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Journal articles on the topic 'Numerical understanding'

Author: Grafiati

Published: 24 June 2021

Last updated: 13 February 2022

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1

Gómez, Daniel O., and Pablo D. Mininni. "Understanding turbulence through numerical simulations." Physica A: Statistical Mechanics and its Applications 342, no. 1-2 (October 2004): 69–75. http://dx.doi.org/10.1016/j.physa.2004.04.061.

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2

Sophian, Catherine. "Infants' understanding of numerical transformations." Infant Behavior and Development 9 (April 1986): 351. http://dx.doi.org/10.1016/s0163-6383(86)80357-5.

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3

Sophian, Catherine, and Norene Adams. "Infants' understanding of numerical transformations." British Journal of Developmental Psychology 5, no. 3 (September 1987): 257–64. http://dx.doi.org/10.1111/j.2044-835x.1987.tb01061.x.

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4

Bornemann, Folkmar. "A model for understanding numerical stability." IMA Journal of Numerical Analysis 27, no. 2 (April 1, 2007): 219–31. http://dx.doi.org/10.1093/imanum/drl037.

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5

Norton, Anderson, and Julie Nurnberger-Haag. "Bridging frameworks for understanding numerical cognition." Journal of Numerical Cognition 4, no. 1 (June 7, 2018): 1–8. http://dx.doi.org/10.5964/jnc.v4i1.160.

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6

Flagello, D. G., A. E. Rosenbluth, C. Progler, and J. Armitage. "Understanding high numerical aperture optical lithography." Microelectronic Engineering 17, no. 1-4 (March 1992): 105–8. http://dx.doi.org/10.1016/0167-9317(92)90021-i.

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7

Patrick, Chris. "Understanding supersonic combustion with numerical simulation." Scilight 2021, no. 21 (May 21, 2021): 211106. http://dx.doi.org/10.1063/10.0005106.

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8

Singh, Srishti, Shubham Agrawal, and Attreyee Ghosh. "Understanding Deep Earth Dynamics:A Numerical Modelling Approach." Current Science 112, no. 07 (April 1, 2017): 1463. http://dx.doi.org/10.18520/cs/v112/i07/1463-1473.

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9

Hart, R. "Enhancing rock stress understanding through numerical analysis." International Journal of Rock Mechanics and Mining Sciences 40, no. 7-8 (October 2003): 1089–97. http://dx.doi.org/10.1016/s1365-1609(03)00116-3.

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10

Apuani, Tiziana, Claudia Corazzato, Andrea Merri, and Alessandro Tibaldi. "Understanding Etna flank instability through numerical models." Journal of Volcanology and Geothermal Research 251 (February 2013): 112–26. http://dx.doi.org/10.1016/j.jvolgeores.2012.06.015.

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11

Yamagata, Kyoko, and Wakaba Koike. "Longitudinal Study of Numerical Understanding and Production." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 1PM—118–1PM—118. http://dx.doi.org/10.4992/pacjpa.79.0_1pm-118.

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12

Russo, J. Edward. "Understanding the effect of a numerical anchor." Journal of Consumer Psychology 20, no. 1 (January 2010): 25–27. http://dx.doi.org/10.1016/j.jcps.2009.12.006.

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13

Sun, Hao, Adwait Chawathé, Hussein Hoteit, Xundan Shi, and Lin Li. "Understanding Shale Gas Flow Behavior Using Numerical Simulation." SPE Journal 20, no. 01 (January 2, 2015): 142–54. http://dx.doi.org/10.2118/167753-pa.

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Summary Shale gas has changed the energy equation around the world, and its impact has been especially profound in the United States. It is now generally agreed that the fabric of shale systems comprises primarily organic matter, inorganic material, and natural fractures. However, the underlying flow mechanisms through these multiporosity and multipermeability systems are poorly understood. For instance, debate still exists about the predominant transport mechanism (diffusion, convection, and desorption), as well as the flow interactions between organic matter, inorganic matter, and fractures. Furthermore, balancing the computational burden of precisely modeling the gas transport through the pores vs. running full reservoir scale simulation is also contested. To that end, commercial reservoir simulators are developing new shale gas options, but some, for expediency, rely on simplification of existing data structures and/or flow mechanisms. We present here the development of a comprehensive multimechanistic (desorption, diffusion, and convection), multiporosity (organic materials, inorganic materials, and fractures), and multipermeability model that uses experimentally determined shale organic and inorganic material properties to predict shale gas reservoir performance. Our multimechanistic model takes into account gas transport caused by both pressure driven convection and concentration driven diffusion. The model accounts for all the important processes occurring in shale systems, including desorption of multicomponent gas from the organics' surface, multimechanistic organic/inorganic material mass transfer, multimechanistic inorganic material/fracture network mass transfer, and production from a hydraulically fractured wellbore. Our results show that a dual porosity, dual permeability (DPDP) model with Knudsen diffusion is generally adequate to model shale gas reservoir production. Adsorption can make significant contributions to original gas in place, but is not important to gas production because of adsorption equilibrium. By comparing triple porosity, dual permeability; DPDP; and single porosity, single permeability formulations under similar conditions, we show that Knudsen diffusion is a key mechanism and should not be ignored under low matrix pressure (Pematrix) cases, whereas molecular diffusion is negligible in shale dry gas production. We also guide the design of fractures by analyzing flow rate limiting steps. This work provides a basis for long term shale gas production analysis and also helps define value adding laboratory measurements.

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14

Jaroszyńska-Wolińska, Justyna, and Szymon Malinowski. "Numerical methods in understanding reaction pathways NOx oxidation." Budownictwo i Architektura 15, no. 3 (September 1, 2016): 075–81. http://dx.doi.org/10.24358/bud-arch_16_153_06.

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Different quantum chemical models were applied in energetic analysis of process of oxidation of NO and NO2 through reaction with ozone generated by non-thermal equilibrium (low temperature), atmospheric pressure plasma. The potential energy surfaces of systems comprising NO and NO2 with ozone were characterized. The NOx oxidation processes well known, at the molecular level, were modelled by ab initio quantum methods to calculate the total reaction energy, Et, of each step in the reaction chain. Chemistry was further applied in an attempt to detect the presence of any transition states to calculate the activation energy, Ea, of reactions (1) NO + O3 and (2) NO2 + O3 using the MP2 level of theory with three different basis sets and fine potential energy scan resolution.

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15

Thorsos, Eric I. "The numerical approach to understanding rough surface scattering." Journal of the Acoustical Society of America 83, S1 (May 1988): S9. http://dx.doi.org/10.1121/1.2025626.

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16

Boysen, Sarah T., and Karen I. Hallberg. "Primate Numerical Competence: Contributions Toward Understanding Nonhuman Cognition." Cognitive Science 24, no. 3 (September 2000): 423–43. http://dx.doi.org/10.1207/s15516709cog2403_4.

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17

McEvoy, John, and A. Mona O’Moore. "Number Conservation: A Fair Assessment of Numerical Understanding?" Irish Journal of Psychology 12, no. 3 (January 1991): 325–37. http://dx.doi.org/10.1080/03033910.1991.10557848.

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18

Ghosh, Karthik, and Amit K. Ghosh. "Understanding Statistical Information: The Problem of Numerical Interpretation." Archives of Internal Medicine 163, no. 18 (October 13, 2003): 2248. http://dx.doi.org/10.1001/archinte.163.18.2248-a.

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19

Puri, U. C., and T. Uomoto. "Numerical modeling—A new tool for understanding shotcrete." Materials and Structures 32, no. 4 (May 1999): 266–72. http://dx.doi.org/10.1007/bf02479596.

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20

Grabauskienė, Vaiva, and Kristina Siminauskienė. "THE UNDERSTANDING OF NUMERICAL EXPRESSIONS IN I-VI CLASSES." ŠVIETIMAS: POLITIKA, VADYBA, KOKYBĖ / EDUCATION POLICY, MANAGEMENT AND QUALITY 4, no. 2 (September 25, 2012): 6–15. http://dx.doi.org/10.48127/spvk-epmq/12.4.06a.

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The article analyzes the understanding of numerical expressions in primary and secondary schools. Algebraic education begins in primary school by numerical expressions calculations and transformations tasks. Understanding of these tasks remains important later while doing any of expressions, equations and inequalities tasks. The numerical expressions errors made by students show learning difficulties and inadequate understanding of concepts. This article presents 120 I–VI classes students numerical expressions solutions mistakes qualitative continent analysis. It revealed such problems as misunderstanding ideas of positional decimal counting system, constructive learning of rules, the lack of practical experience in transformating numerical expressions. There are proposed possiblities to improve the situation by paying more attention to decimal structure and the positions of numbers in primary classes, multipurpose numerical expressions analysis, the increasing of motivation to solving numerical expressions. Key words: errors of numerical expressions, links between arithmetic and algebra, primary and secondary school.

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21

Zamarian, Laura, Katharina M. A. Fürstenberg, Nadia Gamboz, and Margarete Delazer. "Understanding of Numerical Information during the COVID-19 Pandemic." Brain Sciences 11, no. 9 (September 17, 2021): 1230. http://dx.doi.org/10.3390/brainsci11091230.

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Media news during the Coronavirus Disease 2019 (COVID-19) pandemic often entail complex numerical concepts such as exponential increase or reproduction number. This study investigated whether people have difficulties in understanding such information and whether these difficulties are related to numerical competence, reflective thinking, and risk proneness. One hundred sixty-three participants provided answers to a numeracy scale focusing on complex numerical concepts relevant to COVID-19 (COV Numeracy Scale). They also provided responses to well-established objective and subjective scales, questions about affective states, and questions about the COVID-19 pandemic. Higher scores on the COV Numeracy Scale correlated with higher scores on the Health Numeracy Scale, in the Cognitive Reflection Test (CRT), and in self-assessments of verbal comprehension, mathematical intelligence, and subjective numeracy. Interestingly, scores on the COV Numeracy Scale also positively correlated with the number of consulted information sources about COVID-19. Accuracy in the CRT emerged as a significant predictor, explaining ca. 14% of variance on the COV Numeracy Scale. The results suggest that people with lower reflective thinking skills and lower subjective and objective numerical competence can be more at disadvantage when confronted with COVID-related numerical information in everyday life. These findings advise caution in the communication of relevant public health information that entails complex numerical concepts.

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22

Stanev, Emil. "Understanding Black Sea Dynamics: Overview of Recent Numerical Modeling." Oceanography 18, no. 2 (June 1, 2005): 56–75. http://dx.doi.org/10.5670/oceanog.2005.42.

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23

UMENO, Yoshitaka, and Atsushi KUBO. "Understanding Mechanism of Crack Velocity Transition by Numerical Simulation." NIPPON GOMU KYOKAISHI 92, no. 9 (2019): 347–51. http://dx.doi.org/10.2324/gomu.92.347.

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24

Rentschler, M., M. Neuhauser, J. C. Marongiu, and E. Parkinson. "Understanding casing flow in Pelton turbines by numerical simulation." IOP Conference Series: Earth and Environmental Science 49 (November 2016): 022004. http://dx.doi.org/10.1088/1755-1315/49/2/022004.

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25

Koike, Wakaba, and Kyoko Yamagata. "Numerical Understanding and Production in Pre-School Children (8)." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 3AM—119–3AM—119. http://dx.doi.org/10.4992/pacjpa.79.0_3am-119.

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26

Banks, James, and Zoë Oldfield. "Understanding Pensions: Cognitive Function, Numerical Ability and Retirement Saving." Fiscal Studies 28, no. 2 (June 2007): 143–70. http://dx.doi.org/10.1111/j.1475-5890.2007.00052.x.

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27

Dimits, Andris M., Chiaming Wang, Russel Caflisch, Bruce I. Cohen, and Yanghong Huang. "Understanding the accuracy of Nanbu’s numerical Coulomb collision operator." Journal of Computational Physics 228, no. 13 (July 2009): 4881–92. http://dx.doi.org/10.1016/j.jcp.2009.03.041.

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28

Sinclair, Anne, Anne Garin, and Chantal Tièche-Christinat. "Constructing and understanding of place value in numerical notation." European Journal of Psychology of Education 7, no. 3 (September 1992): 191–207. http://dx.doi.org/10.1007/bf03172825.

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29

Agrillo, Christian, and Angelo Bisazza. "Understanding the origin of number sense: a review of fish studies." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1740 (January 2018): 20160511. http://dx.doi.org/10.1098/rstb.2016.0511.

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The ability to use quantitative information is thought to be adaptive in a wide range of ecological contexts. For nearly a century, the numerical abilities of mammals and birds have been extensively studied using a variety of approaches. However, in the last two decades, there has been increasing interest in investigating the numerical abilities of teleosts (i.e. a large group of ray-finned fish), mainly due to the practical advantages of using fish species as models in laboratory research. Here, we review the current state of the art in this field. In the first part, we highlight some potential ecological functions of numerical abilities in fish and summarize the existing literature that demonstrates numerical abilities in different fish species. In many cases, surprising similarities have been reported among the numerical performance of mammals, birds and fish, raising the question as to whether vertebrates' numerical systems have been inherited from a common ancestor. In the second part, we will focus on what we still need to investigate, specifically the research fields in which the use of fish would be particularly beneficial, such as the genetic bases of numerical abilities, the development of these abilities and the evolutionary foundation of vertebrate number sense. This article is part of a discussion meeting issue ‘The origins of numerical abilities’.

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30

Chen, Zikuan, and Vince Calhoun. "Understanding the Morphological Mismatch between Magnetic Susceptibility Source and T2* Image." Magnetic Resonance Insights 6 (January 2013): MRI.S11920. http://dx.doi.org/10.4137/mri.s11920.

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Background and Purpose Recent research has shown that a T2* image (either magnitude or phase) is not identical to the internal spatial distribution of a magnetic susceptibility (χ) source. In this paper, we examine the reasons behind these differences by looking into the insights of T2*-weighted magnetic resonance imaging (T2*MRI) and provide numerical characterizations of the source/image mismatches by numerical simulations. Methods For numerical simulations of T2*MRI, we predefine a 3D χ source and calculate the complex-valued T2* image by intravoxel dephasing in presence and absence of diffusion. We propose an empirical α-power model to describe the overall source/image transformation. For a Gaussian-shaped χ source, we numerically characterize the source/image morphological mismatch in terms of spatial correlation and FWHM (full width at half maximum). Results In theory, we show that the χ-induced fieldmap is morphologically different from the χ source due to dipole effect, and the T2* magnitude image is related to the fieldmap by a quadratic transformation in the small phase angle regime, which imposes an additional morphological change. The numerical simulations with a Gaussian-shaped χ source show that a T2* magnitude image may suffer an overall source/image morphological shrinkage of 20% to 25% and that the T2* phase image is almost identical to the fieldmap thus maintaining a morphological mismatch from the χ source due to dipole effect. Conclusion The morphological mismatch between a bulk χ source and its T2* image is caused by the 3D convolution during tissue magnetization (dipole effect), the nonlinearity of the T2* magnitude and phase calculation, and the spin diffusion effect. In the small phase angle regime, the T2* magnitude exhibits an overall morphological shrinkage, and the T2* phase image suffers a dipole effect but maintains the χ-induced fieldmap morphology.

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31

Osman, Ibrahim R., Misfer S. El Souloli, and Khaled H. Kashan. "Numerical Concepts between the Reality of Elementary Students' Understanding and their Teachers' Beliefs towards this Understanding in Saudi Arabia." Journal of Educational and Psychological Studies [JEPS] 8, no. 2 (March 1, 2014): 319. http://dx.doi.org/10.24200/jeps.vol8iss2pp319-332.

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The aim of the study was to identify the understanding of elementary school students of numerical concepts, and the beliefs of their teachers about this understanding. To achieve this goal, the content of the mathematics sixth grade textbook was analyzed to identify the numerical concepts. Then, a conceptual diagnostic test consisting of 24 multiple-choice questions to measure the understanding of numerical concepts was designed. Another questionnaire was built to measure the teachers' beliefs about their students understanding of these concepts. The teachers' questionnaire included 24 concepts as well. The sample size was 1411 male and female students; and the sample size of teachers was 528 mathematics teachers. Both were drawn randomly from three cities in the Kingdom of Saudi Arabia. The test of students showed that about 4.2% of the numerical concepts were highly understandable, whereas the questionnaire showed that teachers believed their students understood 17% of the concepts. Furthermore, students moderately understood about 37.5% of the concepts, while the questionnaire showed that teachers believed their students moderately undersood about 83% of the concepts. Moreover, the test showed that about 58.3% of the concepts were poorly understood, whereas the questionnaire showed that there were no concepts that would be poorly understood. The study found that there were statistically significant differences due to gender, credential, and experience; however, there was no statistically significant difference attributed to training on teachers' beliefs about students' understanding of mathematics.

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32

Bogaerts, Annemie, Kathleen De Bleecker, Violeta Georgieva, Dieter Herrebout, Ivan Kolev, Myriam Madani, and Erik Neyts. "Numerical modeling for a better understanding of gas discharge plasmas." High Temperature Material Processes (An International Quarterly of High-Technology Plasma Processes) 9, no. 3 (2005): 321–44. http://dx.doi.org/10.1615/hightempmatproc.v9.i3.10.

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33

SHINJO, Junji, and Akira UMEMURA. "Detailed Numerical Simulation toward Understanding the Liquid Spray Formation Mechanisms." JAPANESE JOURNAL OF MULTIPHASE FLOW 25, no. 4 (2011): 331–38. http://dx.doi.org/10.3811/jjmf.25.331.

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34

Fuson, Karen C., Laura Grandau, and Patricia A. Sugiyama. "Early Childhood Corner: Achieving Numerical Understanding for All Young Children." Teaching Children Mathematics 7, no. 9 (May 2001): 522–26. http://dx.doi.org/10.5951/tcm.7.9.0522.

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Young children aged 3 to 7 can learn a great deal about numbers. In a home or daycare environment, this learning can occur as children experience daily routines. Young children will learn to count, match, see, and compare numbers if caregivers or older children count, show objects, and point out small numbers of things. Such informal teaching can be done while children play, eat, get dressed, go up and down stairs, jump, and otherwise move through the day. These activities are engaging and fun but need to be encouraged and modeled by adults or more advanced children in the group. In larger day-care or school settings, numerical understanding results from similar informal learning opportunities combined with more structured experiences that enable all children to engage in supported learning activities with adult and peer modeling and help.

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35

Paolo Ferro, Filippo Berto, and Luca Romanin. "Understanding powder bed fusion additive manufacturing phenomena via numerical simulation." Frattura ed Integrità Strutturale 14, no. 53 (May 11, 2020): 252–84. http://dx.doi.org/10.3221/igf-esis.53.21.

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36

Leka, K. D., and O. Steiner. "Understanding Small Solar Magnetic Structures: Comparing Numerical Simulations to Observations." Astrophysical Journal 552, no. 1 (May 2001): 354–71. http://dx.doi.org/10.1086/320445.

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37

Bartzke, Gerhard, Mark W. Schmeeckle, and Katrin Huhn. "Understanding heavy mineral enrichment using a three-dimensional numerical model." Sedimentology 65, no. 2 (September 30, 2017): 561–81. http://dx.doi.org/10.1111/sed.12392.

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38

Siegler, Robert S. "Improving the Numerical Understanding of Children From Low-Income Families." Child Development Perspectives 3, no. 2 (August 2009): 118–24. http://dx.doi.org/10.1111/j.1750-8606.2009.00090.x.

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39

KOIKE, Wakaba, and Kyoko YAMAGATA. "Development of the Understanding and Production of Numerical Notation (4)." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 1PM—082–1PM—082. http://dx.doi.org/10.4992/pacjpa.77.0_1pm-082.

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40

Mendel, M., S. Hergarten, and H. J. Neugebauer. "On a better understanding of hydraulic lift: A numerical study." Water Resources Research 38, no. 10 (October 2002): 1–1. http://dx.doi.org/10.1029/2001wr000911.

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41

Mahendra, I. Wayan Eka, and Ni Kadek Rini Purwati. "Factor Analysis at Item Test Conceptual Understanding Of Numerical Method." JPI (Jurnal Pendidikan Indonesia) 8, no. 1 (July 12, 2019): 77. http://dx.doi.org/10.23887/jpi-undiksha.v8i1.18289.

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Factor analysis at item test conceptual understanding of numerical method aims to find out whether the conceptual understanding indicators used can confirm a construct and ensure that the test is complied with the indicator. Data analysis is done using confirmatory factor analysis. Based on data analysis, one factor is formed with Eigen value 2.352 and variation is 58.8%. This result shows that the indicators of conceptual understanding ability are valid, and the test prepared in accordance with the indicators of conceptual understanding ability.

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42

Fereres, Sonia, Chris Lautenberger, A. Carlos Fernandez-Pello, David L. Urban, and Gary A. Ruff. "Understanding ambient pressure effects on piloted ignition through numerical modeling." Combustion and Flame 159, no. 12 (December 2012): 3544–53. http://dx.doi.org/10.1016/j.combustflame.2012.08.006.

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43

Ha, Moonsik, Jung Hee Lee, Chang Kyoung Choi, Jae Hyung Kim, and Young Ki Choi. "Understanding the structure of table-type dolmens using numerical analysis." Journal of Mechanical Science and Technology 28, no. 5 (May 2014): 1789–95. http://dx.doi.org/10.1007/s12206-014-0325-x.

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44

OKAWA, HIROTADA. "INITIAL CONDITIONS FOR NUMERICAL RELATIVITY: INTRODUCTION TO NUMERICAL METHODS FOR SOLVING ELLIPTIC PDEs." International Journal of Modern Physics A 28, no. 22n23 (September 20, 2013): 1340016. http://dx.doi.org/10.1142/s0217751x13400162.

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Numerical relativity became a powerful tool to investigate the dynamics of binary problems with black holes or neutron stars as well as the very structure of General Relativity. Although public numerical relativity codes are available to evolve such systems, a proper understanding of the methods involved is quite important. Here, we focus on the numerical solution of elliptic partial differential equations. Such equations arise when preparing initial data for numerical relativity, but also for monitoring the evolution of black holes. Because such elliptic equations play an important role in many branches of physics, we give an overview of the topic, and show how to numerically solve them with simple examples and sample codes written in C ++ and Fortran90 for beginners in numerical relativity or other fields requiring numerical expertise.

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45

Terreni, Jasmin, Andreas Borgschulte, Magne Hillestad, and Bruce D. Patterson. "Understanding Catalysis—A Simplified Simulation of Catalytic Reactors for CO2 Reduction." ChemEngineering 4, no. 4 (November 20, 2020): 62. http://dx.doi.org/10.3390/chemengineering4040062.

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The realistic numerical simulation of chemical processes, such as those occurring in catalytic reactors, is a complex undertaking, requiring knowledge of chemical thermodynamics, multi-component activated rate equations, coupled flows of material and heat, etc. A standard approach is to make use of a process simulation program package. However for a basic understanding, it may be advantageous to sacrifice some realism and to independently reproduce, in essence, the package computations. Here, we set up and numerically solve the basic equations governing the functioning of plug-flow reactors (PFR) and continuously stirred tank reactors (CSTR), and we demonstrate the procedure with simplified cases of the catalytic hydrogenation of carbon dioxide to form the synthetic fuels methanol and methane, each of which involves five chemical species undergoing three coupled chemical reactions. We show how to predict final product concentrations as a function of the catalyst system, reactor parameters, initial reactant concentrations, temperature, and pressure. Further, we use the numerical solutions to verify the “thermodynamic limit” of a PFR and a CSTR, and, for a PFR, to demonstrate the enhanced efficiency obtainable by “looping” and “sorption-enhancement”.

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46

Scherger, Nicole. "Technology Tips: Using Maple to Enhance Students' Understanding of Numerical Integration." Mathematics Teacher 103, no. 1 (August 2009): 76–80. http://dx.doi.org/10.5951/mt.103.1.0076.

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Typically, calculus students are introduced to the simplest numerical approximations of the definite integral through the process of finding the areas of rectangles. Students are initially shown how to use the endpoints of each subinterval to find lower and upper sums, a process that gives them a bound on the actual area. They are then shown, sometimes through a series of labor-intensive computations or through visualization with graphs, that as the number of rectangles, or partitions, increases, the approximations become more and more accurate. Somewhere in this process students are probably also shown how to use midpoints to obtain slightly more accurate numerical approximations. At this point, most calculus courses lead students toward the fundamental theorem of calculus, at which time they learn that they can evaluate a definite integral by finding the antiderivative and evaluating between the limits of integration.

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47

Fan, Shaotao, Cheng Bi Zhao, and You Hong Tang. "Understanding Regular Waves Effect on Ship by Numerical Wave Tank Simulation." Applied Mechanics and Materials 477-478 (December 2013): 259–64. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.259.

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This study establishes three-dimensional numerical wave tank based on the theory of viscous flow to simulate the unsteady motion response of a Wigley advancing in regular heading waves. The governing equations, Reynolds Averaged Navier-Stokes and continuity equations are discretized by finite volume method, a Reynolds-averaged NavierStokes solver is employed to predict the motions of ship, and volume of fluid method is adopted to capture the nonlinear free surface by writing user-defined functions. The outgoing waves are dissipated inside an artificial damping zone located at the rear part (about 1-2 wave lengths) of the wave tank. The numerical simulation results are compared with theoretical and experimental data from Delft University of Technology, and show good agreement with them. This research can be used to further analyze and predict hydrodynamic performance of ship and marine floating structures in waves and help to extend the applications of numerical wave tank.

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48

Stafylidou, Stamatia, and Stella Vosniadou. "The development of students’ understanding of the numerical value of fractions." Learning and Instruction 14, no. 5 (October 2004): 503–18. http://dx.doi.org/10.1016/j.learninstruc.2004.06.015.

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49

Sugawara, Daisuke, Kazuhisa Goto, and Bruce E. Jaffe. "Numerical models of tsunami sediment transport — Current understanding and future directions." Marine Geology 352 (June 2014): 295–320. http://dx.doi.org/10.1016/j.margeo.2014.02.007.

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50

YAMAGATA, Kyoko, and Wakaba KOIKE. "Early Development of the Understanding and Production of Numerical Notation (3)." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 1EV—142–1EV—142. http://dx.doi.org/10.4992/pacjpa.77.0_1ev-142.

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