ORIGINAL_ARTICLE
A fast Jacobian-Free Newton-Krylov iterative solver for eigenvalue search problems in the reactor physics
The Jacobian-Free Newton-Krylov (JFNK) method has been widely used in solving nonlinear equations arising in many applications. In this paper, the JFNK solver is examined as an alternative to the traditional power iteration method for calculation of the fundamental eigenmode in reactor analysis based on even-parity neutron transport theory. Since the Jacobian is not formed the only extra storage required is associated with the workspace of the Krylov solver used at every Newton step. A new nonlinear function is developed for the even-parity neutron transport equation utilized to solve the eigenvalue problem using the JFNK. This Newton-based method is compared with the standard iterative power method for a number of multi-groups, one and two dimensional neutron transport benchmarks. The results show that the proposed algorithm generally ends with fewer iterations and shorter run times than those of the traditional power method.
http://rpe.kntu.ac.ir/article_89324_7e3d4462f9b3f73668d6ee37d10c443e.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
1
9
10.22034/rpe.2020.89324
JFNK method
Even-parity neutron transport equation
Eigenvalue search
Nonlinear systems
Mohammadreza
Abbasi
m_abbasi@sbu.ac.ir
true
1
Faculty of Engineering, Shahid Beheshti University, G.C, P.O. Box: 1983963113, Tehran, Iran
Faculty of Engineering, Shahid Beheshti University, G.C, P.O. Box: 1983963113, Tehran, Iran
Faculty of Engineering, Shahid Beheshti University, G.C, P.O. Box: 1983963113, Tehran, Iran
LEAD_AUTHOR
Abbassi, M., Zolfaghari, A., Minuchehr, A., et al. (2011). An adaptive finite element approach for neutron transport equation. Nuclear Engineering and Design, 241(6):2143–2154.
1
Ackroyd, R. (1962). Geometrical methods for determining the accuracy of approximate solutions of the Boltzmann equation: Part I one-group scattering and absorbing media. Journal of
2
Mathematics and Mechanics, pages 811–850. Ackroyd, R. (1978). A finite element method for neutron transport. Some theoretical considerations. Annals of Nuclear Energy, 5(2):75–94.
3
Ackroyd, R., Fletcher, J., Goddard, A., et al. (1987). Some recent developments in finite element methods for neutron transport. In Advances in Nuclear Science and Technology, pages 381–483. Springer.
4
Ackroyd, R. and Pendlebury, E. (1961). Survey of theoretical calculation methods, article in criticality control. In Karlsrush Symposium, OECD, European Nuclear Energy Agency.
5
Ackroyd, R. T. (1997). Finite element methods for particle transport: applications to reactor and radiation physics. Research Studies Press.
6
Adams, M. L. and Larsen, E. W. (2002). Fast iterative methods for discrete-ordinates particle transport calculations. Progress in Nuclear Energy, 40(1):3–159.
7
Allen, E. and Berry, R. (2002). The inverse power method for calculation of multiplication factors. Annals of Nuclear Energy, 29(8):929–935.
8
Brown, P. N. and Saad, Y. (1990). Hybrid Krylov methods for nonlinear systems of equations. SIAM Journal on Scientific and Statistical Computing, 11(3):450–481.
9
Chan, T. F. and Jackson, K. R. (1984). Nonlinearly preconditioned Krylov subspace methods for discrete Newton algorithms. SIAM Journal on scientific and statistical computing, 5(3):533–542.
10
Downar, T., Lee, D., Xu, Y., and Kozlowski, T. (2004). PARCS v2. 6, US NRC core neutronics simulator. School of Nuclear Engineering, Purdue University.
11
Downar, Y. (2005). The implementation of matrix free Newton/ Krylov methods based on a fixed point iteration.
12
Duderstadt, J. J., Hamilton, L. J., et al. (1976). Nuclear reactor analysis, volume 84. Wiley New York.
13
Eisenstat, S. C. and Walker, H. F. (1996). Choosing the forcing terms in an inexact Newton method. SIAM Journal on Scientific Computing, 17(1):16–32.
14
Gear, C. W. and Saad, Y. (1983). Iterative solution of linear equations in ODE codes. SIAM journal on scientific and statistical computing, 4(4):583–601.
15
Gupta, A. and Modak, R. (2004). Krylov sub-space methods for K-eigenvalue problem in 3-D neutron transport. Annals of Nuclear Energy, 31(18):2113–2125.
16
Hageman, L. A. and Young, D. M. (2012). Applied iterative methods. Courier Corporation. Hestenes, M. R. and Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems, volume 49. NBS Washington, DC.
17
Hongchun, W., Pingping, L., Yongqiang, Z., et al. (2007). Transmission probability method based on triangle meshes for solving unstructured geometry neutron transport problem. Nuclear engineering and design, 237(1):28–37.
18
Knoll, D. A. and Keyes, D. E. (2004). Jacobian-free Newton-Krylov methods: a survey of approaches and applications. Journal of Computational Physics, 193(2):357–397.
19
Lewis, E. E. and Miller, W. F. (1984). Computational methods of neutron transport.
20
Mahadevan, V. and Ragusa, J. (2008). Novel hybrid scheme to compute several dominant eigenmodes for reactor analysis problems.
21
Martin, W. (2010). Nonlinear acceleration methods for even parity neutron transport.
22
Reid, J. K. (1971). On the method of conjugate gradients for the solution of large sparse systems of linear equations. In Pro. the Oxford conference of institute of mathematics and its applications, pages 231–254.
23
Rose, P. (1983). Proceedings: thermal-reactor benchmark calculations, techniques, results, and applications. Technical report, Brookhaven National Lab.
24
Saad, Y. (2003). Iterative methods for sparse linear systems, volume 82. siam.
25
Saad, Y. and Schultz, M. H. (1986). GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on scientific and statistical computing, 7(3):856–869.
26
Synge, J. L. (1957). The hypercircle in mathematical physics. University Press Cambridge.
27
Verd´u, G., Mir´o, R., Ginestar, D., et al. (1999). The implicit restarted Arnoldi method, an efficient alternative to solve the neutron diffusion equation. Annals of nuclear energy, 26(7):579–593.
28
Vladimirov, V. (1963). Mathematical problems in the onevelocity theory of particle transport. Technical report, Atomic Energy of Canada Limited.
29
Wood, J. and Williams, M. (1984). Recent progress in the application of the finite element method to the neutron transport equation. Progress in Nuclear Energy, 14(1):21–40.
30
ORIGINAL_ARTICLE
Optimization of beamline diameter in spot scanning proton therapy for minimization of secondary particles using finite element method
Collision of protons with background gas and beamline wall in proton therapy causes the creation of secondary particles, e.g. neutrons, which results in more difficulties in curing the tumors. In the present simulation-based study, the optimum diameter of proton beamline was determined to minimize the production of secondary particles in the presence of electric field with the magnitude of 50 kV/m, perpendicular equal magnetic fields of 0.7 T, and background gas of argon under Bounce boundary conditions via finite element method. The results showed that the optimum diameter of the beamline for minimization of the secondary particles in the spot scanning proton therapy in the aforementioned conditions was 7 mm. Also, the values of drift velocities of protons were plotted in different time steps of 10 ns to 50 ns for the optimized size of the beamline. Due to few interactions of forwarding particles with background gas, the results showed that the forwarding particles in the propagation direction have greater velocities than those of rear particles. The results can be used in spot scanning proton therapy for curing the localized cancers.
http://rpe.kntu.ac.ir/article_89327_2a4e912f15a18af50b8b9ed1433c44df.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
11
16
10.22034/rpe.2020.89327
Spot Scanning Proton Therapy
Beamline Diameter
Secondary Particles
Finite Element Method
Amir
Veiskarami
true
1
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Mahdi
Sadeghi
true
2
Iran University of Medical Sciences, Medical Physics Department, Tehran, Iran
Iran University of Medical Sciences, Medical Physics Department, Tehran, Iran
Iran University of Medical Sciences, Medical Physics Department, Tehran, Iran
AUTHOR
Dariush
Sardari
true
3
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Shahryar
Malekie
smaleki@aeoi.org.ir
true
4
Radiation Application Research School, Nuclear Science & Technology Research Institute, PO Box 31485-498, Karaj, Iran
Radiation Application Research School, Nuclear Science & Technology Research Institute, PO Box 31485-498, Karaj, Iran
Radiation Application Research School, Nuclear Science & Technology Research Institute, PO Box 31485-498, Karaj, Iran
LEAD_AUTHOR
Chang, J. Y., Jabbour, S. K., De Ruysscher, D., et al. (2016). Consensus statement on proton therapy in early-stage and locally advanced non–small cell lung cancer. International Journal of Radiation Oncology* Biology* Physics, 95(1):505–516.
1
COMSOL (1994). Guide, comsol multiphysics users. Inc. 2006.-708 p.
2
COMSOL (2013). Comsol multiphysics user guide (version 4.3 a). COMSOL, AB, pages 39–40.
3
Deasy, J. (1994). ICRU Report 49, stopping powers and ranges for protons and alpha particles. Medical Physics, 21(5):709–710.
4
Elnahal, S. M., Kerstiens, J., Helsper, R. S., et al. (2013). Proton beam therapy and accountable care: the challenges ahead. International Journal of Radiation Oncology* Biology* Physics, 85(4):e165–e172.
5
Gottschalk, B. (2006). Neutron dose in scattered and scanned proton beams: In regard to Eric J. Hall (Int J Radiat Oncol Biol Phys 2006; 65: 1–7). International Journal of Radiation Oncology* Biology* Physics, 66(5):1594.
6
Grevillot, L., Bertrand, D., Dessy, F., et al. (2011). A Monte Carlo pencil beam scanning model for proton treatment plan simulation using GATE/GEANT4. Physics in Medicine and Biology, 56(16):5203.
7
Haberer, T., Becher, W., Schardt, D., et al. (1993). Magnetic scanning system for heavy ion therapy. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 330(1-2):296–305.
8
Hall, E. J. (2006). Intensity-modulated radiation therapy, protons, and the risk of second cancers. International Journal of Radiation Oncology* Biology* Physics, 65(1):1–7.
9
Ho, E. S., Barrett, S. A., and Mullaney, L. M. (2017). A review of dosimetric and toxicity modeling of proton versus photon craniospinal irradiation for pediatrics medulloblastoma. Acta Oncologica, 56(8):1031–1042.
10
Hyer, D. E., Hill, P. M.,Wang, D., et al. (2014). Effects of spot size and spot spacing on lateral penumbra reduction when using a dynamic collimation system for spot scanning proton therapy. Physics in Medicine and Biology, 59(22):N187.
11
Jansen, E. P., Dewit, L. G., van Herk, M., et al. (2000). Target volumes in radiotherapy for high-grade malignant glioma of the brain. Radiotherapy and Oncology, 56(2):151–156.
12
Jia, S. B., Hadizadeh, M. H., Mowlavi, A. A., et al. (2014). Evaluation of energy deposition and secondary particle production in proton therapy of brain using a slab head phantom. Reports of Practical Oncology and Radiotherapy, 19(6):376–384.
13
Jiang, H., Wang, B., Xu, X. G., et al. (2005). Simulation of organ-specific patient effective dose due to secondary neutrons in proton radiation treatment. Physics in Medicine and Biology, 50(18):4337.
14
Kraft, G. (2000). Tumor therapy with heavy charged particles. Progress in Particle and Nuclear Physics, 45:S473–S544.
15
Lin, R., Hug, E. B., Schaefer, R. A., et al. (2000). Conformal proton radiation therapy of the posterior fossa: a study comparing protons with three-dimensional planned photons in limiting dose to auditory structures. International Journal of Radiation Oncology* Biology* Physics, 48(4):1219–1226.
16
Lomax, A. J., B¨ohringer, T., Bolsi, A., et al. (2004). Treatment planning and verification of proton therapy using spot scanning: Initial experiences. Medical Physics, 31(11):3150–3157.
17
Lomax, A. J., Bortfeld, T., Goitein, G., et al. (1999). A treatment planning inter-comparison of proton and intensity modulated photon radiotherapy. Radiotherapy and Oncology, 51(3):257–271.
18
Malekie, S. and Ziaie, F. (2017). A two-dimensional simulation to predict the electrical behavior of carbon nanotube/polymer composites. Journal of Polymer Engineering, 37(2):205–210.
19
McDonald, M. W. and Fitzek, M. M. (2010). Proton therapy. Current problems in cancer, 34(4):257.
20
Newhauser, W. D. and Zhang, R. (2015). The physics of proton therapy. Physics in Medicine and Biology, 60(8):R155.
21
Owen, H., Holder, D., Alonso, J., et al. (2014). Technologies for delivery of proton and ion beams for radiotherapy. International Journal of Modern Physics A, 29(14):1441002.
22
Pedroni, E., Bacher, R., Blattmann, H., et al. (1995). The 200-MeV proton therapy project at the Paul Scherrer Institute: Conceptual design and practical realization. Medical Physics, 22(1):37–53.
23
Poenisch, F., Gillin, M., Sahoo, N., et al. (2016). SU-F-T-165: Daily QA analysis for spot scanning beamline. Medical Physics, 43(6Part15):3499–3500.
24
Reddy, J. N. (2005). An introduction to the finite element method. 3rd Edition, volume 2. McGraw-Hill New York.
25
Ricardi, U., Dabaja, B., and Hodgson, D. (2017). Proton therapy in mediastinal Hodgkin lymphoma: moving from dosimetric prediction to clinical evidence.
26
Schlegel, W., Bortfeld, T., Grosu, A.-L., et al. (2008). New Technologies in Radiation Oncology. Journal of Nuclear Medicine, 49(4):683–684.
27
Schneider, U., Agosteo, S., Pedroni, E., et al. (2002). Secondary neutron dose during proton therapy using spot scanning. International Journal of Radiation Oncology*Biology*Physics, 53(1):244–251.
28
Smith, B., Gelover, E., Moignier, A., et al. (2016). A treatment plan comparison between dynamic collimation and a fixed aperture during spot scanning proton therapy for brain treatment. Medical Physics, 43(8Part1):4693–4699.
29
Søbstad, J. M. (2017). Monte Carlo based comparison of constant vs. variable RBE for proton therapy patients. Master’s thesis, The University of Bergen.
30
Timmermann, B., Schuck, A., Niggli, F., et al. (2007). Spotscanning proton therapy for malignant soft tissue tumors in childhood: First experiences at the Paul Scherrer Institute. International Journal of Radiation Oncology* Biology*Physics, 67(2):497–504.
31
van de Water, T. A., Lomax, A. J., Bijl, H. P., et al. (2012). Using a reduced spot size for intensity-modulated proton therapy potentially improves salivary gland-sparing in oropharyngeal cancer. International Journal of Radiation Oncology*Biology* Physics, 82(2):e313–e319.
32
Weber, D. C., Rutz, H. P., Pedroni, E. S., et al. (2005). Results of spot-scanning proton radiation therapy for chordoma and chondrosarcoma of the skull base: the Paul Scherrer Institut experience. International Journal of Radiation Oncology*Biology* Physics, 63(2):401–409.
33
Yao, W., Merchant, T. E., and Farr, J. B. (2016). A correction scheme for a simplified analytical random walk model algorithm of proton dose calculation in distal Bragg peak regions. Physics in Medicine and Biology, 61(20):7397.
34
ORIGINAL_ARTICLE
Flow regimes classification and prediction of volume fractions of the gas-oil-water three-phase flow using Adaptive Neuro-fuzzy Inference System
The used metering technique in this study is based on the dual energy (Am-241 and Cs-137) gamma ray attenuation. Two transmitted NaI detectors in the best orientation were used and four features were extracted and applied to the model. This paper highlights the application of Adaptive Neuro-fuzzy Inference System (ANFIS) for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems. In fact, the aim of the current study is to recognize the flow regimes based on dual energy broad-beam gamma-ray attenuation technique using ANFIS. In this study, ANFIS is used to classify the flow regimes (annular, stratified, and homogenous) and predict the value of volume fractions. To start modeling, sufficient data are gathered. Here, data are generated numerically using MCNPX code. In the next step, ANFIS must be trained. According to the modeling results, the proposed ANFIS can correctly recognize all the three different flow regimes, and other ANFIS networks can determine volume fractions with MRE of less than 2% according to the recognized regime, which shows that ANFIS can predict the results precisely.
http://rpe.kntu.ac.ir/article_89328_509eabeac0a03484f5997e9fc57e0e5d.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
17
26
10.22034/rpe.2020.89328
Three-phase flow
Pattern recognition
Volume fraction
Adaptive neuro-fuzzy inference system
Monte Carlo simulation
Gholam Hossein
Roshani
true
1
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
Alimohammad
Karami
true
2
Mechanical Engineering Department, Razi University, Kermanshah, Iran
Mechanical Engineering Department, Razi University, Kermanshah, Iran
Mechanical Engineering Department, Razi University, Kermanshah, Iran
AUTHOR
Ehsan
Nazemi
true
3
Nuclear Science and Technology Research Institute, Tehran, Iran
Nuclear Science and Technology Research Institute, Tehran, Iran
Nuclear Science and Technology Research Institute, Tehran, Iran
AUTHOR
Cesar
Marques Salgado
otero@ien.gov.br
true
4
Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970 Rio de Janeiro, Brazil
Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970 Rio de Janeiro, Brazil
Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970 Rio de Janeiro, Brazil
LEAD_AUTHOR
Abouelwafa, M. and Kendall, E. (1980). The measurement of component ratios in multiphase systems using alpha-ray attenuation. Journal of Physics E: Scientific Instruments, 13(3):341.
1
Aghakhani, M., Ghaderi, M., Karami, A., et al. (2014). Combined effect of TiO2 nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic. The International Journal of Advanced Manufacturing Technology, 70(1-4):63–72.
2
Aghakhani, M., Jalilian, M. M., and Karami, A. (2012). Prediction of weld bead dilution in GMAW process using fuzzy logic. In Applied Mechanics and Materials, volume 110, pages 3171–3175. Trans Tech Publ.
3
Amiri, A., Karami, A., Yousefi, T., and Zanjani, M. (2012). Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders. Polish Journal of Chemical Technology, 14(4):46–52.
4
Bishop, C. M. and James, G. D. (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 327(2-3):580–593.
5
Gulley, N. and Jang, J.-S. R. (1995). Fuzzy logic toolbox user’s guide. The MathWorks, Inc, 24.
6
Jang, J., Sun, C., and Mizutani, E. (1997). Neuro-fuzzy and soft computing, (1997). PTR Prentice Hall.
7
Jang, J.-S. and Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3):378–406.
8
Karami, A., Akbari, E., Rezaei, E., et al. (2013a). Neurofuzzy modeling of the free convection from vertical arrays of isothermal cylinders. Journal of Thermophysics and Heat Transfer, 27(3):588–592.
9
Karami, A., Rezaei, E., Rahimi, M., et al. (2013b). Modeling of heat transfer in an air cooler equipped with classic twisted tape inserts using adaptive neuro-fuzzy inference system. Chemical Engineering Communications, 200(4):532–542.
10
Karami, A., Rezaei, E., Shahhosseni, M., et al. (2012). Fuzzy logic to predict the heat transfer in an air cooler equipped with different tube inserts. International Journal of Thermal Sciences, 53:141–147.
11
Karami, A., Yousefi, T., Ebrahimi, S., et al. (2013c). Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate. Heat and Mass Transfer, 49(6):789–798.
12
Karami, A., Yousefi, T., Harsini, I., et al. (2015). Neurofuzzy modeling of the free convection heat transfer from a wavy surface. Heat Transfer Engineering, 36(9):847–855.
13
Karami, A., Yousefi, T., Mohebbi, S., et al. (2014). Prediction of free convection from vertical and inclined rows of horizontal isothermal cylinders using ANFIS. Arabian Journal for Science and Engineering, 39(5):4201–4209.
14
Karami, A., Yousefi, T., Rezaei, E., et al. (2016). Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic. The International Journal of Multiphysics, 6(1).
15
Khorsandi, M., Feghhi, S., Salehizadeh, A., et al. (2013). Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network. Radiation Measurements, 59:183–187.
16
Nazemi, E., Roshani, G., Feghhi, S., et al. (2016). Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. International Journal of Hydrogen Energy, 41(18):7438–7444.
17
Pelowitz, D. B. et al. (2005). MCNPXTM user’s manual. Los Alamos National Laboratory, Los Alamos.
18
Rezaei, E., Karami, A., Yousefi, T., et al. (2012). Modeling the free convection heat transfer in a partitioned cavity using ANFIS. International Communications in Heat and Mass Transfer, 39(3):470–475.
19
Roshani, G., Feghhi, S., Mahmoudi-Aznaveh, A., et al. (2014). Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement, 51:34–41.
20
Roshani, G., Feghhi, S., and Setayeshi, S. (2015). Dual modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network. Radiation Measurements, 82:154–162.
21
Roshani, G., Nazemi, E., and Roshani, M. (2017a). Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Computing and Applications, 28(1):1265–1274.
22
Roshani, G., Nazemi, E., and Roshani, M. (2017b). Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement, 100:122–130.
23
Salgado, C. M., Brand˜ao, L. E., Schirru, R., et al. (2009). Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Applied Radiation and Isotopes, 67(10):1812–1818.
24
Salgado, C. M., Pereira, C. M., Schirru, R., et al. (2010). Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks. Progress in Nuclear Energy, 52(6):555–562.
25
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, (1):116–132.
26
Thorn, R., Johansen, G., and Hammer, E. (1997). Recent developments in three-phase flow measurement. Measurement Science and Technology, 8(7):691.
27
Yousefi, T., Karami, A., Rezaei, E., et al. (2012). Fuzzy modeling of the forced convection heat transfer from a V-shaped plate exposed to an air slot jet. Heat Transfer–Asian Research, 41(5):430–443.
28
ORIGINAL_ARTICLE
A simulation study on neutronic behavior of non-fissionable and fissionable materials of different geometries as spallation targets in ADS
Spallation process is the most significant process for neutron generation in industry and medicine. This process has been used in the subcritical reactor core. In this research, we study the neutronic behavior of non-fissionable and fissionable spallation targets consists of U-238, Th-232, Lead Bismuth Eutectic (LBE) and W-184 materials in cylindrical and conic shapes using MCNPX code. Neutronic parameters consist of spallation neutron yield, deposition energy, and angular spectrum of the neutron output. The gas production rate and residual mass spectrum were investigated. The results of this research indicate that the shape of the target must be selected based on target material and operational purposes. The number of neutrons per energy unit is stable at energies higher than 1 GeV, and the rate of change in neutron generation has been reduced after that. Furthermore, hydrogen is the principal factor in swelling of spallation target and consists of about 88% of gas production. It was found that a target of LBE provides the most favorite parameters for both neutronic and physical properties.
http://rpe.kntu.ac.ir/article_89329_fb6e92f4f7e8c1cc7cd4a10eb94de96f.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
29
36
10.22034/rpe.2020.89329
MCNPX code
Spallation process
Neutronic parameters
Spallation targets
Mohammad Amin
Amirkhani
true
1
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
AUTHOR
Mostafa
Hassanzadeh
mhasanzadeh@aeoi.org.ir
true
2
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
LEAD_AUTHOR
Safar Ali
Safari
true
3
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
Nuclear Science & Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, AEOI, Tehran, Iran
AUTHOR
Abderrahim, H. A., Kupschus, P., Malambu, E., et al. (2001). MYRRHA: A multipurpose accelerator driven system for research & development. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 463(3):487–494.
1
Ammerman, C., Woloshun, K., He, X., et al. (2001). Conceptual designs for a spallation neutron target constructed of a helium cooled, packed bed of tungsten particles. In ANS Winter Meeting, Los Alamos National Laboratory.
2
Bertini, H. W. (1963). Low-energy intranuclear cascade calculation. Physical Review, 131(4):1801.
3
Brown, F. B. et al. (2000). MCNP–A General Monte Carlo N-Particle Transport Code, Version 4C. Los Alamos National Laboratory, Oak Ridge, TN.
4
Bungau, C., Tygier, S., Barlow, R., et al. (2008). Accelerator driven systems for energy production and waste transmutation. EPAC.
5
Cheng, X., Batta, A., Tak, N.-I., et al. (2006). Thermalhydraulic analysis of the TRADE spallation target. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 562(2):855–858.
6
Demirkol, ˙I. and Tel, E. (2011). Multiplicity of particles per primary reaction at 1500 MeV for the nuclei used on the accelerator-driven systems. Annals of Nuclear Energy, 38(5):1078–1083.
7
DoE, A. (1999). Roadmap for developing accelerator transmutation of waste (ATW) technology. In Washington: A Report to Congress.
8
Feghhi, S. A. H., Gholamzadeh, Z., and Tenreiro, C. (2014). Investigation of the optimal material type and dimension for spallation targets using simulation methods. Journal of Theoretical and Applied Physics, 8(1):1.
9
Hassanzadeh, M. and Feghhi, S. (2015). Calculation of the spallation target neutronic parameters in Accelerator Driven Subcritical TRIGA reactor. Annals of Nuclear Energy, 85:326–330.
10
Hughes, H. G. et al. (2002). MCNPX user’s manual, Version 2.4.0. Los Alamos National Laboratory, Los Alamos.
11
Johnson, J., Gabriel, T., and Bartine, D. (1985). Accelerator breeder nuclear fuel production: concept evaluation of a modified design for ORNL’s proposed TME-ENFP. Technical report, Oak Ridge National Lab.
12
Kawai, M., Furusaka, M., Kikuchi, K., et al. (2003). R&d of a mw-class solid-target for a spallation neutron source. Journal of Nuclear Materials, 318:38–55.
13
Kimura, M. (1992). Neutron production, moderation, and characterization of sources. Lns. Pnpi. Spb. Ru.
14
Kumar, V., Kumawat, H., Goel, U., et al. (2003). Neutron spallation source and the dubna cascade code. Pramana, 60(3):469–481.
15
Mantha, V., Mohanty, A., and Satyamurthy, P. (2007). Thermal hydraulic studies of spallation target for one-way coupled indian accelerator driven systems with low energy proton beam. Pramana, 68(2):355–363.
16
Mellier, F. et al. (2005). The MUSE experiments for sub critical neutronics validation. Final Report to the European Commission.
17
Prael, R. E. and Lichtenstein, H. (1989). User guide to LCS: the LAHET code system. Group, 10:6.
18
Pyeon, C. H., Misawa, T., Lim, J.-Y., et al. (2009). First injection of spallation neutrons generated by high-energy protons into the Kyoto University Critical Assembly. Journal of Nuclear Science and Technology, 46(12):1091–1093.
19
Rubbia, C., Aleixandre, J., Andriamonje, S., et al. (2001). A european roadmap for developing accelerator driven systems (ADS) for nuclear waste incineration. ENEA Report, 88.
20
Rubbia, C., Roche, C., Rubio, J. A., et al. (1995). Conceptual design of a fast neutron operated high power energy amplifier. Technical report.
21
Salvatores, M., Slessarev, I., Ritter, G., et al. (1998). Longlived radioactive waste transmutation and the role of accelerator driven (hybrid) systems. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 414(1):5–20.
22
Sokolov, F., Fukuda, K., and Nawada, H. (2005). Thorium fuel cyclepotential benefits and challenges. IAEATECDOC-1450, IAEA, Vienna, Austria.
23
Tak, N., Neitzel, H., Chen, H., et al. (2004). Thermal hydraulic analysis of window target unit for LBE cooled XADS. Forschungszentrum Karlsruhe.
24
Tak, N.-i., Neitzel, H.-J., and Cheng, X. (2005). Computational fluid dynamics analysis of spallation target for experimental accelerator-driven transmutation system. Nuclear Engineering and Design, 235(7):761–772.
25
Taninaka, H., Hashimoto, K., Pyeon, C. H., et al. (2011). Determination of subcritical reactivity of a thermal accelerator driven system from beam trip and restart experiment. Journal of Nuclear Science and Technology, 48(6):873–879.
26
Uesugi, T., Mori, Y., Horii, H., et al. (2008). Ffags for the ERIT and ADS projects at KURRI. Energy (MeV), 70(80):90.
27
Yariv, Y. and Fraenkel, Z. (1979). Intranuclear cascade calculation of high-energy heavy-ion interactions. Physical Review C, 20(6):2227.
28
ORIGINAL_ARTICLE
An analytical and Monte Carlo investigation of the sufficiency of the present shielding of PET/CT imaging system at Tehran's Shariati hospital
By the rapid development of imaging systems such as PET/CT for diagnosis of cancer, the protection of staff and public has become a main health concern. Due to serious and irreversible harms of ionization radiations, protection of all those who are exposed is the main concern of health issues. The main basis of the calculation of the shielding design in the medical imaging systems is that the absorbed dose should not exceed the allowed limit. In this study, the current shielding status of the PET/CT installations in Tehran's Shariati hospital was investigated using the MCNPX Monte Carlo code to ensure that the dose limits for both the controlled and uncontrolled area are not violated. The proposed simulation method was benchmarked with a validated analytical method. Shariati hospital provides services to four patients every day, leading to a dose rate in the range of 2.6 × 10-6 to 9.35 × 10-3 mSv/week. The minimum dose rate in this range represents the value behind the door of the waiting room (public uncontrolled area), while the maximum in this range corresponds to the value behind the glass of the scanner room (operator controlled area). The simulation results for 8 patients/day in this center showed that the dose rate behind the wall of the injection room will increase from 4.88 ×10-6 mSv/week to 2.81 × 10-2 mSv/week, which is well below the recommended levels. This indicates that the present shielding is adequate for up to four more patients per day.
http://rpe.kntu.ac.ir/article_89330_01a8126803f87afc555a3373d515dc99.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
37
41
10.22034/rpe.2020.89330
Positron-emission-tomography
Computed-tomography shielding
Dose rate
Monte Carlo Method
Analytical method
Fereshteh
Gholami
true
1
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
AUTHOR
Ehsan
Alibeigi
true
2
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
AUTHOR
Mojtaba
Shamsaei-Zafarghandi
true
3
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
Faculty of Nuclear Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
AUTHOR
Ehsan
Nazemi
enazemi@aeoi.org.ir
true
4
Nuclear Science and Technology Research Institute, Tehran, Iran
Nuclear Science and Technology Research Institute, Tehran, Iran
Nuclear Science and Technology Research Institute, Tehran, Iran
LEAD_AUTHOR
Archer, B. R., Thornby, J., and Bushong, S. C. (1983). Diagnostic X-ray shielding design based on an empirical model of photon attenuation. Health physics, 44(5):507–517.
1
Bresnahan, M. E. and Shrestha, B. (2012). Potential shielding for a positron emission tomography (PET) suite. Journal of Biomedical Graphics and Computing, 2(1):89.
2
Coker, A. L. (2007). PET/CT shielding design comparisons. PhD thesis, Texas A&M University.
3
ICRP (1971). Committee 3 Task Group, P. Grande and MC ORiordan, chairman,Data for Against Ionizing Radiation from External Sources: Supplement to ICRP Publication 15, ICRP-21, International Commission on Radiological Protection.
4
Johnson, T. E. and Birky, B. K. (2012). Health physics and radiological health. Lippincott Williams & Wilkins.
5
Madsen, M. T., Anderson, J. A., Halama, J. R., et al. (2006). AAPM task group 108: PET and PET/CT shielding requirements. Medical Physics, 33(1):4–15.
6
Mawlawi, O., Pan, T., and Macapinlac, H. A. (2006). PET/CT imaging techniques, considerations, and artifacts. Journal of Thoracic Imaging, 21(2):99–110.
7
Pelowitz, D. B. et al. (2005). MCNPXTM user’s manual. Los Alamos National Laboratory, Los Alamos.
8
Powsner, R. A. and Powsner, E. R. (2008). Essential nuclear medicine physics. John Wiley & Sons.
9
Shamsaei Zafarghandi, M. et al. (2014). Study the shielding for PET/CT imaging installations of Shariati hospital of Tehran with Monte Carlo code. Iranian Journal of Radiation Safety and Measurement, 2(2):13–20.
10
ORIGINAL_ARTICLE
ThO2 spent fuel assembly’s gamma dose rate dependency to burnup and cooling time
Today thorium based fuels are being investigated as an alternative fuel technology. However, the majority of thorium fuel research studies are limited to reactor physics investigations, which leaves a gap for dose evaluation and shielding concerns of such spent fuels. The present work investigates thorium oxide fuel assemblies in Tehran research reactor. The fuel gamma dose rates are calculated at different burnups and cooling times. A comparison between the reactor routine fuel and the thorium oxide fuel is conducted to reveal the thorium-based fuel application shielding challenges. The obtained results showed that inverse to U3O8-Al routine fuel the spent ThO2 gamma dose rates are completely dependent to the burnup values. In addition, for transporting the spent ThO2 fuel with the routine transport casks there is needed to be waited for the higher cooling times than U3O8-Al transportation time or construction of thicker transport casks is needed for transportation of the thorium-based spent fuels at shorter times.
http://rpe.kntu.ac.ir/article_104833_bb09c6c1026b467cca96ab0547744c5f.pdf
2020-08-01T11:23:20
2021-04-11T11:23:20
43
48
10.22034/rpe.2020.104833
Gamma dose rate
Thorium spent fuel
Computational calculations
MCNPX code
Zohreh
Gholamzadeh
cadmium_109@yahoo.com
true
1
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
LEAD_AUTHOR
Mohadeseh
Gholshanian
m.golshanian@gmail.com
true
2
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
AUTHOR
Seyed Mohammad
Mirvakili
mmirvakili@aeoi.org.ir
true
3
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
AUTHOR
Croff, A. G. (1980). User’s manual for the ORIGEN2 computer code. Technical report, Oak Ridge National Lab.
1
Fensin, M. L. (2008). Development of the MCNPX depletion capability: A Monte Carlo linked depletion method that automates the coupling between MCNPX and CINDER90 for high fidelity burnup calculations. University of Florida.
2
Frybort, J. (2014). Comparison of the radiological hazard of thorium and uranium spent fuels from VVER-1000 reactor. Radiation Physics and Chemistry, 104:408–413.
3
IAEA-TECDOC (2000). Thorium based fuel options for the generation of electricity: Developments in the 1990s. IAEA TECDOC–1155, page 79.
4
IAEA-TECDOC (2005). Nuclear fuel cycle. Material Section. Thorium fuel cycle-potential benefits and challenges-IAEATECDOC-1450. Technical report, Technical report, IAEA, International Atomic Energy Agency.
5
Kang, J. and von Hippel, F. N. (2001). U-232 and the proliferation-resistance of U-233 in spent fuel. Science & Global Security, 9(1):1–32.
6
Mirvakili, S., Keyvani, M., Arshi, S. S., et al. (2012). Possibility evaluation of eliminating the saturated control fuel element from Tehran research reactor core. Nuclear Engineering and Design, 248:197–205.
7
Naymushin, A., Chertkov, Y., Lebedev, I., et al. (2016). Use of thorium in thermal-neutron reactors: Computation model and comparison of neutronic codes. Journal of Industrial Pollution Control, 32(2):428–431.
8
Pelowitz, D. B. et al. (2005). MCNPX user’s manual. Los Alamos National Laboratory, Los Alamos.
9
Srinivasan, P., Ganesan, S., Sharma, D., et al. (2006). Estimation of dose rates on the phwr irradiated thorium oxide bundles based on barc updated nuclear data for origen code.
10
In National Workshop on Nuclear Data, NWND-2006, Mangalore, India, pages 8–11.
11
USNRC (1997). Standard review plan for dry cask storage systems, US Nuclear Regulatory Commission and others. NUREG-1536, 1.
12
Wojtaszek, D., Colton, A., Bromley, B., et al. (2018). A scenario analysis of once-through thorium fuel cycles with pressure tube HWRs in Canada. Annals of Nuclear Energy, 111:152–162.
13