Physical and numerical modeling

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Physical characteristics and dynamics of the coastal Latex09 Eddy derived from in situ data and numerical modeling.

Physical characteristics and dynamics of the coastal Latex09 Eddy derived from in situ data and numerical modeling.

Physical char- acteristics and dynamics of the coastal Latex09 Eddy derived from in situ data and numerical modeling.4. Journal of Geophysical Research.5[r]

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Adsorption of dye on a Tunisian unsaturated layered soil: physical and numerical modeling

Adsorption of dye on a Tunisian unsaturated layered soil: physical and numerical modeling

In environment engineering problems, solute transport in multi layered porous media is often observed, such as leachate transfer in landfills, con- taminant diffusion in capping layers over contami- nated sediment and stratified soils. Compared to sol- ute transport in single layered problem, the transport parameters in each layer may be different, resulting in a jump of parameters at the interface of the adjacent layer [12, 34]. Several researchers have proved that the creation of layered soil may be an efficient method to rise the soil water retention and protect the soil from pollution with dissolved toxicants [25, 26, 31]. In fact, Solute transport in layered soils have been studied in many researchers [24] presents the combined effect of the capillary barrier and soil layer slope on the trans- port of water, bromide and nanoparticles through an unsaturated soil. They showed that under the effect of the capillary barrier water accumulated at the interface of the two materials and the sloped structure deflects flow in contrast to the structure with zero slope [33] proves that the contaminant spread faster in stratified field with a soft and highly permeable top layer and they concluded that soil parameters of the top layer are more critical than the lower but the results can be changed by controlling the thicknesses of layers [7] found that both experimental exploration and numerical simulation show that the three-layer capillary barrier cover system performs as inhibitor to minimize pollutant percolation and in capillary barrier, the MB kinetic adsorption is inversely proportional to the flow velocity.
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Adsorption of dye on a Tunisian unsaturated layered soil: physical and numerical modeling

Adsorption of dye on a Tunisian unsaturated layered soil: physical and numerical modeling

Validation of the three-layered model. The adsorp- tion-desorption and transport behavior of MB in a three layered soil has been studied using column experiments by [7] and simulated in this study using the finite element method. The initial MB concentra- tion was 100 mg/ L and the bed height was 10 cm with 1 cm of silty soil on the top, 2 cm of clay on the bottom and 7 cm of sand in the middle of the column. Break- through curves obtained on MB adsorption by the 3 lay- ers of soils at f low rate of 0.0261 mL/min are presented in Fig. 2a. It is clear from the figure that the advection dispersion model gives a good reproduction of mech- anisms of transfer in layered soil and appears to be a useful tool to better understand the physical processes and the effect of capillary barrier of MB transport in unsaturated heterogeneous soil.
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Molecular modeling for physical property prediction

Molecular modeling for physical property prediction

This can only be done by improving process knowledge and performance at all scales, down to the atomic one. Current experimental and modeling approaches assess with difficultly such sub-micronic scales. In experiments, how to conceive experimental devices small enough and introduce them in molecular systems without affecting irreversibly the phenomena that they look at? In modeling and simulation, which hypotheses are still relevant? how to handle boundary effects? Numerical difficulties may arise along with the necessity of defining new parameters… that will be adjustable ones as no experiments can obtain them. This latter statement is particularly true for energetic interaction parameters like binary interaction parameters in current liquide – vapor equilibrium macroscopic thermodynamic models based on activity coefficient approach or on equation of state approach. In all processes, study of phenomena attributed to energetic interactions has always been left over for a time … that has come:
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Nonlinear and dispersive numerical modeling of nearshore waves

Nonlinear and dispersive numerical modeling of nearshore waves

The domain can be decomposed into two zones of interest where different physical processes are important. First, over the flat bottom ( x ≤ 19.88m), wave dissipation is caused by bulk viscos- ity and bottom friction. Second, on the slope, the effects of wave shoaling (increase of the wave amplitude due to the water depth decrease) become important and compete with the energy dissi- pation (19.88 m ≤ x ≤ 25 m). Over the flat bottom, the amplitude of the solitary waves decreases (Figure 4.13 a and b for  = 0.091 and  = 0.409, respectively) due to these dissipative pro- cesses. The results of four simulations for each value of  are presented in Figure 4.13 to evaluate the influence of the different sources of energy dissipation on the decay rate. Without viscosity (light blue line), the wave amplitude remains constant. The simulations with only the bulk vis- cosity terms (slip bottom condition) and with ν = 7.10 −6 m 2 /s show only a weak amplitude decay and are close to the simulations without viscosity ( ν = 0 m 2 /s). When the bottom fric- tion term (no-slip bottom condition) is added, the soliton amplitudes decrease significantly. The primary source of energy dissipation is bottom friction. This effect becomes more pronounced for larger wave heights that have larger horizontal velocities at the bottom. The value of the viscosity required to best fit the experimental data is slightly higher ( ν = 7.10 −6 m 2 /s) than the kinematic viscosity of water ( ν = 10 −6 m 2 /s). Using a Boussineq model to simulate these experiments, Liu et al. ( 2006 ) found the same decay rate with a viscosity of 10 −6 m 2 /s when taking into account the boundary layers on the walls of the wave flume. Here this dissipation is not taken into account, which could explain the higher value of ν adjusted to obtain the same decay rate as in the experiments. The same value of viscosity is used to fit the experimental data for the two wave heights, showing the insensitivity of this value to the wave non-linearity, for the considered range of conditions.
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Modeling and numerical simulation of a grand piano.

Modeling and numerical simulation of a grand piano.

Introduction After a key has been engaged, a nonlinear hammer strikes either one, two or three strings. The percussive timbre of the piano is attributed to the presence of a lon- gitudinal vibration in the string, which is nonlinearly cou- pled to the transversal vibration thanks to a geometrically exact description of the string (see [1]). The transversal and longitudinal vibrations of the strings are transmitted to the structure through the bridge, thanks to a nonstan- dard coupling condition. A Reissner Mindlin plate model is used to describe the soundboard, which radiates the sound in the air. All the couplings of the continuous sys- tem are reciprocal so that the global energy is preserved, or decaying if physical dissipation is introduced.
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Modeling the piano. Numerical Aspects.

Modeling the piano. Numerical Aspects.

1 Introduction The nonlinear parts of the problem (hammer- strings interaction, string vibration), the couplings between the subsystems and, more generally, the size of the problem in terms of computational burden, re- quires to guarantee the long-term numerical stabil- ity. In the context of wave equations, and in mu- sical acoustics particularly, a classical and efficient technique to achieve this goal is to design numerical schemes based on the formulation of a discrete en- ergy which is either constant or decreasing with time (see [7], [5]). Ensuring the positivity of the discrete energy, consistent with the continuous energy of the physical system, yields to a priori estimates for the unknowns of the problem, leading to the stability of the method. For most numerical schemes this im- poses a restriction on the discretization parameters, as, for example, an upper bound for the time step.
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Workshop on numerical and physical modelling in multiphase flowsa cross-fertilization approach

Workshop on numerical and physical modelling in multiphase flowsa cross-fertilization approach

Subjects to be discussed (not exhaustive) • the different choices for the basic model, typically one-fluid model, two- fluids model or multi-fluids models; • treatment of interfaces: deterministic treatment or statistical description ? • Numerics versus physical modeling? Is the better, more realistic or

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Numerical simulation and modeling of ice shedding: Process initiation

Numerical simulation and modeling of ice shedding: Process initiation

9. Conclusion and perspectives A possible ice detachment mechanism has been proposed and modeled using damage mechanics. Empirical relations were iden- tified to determine the mechanical properties of atmospheric ice. This modeling strategy was first assessed on a double edge notched test case for which experimental results were available. We then considered a simple dumbell specimen in order to calibrate and identify the model parameters. With numerical results in good agreement with experimental data, two numerical experiments were defined. These simulations served to test the proposed ice shedding mechanism. They were both based on the same kind of teardrop ice shape. For both cases detachment was predicted before the whole contact length was melted. We therefore conclude that this type of shedding mechanism is relevant. Hence we will take this mechanism into account when further investigating the physical functioning of an ETIPS. These results may also be helpful in developing simplified ice shedding prediction models.
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Mathematical modeling and numerical simulation of innovative electronic nanostructures

Mathematical modeling and numerical simulation of innovative electronic nanostructures

Physical phenomena generated in these innovative electronic devices are extremely complex and still not well understood. For example, a minor variation in the crystal structure of a nanodevice may have a significant influence in its electric properties. Also, for a threshold value of the spin current, the magnetization in a ferromagnetic multilayer material can be switched. Thus, in order to predict their behavior, to access their performance limits and to design new configurations, an important experimental test battery is necessary. Of course, this approach is long and expensive. That is why the modeling and the numerical simulation can play an important role to improve the performance of such devices. One advantage of the numerical approach is that it works with virtual device. We can proceed to “ideal” tests, without taking in account the process constraints. This flexibility is important for the understanding advancements in nanoelectronics and spintronics. A parametric study, which consists of varying one or more characteristics of the device (length, doping profile, applied voltage, injected current...), can allow to understand the mechanisms and even to predict some singular behaviors. The numerical simulations are also useful to determine an adequate interval of values for the different parameters in order to perform the experimental tests in an optimal way. They acts as a complementary tool to the physical experiments.
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Variability modeling and numerical biomarkers design in cardiac electrophysiology

Variability modeling and numerical biomarkers design in cardiac electrophysiology

2.2.5 Physical DOFs reduction: clustered sensitivities (CS) algo- rithm As explained before, the dual variable formulation of the optimization problem transfers the resolution effort onto the solution of a system whose size is the number of DOFs in the physical domain times the number of moments. However, in many practical applications, as for instance when models are described by PDEs, the number of DOFs used to discretize the solution in the physical domain is large, making the Hessian matrix inversion computationally intensive. Aside from the sheer computational cost of linear algebra operations, dealing with many large simulations – say thousands of simulations counting millions of DOFs – poses undeniable issues in terms of storage capacity and Input/Output computer operations. The main idea to reduce the computational cost is to retain only the subsets of the physical domain in which the observable conveys more information about the variability of the parameters. Consider for instance a region in which the observable does not vary, or its variation amplitude is lower than the noise level: then, matching the moments in this region will certainly not convey any meaningful information about the parameters. Even worse, it may increase the Hessian condition number and degrade the overall accuracy of the method. It may also happen that part of the data is redundant, meaning that the observable exhibits the same variations with respect to the parameters in two different DOFs. In this section, we propose an algorithm that selects a subset S of the full set of DOFs D. This subset is then used in the OMM inverse procedure described before. Notice that we are not interested in building a low-dimensional surrogate model with fewer outputs. On the contrary, we aim at developing a non-intrusive approach where we only choose to discard some outputs of the high fidelity model. To do that, we propose the following gradient-based algorithm which is rooted in the global sensitivity analysis of the model.
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Numerical simulation and modeling of ice shedding: Process initiation

Numerical simulation and modeling of ice shedding: Process initiation

9. Conclusion and perspectives A possible ice detachment mechanism has been proposed and modeled using damage mechanics. Empirical relations were iden- tified to determine the mechanical properties of atmospheric ice. This modeling strategy was first assessed on a double edge notched test case for which experimental results were available. We then considered a simple dumbell specimen in order to calibrate and identify the model parameters. With numerical results in good agreement with experimental data, two numerical experiments were defined. These simulations served to test the proposed ice shedding mechanism. They were both based on the same kind of teardrop ice shape. For both cases detachment was predicted before the whole contact length was melted. We therefore conclude that this type of shedding mechanism is relevant. Hence we will take this mechanism into account when further investigating the physical functioning of an ETIPS. These results may also be helpful in developing simplified ice shedding prediction models.
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Numerical modeling of olfaction

Numerical modeling of olfaction

22 intuitive, is rather delicate to define in perfumery. Molecular weight, vapor pressure (which directly relates to the quantity of molecules present in the gas phase) or logP (water / octanol partition coefficient) are typically good indicators of this volatility. (8) This definition, focused on the molecule, has its flaws. Oxygen, nitrogen or methane are perfect counterexamples. None of them has a smell, although their physical and chemical characteristics (they are volatile and hydrophobic) correspond to the criteria stated above. Among these highly hydrophobic molecules the absence of stimulation of olfactory receptors could be at the core of the absence of odor. But then, how to explain the tenacious smell of ozone (O3, which comes from the Greek ozô, which means “to exhale an odor”), causing the characteristic smell of photocopy rooms? Concentration can also influence the smell of a molecule. Again, the following example highlights the difficulty to establish a structure-odor relationship: 4-mercapto-4-methylpentan-2-one molecule (called “cat ketone”) has an odor of cat urine at high concentration while its dilution gives it a note of “blackcurrant” or “cabernet- sauvignon”.
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Numerical modeling of salt and gypsum dissolution: test case and comparison

Numerical modeling of salt and gypsum dissolution: test case and comparison

Figure 1: Land Subsidence (sinkhole) in Central Kansas related to Salt dissolution. Using dissolution modeling also enables the optimization of the industrial dissolution process. 7KLVRSWLPL]DWLRQFDQUHODWHIRUH[DPSOHWRWKHLQWHQVLW\RIWKHLQSXWÀRZWKHWHPSHUDWXUHRI WKHLQMHFWHGÀXLGWKHGHJUHHRIVDWXUDWLRQRIWKHLQOHWÀXLGWKHORFDWLRQRIWKHLQMHFWLRQZHOOV etc. Rock dissolution is undoubtedly a multi-scale and multiphysics problem raising several questions. One concerns an accurate description of solid-liquid interface recession at the macro-scale level. In order to reach this goal, it is essential to have a precise mathematical formalization of physicochemical and transport mechanisms at the micro scale level. The second concerns the applicability to large spatial scale. Finally, strong coupling with other physical processes, in particular geomechanical behavior, must be considered.
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Reliable hydraulic numerical modeling with multiblock grids and linked models

Reliable hydraulic numerical modeling with multiblock grids and linked models

Such optimized hydraulic numerical modeling is presented in this paper with a multiblock multimodel 2D solver and its linking to a 1D model. Both models, based on mass and momentum conservation equations, use the same finite volume technique together with the same flux vector splitting and explicit time integration scheme. This makes their linking easy and ensures its physical meaning. The 2D model uses multiblock regular grids, enabling the variation of the mesh size in different locations of the computation area as well as the mathematical model to be solved. The modeling can thus be locally enhanced by considering extended models, taking into account sediment transport or turbulence effects for example. These techniques enable to simulate in a unified way very large both free surface and pressurized flow hydraulic systems with a very fine discretization and/or sophisticated mathematical models in local areas, without decreasing the models reliability and precision nor prohibitively increasing calculation time and memory requirements.
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Hollow cathode modeling: II. Physical analysis and parametric study

Hollow cathode modeling: II. Physical analysis and parametric study

We then moved on to a parametric study of the cathode and used our numerical model to assess the influence of the discharge current and mass flow rate on the cathode operation from the perspective of the discharge potential, current and power balance, and plasma oscillations in the plume. The trends obtained here help to understand the phenomena which limit the useful operating envelope of this cathode: at low discharge current (5 ), we expect an accelerated sputtering of the emitter due to ion bombardment while at high discharge current (greater than 16  ), it seems that the high emission current density might lead to a quick evaporation of the emitter. At high discharge current, we also observed strong plasma potential fluctuations (tens of volts) in the keeper orifice which could accelerate ions towards the orifice plate and the keeper electrode and quickly erode these elements of the cathode. Some evidence showing that the visual aspect of the simulated plume plasma in high discharge current conditions may be similar to the experimentally observed plume mode was also presented. We stress that the phenomena observed at high discharge current could be impacted by the addition of an applied magnetic field, which our model does not include. Therefore, results obtained here are more readily applicable to hollow cathodes designed for Hall Thrusters. The impact of the set mass flow rate was discussed as well, based on its influence on electron emission in the interior region of the cathode. Lastly we analyzed the influence of the cathode radius on the discharge and established some trends which might provide some guidance in the development of new high discharge current hollow cathodes for Hall Thrusters.
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Linear Friction Welding of Aeronautical alloys Modeling and Numerical Simulation

Linear Friction Welding of Aeronautical alloys Modeling and Numerical Simulation

6. Garcia, A. M. M. (2011). “Blisk fabrication by linear friction welding advances in gas turbine technology” in Advances in Gas Turbine Technology edited by Dr. Ernesto Benini (InTech, 2011). pp. 411-434. 7. Guo,Z.,Turner,R.,DaSilva,A.D.,Sauders,N.,Schroeder,F.,Cetlin,P.R.,and Schille,J.-P. Physical and numerical simulation of materials, 762, 266-276(2013).

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Physical parameters for piano modeling

Physical parameters for piano modeling

Abstract: This document lists the physical parameters used by the authors when performing numerical simulations of the piano. We first give the parameters used for the soundboard and the air. Then, the hammer parameters are given. Finally, strings parameters are issued and two cases are considered : with (realistic) or without (virtual) wrapped strings. When the strings are considered wrapped, their length is the effective length measured on the reference piano, but we consider that they are made of a virtual material with a higher density. When the strings are considered unwrapped, the material is steel, and to achieve the very bass notes without increasing inharmonicity too much, we have increased the length up to almost 6 meters.
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Modeling, numerical analysis and simulations of breathing

Modeling, numerical analysis and simulations of breathing

This chapter is concerned with the choice of the suitable boundary conditions and its associated formulations used to solve this problem. Indeed, whatever these conditions are, the numerical problem to be solved must be mathematically well posed. In some real-life situations, and it is the case for the lung, it is natural to prescribe a pressure on some part of the boundary. From a mathematical point of view, the pressure is only a Lagrange multiplier in the incompressible Navier–Stokes system, allowing to keep the velocity divergence free at any point. However, it is also a quantity with a physical meaning, and many papers deal with this kind of boundary conditions (see e.g. [103, 61, 9]). Unfortunately one cannot prescribe only the value of the pressure on the boundary, since such a problem is known to be ill-posed. Then the fact that boundary conditions involving pressures are often more suitable for this kind of modeling problems implies that the formulation of the Navier–Stokes equations has to be selected carefully in order to guarantee not only that their associated boundary conditions are physically relevant for this kind of modeling but also that the whole system is mathematically well-posed.
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Martian Infrasound: Numerical Modeling and Analysis of InSight's Data

Martian Infrasound: Numerical Modeling and Analysis of InSight's Data

In the tropospheric waveguide direction, no rays are reaching the ground up to a certain range. This region is called an acoustic ”shadow zone”, a feature of infrasound propagation frequently observed on Earth (de Groot-Hedlin, 2017). During nighttime on Mars, the shadow zone has a finite length due to the presence of large-amplitude tropospheric winds (Garcia et al., 2017). The end of the shadow zone is marked by a jump in the density of rays guided at low altitudes, translating into an amplitude increase in the full-wave simulation. This feature repeats as rays bounce on the ground at regular intervals. We note that each tropospheric refraction signature is composed of two successive impulses that appear when the acoustic source is located above the ground. This is because one impulse stems from the up-going wave, and the other from the down-going wave reflected off of the ground. From ray tracing simulations we expect an increase of the time delay with the source height; and expect only one phase for a source on the ground. Rays with larger launch angles sound the troposphere higher and reach larger distances. A decrease of the density of rays combined to longer propagation paths explain the rapidly decreasing amplitude with distance described above with the full-wave simulations.
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