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Journal articles on the topic 'Low-order method'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Yee, B. C., A. B. Wollaber, T. S. Haut, and H. Park. "A Stable 1D Multigroup High-Order Low-Order Method." Journal of Computational and Theoretical Transport 46, no. 1 (July 13, 2016): 46–76. http://dx.doi.org/10.1080/23324309.2016.1187172.

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2

Park, H. "Toward Asymptotic Diffusion Limit Preserving High-Order, Low-Order Method." Nuclear Science and Engineering 194, no. 11 (July 9, 2020): 952–70. http://dx.doi.org/10.1080/00295639.2020.1769390.

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3

Wollaber, A. B., H. Park, R. B. Lowrie, R. M. Rauenzahn, and M. A. Cleveland. "Multigroup Radiation Hydrodynamics with a High-Order–Low-Order Method." Nuclear Science and Engineering 185, no. 1 (January 2, 2017): 117–29. http://dx.doi.org/10.13182/nse16-45.

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4

Sudhakar, K., and G. R. Shevare. "Low-order panel method for internal flows." Journal of Aircraft 28, no. 4 (April 1991): 286–88. http://dx.doi.org/10.2514/3.46026.

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5

FUJIWARA, Toshitaka. "Design Method and Application of Low Order Compensator." Transactions of the Society of Instrument and Control Engineers 24, no. 7 (1988): 761–63. http://dx.doi.org/10.9746/sicetr1965.24.761.

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6

Turuwhenua, Jason. "An Improved Low Order Method for Corneal Reconstruction." Optometry and Vision Science 85, no. 3 (March 2008): E211—E218. http://dx.doi.org/10.1097/opx.0b013e318164ee9b.

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7

Kawai, Fukiko. "Design Method for Low-Order Disturbance Feedback Control." IEEJ Transactions on Electronics, Information and Systems 136, no. 5 (2016): 690–96. http://dx.doi.org/10.1541/ieejeiss.136.690.

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8

Korchowiec, Jacek, and Jakub Lewandowski. "Elongation cutoff technique: low-order scaling SCF method." Journal of Molecular Modeling 14, no. 8 (April 2, 2008): 651–58. http://dx.doi.org/10.1007/s00894-008-0295-y.

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9

Li, Ji-Cheng, Na-Na Wang, and Xu Kong. "Uzawa-Low method and preconditioned Uzawa-Low method for three-order block saddle point problem." Applied Mathematics and Computation 269 (October 2015): 626–36. http://dx.doi.org/10.1016/j.amc.2015.07.107.

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10

Micunovic, Milan. "Low order inelastic micromorphic polycrystals." Theoretical and Applied Mechanics, no. 28-29 (2002): 235–60. http://dx.doi.org/10.2298/tam0229235m.

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The paper deals with some fundamental issues essential for constitutive modelling of plastic behaviour of metals. Geometric and kinematic aspects of intragranular as well as intergranular plastic deformation of polycrystals are discussed. Homogeneous grain strains are composed into the resulting behaviour of representative volume element (RVE). A homogenization of total, plastic and elastic strains has been done. Constitutive equations by a self consistent method have been discussed. A simplest case of higher gradient theory is discussed. Elastic strain is covered by the efiective field homogenization method inside a RVE. It is underlined that plastic stretching and plastic spin are not independent. .

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11

Yan, Cheng, Xiuli Shen, and Fushui Guo. "Novel Two-Stage Method for Low-Order Polynomial Model." Mathematical Problems in Engineering 2018 (July 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/8156390.

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One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

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12

Lilja, M. "A Frequency Domain Method for Low Order Controller Design." IFAC Proceedings Volumes 23, no. 8 (August 1990): 223–28. http://dx.doi.org/10.1016/s1474-6670(17)52099-6.

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13

Li, Ya, Junjie Yang, Qi Feng, and Bingo Wing-Kuen Ling. "A Design Method for Low-Order Accurate Parallel Graphic Equalizers." Journal of the Audio Engineering Society 70, no. 4 (May 11, 2022): 284–93. http://dx.doi.org/10.17743/jaes.2021.0066.

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14

Saebø, Svein, and Peter Pulay. "A low-scaling method for second order Møller–Plesset calculations." Journal of Chemical Physics 115, no. 9 (September 2001): 3975–83. http://dx.doi.org/10.1063/1.1389291.

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15

Grace, S. M. "Fan broadband interaction noise modeling using a low-order method." Journal of Sound and Vibration 346 (June 2015): 402–23. http://dx.doi.org/10.1016/j.jsv.2015.02.013.

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16

Sun, Xiuxia, and Jianqin Mao. "Low-order controller design based on LMI using projection method." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3466–71. http://dx.doi.org/10.1016/s1474-6670(17)56592-1.

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17

Morgan, C. "A low-order modelling method for Z-domain transfer functions." Transactions of the Institute of Measurement and Control 9, no. 3 (July 1987): 165–68. http://dx.doi.org/10.1177/014233128700900310.

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18

Birajdar, Gajanan K., and Vijay H. Mankar. "Blind method for low-order interpolation detection in digital images." Australian Journal of Forensic Sciences 51, no. 3 (September 14, 2017): 318–30. http://dx.doi.org/10.1080/00450618.2017.1363289.

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19

Cao, Jie, and Ya-lei You. "New method for low order spectral model and its application." Applied Mathematics and Mechanics 27, no. 4 (April 2006): 477–84. http://dx.doi.org/10.1007/s10483-006-0407-z.

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20

Goleniewski, G. "Low-order mixed method finite elements in non-linear elasticity." Communications in Applied Numerical Methods 7, no. 1 (January 1991): 57–63. http://dx.doi.org/10.1002/cnm.1630070109.

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21

Li, Jianzhong, Yide Wang, Cédric Le Bastard, Zongze Wu, and Shaoyang Men. "Low-Complexity High-Order Propagator Method for Near-Field Source Localization." Sensors 19, no. 1 (December 23, 2018): 54. http://dx.doi.org/10.3390/s19010054.

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In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

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22

LU, HONGQIANG. "HIGH-ORDER DISCONTINUOUS GALERKIN SOLUTION OF LOW-RE VISCOUS FLOWS." Modern Physics Letters B 23, no. 03 (January 30, 2009): 309–12. http://dx.doi.org/10.1142/s0217984909018278.

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In this paper, the BR2 high-order Discontinuous Galerkin (DG) method is used to discretize the 2D Navier-Stokes (N-S) equations. The nonlinear discrete system is solved using a Newton method. Both preconditioned GMRES methods and block Gauss-Seidel method can be used to solve the resulting sparse linear system at each nonlinear step in low-order cases. In order to save memory and accelerate the convergence in high-order cases, a linear p-multigrid is developed based on the Taylor basis instead of the GMRES method and the block Gauss-Seidel method. Numerical results indicate that highly accurate solutions can be obtained on very coarse grids when using high order schemes and the linear p-multigrid works well when the implicit backward Euler method is employed to improve the robustness.

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23

Yamashita, Yasuki, and Yoichi Takebayashi. "Method and apparatus for speech recognition using both low‐order and high‐order parameter analyzation." Journal of the Acoustical Society of America 97, no. 2 (February 1995): 1365. http://dx.doi.org/10.1121/1.412156.

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24

Xinxin, JIA, WANG Lei, ZHANG Hao, SUN Xiaoling, DUAN Liya, and WANG Xin. "A Low-Order Model Method for 2-Phase Oil Reservoir Simulation." 应用数学和力学 42, no. 9 (2021): 891–99. http://dx.doi.org/10.21656/1000-0887.410235.

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25

Darby, Christopher L., William W. Hager, and Anil V. Rao. "Direct Trajectory Optimization Using a Variable Low-Order Adaptive Pseudospectral Method." Journal of Spacecraft and Rockets 48, no. 3 (May 2011): 433–45. http://dx.doi.org/10.2514/1.52136.

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26

PADOVANI, E., E. PRIOLO, and G. SERIANI. "LOW AND HIGH ORDER FINITE ELEMENT METHOD: EXPERIENCE IN SEISMIC MODELING." Journal of Computational Acoustics 02, no. 04 (December 1994): 371–422. http://dx.doi.org/10.1142/s0218396x94000233.

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The finite element method (FEM) is a numerical technique well suited to solving problems of elastic wave propagation in complex geometries and heterogeneous media. The main advantages are that very irregular grids can be used, free surface boundary conditions can be easily taken into account, a good reconstruction is possible of irregular surface topography, and complex geometries, such as curved, dipping and rough interfaces, intrusions, cusps, and holes can be defined. The main drawbacks of the classical approach are the need for a large amount of memory, low computational efficiency, and the possible appearance of spurious effects. In this paper we describe some experience in improving the computational efficiency of a finite element code based on a global approach, and used for seismic modeling in geophysical oil exploration. Results from the use of different methods and models run on a mini-superworkstation APOLLO DN10000 are reported and compared. With Chebyshev spectral elements, great accuracy can be reached with almost no numerical artifacts. Static condensation of the spectral element's internal nodes dramatically reduces memory requirements and CPU time. Time integration performed with the classical implicit Newmark scheme is very accurate but not very efficient. Due to the high sparsity of the matrices, the use of compressed storage is shown to greatly reduce not only memory requirements but also computing time. The operation which most affects the performance is the matrix-by-vector product; an effective programming of this subroutine for the storage technique used is decisive. The conjugate gradient method preconditioned by incomplete Cholesky factorization provides, in general, a good compromise between efficiency and memory requirements. Spectral elements greatly increase its efficiency, since the number of iterations is reduced. The most efficient and accurate method is a hybrid iterative-direct solution of the linear system arising from the static condensation of high order elements. The size of 2D models that can be handled in a reasonable time on this kind of computer is nowadays hardly sufficient, and significant 3D modeling is completely unfeasible. However the introduction of new FEM algorithms coupled with the use of new computer architectures is encouraging for the future.

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27

TURUWHENUA, JASON, and JON HENDERSON. "A Novel Low-Order Method for Recovery of the Corneal Shape." Optometry and Vision Science 81, no. 11 (November 2004): 863–71. http://dx.doi.org/10.1097/01.opx.0000145023.74460.ee.

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28

Drimer, N., and Y. Agnon. "An improved low-order boundary element method for breaking surface waves." Wave Motion 43, no. 3 (January 2006): 241–58. http://dx.doi.org/10.1016/j.wavemoti.2005.09.006.

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29

Wang, Youjiang, Moustafa Abdel-Maksoud, Keqi Wang, and Baowei Song. "Prediction of tip vortex cavitation inception with low-order panel method." Ocean Engineering 125 (October 2016): 124–33. http://dx.doi.org/10.1016/j.oceaneng.2016.08.013.

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30

Botti, Michele, Daniele A. Di Pietro, and Alessandra Guglielmana. "A low-order nonconforming method for linear elasticity on general meshes." Computer Methods in Applied Mechanics and Engineering 354 (September 2019): 96–118. http://dx.doi.org/10.1016/j.cma.2019.05.031.

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31

Dardalhon, F., J. C. Latche, and S. Minjeaud. "Analysis of a projection method for low-order nonconforming finite elements." IMA Journal of Numerical Analysis 33, no. 1 (May 28, 2012): 295–317. http://dx.doi.org/10.1093/imanum/drr053.

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32

Tsang, K. M., W. L. Lo, and A. B. Rad. "Frequency Response Testing and Low-Order Modelling by Relay Oscillation Method." HKIE Transactions 6, no. 2 (January 1999): 42–47. http://dx.doi.org/10.1080/1023697x.1999.10667805.

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33

de Magistris, Massimiliano, Luciano De Tommasi, Antonio Maffucci, and Giovanni Miano. "Low-Order Identification of Interconnects With the Generalized Method of Characteristics." IEEE Transactions on Electromagnetic Compatibility 49, no. 3 (August 2007): 670–76. http://dx.doi.org/10.1109/temc.2007.902195.

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34

Carstensen, Carsten, and Sophie Puttkammer. "A low-order discontinuous Petrov–Galerkin method for the Stokes equations." Numerische Mathematik 140, no. 1 (April 9, 2018): 1–34. http://dx.doi.org/10.1007/s00211-018-0965-3.

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35

Liang, Yinshan, and Jiangling Lu. "Direct Low Order Rational Approximations for Fractional Order Systems in Narrow Frequency Band: A Fix-Pole Method." Journal of Circuits, Systems and Computers 26, no. 04 (December 6, 2016): 1750065. http://dx.doi.org/10.1142/s0218126617500657.

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Rational approximations for fractional order systems are discussed in this paper. A fix-pole method is derived whereby a fractional order system is efficiently approximated by a low order integer system via simple algebraic algorithms. An adjacent congruent triangle (ACT) criterion is presented to analyze our method and compare it with existing results in a unified framework. The proposed fix-pole method has a trade-off that sacrifices partly the fitting error in exchange for simple approximations of complicated fractional order systems. The quality of the fix-pole approximations can be improved to be acceptable in practical or numerical implementations by narrowing the fitting frequency band. Finally, numerical examples illustrate the effectiveness of our methods.

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36

Khan, Tareq. "Low Complexity Fluctuation Measurement in Image Processing Considering Order." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4253. http://dx.doi.org/10.11591/ijece.v8i6.pp4253-4257.

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The standard deviation can measure the spread out of a set of numbers and entropy can measure the randomness. However, they do not consider the order of the numbers. This can lead to misleading results where the order of the numbers is vital. An image is a set of numbers (i.e. pixel values) that is sensitive to order. In this paper, a low complexity and efficient method for measuring the fluctuation is proposed considering the order of the numbers. The proposed method sums up the changes of consecutive numbers and can be used in image processing applications. Simulation shows that the proposed method is 8 to 33 times faster than other related works.

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37

sci, global. "A Stabilized Low Order Finite Element Method for Three Dimensional Elasticity Problems." Numerical Mathematics: Theory, Methods and Applications 13, no. 2 (June 2020): 281–95. http://dx.doi.org/10.4208/nmtma.oa-2019-0034.

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38

Lu, Xinjiang, Bowen Xu, and Pingzhong He. "A novel low-order spatiotemporal modeling method for nonlinear distributed parameter systems." Journal of Process Control 106 (October 2021): 84–93. http://dx.doi.org/10.1016/j.jprocont.2021.08.010.

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39

Syms, G. F. "Low-Order Method for Predicting Aerodynamic Performance Degradation Due to Ground Icing." Journal of Aircraft 39, no. 1 (January 2002): 59–64. http://dx.doi.org/10.2514/2.2895.

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40

Akamatsu, Mikio. "Applicability of Higher-Order TVD Method to Low Mach Number Compressible Flows." Transactions of the Japan Society of Mechanical Engineers Series B 61, no. 583 (1995): 974–81. http://dx.doi.org/10.1299/kikaib.61.974.

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41

Sahin, I., J. Crane, and K. Watson. "Added Mass Coefficients for Submerged Bodies by a Low-Order Panel Method." Journal of Fluids Engineering 115, no. 3 (September 1, 1993): 452–56. http://dx.doi.org/10.1115/1.2910159.

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The added mass coefficients for two and three-dimensional submerged bodies were calculated using a low-order panel code. The source and dipole strengths, and the panel surface area for each panel, were used to compute the integrals needed for added mass in all six degrees of motions. Several applications of this method were used in comparing the results with the theoretical, when available, experimental or other numerical results. The method was found to be successful in predicting the added mass coefficients using relatively low numbers of panels.

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42

Pan, Zhibin, Xiaoxiao Ma, Xiaoman Deng, and Sen Hu. "Low bit-rate information hiding method based on search-order-coding technique." Journal of Systems and Software 86, no. 11 (November 2013): 2863–69. http://dx.doi.org/10.1016/j.jss.2013.06.066.

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43

Sahin, Iskender, and Noriaki Okita. "Flow around circular and noncircular cylinders by a low-order panel method." Ocean Engineering 25, no. 7 (July 1998): 529–39. http://dx.doi.org/10.1016/s0029-8018(97)00026-7.

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44

Lee, Ho Woo, Jong Min Moon, Byung Kyu Kim, Jong Geun Park, and Se Won Lee. "A simple eigenvalue method for low-order D-BMAP/G/1 queues." Applied Mathematical Modelling 29, no. 3 (March 2005): 277–88. http://dx.doi.org/10.1016/j.apm.2004.07.004.

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45

Salaun, M., and S. Salmon. "Low-order finite element method for the well-posed bidimensional Stokes problem." IMA Journal of Numerical Analysis 35, no. 1 (January 23, 2014): 427–53. http://dx.doi.org/10.1093/imanum/drt063.

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46

Bizzari, Romain, Dorian Lahbib, Antoine Dauptain, Florent Duchaine, Stephane Richard, and Franck Nicoud. "Low order modeling method for assessing the temperature of multi-perforated plates." International Journal of Heat and Mass Transfer 127 (December 2018): 727–42. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2018.07.059.

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47

Ma, Chengbin, Junyi Cao, and Yue Qiao. "Polynomial-Method-Based Design of Low-Order Controllers for Two-Mass Systems." IEEE Transactions on Industrial Electronics 60, no. 3 (March 2013): 969–78. http://dx.doi.org/10.1109/tie.2012.2188869.

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48

Sideridis, A. B., and T. E. Simos. "A low-order embedded Runge—Kutta method for periodic initial-value problems." Journal of Computational and Applied Mathematics 44, no. 2 (December 1992): 235–44. http://dx.doi.org/10.1016/0377-0427(92)90013-n.

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49

Devia Narváez, Diana Marcela, Fernando Mesa, and German Correa-Vélez. "Numerical comparison by different methods (second order Runge Kutta methods, Heun method, fixed point method and Ralston method) to differential equations with initial condition." Scientia et Technica 25, no. 2 (June 30, 2020): 299–305. http://dx.doi.org/10.22517/23447214.24446.

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This manuscript contains a detailed comparison between numerical solution methods of ordinary differential equations, which start from the Taylor series method of order 2, stating that this series hinders calculations for higher order derivatives of functions of several variables, so that the Runge Kutta methods of order 2 are implemented, which achieve the required purpose avoiding the cumbersome calculations of higher order derivatives. In this document, different variants of the Runge-Kutta methods of order 2 will be exposed from an introduction and demonstration of the connection of these with the Taylor series of order 2, these methods are: the method of Heun, the method of midpoint and the Ralston method. It will be observed from the solution of test differential equations its respective error with respect to the analytical solution, obtaining an error index dictated by the mean square error EMC. Through this document we will know the best numerical approximation to the analytical solution of the different PVI (initial value problems) raised, also fixing a solution pattern for certain problems, that is, the appropriate method for each type of problem will be stipulated. It was observed that the Ralston method presented greater accuracy followed by the midpoint method and the Heun method, in the other PVI it is observed that the midpoint method yields the best numerical solution since it has a very low EMC and difficult to reach by the other methods.

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50

Wang, Mao Cai, Han Ping Hu, Guang Ming Dai, and Lei Pen. "Constructing Elliptic Curve Including Subgroup with Low Hamming Order." Advanced Materials Research 113-116 (June 2010): 6–9. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.6.

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In practical applications of pairing-based cryptosystems, the efficiency of pairing computation is a crucial factor. Recently, there have been many improvements for the computation of Tate pairing, which focuses on the arithmetical operations under given elliptic curve. Based to the characteristics that Miller’s algorithm will be improved tremendous if there are subgroups with order of low hamming prime above the elliptic curve, an algorithm of generating primes of low hamming with weight 3 is given in this paper. Then, we present an effective generation method of elliptic curve, which enable it feasible that there is certain some subgroup of low hamming prime order. The improvement of paring computation is marked above the elliptic curve generating by our method.

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