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SVIP-N 1.0: An integrated visualization platform for neutronics analysis
Fusion Engineering and Design 85 (2010) 1527–1530
Contents lists available at ScienceDirect
Fusion Engineering and Design journal homepage: www.elsevier.com/locate/fusengdes
SVIP-N 1.0: An integrated visualization platform for neutronics analysis Yuetong Luo a,b,c,∗ , Pengcheng Long b,c , Guoyong Wu a , Qin Zeng b,c , Liqin Hu b,c , Jun Zou b,c a
School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui 230031, China c School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China b
a r t i c l e
i n f o
Article history: Available online 4 May 2010 Keywords: Visualization Neutronics analysis Mixed-rendering Volume clipping
a b s t r a c t Post-processing is an important part of neutronics analysis, and SVIP-N 1.0 (scientific visualization integrated platform for neutronics analysis) is designed to ease post-processing of neutronics analysis through visualization technologies. Main capabilities of SVIP-N 1.0 include: (1) ability of manage neutronics analysis result; (2) ability to preprocess neutronics analysis result; (3) ability to visualization neutronics analysis result data in different way. The paper describes the system architecture and main features of SVIP-N, some advanced visualization used in SVIP-N 1.0 and some preliminary applications, such as ITER. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Neutronics analysis is the foundation of reactor physics design, radiation shield analysis, fuel management optimization and nuclear safety analysis etc. Because neutronics analysis tasks are becoming more and more complex, and development of HPC (highperformance computing) makes it possible to perform more and more accurate neutronics analysis, the result data of neutronics analysis codes are becoming huger and huger (in the remained part of the paper, the result data of neutronics analysis codes is called as neutronics analysis data). Huger data contains more information and knowledge, but it also makes data analysis and processing more difficult, so it is necessary to find efficient and effective tools for huge data processing and analysis. Visualization converts data into images and/or animation to take advantages of human visual system, which can process image/animation quickly in parallel way. Visualization has been regarded as one of essential tools for huge data processing and analysis. Since scientific visualization be first presented in 1987 [1], many wonderful visualization systems such as AVS, IRIS Explorer, and VolView have been developed; and various visualization systems have been applied in many disciplines such as medical, Meteorology, CFD and Molecular Modeling. Although visualization has gotten wonderful achievements, there are still many challenges, one of them is how to cooperate with application domain for further development [2]. The report [2] also points out that cooperation between visualization and application domain is the trend of visualization.
∗ Corresponding author. Tel.: +86 551 2901377; fax: +86 551 5591397. E-mail address: hfl[email protected] (Y. Luo). 0920-3796/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fusengdes.2010.04.016
SVIP-N 1.0 is the result of close cooperation between visualization and neutronics analysis; it is developed by FDS team. FDS team is an inter-disciplines team with people from both nuclear physic science and computer science, which works on fusion reactor design [3], neutronics analysis [4,5] and development of related software [6,7]. To be an integrated environment for neutronics data analysis, besides the function of neutronics analysis data visualization, SVIP-N 1.0 also supports neutronics analysis data management and preprocessing, because all of them are necessary functions of neutronics analysis data processing. 2. Main functions and architecture of SVIP-N SVIP-N 1.0 takes ACIS [8] as 3D geometry engine and VTK [9] as visualization toolkits, and is developed in Visual C++. SVIP-N’s components architecture is shown in Fig. 1. Currently, SVIP-N provides the following three main functions. 2.1. Neutronics analysis data management For visualization software always involves many data, convenient data management is necessary for visualization software. For both geometry model and neutronics analysis data are involved in SVIP-N, and both geometry model and neutronics analysis data can be in various formats, data management is necessary and complex. The goal of SVIP-N’s data management module is to support most related data directly. For geometry model, SVIP-N 1.0 supports most neutral formats, such as sat, WRL, STEP; for neutronics analysis data, besides neutral formats such as RAW and VTI are supported, SVIP-N can import some neutronics analysis codes’ output
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Fig. 1. Components architecture of SVIP-N.
file directly. SVIP-N 1.0 can read output file of TORT and MCNP directly. 2.2. Neutronics analysis data preprocessing In most case, original neutronics analysis data cannot be visualized directly, there are two main possible reasons: (1) The data to be visualized does not exist in the original data, preprocessing step is needed to create the data from original data according to some rules. For example, we often combine some fine group into a coarse-group for visualization. (2) It is because of limitations of some visualization technologies, one typical example is DVR-based visualization, which require the input data to be volume data, but most neutronics analysis data is not volume data, so preprocessing function is needed to convert neutronics data into volume data. Data preprocessing is an integral part of data visualization, so it is reasonable and necessary to integrate data preprocessing function into data visualization platform. According to features of neutronics analysis data, SVIP-N 1.0 implements the following neutronics analysis data preprocessing functions: such as flux group extraction, flux group collapse, flux normalization and flux dataset regularization.
Fig. 2. Flowchart of mixed-rendering of geometry model and neutronics analysis data.
Based on “depth-peeling” method, mixed-rendering of geometry model and neutronics analysis data is presented [5], whose pipeline is shown in Fig. 2. SVIP-N 1.0 implements mixed-rendering method by taking advantages of modern programmable GPU (graphic process unit). For more detail of the method please refer to [10]. 3.2. Geometry model based neutronics analysis data clipping When visualize neutronics analysis data, user may only concern the data inside some components such as vacuum vessel, but it is very difficult for DVR to only display the data inside the given components. The function to keep selected part of neutronics data is referred as neutronics analysis data clipping. For SVIP-N specifies the selected part through geometry model of components, we call the method as geometry model based neutronics analysis data clipping.
2.3. Neutronics analysis data visualization Visualization is SVIP-N’s core function. Because neutronics analysis data is in essence volume data, SVIP-N has implements most popular volume data visualization methods: slice-based visualization, iso-surface based visualization and DVR (direct volume rendering) based visualization. DVR is one of the most important visualization technologies, but naive DVR cannot satisfy some special requirements of SVIPN. Based on DVR method, two advanced visualization technologies are presented according to the requirements of neutronics analysis data visualization. 3. Advanced visualization technologies Two advanced visualization technologies are to be introduced in Sections 3.1 and 3.2, respectively. 3.1. Mixed-rendering of geometry model and neutronics analysis data During neutronics analysis data analysis, geometry model is very helpful, so it is very useful to display neutronics analysis data and the corresponding geometry in the same scene. DVR is an important neutronics analysis data visualization method for it can reflect the whole volume data without concentrating on certain features of interest (e.g., iso-surface), but it cannot embedded geometry model information into its result image.
Fig. 3. Flowchart of geometry model based neutronics analysis data clipping.
Y. Luo et al. / Fusion Engineering and Design 85 (2010) 1527–1530
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Fig. 6. Iso-surface based visualization.
4. Preliminary application of SVIP-N 1.0
Fig. 4. ITER benchmark model.
The method’s pipeline is shown in Fig. 3, it converts given components’ geometry model into “clipping texture” firstly, and then the reconstructive DVR (or mixed-rendering method) realize neutronics analysis data clipping through customized fragment shader program, which decides whether remain an fragment through clipping texture fetching. For more detail please refer to [11].
SVIP-N 1.0 aims to ease post-processing of neutronics analysis data, which works under MS Windows XP/NT/2000/2003 operating systems. It could be experienced under an agreement with FDS team. The common SVIP-N 1.0 based post-processing process includes the following steps. (1) Data importing: Import one or more neutronics analysis data into SVIP-N, and corresponding geometry model can also be imported into SVIP-N, but it is not necessary. Now, output file of TORT and MCNP can be imported into SVIP-N directly. (2) Data preprocessing: According to post-processing task, one or more data preprocessing operation to be applied to neutronics analysis data. (3) Data visualization: Choose suitable visualization method to visualize data according to user’s task. Step 2 and Step 3 are iterative process.
Fig. 5. SN geometry model.
Fig. 7. DVR-based visualization.
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neutronics analysis data preprocessing and neutronics analysis data visualization into an integrated environment. Besides provide common visualization method such as slice-based visualization; iso-surface-based visualization and DVR-based visualization, it presents and implemented two advanced visualization methods according to special requirement of SVIP-N 1.0 by taking advantages of modern GPU. ITER benchmark model has been used to test SVIP-N, the result shows SVIP-N 1.0 is helpful for processing neutronics analysis data. For data management, besides TORT output file of TORT and MCNP, more other neutronics analysis codes’ output files are to be supported directly; and user interface to be optimized according to user’s feedback. References
Fig. 8. Mixed-rendering based visualization.
Following above process, preliminary application to ITER has been performed on a commodity PC equipped with CPU of CORE 2 DUO 1.86 GHz, GPU of Geforce 7600 and 2G memory. ITER is a joint international research and development project that aims to demonstrate the scientific and technical feasibility of fusion power. ITER benchmark model [12], as shown in Fig. 4 is provided by ITER international team to evaluate the CAD/MCNP programs [13] being developed by the ITER participant teams. SNAM 2.1 [14] is used to convert ITER benchmark CAD model into SN model with 408 × 8 × 570 calculation grid (as shown in Fig. 5), and VisualBUS [15] is used to perform neutronics analysis calculation, and the output data is of 5.06G, which consists of 175 groups of neutron flux density and 42 groups of photon flux density. SVIP-N is used to process 5.06G output data, The snapshots of iso-surface based visualization, DVR-based visualization and mixed-rendering based visualization are shown in Figs. 6–8 respectively. 5. Conclusions and future work SVIP-N 1.0 is an integrated platform for neutronics analysis data visualization; it integrates neutronics analysis data management,
[1] B.H. McCormick, T.A. DeFanti, M.D. Brown, Visualization in scientific computing, Computer Graphics 21 (6) (1987) 1–14. [2] C. Johnson, R. Moorhead, T. Munzner, et al., NIH/NSF: Visualization Research Challenges 2006, IEEE Press, 2006. [3] Y. Wu, FDS Team, Conceptual design activities of FDS series fusion power plants in China, Fusion Engineering and Design 81 (23–24) (2006) 2713–2718. [4] Yican Wu, Zhongsheng Xie, Ulrich Fischer Nuclear. A discrete ordinates nodal method for one-dimensional neutron transport numerical calculation in curvilinear geometries, Science and Engineering 133 (1999) 350–357. [5] S. Zheng, M. Chen, J. Li, Q. Zeng, L. Lu, Y. Li, A. Ding, H. Hu, Neutronics analysis for the test blanket modules proposed for EAST and ITER, Nuclear Fusion 47 (8) (2007) 1053–1056. [6] Y. Wu, FDS Team, CAD-based interface programs for fusion neutron transport simulation, Fusion Engineering and Design 84 (2009) 1987–1992. [7] P. Long, et al., Development and application of SN Auto-Modeling Tool SNAM 2.1, Fusion Engineering and Design 85 (2010) 1113–1116. [8] J. Comey, T. Lim, 3D Modeling with ACIS [M], Saxe-coburg Publications, Stirling, UK, 2001. [9] W. Schroeder, K. Martin, B. Lorensen, The Visualization Toolkit: An Object Oriented Approach to 3D Graphics [M], 3rd edition, Kitware, Inc. Publisher, New York, 2003. [10] Y.T. Luo, Y. Xue, J. Sun, “Depth peeling” based transparency rendering and its application [J], Chinese Journal of Scientific Instrument 28 (8) (2007) 596–599 (in Chinese). [11] Y.T. Luo, G.Y. Wu, P.C. Long, Multi-resolution clipping texture based volume clipping technology [C], in: Proceedings of Progress of Computer Technology and Application 2009, University of Science and Technology of China Press, Hefei, 2009, 603–666 (in Chinese). [12] ITER ITA Task Agreement—Testing and Application of a CAD/MCNP Interface Programme for ITER Neutronics Design Calculations, ITA-73-08-CN, 2005. [13] Y. Li, L. Lu, A. Ding, H. Hu, Q. Zeng, S. Zheng, Y. Wu, Benchmarking of MCAM 4.0 with the ITER 3D model, Fusion Engineering and Design 82 (2007) 2861–2866. [14] H. Hu, Y. Wu, M. Chen, Q. Zeng, A. Ding, S. Zheng, et al., Benchmarking of SNAM with the ITER 3D model, Fusion Engineering and Design 82 (2007) 2867–2871. [15] Y.C. Wu, J.J. Li, M.L. Chen, et al., An integrated Multi-functional neutronics calculation and analysis code system: VisualBUS [J], Chinese Journal of Nuclear Science and Engineering 27 (4) (2007) 365–373 (in Chinese).
Contents lists available at ScienceDirect
Fusion Engineering and Design journal homepage: www.elsevier.com/locate/fusengdes
SVIP-N 1.0: An integrated visualization platform for neutronics analysis Yuetong Luo a,b,c,∗ , Pengcheng Long b,c , Guoyong Wu a , Qin Zeng b,c , Liqin Hu b,c , Jun Zou b,c a
School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui 230031, China c School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China b
a r t i c l e
i n f o
Article history: Available online 4 May 2010 Keywords: Visualization Neutronics analysis Mixed-rendering Volume clipping
a b s t r a c t Post-processing is an important part of neutronics analysis, and SVIP-N 1.0 (scientific visualization integrated platform for neutronics analysis) is designed to ease post-processing of neutronics analysis through visualization technologies. Main capabilities of SVIP-N 1.0 include: (1) ability of manage neutronics analysis result; (2) ability to preprocess neutronics analysis result; (3) ability to visualization neutronics analysis result data in different way. The paper describes the system architecture and main features of SVIP-N, some advanced visualization used in SVIP-N 1.0 and some preliminary applications, such as ITER. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Neutronics analysis is the foundation of reactor physics design, radiation shield analysis, fuel management optimization and nuclear safety analysis etc. Because neutronics analysis tasks are becoming more and more complex, and development of HPC (highperformance computing) makes it possible to perform more and more accurate neutronics analysis, the result data of neutronics analysis codes are becoming huger and huger (in the remained part of the paper, the result data of neutronics analysis codes is called as neutronics analysis data). Huger data contains more information and knowledge, but it also makes data analysis and processing more difficult, so it is necessary to find efficient and effective tools for huge data processing and analysis. Visualization converts data into images and/or animation to take advantages of human visual system, which can process image/animation quickly in parallel way. Visualization has been regarded as one of essential tools for huge data processing and analysis. Since scientific visualization be first presented in 1987 [1], many wonderful visualization systems such as AVS, IRIS Explorer, and VolView have been developed; and various visualization systems have been applied in many disciplines such as medical, Meteorology, CFD and Molecular Modeling. Although visualization has gotten wonderful achievements, there are still many challenges, one of them is how to cooperate with application domain for further development [2]. The report [2] also points out that cooperation between visualization and application domain is the trend of visualization.
∗ Corresponding author. Tel.: +86 551 2901377; fax: +86 551 5591397. E-mail address: hfl[email protected] (Y. Luo). 0920-3796/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fusengdes.2010.04.016
SVIP-N 1.0 is the result of close cooperation between visualization and neutronics analysis; it is developed by FDS team. FDS team is an inter-disciplines team with people from both nuclear physic science and computer science, which works on fusion reactor design [3], neutronics analysis [4,5] and development of related software [6,7]. To be an integrated environment for neutronics data analysis, besides the function of neutronics analysis data visualization, SVIP-N 1.0 also supports neutronics analysis data management and preprocessing, because all of them are necessary functions of neutronics analysis data processing. 2. Main functions and architecture of SVIP-N SVIP-N 1.0 takes ACIS [8] as 3D geometry engine and VTK [9] as visualization toolkits, and is developed in Visual C++. SVIP-N’s components architecture is shown in Fig. 1. Currently, SVIP-N provides the following three main functions. 2.1. Neutronics analysis data management For visualization software always involves many data, convenient data management is necessary for visualization software. For both geometry model and neutronics analysis data are involved in SVIP-N, and both geometry model and neutronics analysis data can be in various formats, data management is necessary and complex. The goal of SVIP-N’s data management module is to support most related data directly. For geometry model, SVIP-N 1.0 supports most neutral formats, such as sat, WRL, STEP; for neutronics analysis data, besides neutral formats such as RAW and VTI are supported, SVIP-N can import some neutronics analysis codes’ output
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Fig. 1. Components architecture of SVIP-N.
file directly. SVIP-N 1.0 can read output file of TORT and MCNP directly. 2.2. Neutronics analysis data preprocessing In most case, original neutronics analysis data cannot be visualized directly, there are two main possible reasons: (1) The data to be visualized does not exist in the original data, preprocessing step is needed to create the data from original data according to some rules. For example, we often combine some fine group into a coarse-group for visualization. (2) It is because of limitations of some visualization technologies, one typical example is DVR-based visualization, which require the input data to be volume data, but most neutronics analysis data is not volume data, so preprocessing function is needed to convert neutronics data into volume data. Data preprocessing is an integral part of data visualization, so it is reasonable and necessary to integrate data preprocessing function into data visualization platform. According to features of neutronics analysis data, SVIP-N 1.0 implements the following neutronics analysis data preprocessing functions: such as flux group extraction, flux group collapse, flux normalization and flux dataset regularization.
Fig. 2. Flowchart of mixed-rendering of geometry model and neutronics analysis data.
Based on “depth-peeling” method, mixed-rendering of geometry model and neutronics analysis data is presented [5], whose pipeline is shown in Fig. 2. SVIP-N 1.0 implements mixed-rendering method by taking advantages of modern programmable GPU (graphic process unit). For more detail of the method please refer to [10]. 3.2. Geometry model based neutronics analysis data clipping When visualize neutronics analysis data, user may only concern the data inside some components such as vacuum vessel, but it is very difficult for DVR to only display the data inside the given components. The function to keep selected part of neutronics data is referred as neutronics analysis data clipping. For SVIP-N specifies the selected part through geometry model of components, we call the method as geometry model based neutronics analysis data clipping.
2.3. Neutronics analysis data visualization Visualization is SVIP-N’s core function. Because neutronics analysis data is in essence volume data, SVIP-N has implements most popular volume data visualization methods: slice-based visualization, iso-surface based visualization and DVR (direct volume rendering) based visualization. DVR is one of the most important visualization technologies, but naive DVR cannot satisfy some special requirements of SVIPN. Based on DVR method, two advanced visualization technologies are presented according to the requirements of neutronics analysis data visualization. 3. Advanced visualization technologies Two advanced visualization technologies are to be introduced in Sections 3.1 and 3.2, respectively. 3.1. Mixed-rendering of geometry model and neutronics analysis data During neutronics analysis data analysis, geometry model is very helpful, so it is very useful to display neutronics analysis data and the corresponding geometry in the same scene. DVR is an important neutronics analysis data visualization method for it can reflect the whole volume data without concentrating on certain features of interest (e.g., iso-surface), but it cannot embedded geometry model information into its result image.
Fig. 3. Flowchart of geometry model based neutronics analysis data clipping.
Y. Luo et al. / Fusion Engineering and Design 85 (2010) 1527–1530
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Fig. 6. Iso-surface based visualization.
4. Preliminary application of SVIP-N 1.0
Fig. 4. ITER benchmark model.
The method’s pipeline is shown in Fig. 3, it converts given components’ geometry model into “clipping texture” firstly, and then the reconstructive DVR (or mixed-rendering method) realize neutronics analysis data clipping through customized fragment shader program, which decides whether remain an fragment through clipping texture fetching. For more detail please refer to [11].
SVIP-N 1.0 aims to ease post-processing of neutronics analysis data, which works under MS Windows XP/NT/2000/2003 operating systems. It could be experienced under an agreement with FDS team. The common SVIP-N 1.0 based post-processing process includes the following steps. (1) Data importing: Import one or more neutronics analysis data into SVIP-N, and corresponding geometry model can also be imported into SVIP-N, but it is not necessary. Now, output file of TORT and MCNP can be imported into SVIP-N directly. (2) Data preprocessing: According to post-processing task, one or more data preprocessing operation to be applied to neutronics analysis data. (3) Data visualization: Choose suitable visualization method to visualize data according to user’s task. Step 2 and Step 3 are iterative process.
Fig. 5. SN geometry model.
Fig. 7. DVR-based visualization.
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neutronics analysis data preprocessing and neutronics analysis data visualization into an integrated environment. Besides provide common visualization method such as slice-based visualization; iso-surface-based visualization and DVR-based visualization, it presents and implemented two advanced visualization methods according to special requirement of SVIP-N 1.0 by taking advantages of modern GPU. ITER benchmark model has been used to test SVIP-N, the result shows SVIP-N 1.0 is helpful for processing neutronics analysis data. For data management, besides TORT output file of TORT and MCNP, more other neutronics analysis codes’ output files are to be supported directly; and user interface to be optimized according to user’s feedback. References
Fig. 8. Mixed-rendering based visualization.
Following above process, preliminary application to ITER has been performed on a commodity PC equipped with CPU of CORE 2 DUO 1.86 GHz, GPU of Geforce 7600 and 2G memory. ITER is a joint international research and development project that aims to demonstrate the scientific and technical feasibility of fusion power. ITER benchmark model [12], as shown in Fig. 4 is provided by ITER international team to evaluate the CAD/MCNP programs [13] being developed by the ITER participant teams. SNAM 2.1 [14] is used to convert ITER benchmark CAD model into SN model with 408 × 8 × 570 calculation grid (as shown in Fig. 5), and VisualBUS [15] is used to perform neutronics analysis calculation, and the output data is of 5.06G, which consists of 175 groups of neutron flux density and 42 groups of photon flux density. SVIP-N is used to process 5.06G output data, The snapshots of iso-surface based visualization, DVR-based visualization and mixed-rendering based visualization are shown in Figs. 6–8 respectively. 5. Conclusions and future work SVIP-N 1.0 is an integrated platform for neutronics analysis data visualization; it integrates neutronics analysis data management,
[1] B.H. McCormick, T.A. DeFanti, M.D. Brown, Visualization in scientific computing, Computer Graphics 21 (6) (1987) 1–14. [2] C. Johnson, R. Moorhead, T. Munzner, et al., NIH/NSF: Visualization Research Challenges 2006, IEEE Press, 2006. [3] Y. Wu, FDS Team, Conceptual design activities of FDS series fusion power plants in China, Fusion Engineering and Design 81 (23–24) (2006) 2713–2718. [4] Yican Wu, Zhongsheng Xie, Ulrich Fischer Nuclear. A discrete ordinates nodal method for one-dimensional neutron transport numerical calculation in curvilinear geometries, Science and Engineering 133 (1999) 350–357. [5] S. Zheng, M. Chen, J. Li, Q. Zeng, L. Lu, Y. Li, A. Ding, H. Hu, Neutronics analysis for the test blanket modules proposed for EAST and ITER, Nuclear Fusion 47 (8) (2007) 1053–1056. [6] Y. Wu, FDS Team, CAD-based interface programs for fusion neutron transport simulation, Fusion Engineering and Design 84 (2009) 1987–1992. [7] P. Long, et al., Development and application of SN Auto-Modeling Tool SNAM 2.1, Fusion Engineering and Design 85 (2010) 1113–1116. [8] J. Comey, T. Lim, 3D Modeling with ACIS [M], Saxe-coburg Publications, Stirling, UK, 2001. [9] W. Schroeder, K. Martin, B. Lorensen, The Visualization Toolkit: An Object Oriented Approach to 3D Graphics [M], 3rd edition, Kitware, Inc. Publisher, New York, 2003. [10] Y.T. Luo, Y. Xue, J. Sun, “Depth peeling” based transparency rendering and its application [J], Chinese Journal of Scientific Instrument 28 (8) (2007) 596–599 (in Chinese). [11] Y.T. Luo, G.Y. Wu, P.C. Long, Multi-resolution clipping texture based volume clipping technology [C], in: Proceedings of Progress of Computer Technology and Application 2009, University of Science and Technology of China Press, Hefei, 2009, 603–666 (in Chinese). [12] ITER ITA Task Agreement—Testing and Application of a CAD/MCNP Interface Programme for ITER Neutronics Design Calculations, ITA-73-08-CN, 2005. [13] Y. Li, L. Lu, A. Ding, H. Hu, Q. Zeng, S. Zheng, Y. Wu, Benchmarking of MCAM 4.0 with the ITER 3D model, Fusion Engineering and Design 82 (2007) 2861–2866. [14] H. Hu, Y. Wu, M. Chen, Q. Zeng, A. Ding, S. Zheng, et al., Benchmarking of SNAM with the ITER 3D model, Fusion Engineering and Design 82 (2007) 2867–2871. [15] Y.C. Wu, J.J. Li, M.L. Chen, et al., An integrated Multi-functional neutronics calculation and analysis code system: VisualBUS [J], Chinese Journal of Nuclear Science and Engineering 27 (4) (2007) 365–373 (in Chinese).