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Zhang H.,Changan University | Yan H.,Aeronautics Computing Technique Research Institute
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | Year: 2013

The accuracy of the drag prediction was investigated by simulating the transonic flow fields around the DLR-F6 wing-body (WB) and wing-body-nacelle-pylon (WBNP) configurations. The computations were performed using fully turbulence boundary-layer and fixed position transition boundary-layer respectively. Multiple sets of grids with different densities were then employed. The drag, drag increments by adding the nacelle and the pylon and the effects of grid and transition were also estimated. The results show that grid refinement leads to convergent results for two configurations, and the predicted surface pressure distributions on the wing and nacelle are in agreement with the experimental data. When comparing the experiment data, the predicted incremental drag was over estimated by about 3 drag counts, 0.000 3, but better than the results obtained by using other software. The computed results show that grid refinement had little effect on the wall surface pressure distributions, but obvious effect on drag, especially the pressure drag. Relative to the whole turbulence model, transition had obvious effect on drag, particularly on friction drag, but almost no effect on nacelle/pylon induced incremental drag. Source

Zheng Q.-Y.,Xidian University | Zheng Q.-Y.,Changan University | Liu S.-Y.,Xidian University | Liang Y.-H.,Aeronautics Computing Technique Research Institute
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | Year: 2010

The DLR-F4 wing-body model is a simplified geometric simulation of current subsonic commercial aircraft intended to help evaluate simulations predicting aircraft drag. The accuracy of computed drag around it was investigated, and in particular, the effects of grid and turbulence models were analyzed. This was done by solving the Reynolds-averaged Navier-Stokes equations (RANS) coupled with the Spalart-Allmaras and Baldwin-Lomax turbulence models. A high quality multi-block structured patched grid around the wing-body configuration was generated using the hypercube concept. Mesh refinement was performed to investigate the effects of the grid's density. The results showed that computed lift was slightly affected by turbulence models and the grid's density. Computed drag was significantly affected by both turbulence models and the grid's density. The pressure coefficient distribution was slightly affected by the grid's density. This research showed that the accuracy of computed drag can be improved by decreasing the grid interval between the object plane and the first-layer mesh as well as by properly increasing the density of the grids. Source

Sun B.,Northwestern Polytechnical University | Dong Y.,Northwestern Polytechnical University | Ye H.,Aeronautics Computing Technique Research Institute
Proceedings - IEEE 9th International Conference on Ubiquitous Intelligence and Computing and IEEE 9th International Conference on Autonomic and Trusted Computing, UIC-ATC 2012 | Year: 2012

As the development of the large-scale and complicated software, especially in embedded system, nonfunctional properties of system, such as timing, reliability, safety and security, have become more and more important on impacting and restricting the behaviors of software system. One of the emerging challenges is how to test these properties in the phase of model-based software design. This paper aims to solve two essential problems in model-based testing: i) how to test model dynamically; ii) how to improve the efficiency of model-based testing. An enhancing adaptive random testing is investigated to generate test cases for AADL model-based testing in order to guarantee the system architecture and computing trustworthy. This methodology makes up the deficiency of adaptive random testing in dealing with the nonnumeric data. A case study is presented and illustrates that its efficiency is higher than traditional random testing. © 2012 IEEE. Source

Cui X.,Xidian University | Cui X.,Aeronautics Computing Technique Research Institute | Yang J.,Xidian University
Proceedings - 2012 International Conference on Computer Science and Information Processing, CSIP 2012 | Year: 2012

As the networks have been broadly used everywhere such as national defense, military, bank and so on, security of data transported on network has become a hot issue. Public key cryptographic algorithms are widely applied in network communication. RSA has been used for a long time as a traditional public key cryptographic system, but it seems not able to meet user's higher security demands. In recent years, ECC(Elliptic Curve Cryptography) has been adopted more and more broadly because of its highest security of the same length bit. In addition, it also has the advantage of less computation overheads, less bandwidth demand and so on. The speed of encryption and decryption of ECC is greatly affected by point multiplication, which is very time-consuming. In this study, an FPGA(Field Programmable Gate Array) based processor is implemented for ECC, which parallelizes the computing of ECC at bit-level and gains a considerable speed-up. The ECC processor is fully implemented with hardware which supports key length of 113-bit, 163-bit and 193-bit. Algorithms suitable for hardware implementation are applied to make the processor more efficient. There are four kinds of unit in the processor: arithmetic logic unit, controlling unit, and input/output system. The units communicate with each other thought bus in FPGA device. © 2012 IEEE. Source

Lu C.,Beihang University | Lu C.,Science and Technology on Reliability and Environmental Engineering Laboratory | Yuan H.,Beihang University | Yuan H.,Science and Technology on Reliability and Environmental Engineering Laboratory | Tang Y.,Aeronautics Computing Technique Research Institute
Journal of Vibroengineering | Year: 2014

Bearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns. © JVE INTERNATIONAL LTD. Source

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