Relevant bibliographies by topics / Dispersion functions

Academic literature on the topic 'Dispersion functions'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Dispersion functions"

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Percival, D. J., and P. A. Robinson. "Generalized plasma dispersion functions." Journal of Mathematical Physics 39, no. 7 (July 1998): 3678–93. http://dx.doi.org/10.1063/1.532460.

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Robinson, P. A. "Relativistic plasma dispersion functions." Journal of Mathematical Physics 27, no. 5 (May 1986): 1206–14. http://dx.doi.org/10.1063/1.527127.

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Camacho, J. "Thermodynamic functions for Taylor dispersion." Physical Review E 48, no. 3 (September 1, 1993): 1844–49. http://dx.doi.org/10.1103/physreve.48.1844.

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Ideguchi, Shinsuke, Yuichi Tashiro, Takuya Akahori, Keitaro Takahashi, and Dongsu Ryu. "FARADAY DISPERSION FUNCTIONS OF GALAXIES." Astrophysical Journal 792, no. 1 (August 14, 2014): 51. http://dx.doi.org/10.1088/0004-637x/792/1/51.

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Katsuura, Hidefumi. "Dispersion points and continuous functions." Topology and its Applications 28, no. 3 (April 1988): 233–40. http://dx.doi.org/10.1016/0166-8641(88)90044-2.

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Melrose, D. B., J. I. Weise, and J. McOrist. "Relativistic quantum plasma dispersion functions." Journal of Physics A: Mathematical and General 39, no. 27 (June 21, 2006): 8727–40. http://dx.doi.org/10.1088/0305-4470/39/27/011.

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Robinson, P. A. "Relativistic and nonrelativistic plasma dispersion functions." Journal of Mathematical Physics 30, no. 11 (November 1989): 2484–87. http://dx.doi.org/10.1063/1.528528.

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Pernal, Katarzyna, and Krzysztof Szalewicz. "Third-order dispersion energy from response functions." Journal of Chemical Physics 130, no. 3 (January 21, 2009): 034103. http://dx.doi.org/10.1063/1.3058477.

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Dubrovskii, V. G. "Dispersion of scale-invariant size-distribution functions." Technical Physics Letters 43, no. 5 (May 2017): 413–15. http://dx.doi.org/10.1134/s1063785017050029.

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LUO, Q., and D. B. MELROSE. "Approximate plasma dispersion functions at relativistic temperatures." Journal of Plasma Physics 70, no. 6 (December 2004): 709–18. http://dx.doi.org/10.1017/s0022377804002867.

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Dissertations / Theses on the topic "Dispersion functions"

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Alves, Claudia Marins. "Stochastic models for the treatment of dispersion in the atmosphere." Laboratório Nacional de Computação Científica, 2006. http://www.lncc.br/tdmc/tde_busca/arquivo.php?codArquivo=135.

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Lagrangian stochastic models are a largely used tool in the study of passive substances dispersion inside the Atmospheric Boundary Layer. Its application is related to the trajectory computation of thousands of particles, that numerically simulate the dispersion of suspense substances in the atmosphere. In this study, the basic concepts related to the Lagrangian stochastic modelling are presented and discussed together with its main characteristics and its computational implementation, to the study of particles dispersion in the atmosphere. In a computational experiment, the obtained results are compared with observational data from the TRACT experiment, that took place in Europe in 1992. The input data needed for the dispersion model are extracted from simulations with the numerical weather forecast model RAMS. Dispersion over Rio de Janeiro region is also tested in a second experiment.
Modelos Lagrangianos estocásticos constituem ferramenta muito utilizada no estudo da dispersão de substâncias passivas na Camada Limite Atmosférica. Sua aplicação consiste em calcular a trajetória de milhares de partículas, que simulam numericamente a dispersão de uma substância em suspensão na atmosfera. Nesta tese, são apresentados e discutidos os conceitos básicos relacionados à Modelagem Lagrangiana Estocástica de Partículas, bem como suas principais características e sua implementação computacional, para o estudo da dispersão de partículas na atmosfera. Numa experimentação computacional, comparam-se os resultados obtidos com dados observacionais provenientes do experimento TRACT, realizado na Europa em 1992. Os dados de entrada necessários ao modelo de dispersão são extraídos de simulações do modelo de previsão numérica do tempo RAMS. A dispersão sobre o Estado do Rio de Janeiro é também testada em um segundo experimento.
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Tjulin, Anders. "Waves in space plasmas : Lower hybrid cavities and simple-pole distribution functions." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3527.

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Dreiling, Jennifer [Verfasser]. "Crustal structures in southern Madagascar and Sri Lanka in the context of Gondwana’s assembly and break-up : A study based on surface wave dispersion and receiver functions / Jennifer Dreiling." Berlin : Freie Universität Berlin, 2020. http://d-nb.info/121203175X/34.

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Goncalves, Juliana Bittencourt. "EMPREGO DE UM MODELO DE DISPERSÃO TURBULENTO NO ESTUDO DA UNIVERSALIDADE DA TAXA DE DISSIPAÇÃO DA ENERGIA." Universidade Federal de Santa Maria, 2010. http://repositorio.ufsm.br/handle/1/10254.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
This study employed different autocorrelation functions and Maclaurin series expansions in the derivation of expressions describing the dissipation rate of turbulent kinetic energy. These expressions have the same functional form, but are described in terms of different numerical coefficients. The values obtained for the numerical coefficients were used in a Lagrangian stochastic dispersion model to simulate the dispersion of contaminants in the Planetary Boundary Layer (PBL). The simulation results were compared with concentration data observed in the Copenhagen experiment. The good performance of the parameterization and analysis through statistical indices showed that the mathematical relationships that describe the turbulent dissipation rate present an uncertainty. The analysis developed in this study indicates that there is no a universal functional form describing the dissipation rate of turbulent energy.
Neste estudo foram empregadas diferentes funções de autocorrelação e expansões em série de Maclaurin na derivação de expressões que descrevem a taxa de dissipação da energia cinética turbulenta. Estas expressões apresentam a mesma forma funcional, porém são descritas em termos de diferentes coeficientes numéricos. Os valores obtidos para os coeficientes numéricos foram empregados em um modelo de dispersão estocástico Lagrangiano para simular a dispersão de contaminantes na Camada Limite Planetária (CLP). Os resultados das simulações foram comparados com dados de concentração do experimento de Copenhagen. O bom desempenho da parametrização e a análise através de índices estatísticos permitiram concluir que as relações matemáticas que descrevem a taxa de dissipação da turbulenta, apresentam uma incerteza. A análise desenvolvida nesse estudo permite concluir que não existe uma forma funcional universal descrevendo a taxa de dissipação de energia turbulenta.
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Gibbons, Luke J. "Nanocomposite Dispersion: Quantifying the Structure-Function Relationship." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77214.

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The dispersion quality of nanoinclusions within a matrix material is often overlooked when relating the effect of nanoscale structures on functional performance and processing/property relationships for nanocomposite materials. This is due in part to the difficulty in visualizing the nanoinclusion and ambiguity in the description of dispersion. Understanding the relationships between the composition of the nanofiller, matrix chemistry, processing procedures and resulting dispersion is a necessary step to tailor the physical properties. A method is presented that incorporates high-contrast imaging, an emerging scanning electron microscopy technique to visualize conductive nanofillers deep within insulating materials, with various image processing procedures to allow for the quantification and validation of dispersion parameters. This method makes it possible to quantify the dispersion of various single wall carbon nanotube (SWCNT)-polymer composites as a function of processing conditions, composition of SWCNT and polymer matrix chemistry. Furthermore, the methodology is utilized to show that SWCNT dispersion exhibits fractal-like behavior thus allowing for simplified quantitative dispersion analysis. The dispersion analysis methodology will be corroborated through comparison to results from small angle neutron scattering dispersion analysis. Additionally, the material property improvement of SWCNT nanocomposites are linked to the dispersion state of the nanostructure allowing for correlation between dispersion techniques, quantified dispersion of SWCNT at the microscopic scale and the material properties measured at the macroscopic scale.
Ph. D.
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Johnson, Erin R. "A density-functional theory including dispersion interactions." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/926.

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Alison, John Michael. "A dielectric study of lossy materials over the frequency range four to eighty-two gigahertz." Thesis, King's College London (University of London), 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263831.

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Mididoddi, Rajiv. "Investigation on material dispersion as a function of pressure and temperature for sensor design." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,109.

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Thesis (M.S.)--University of New Orleans, 2004.
Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering."--Thesis t.p. Vita. Includes bibliographical references.
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Nisa, Khoirin. "On multivariate dispersion analysis." Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2025.

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Cette thèse examine la dispersion multivariée des modelés normales stables Tweedie. Trois estimateurs de fonction variance généralisée sont discutés. Ensuite dans le cadre de la famille exponentielle naturelle deux caractérisations du modèle normal-Poisson, qui est un cas particulier de modèles normales stables Tweedie avec composante discrète, sont indiquées : d'abord par fonction variance et ensuite par fonction variance généralisée. Le dernier fournit la solution à un problème particulier d'équation de Monge-Ampère. Enfin, pour illustrer l'application de la variance généralisée des modèles Tweedie stables normales, des exemples à partir des données réelles sont fournis
This thesis examines the multivariate dispersion of normal stable Tweedie (NST) models. Three generalize variance estimators of some NST models are discussed. Then within the framework of natural exponential family, two characterizations of normal Poisson model, which is a special case of NST models with discrete component, are shown : first by variance function and then by generalized variance function. The latter provides a solution to a particular Monge-Ampere equation problem. Finally, to illustrate the application of generalized variance of normal stable Tweedie models, examples from real data are provided
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Pilemalm, Robert. "Dispersion forces in a four-component density functional theory framework." Thesis, Linköping University, Department of Physics, Chemistry and Biology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18487.

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The main purpose of this thesis is to implement the Gauss--Legendre quadrature for the dispersion coefficient. This has been done and can be now be made with different number of points. The calculations with this implementation has shown that the relativistic impact on helium, neon, argon and krypton is largest for krypton, that has the highest charge of its nucleus. It was also seen that the polarizability of neon as a function of the imaginary angular frequency decreases monotonically from a static value.

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Books on the topic "Dispersion functions"

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LeNeveu, D. M. Radionuclide response functions for the convection-dispersion equation from a point source along the axis of nested cylindrical media. Pinawa, MB: Whiteshell Laboratories, 1996.

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Wang, Yinkun. Energy dispersive x-ray diffraction system: A response function for the CZT detector and an analysis of noise a low momentum transfer arguments. Sudbury, Ont: Laurentian University, School of Graduate, 2006.

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F, Roach G., and Dassios G, eds. Mathematical methods in scattering theory and biomedical technology: Proceedings of a workshop dedicated to Professor Gary Roach. Harlow: Longman, 1998.

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Fried, Burton D., and Samuel D. Conte. Plasma Dispersion Function: The Hilbert Transform of the Gaussian. Elsevier Science & Technology Books, 2015.

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Oktay, Baysal, and United States. National Aeronautics and Space Administration., eds. Investigation of dispersion-relation-preserving scheme and spectral analysis methods for acoustic waves. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Oktay, Baysal, and United States. National Aeronautics and Space Administration., eds. Investigation of dispersion-relation-preserving scheme and spectral analysis methods for acoustic waves. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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United States. National Aeronautics and Space Administration., ed. Asymptotic boundary conditions for dissipative waves: General theory. [Washington, D.C.]: NASA, 1990.

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Wright, A. G. Statistical processes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0004.

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Two statistical processes affect performance: one concerns photon detection at the photocathode (binomial); and the other, gain at each dynode (Poisson). The combined statistical processes dictate resolution, both timing and pulse height. They are best examined using generating functions that are both elegant and capable of providing answers more efficiently than traditional approaches. The requirement for steady and pulsed light sources is an important one for testing and setting up procedures. The use of moments to test the quality of performance is illustrated for a steady DC light source. Amplification provided by a dynode stack is a cascade process, leading to dispersion in gain, and is also ideally handled with generating functions. Theory is developed for essentially continuous pulse height distributions, such as those produced by a multichannel analyser. Arrival time statistics for scintillators are investigated analytically and by Monte Carlo simulation. Treatment is given for dead time and scaling.
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Georgiev, Vladimir Simeonov, Raffaele Scandone, and Alessandro Michelangeli. Qualitative Properties of Dispersive PDEs. Springer, 2022.

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1922-, Charalambous George, and Doxastakis George, eds. Food emulsifiers: Chemistry, technology, functional properties and applications. Amsterdam: Elsevier, 1989.

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Book chapters on the topic "Dispersion functions"

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Wüthrich, Mario V., and Michael Merz. "Exponential Dispersion Family." In Springer Actuarial, 13–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_2.

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AbstractThis chapter introduces and discusses the exponential family (EF) and the exponential dispersion family (EDF). The EF and the EDF are by far the most important classes of distribution functions for regression modeling. They include, among others, the Gaussian, the binomial, the Poisson, the gamma, the inverse Gaussian distributions, as well as Tweedie’s models. We introduce these families of distribution functions, discuss their properties and provide several examples. Moreover, we introduce the Kullback–Leibler (KL) divergence and the Bregman divergence, which are important tools in model evaluation and model selection.
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Romanazzi, Mario, and Claudio Agostinelli. "Robustness, Dispersion, and Local Functions in Data Depth." In Advances in Theoretical and Applied Statistics, 13–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35588-2_2.

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Lai, Carlo G., and Ali G. Özcebe. "Causal Damping Ratio Spectra and Dispersion Functions in Geomaterials from the Exact Solution of Kramers-Kronig Equations of Viscoelasticity." In Continuous Media with Microstructure 2, 367–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28241-1_24.

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Dobson, J. F. "Dispersion (Van Der Waals) Forces and TDDFT." In Time-Dependent Density Functional Theory, 443–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-35426-3_30.

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Armstrong, Margaret. "Dispersion as a Function of Block Size." In Basic Linear Geostatistics, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58727-6_6.

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Liu, Baihua, Jingwei Zhang, Cong Wang, Cuiqing Teng, Hui Zhang, and Muhuo Yu. "Dispersion of Single-Walled Carbon Nanotubes in Organic Solvents DMAC." In Advanced Functional Materials, 841–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0110-0_91.

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Patel, Ashok R. "CHAPTER 14. Innovative Dispersion Strategies for Creating Structured Oil Systems." In Food Chemistry, Function and Analysis, 308–30. Cambridge: Royal Society of Chemistry, 2017. http://dx.doi.org/10.1039/9781788010184-00308.

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Dobson, John F. "Dispersion (van der Waals) Forces and TDDFT." In Fundamentals of Time-Dependent Density Functional Theory, 417–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23518-4_22.

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Koch, Herbert. "Adapted Function Spaces for Dispersive Equations." In Singular Phenomena and Scaling in Mathematical Models, 49–67. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00786-1_3.

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Laperrière, Luc. "Identifying and Quantifying Functional Elements Dispersions During Functional Analysis." In Geometric Design Tolerancing: Theories, Standards and Applications, 157–70. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5797-5_12.

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Conference papers on the topic "Dispersion functions"

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Narayanaswamy, Arvind. "Near-Field Radiative Transfer, Dispersion Forces, and Dyadic Green’s Functions." In ASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer. ASMEDC, 2009. http://dx.doi.org/10.1115/mnhmt2009-18136.

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Near–field force and energy exchange between two objects due to electrodynamic fluctuations give rise to dispersion forces such as Casimir and van der Waals forces, and thermal radiative transfer exceeding Plancks theory of blackbody radiation. The two phenomena dispersion forces and near–field enhancement of thermal radiation have common origins in the electromagnetic fluctuations. However, dispersion forces have contributions from quantum (zero–point) as well as thermal fluctuations whereas nearfield radiative transfer has contributions from thermal fluctuations alone. The forces are manifested through the Maxwell stress tensor of the electromagnetic field and radiative transfer through the Poynling vector. Both phenomena are elegantly described in terms of the Dyadic Greens function of the vector Helmholtz equation that governs the electromagnetic fields. In this talk, I will focus on the application of the Dyadic Greens function technique to near–field radiative transfer and dispersion forces. Despite the similarities, radiative transfer and forces have important differences that will be stressed on. I will end the talk with some open questions about the Dyadic Greens function formalism and its application to near–field radiative transfer.
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Ma, Yuanwei, Dezhong Wang, Zhilong Ji, and Nan Qian. "Dynamic Correcting Dispersion Parameters of Lagrangian Puff Model in Atmospheric Tracer Experiments." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-30347.

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In atmospheric dispersion models of nuclear accident, the empirical dispersion coefficients were obtained under certain experiment conditions, which is different from actual conditions. This deviation brought in the great model errors. A better estimation of the radioactive nuclide’s distribution could be done by correcting coefficients with real-time observed value. This reverse problem is nonlinear and sensitive to initial value. Genetic Algorithm (GA) is an appropriate method for this correction procedure. Fitness function is a particular type of objective function to achieving the set goals. To analysis the fitness functions’ influence on the correction procedure and the dispersion model’s forecast ability, four fitness functions were designed and tested by a numerical simulation. In the numerical simulation, GA, coupled with Lagrange dispersion model, try to estimate the coefficients with model errors taken into consideration. Result shows that the fitness functions, in which station is weighted by observed value and by distance far from release point, perform better when it exists significant model error. After performing the correcting procedure on the Kincaid experiment data, a significant boost was seen in the dispersion model’s forecast ability.
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Ingber, Amir, Da Wang, and Yuval Kochman. "Dispersion theorems via second order analysis of functions of distributions." In 2012 46th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2012. http://dx.doi.org/10.1109/ciss.2012.6310944.

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Wang, Xiuling, and Darrell W. Pepper. "A Hybrid Numerical Model for Simulating Atmospheric Dispersion." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80095.

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A hybrid numerical model for simulating atmospheric contaminant dispersion is developed. The hybrid numerical scheme employs an hp-adaptive finite element method coupled with a Lagrangian particle transport technique to solve the governing equations for atmospheric flow and species transport. A random walk/stochastic approach is used to generate Lagrangian particles that define the contaminant dispersion traces. A coarse mesh using low order shape functions is initially generated. Both the mesh and shape function order are subsequently refined and enriched in those regions where high computational error exist. Compared with fine mesh and high order numerical solutions, the hybrid scheme produces highly accurate solutions with reduced computational cost. A general probability distribution is used in the particle transport module for the random component of motion due to turbulent diffusion. Results depicting contaminant transport and dispersion in the atmosphere are presented. The computational efficiency of the hybrid numerical model is also discussed.
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Censor, Dan. "Relativistic invariance of dispersion-relations and their associated wave-operators and Green-functions." In 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel (IEEEI). IEEE, 2008. http://dx.doi.org/10.1109/eeei.2008.4736699.

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Hu, Fu-Gang, and Chao-Fu Wang. "Numerical dispersion in DG-FETD method using vector basis functions on brick elements." In 2013 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. IEEE, 2013. http://dx.doi.org/10.1109/aps.2013.6711504.

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Sutherland, John C., Kiley J. Reynolds, and David J. Fisk. "Dispersion functions and factors that determine resolution for DNA sequencing by gel electrophoresis." In Photonics West '96, edited by Gerald E. Cohn, Steven A. Soper, and C. H. Winston Chen. SPIE, 1996. http://dx.doi.org/10.1117/12.237621.

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de Lima, Thuany Patrícia Costa, Hrvoje Tkalčić, Seongryong Kim, and Jordi Julià. "Bayesian Inversion of Receiver Functions and Surface Wave Dispersion Data in the Brazilian Northeast." In 15th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 31 July-3 August 2017. Brazilian Geophysical Society, 2017. http://dx.doi.org/10.1190/sbgf2017-313.

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Malcolm Ng Mou Kehn and Eva Rajo Iglesias. "Moment method analysis of dispersion in SRR-type FSS loaded rectangular waveguides using spectral domain green’s functions and RWG basis functions." In 2007 IEEE Antennas and Propagation Society International Symposium. IEEE, 2007. http://dx.doi.org/10.1109/aps.2007.4395456.

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Malladi, Vijaya V. N. Sriram, Mohammad I. Albakri, Pablo A. Tarazaga, and Serkan Gugercin. "Data-Driven Modeling Techniques to Estimate Dispersion Relations of Structural Components." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8135.

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Dispersion relations describe the frequency-dependent nature of elastic waves propagating in structures. Experimental determination of dispersion relations of structural components, such as the floor of a building, can be a tedious task, due to material inhomogeneity, complex boundary conditions, and the physical dimensions of the structure under test. In this work, data-driven modeling techniques are utilized to reconstruct dispersion relations over a predetermined frequency range. The feasibility of this approach is demonstrated on a one-dimensional beam where an exact solution of the dispersion relations is attainable. Frequency response functions of the beam are obtained numerically over the frequency range of 0–50kHz. Data-driven dynamical model, constructed by the vector fitting approach, is then deployed to develop a state-space model based on the simulated frequency response functions at 16 locations along the beam. This model is then utilized to construct dispersion relations of the structure through a series of numerical simulations. The techniques discussed in this paper are especially beneficial to such scenarios where it is neither possible to find analytical solutions to wave equations, nor it is feasible to measure dispersion curves experimentally. In the present work, actual experimental data is left for future work, but the complete framework is presented here.
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Reports on the topic "Dispersion functions"

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Gok, R., H. Mahdi, H. Al-Shukri, and A. Rodgers. Crustal Structure of Iraq from Receiver Functions and Surface Wave Dispersion. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/894780.

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Lebedev, V. A., M. Bickley, S. Schaffner, J. van Zeijts, G. A. Krafft, and C. Watson. Correction of dispersion and the betatron functions in the CEBAF accelerator. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/378904.

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Lebedev, V. A., M. Bickley, S. Schaffner, J. van Zeijts, G. A. Krafft, and C. Watson. Correction of dispersion and the betatron functions in the CEBAF accelerator. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/10165710.

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Ammon, Charles J., Minoo Kosarian, and Robert B. Hermann. Simultaneous Inversion of Receiver Functions, Multi-Mode Dispersion, and Travel-Time Tomography for Lithospheric Structure Beneath the Middle East and North Africa. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada455320.

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Tsoupas N., H. Huang, F. Meot, J. Morris, and S. Nemesure. An online application to measue the dispersion function in AGS. Office of Scientific and Technical Information (OSTI), May 2013. http://dx.doi.org/10.2172/1087539.

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Fieguth, T., S. Kheifets, and J. J. Murray. Relationship of field components and the matched dispersion function in Arc achromats. Office of Scientific and Technical Information (OSTI), August 1986. http://dx.doi.org/10.2172/5330980.

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Coleman, Matthew, Scott Higinbotham, and Aisha Arroyo. Potential for Concordance between Plurality and Instant-Runoff Election Algorithms as a Function of Ballot Dispersion. Journal of Young Investigators, March 2021. http://dx.doi.org/10.22186/jyi.39.3.32-37.

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Julia, Jordi, Charles J. Ammon, and Robert B. Herrimann. Lithospheric Structure of the Arabian Shield from the Joint Inversion of Receiver Function and Surface-Wave Dispersion Observations. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada456390.

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Jury, William A., and David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, January 1994. http://dx.doi.org/10.32747/1994.7568772.bard.

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Abstract:
This report describes activity conducted in several lines of research associated with field-scale water and solute processes. A major effort was put forth developing a stochastic continuum analysis for an important class of problems involving flow of reactive and non reactive chemicals under steady unsaturated flow. The field-scale velocity covariance tensor has been derived from local soil properties and their variability, producing a large-scale description of the medium that embodies all of the local variability in a statistical sense. Special cases of anisotropic medium properties not aligned along the flow direction of spatially variable solute sorption were analysed in detail, revealing a dependence of solute spreading on subtle features of the variability of the medium, such as cross-correlations between sorption and conductivity. A novel method was developed and tested for measuring hydraulic conductivity at the scale of observation through the interpretation of a solute transport outflow curve as a stochastic-convective process. This undertaking provided a host of new K(q) relationships for existing solute experiments and also laid the foundation for future work developing a self-consistent description of flow and transport under these conditions. Numerical codes were developed for calculating K(q) functions for a variety of solute pulse outflow shapes, including lognormal, Fickian, Mobile-Immobile water, and bimodal. Testing of this new approach against conventional methodology was mixed, and agreed most closely when the assumptions of the new method were met. We conclude that this procedure offers a valuable alternative to conventional methods of measuring K(q), particularly when the application of the method is at a scale (e.g. and agricultural field) that is large compared to the common scale at which conventional K(q) devices operate. The same problem was approached from a numerical perspective, by studying the feasibility of inverting a solute outflow signal to yield the hydraulic parameters of the medium that housed the experiment. We found that the inverse problem was solvable under certain conditions, depending on the amount of noise in the signal and the degree of heterogeneity in the medium. A realistic three dimensional model of transient water and solute movement in a heterogeneous medium that contains plant roots was developed and tested. The approach taken was to generate a single realization of this complex flow event, and examine the results to see whether features were present that might be overlooked in less sophisticated model efforts. One such feature revealed is transverse dispersion, which is a critically important component in the development of macrodispersion in the longitudinal direction. The lateral mixing that was observed greatly exceeded that predicted from simpler approaches, suggesting that at least part of the important physics of the mixing process is embedded in the complexity of three dimensional flow. Another important finding was the observation that variability can produce a pseudo-kinetic behavior for solute adsorption, even when the local models used are equilibrium.
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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.
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