RECONSTRUCTION OF PART OF AN ACTUAL BLAST-WAVE FLOW FIELD TO AGREE WITH EXPERIMENTAL DATA BY USING NUMERICAL METHOD WITH HIGH IDENTIFICATION

In this paper, on the basis of experimental data of two kinds of chemical explosions, the piston-pushing model of spherical blast-waves and the second-order Godunov-type scheme of finite difference methods with high identification to discontinuity are used to the numerical reconstruction of part of an actual hemispherical blast-wave flow field by properly adjusting the moving bounary conditions of a piston. This method is simple and reliable. It is suitable to the evaluation of effects of the blast-wave flow field away from the explosion center.

Estimation of transition redshift based on Reinsch splines

Many schemes have been proposed to define a model-independent con- straint on cosmological dynamics, such as the nonparametric dark energy equation of state ω（z） or the deceleration parameter q（z）. These methods usually contain deriva- tives with respect to observational data with noise. However, there can be large un- certainties when one estimates values with numerical differentiation, especially when noise is significant. We introduce a global numerical differentiation method, first for- mulated by Reinsch, which is smoothed by cubic spline functions, and apply it to the estimation of the transition redshift zt with a simulated expansion rate E（z） based on observational Hubble parameter data. We also discuss some deficiencies and limita-tions of this method.

Evaluation of a Micro-scale Wind Model's Performance over Realistic Building Clusters Using Wind Tunnel Experiments

The simulation performance over complex building clusters of a wind simulation model（Wind Information Field Fast Analysis model, WIFFA） in a micro-scale air pollutant dispersion model system（Urban Microscale Air Pollution dispersion Simulation model, UMAPS） is evaluated using various wind tunnel experimental data including the CEDVAL（Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models） wind tunnel experiment data and the NJU-FZ experiment data（Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data）. The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier–Stokes equations, and can produce very high-resolution（1–5 m） wind fields of a complex neighborhood scale urban building canopy（～ 1 km ×1km） in less than 3 min when run on a personal computer.

Performance analysis of parallel gravitational N-body codes on large GPU clusters

We compare the performance of two very different parallel gravitational N-body codes for astrophysical simulations on large Graphics Processing Unit（GPU） clusters, both of which are pioneers in their own fields as well as on certain mutual scales- NBODY6＋＋ and Bonsai. We carry out benchmarks of the two codes by analyzing their performance, accuracy and efficiency through the modeling of structure decomposition and timing measurements. We find that both codes are heavily optimized to leverage the computational potential of GPUs as their performance has approached half of the maximum single precision performance of the underlying GPU cards. With such performance we predict that a speed-up of200- 300 can be achieved when up to 1k processors and GPUs are employed simultaneously. We discuss the quantitative information about comparisons of the two codes, finding that in the same cases Bonsai adopts larger time steps as well as larger relative energy errors than NBODY6＋＋, typically ranging from10- 50 times larger, depending on the chosen parameters of the codes. Although the two codes are built for different astrophysical applications, in specified conditions they may overlap in performance at certain physical scales, thus allowing the user to choose either one by fine-tuning parameters accordingly.

Fast compression and reconstruction of astronomical images based on compressed sensing

With the fast increase in the resolution of astronomical images, the question of how to process and transfer such large images has become a key issue in astronomy. We propose a new real-time compression and fast reconstruction algorithm for astronomical images based on compressive sensing techniques. We first reconstruct tile Original signal with fewer measurements, according to its compressibility. Then, based on the characteristics of astronomical images, we apply Daubechies orthogonal wavelets to obtain a sparse representation. A matrix representing a random Fourier ensembleis used to obtain a sparse representation in a lower dimensional space. For reconstructing the image, we propose a novel minimum total variation with block addptive sensing to balance the accuracy and eomputation time. Our experimental results show that the proposed algorithm can efficiently reconstruct colorful astronomicai images with high resolution and improve the applicability of compressed sensing.

A novel pure data-selection framework for day-ahead wind power forecasting

Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

Developments of the Three-Dimensional Variational Data Assimilation System for the Nonhydrostatic GRAPES

Based on the original GRAPES（Global/Regional Assimilation and PrEdiction System）3DVAR（p3DAR）, which is defined on isobaric surface,a new three-dimensional variational data assimilation system（m3DVAR） is constructed and used exclusively with the nonhydrostatic GRAPES model in order to reduce the errors caused by spatial interpolation and variable transformation,and to improve the quality of the initial value for operational weather forecasts.Analytical variables of the m3DVAR are fully consistent with predictands of the GRADES model in terms of spatial staggering and physical definition.A different vertical coordinate and the nonhydrostatic condition are taken into account,and a new scheme for solving the dynamical constraint equations is designed for the m3DVAR.To deal with the diffculties in solving the nonlinear balance equation atσlevels,dynamical balance constraints between mass and wind fields are reformulated,and an effective mathematical scheme is implemented under the terrain-following coordinate.Meanwhile,new observation operators are developed for routine observational data,and the background error covariance is also obtained.Currently,the m3DVAR system can assimilate all routine observational data. Multi-variable idealized experiments with single point observations are performed to validate the m3DVAR system.The results show that the system can describe correctly the multi-variable analysis and the relationship of the physical constraints.The difference of innovation and the analysis residual forπalso show that the analysis error of the m3DVAR is smaller than that of the p3DVAR.The T s scores of precipitation forecasts in August 2006 indicate that the m3DVAR system provides reduced errors in the model initial value than the p3DVAR system.Therefore,the m3DVAR system can improve the analysis quality and initial value for numerical weather predictions.