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Relevant bibliographies by topics / Numerical Diffusion

Academic literature on the topic 'Numerical Diffusion'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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Journal articles on the topic "Numerical Diffusion"

1

Jaichuang, Atit, and Wirawan Chinviriyasit. "Numerical Modelling of Influenza Model with Diffusion." International Journal of Applied Physics and Mathematics 4, no. 1 (2014): 15–21. http://dx.doi.org/10.7763/ijapm.2014.v4.247.

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LACHTIOUI, Y., M. MAZROUI, and Y. BOUGHALEB. "COMMENSURABILITY EFFECTS ON DIFFUSION PROCESS IN STEPPED STRUCTURES." Modern Physics Letters B 25, no. 21 (August 20, 2011): 1749–60. http://dx.doi.org/10.1142/s0217984911027005.

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This study deals with the investigation of diffusion process of one-dimensional system with steps for adsorbates interacting via the nearest-neighbor harmonic forces. The results are obtained from numerical studies, utilizing the method of stochastic Langevin dynamics. To study commensurability effects and the role of steps in the behavior of the diffusing particles, we have computed the diffusion coefficient for large concentrations and several interaction strengths. Our numerical results show that the diffusive behavior is reduced for commensurate structure case when the ground state has only one particle per one period of the substrate potential and enhanced for incommensurate density. Furthermore, the dynamic is qualitatively similar to that obtained in the case of no steps but with a clear reduction of the diffusion rate. Implications of these findings are discussed.

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Raymond, William H. "Diffusion and Numerical Filters." Monthly Weather Review 122, no. 4 (April 1994): 757–61. http://dx.doi.org/10.1175/1520-0493(1994)122<0757:danf>2.0.co;2.

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D'Isidoro, M., A. Maurizi, and F. Tampieri. "Effects of resolution on the relative importance of numerical and physical horizontal diffusion in atmospheric composition modelling." Atmospheric Chemistry and Physics 10, no. 6 (March 24, 2010): 2737–43. http://dx.doi.org/10.5194/acp-10-2737-2010.

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Abstract. Numerical diffusion induced by advection has a large influence on concentration of substances in atmospheric composition models. At coarse resolution numerical effects dominate, whereas at increasing model resolution a description of physical diffusion is needed. A method to investigate the effects of changing resolution and Courant number is defined here and is applied to the WAF advection scheme (used in BOLCHEM), evidencing a sub-diffusive process. The spread rate from an instantaneous source caused by numerical diffusion is compared to that produced by the physical diffusion necessary to simulate unresolved turbulent motions. The time at which the physical diffusion process overpowers the numerical spread is estimated, and it is shown to reduce as the resolution increases, and to increase with wind velocity.

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Bengtsson, Lisa, Sander Tijm, Filip Váňa, and Gunilla Svensson. "Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME." Journal of Applied Meteorology and Climatology 51, no. 1 (January 2012): 54–67. http://dx.doi.org/10.1175/jamc-d-11-032.1.

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AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

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GEORGE, E., J. GLIMM, X. L. LI, A. MARCHESE, Z. L. XU, J. W. GROVE, and DAVID H. SHARP. "Numerical methods for the determination of mixing." Laser and Particle Beams 21, no. 3 (July 2003): 437–42. http://dx.doi.org/10.1017/s0263034603213239.

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We present a Rayleigh–Taylor mixing rate simulation with an acceleration rate falling within the range of experiments. The simulation uses front tracking to prevent interfacial mass diffusion. We present evidence to support the assertion that the lower acceleration rate found in untracked simulations is caused, at least to a large extent, by a reduced buoyancy force due to numerical interfacial mass diffusion. Quantitative evidence includes results from a time-dependent Atwood number analysis of the diffusive simulation, which yields a renormalized mixing rate coefficient for the diffusive simulation in agreement with experiment. We also present the study of Richtmyer–Meshkov mixing in cylindrical geometry using the front tracking method and compare it with the experimental results.

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Abrashina-Zhadaeva, Natali. "A SPLITTING TYPE ALGORITHM FOR NUMERICAL SOLUTION OF PDES OF FRACTIONAL ORDER." Mathematical Modelling and Analysis 12, no. 4 (December 31, 2007): 399–408. http://dx.doi.org/10.3846/1392-6292.2007.12.399-408.

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Fractional order diffusion equations are generalizations of classical diffusion equations, treating super‐diffusive flow processes. In this paper, we examine a splitting type numerical methods to solve a class of two‐dimensional initial‐boundary value fractional diffusive equations. Stability, consistency and convergence of the methods are investigated. It is shown that both schemes are unconditionally stable. A numerical example is presented.

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D'Isidoro, M., A. Maurizi, and F. Tampieri. "Effects of resolution on the relative importance of numerical and physical diffusion in atmospheric composition modelling." Atmospheric Chemistry and Physics Discussions 9, no. 5 (October 28, 2009): 22865–81. http://dx.doi.org/10.5194/acpd-9-22865-2009.

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Abstract. Numerical diffusion induced by advection has a large influence on concentration of substances in atmospheric composition models. At coarse resolutions numerical effects dominate, whereas at increasing model resolutions a description of physical diffusion is needed. The effects of changing resolution and Courant number are investigated for the WAF advection scheme (used in BOLCHEM), evidencing a sub-diffusive process. The spreading rate from an instantaneous source is compared with the physical diffusion necessary to simulate unresolved turbulent motions. The time at which the physical diffusion process overpowers the numerical spreading is estimated, and is shown to reduce as the resolution increases, and to increase with wind velocity.

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Hwang, Sooncheol, and Sangyoung Son. "Development of an Advection-diffusion Model Using Depth-integrated Equations Based on GPU Acceleration." Journal of the Korean Society of Hazard Mitigation 21, no. 1 (February 28, 2021): 281–89. http://dx.doi.org/10.9798/kosham.2021.21.1.281.

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A scalar transport model is developed by adding a depth-averaged advection-diffusion equation to Celeris Advent, which is a Boussinesq-type numerical model that utilizes GPU acceleration. The hybrid finite volume-finite difference method is used to guarantee numerical stability along with the high accuracy of the Boussinesq equation. The advective and diffusive terms are numerically discretized using the finite volume and finite difference methods, respectively. Results of a one-dimensional scalar advection benchmark test showed that the scalar advection by the proposed model was very close to the analytical solution without any remarkable numerical diffusion. In addition, two benchmark tests using experimental data from different hydraulic experiments were numerically reproduced, and the computed results and observed data for scalar transport were found to be in good agreement. The developed model is expected to contribute to real-time disaster prediction for contaminant spills and can assist in preparing countermeasures for these types of disasters.

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Somjaivang, Dussadee, and Settapat Chinviriyasit. "Numerical Modeling of an Influenza Epidemic Model with Vaccination and Diffusion." International Journal of Applied Physics and Mathematics 4, no. 1 (2014): 68–74. http://dx.doi.org/10.7763/ijapm.2014.v4.257.

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Dissertations / Theses on the topic "Numerical Diffusion"

1

Lunney, Michael E. "Numerical dynamics of reaction-diffusion equations." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ61659.pdf.

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Abercrombie, Stuart Christopher Benedict. "Numerical simulation of diffusion controlled reactions." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401748.

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Gryaznov, Denis, Juergen Fleig, and Joachim Maier. "Numerical study of grain boundary diffusion." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-195828.

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Nagaiah, Chamakuri. "Adaptive numerical simulation of reaction-diffusion systems." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985277882.

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Patel, Mayur K. "On the false-diffusion problem in the numerical modelling of convection-diffusion processes." Thesis, University of Greenwich, 1986. http://gala.gre.ac.uk/8697/.

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This thesis is concerned with the classification and evaluation of various numerical schemes that are available for computing solutions for fluid-flow problems, and secondly, with the development of an improved numerical discretisation scheme of the finite-volume type for solving steady-state differential equations for recirculating flows with and without sources. In an effort to evaluate the performance of the various numerical schemes available, some standard test cases were used. The relative merits of the schemes were assessed by means of one-dimensional laminar flows and two-dimensional laminar and turbulent flows, with and without sources. Furthermore, Taylor series expansion analysis was also utilised to examine the limitations that were present. The outcome of this first part of the work was a set of conclusions, concerning the accuracy of the numerous schemes tests, vis-a-vis their stability, ease of implementation, and computational costs. It is hoped that these conclusions can be used by `computational fluid-dynamics' practitioners in deciding on an optimum choice of scheme for their particular problem. From the understanding gained during the first part of the study, and in an effort to combine the attributes of a successful discretisation scheme, eg positive coefficients. conservation and the elimination of 'false-diffusion', a new flow-oriented finite-volume numerical scheme was devised and applied to several test cases in order to evaluate its performance. The novel approach in formulating the new CUPID* scheme (for Corner UPw^nDing) underlines the idea of focussing attention at the control-volume corners rather than at the control-volume cell-faces. In two-dimensions, this leads to an eight neighbour influence for the central grid point value, depending on the flow-directions at the corners of the control-volume. In the formulation of the new scheme, false-diffusion is considered from a pragmatic perspective, with emphasis on physics rather than on strict mathematical considerations such as the order of discretisation, etc. The accuracy of the UPSTREAM scheme (for JJPwind in STREAMIines) indicates that although it is formally only first-order accurate, it considerably reduces 'false-diffusion'. Scalar transport calculations (without sources) show that the UPSTREAM scheme predicts bounded solutions which are more accurate than the upwind-difference scheme and the unbounded skew-upstream-difference scheme. Furthermore, for laminar and turbulent flow calculations, improved results are obtained when compared with the performances of the other schemes. The advantage of the UPSTREAM-difference scheme is that all the influence coefficients are always positive and thus the coefficient matrices are suitable for iterative solution procedures. Finally, the stability and convergence characteristics are similar to those of the upwind-difference scheme, eg converged solutions are guaranteed. What cannot be guaranteed, however, is the conservatism of the scheme and it is recommended that future work should be directed towards improving that disadvantage.

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Gryaznov, Denis, Juergen Fleig, and Joachim Maier. "Numerical study of grain boundary diffusion: size effects." Diffusion fundamentals 2 (2005) 49, S. 1-2, 2005. https://ul.qucosa.de/id/qucosa%3A14382.

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Ferguson, R. C. "Numerical techniques for the drift-diffusion semiconductor equations." Thesis, University of Bath, 1996. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362239.

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Moroney, Benjamin F., Timothy Stait-Gardner, Gang Zheng, and William S. Price. "Numerical analysis of NMR diffusion experiments in complex systems." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-185579.

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Ryu, Seungoh. "Numerical modeling of the carbonate and the sandstone formations." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-192171.

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It is of interest in various scientific and industrial contexts to make a reliable estimation of the transport properties of porous media via more accessible probes such as NMR that yield information on static pore geometry and porosity. When the pore geometry is simple, there are empirical recipes that have long proven reliable in bridging the gap. For heterogeneous systems, such recipes fail to give a consistent prediction and invite case-by-case modifications. This is just one of many indications that the complex pore geometry erodes the predictive power of empirical laws that work well in simpler situations. Heterogeneity combined with sizeable diffusive coupling in extended pore space further undermines the validity of the MR interpretation based on simple pore geometry. On top of this, possible spatial variation of surface relaxivity may further complicate the interpretation. Resolution of these issues for real life samples requires elaborate simulations in tandem with experimental verifications on the shared pore geometry. We report on a recent progress which allows combined parallel Lattice Boltzmann and random walk simulations to study transport and diffusion properties in various types of pore geometry, from simple 2D micro-fluidic mazes, 3D glass-bead packs and sandstones to more complex carbonates.

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Meral, Gulnihal. "Numerical Solution Of Nonlinear Reaction-diffusion And Wave Equations." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610568/index.pdf.

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In this thesis, the two-dimensional initial and boundary value problems (IBVPs) and the one-dimensional Cauchy problems defined by the nonlinear reaction- diffusion and wave equations are numerically solved. The dual reciprocity boundary element method (DRBEM) is used to discretize the IBVPs defined by single and system of nonlinear reaction-diffusion equations and nonlinear wave equation, spatially. The advantage of DRBEM for the exterior regions is made use of for the latter problem. The differential quadrature method (DQM) is used for the spatial discretization of IBVPs and Cauchy problems defined by the nonlinear reaction-diffusion and wave equations. The DRBEM and DQM applications result in first and second order system of ordinary differential equations in time. These systems are solved with three different time integration methods, the finite difference method (FDM), the least squares method (LSM) and the finite element method (FEM) and comparisons among the methods are made. In the FDM a relaxation parameter is used to smooth the solution between the consecutive time levels. It is found that DRBEM+FEM procedure gives better accuracy for the IBVPs defined by nonlinear reaction-diffusion equation. The DRBEM+LSM procedure with exponential and rational radial basis functions is found suitable for exterior wave problem. The same result is also valid when DQM is used for space discretization instead of DRBEM for Cauchy and IBVPs defined by nonlinear reaction-diffusion and wave equations.

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Books on the topic "Numerical Diffusion"

1

Bouzon, J. Mathematical and numerical treatment of diffusion. Englewood Cliffs, NJ: PTR Prentice Hall, 1994.

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1941-, Vreugdenhil Cornelis Boudewijn, and Koren Barry, eds. Numerical methods for advection--diffusion problems. Braunschweig: Vieweg, 1993.

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Mei, Zhen. Numerical Bifurcation Analysis for Reaction-Diffusion Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04177-2.

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Mei, Zhen. Numerical Bifurcation Analysis for Reaction-Diffusion Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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United States. National Aeronautics and Space Administration, ed. Numerical calculation of subsonic jets in crossflow with reduced numerical diffusion. [Washington, D.C.]: National Aeronautics and Space Administration, 1985.

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6

Mendes, Nathan, Marx Chhay, Julien Berger, and Denys Dutykh. Numerical Methods for Diffusion Phenomena in Building Physics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31574-0.

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United States. National Aeronautics and Space Administration., ed. Order of accuracy of QUICK and related convection-diffusion schemes. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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Mavriplis, Dimitri. Multigrid approaches to non-linear diffusion problems on unstructured meshes. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2001.

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9

Hundsdorfer, Willem, and Jan Verwer. Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-09017-6.

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1946-, Verwer J. G., ed. Numerical solution of time-dependent advection-diffusion-reaction equations. Berlin: Springer, 2003.

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Book chapters on the topic "Numerical Diffusion"

1

Joyce, Philip. "Diffusion Lattice Model." In Practical Numerical C Programming, 185–96. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6128-6_12.

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Janssen, Jacques, Oronzio Manca, and Raimondo Manca. "Numerical Methods." In Applied Diffusion Processes from Engineering to Finance, 219–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118578339.ch8.

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Kalz, Erik. "Numerical Results." In Diffusion under the Effect of Lorentz Force, 23–31. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-39518-6_3.

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Quarteroni, Alfio. "Diffusion-transport-reaction equations." In Numerical Models for Differential Problems, 315–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49316-9_13.

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Quarteroni, Alfio. "Diffusion-transport-reaction equations." In Numerical Models for Differential Problems, 291–338. Milano: Springer Milan, 2014. http://dx.doi.org/10.1007/978-88-470-5522-3_12.

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Horgmo Jæger, Karoline, and Aslak Tveito. "The Diffusion Equation." In Differential Equations for Studies in Computational Electrophysiology, 21–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30852-9_3.

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AbstractThe diffusion equation appears in many applications in science and engineering, and computational physiology is no exception. In its most basic form, the diffusion equation is also useful as an example of how to deal with a PDE using numerical methods. We will start by considering it as a stand-alone model, but in the next chapterswe will study it in combination with non-linear ODEs. This chapter therefore serves as a warm-up for the more complex models. We will also follow the path we started above. In the very simplest case of an ODE, we found a formula for the solution of both the differential equation and the numerical scheme approximating the equation. One nice consequence of this is that, as we saw above, we can explicitly study the error introduced by the numerical approximation. In this chapter, we will use the same approach to study the error of the numerical approximation of the diffusion equation.

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Vreugdenhil, Cornelis B. "Numerical Accuracy for Convection—Diffusion." In Computational Hydraulics, 61–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-95578-5_12.

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Vreugdenhil, Cornelis B. "Numerical Accuracy for Diffusion Problems." In Computational Hydraulics, 43–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-95578-5_8.

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Pereira da Silva, Luciano, Messias Meneguette Junior, and Carlos Henrique Marchi. "Numerical Modeling of Heat Diffusion." In Numerical Solutions Applied to Heat Transfer with the SPH Method, 7–49. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28946-0_2.

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Mei, Zhen. "Reaction-Diffusion Equations." In Numerical Bifurcation Analysis for Reaction-Diffusion Equations, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04177-2_1.

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Conference papers on the topic "Numerical Diffusion"

1

Timofte, Claudia, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Upscaling in Reaction-Diffusion Problems." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790202.

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Berres, Stefan. "Identification of Piecewise Linear Diffusion Function in Convection‐Diffusion Equation with Overspecified Boundary." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2991043.

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Şerban, Sorina, Laura Strugariu, Ludovic Dan Lemle, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Teaching Molecular Diffusion by Using ChimUniv." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636788.

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Kowar, Richard, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Real-Valued Semigroups and (Causal) Diffusion." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636876.

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Skramlik, Jan, Miloslav Novotny, Karel Suhajda, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Modeling of Diffusion in Porous Medium." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636975.

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Tracinà, Rita, Mariano Torrisi, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Quasi Self-adjoint Reaction Diffusion Systems." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637880.

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Koprucki, Thomas, and Klaus Gartner. "Discretization scheme for drift-diffusion equations with strong diffusion enhancement." In 2012 12th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD). IEEE, 2012. http://dx.doi.org/10.1109/nusod.2012.6316560.

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Guirardello, R. "A variational formulation for the diffusion equation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756662.

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Dan Lemle, Ludovic, Flavius Lucian Pater, Adela Berdie, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Kato’s Type Inequality for Symmetric Diffusion Operators." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636782.

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Mustaffa, I., I. Mizuar, M. M. M. Aminuddin, and Y. Dasril. "Numerical discretization for nonlinear diffusion filter." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICoMEIA 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4916039.

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Reports on the topic "Numerical Diffusion"

1

Kellogg, R. B. Numerical Solution of Convection Diffusion Equation. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada244563.

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2

Dai, William. Numerical Diffusion (Mixing) of Material in Numerical Simulations of Hydrodynamics. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1778735.

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Aziz, A. K., A. B. Stephens, and Manil Suri. Numerical Methods for Reaction-Diffusion Problems with Non-Differentiable Kinetics. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada185405.

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Rumminger, Marc D., and Gregory T. Linteris. Numerical modeling of counterflow diffusion flames inhibited by iron pentacarbonyl. Gaithersburg, MD: National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6243.

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Clayton, John D., Peter W. Chung, Michael A. Greenfield, and WIlliam D. Nothwang. Numerical Methods for Analysis of Charged Vacancy Diffusion in Dielectric Solids. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada459751.

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Weinacht, Daniel J. Coupled elastic-plastic thermomechanically assisted diffusion: Theory development, numerical implementation, and application. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/176804.

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Pruess, Karsten, and Keni Zhang. Numerical Modeling Studies of The Dissolution-Diffusion-Convection ProcessDuring CO2 Storage in Saline Aquifers. Office of Scientific and Technical Information (OSTI), November 2008. http://dx.doi.org/10.2172/944124.

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Prasad, K., C. Li, K. Kailasanath, C. Ndubizu, and R. Ananth. Numerical Modeling of Fire Suppression Using Water Mist. 1. Gaseous Methane-Air Diffusion Flames. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada337904.

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Manzini, Gianmarco, Andrea Cangiani, and Oliver Sutton. Numerical results using the conforming VEM for the convection-diffusion-reaction equation with variable coefficients. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1159206.

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Prasad, K., C. Li, and K. Kailasanath. Numerical Modeling of Fire Suppression Using Water Mist. 2. An Optimization Study on Jet Diffusion Flames. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada349379.

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