PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The Use of the CUDA Architecture to Increase the Computing Effectiveness of the Simulation Module of a Ceramic Mould Quality Forecasting System

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents practical capabilities of a system for ceramic mould quality forecasting implemented in an industrial plant (foundry). The main assumption of the developed solution is the possibility of eliminating a faulty mould from a production line just before the casting operation. It allows relative savings to be achieved, and faulty moulds, and thus faulty castings occurrence in the production cycle to be minimized. The numerical computing module (the DEFFEM 3D package), based on the smoothed particle hydrodynamics (SPH) is one of key solutions of the system implemented. Due to very long computing times, the developed numerical module cannot be effectively used to carry out multi-variant simulations of mould filling and solidification of castings. To utilize the benefits from application of the CUDA architecture to improve the computing effectiveness, the most time consuming procedure of looking for neighbours was parallelized (cell-linked list method). The study is complemented by examples of results of performance tests and their analysis.
Rocznik
Strony
5--12
Opis fizyczny
Bibliogr. 15 poz., rys., tab., wykr.
Twórcy
autor
  • AGH - University of Science and Technology, Department of Applied Computer Science and Modeling, Kraków
autor
  • AGH - University of Science and Technology, Department of Plastic Processing and Metallurgy of Non-Ferrous Metals, Kraków
  • AGH - University of Science and Technology, Department of Applied Computer Science and Modeling, Kraków
autor
  • AGH - University of Science and Technology, Department of Applied Computer Science and Modeling, Kraków
Bibliografia
  • [1] Atlas of Wax Pattern Defects. Investment Casting Institute, 2004.
  • [2] Atlas of Shell Defects. Montvale, Investment Casting Institute, 2004.
  • [3] Monaghan, J.J. (1992). Smoothed particle hydrodynamics. Annual Review of Astronomy and Astrophysics. 30, 543-574.
  • [4] Monaghan, J.J. (2005). Smoothed particle hydrodynamics. Reports on Progress in Physics. 68, 1703-1759.
  • [5] Cleary, P.W., Ha, J., Prakash, M. & Nguyen, T. (2006). 3D SPH flow predictions and validation for high pressure die casting of automotive component. Applied Mathematical Modelling. 30(11), 1406-1427.
  • [6] Abdelrahman, A., Fouad, M. (2017). High performance cuda aes implementation: a quantitative performance analysis approach, Computing Conference, July, 18-20 London, UK.
  • [7] Manavski, S.A. (2007). CUDA compatible GPU as an efficient hardware accelerator for aes cryptography, IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 24-27 November, Dubai, United Arab Emirates.
  • [8] Kalaiselvi, T., Sriramakrishnan, P. & Somasundaram, K. Survey of using GPU CUDA programming model in medical image analysis. Informatics in Medicine. 9, 133-144.
  • [9] Shi, L., Liu, W., Zhang, H., Xie, Y. & Wang, D. (2012). A survey of GPU-based medical image computing techniques. Quant Imaging Med Surg. 2(3), 188-206.
  • [10] Lee, H., Kim, B., Lee, K., Jung, H. (2016). Acceleration of computational fluid dynamics analysis by using multiple GPUs, Int'l Conf. Bioinformatics and Computational Biology. BIOCOMP'16, CSREA Press.
  • [11] Brandvik, T., Pullan, G. (2008). Acceleration of a 3D Euler solver using commodity graphics hardware,46th AIAA Aerospace Sciences Meeting.
  • [12] Huqqania, A.A.., Schikutaa, E., Yea, S., Chena, P. (2013). Multicore and GPU parallelization of neural networks for face recognition, Elsevier, International Conference on Computational Science, ICCS 2013.
  • [13] Uetz, R., Behnke, S. (2009). Large-scale object recognition with CUDA-accelerated hierarchical neural networks, Proceedings of the 1st IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009).
  • [14] Akbariyeh, A. (2012). Large scale finite element analysis using gpu parallel computing, PhD Thesis.
  • [15] Hojny, M. (2018). Modeling of Steel Deformation in the Semi-Solid State, Switzerland.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9317526b-4440-40ef-9802-b0dd1e3d778b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.