forebody nose. Computation showed that it is shock/shock interaction and compressing supersonic flow post forebody shock that increases cowl lip heat transfer for hypersonic vehicle with airbreathing scramjet. The same situation also appears in the sidewall leading edge. Further research is conducted for H=20-30km Ma=6 and 10 flow with different deflect angle and multistage compression. These conclusions must be considered in the thermal protection and structure design.