Modeling of magnetized expanding plasmas

The influence of electric fields and neutral particles on the plasma sheath at ITER divertor conditions

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine and deep learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine and deep learning approaches to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-picked features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand selected features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches. Additional information about individual files is in the accompanying CSV file (item_metadata.csv). This dataset was originally deposited in the Simon Fraser University institutional repository.

This forthcoming publication has been cancelled

Magnetically Enhanced Plasma

Development and assessment of adjoint sensitivity analysis method for transient two-phase flow simulations

A model for fuel ‐air mixing in diffusion e ames is presented and applied to study the mixing and quenching of methane‐air e ames. The model is based on the ideal gas law, the energy equation, the equation of continuity, and Arrhenius form of rate equation and is, therefore, strictly valid for mixtures having low density, that is, for lowpressurecombustors.Intheabsenceofpreferentialdiffusion,chemicalreactionscauseanunbalancedconsumption offuelandoxygeninnonstoichiometrice ames.Untilthedesiredequivalenceratioisachieved,enhancedpreferential diffusion ofoxygen orfuel isrequired in fuel-rich orfuel-lean e ames, respectively. Aftera desiredequivalenceratio is achieved, preferential diffusion of oxygen or fuel should bereduced to the exact level required to compensate for the unbalanced consumption of fuel and air. In the absence of these conditions, e ame chemistry cannot be strictly controlled. In addition, unless the desired equivalence ratio is at a position of stable equilibrium over an extended range of operational conditions, the e ame may be quenched. Net transport of fuel or oxygen due to diffusion is correlated with distributions of pressure,temperature, velocity, mass fraction of species,and heat transfer through radiation and conduction. Results show that negative rates of pressure (or positive rates of temperature ) and positive rates of pressure (or negative rates of temperature ) can enhance preferential diffusion of oxygen and fuel, respectively. Negative velocity divergence also enhances the diffusion of oxygen, whereas positive velocity divergenceenhances thediffusion offuel. Recirculation of burntgasesimproves thestability ofall e ames. Forrates of pressure of less than 1 atm/s, heat addition through conduction or radiation can provide a position of stable equilibrium for fuel-rich e ames. A position of stable equilibrium can be provided for both fuel-rich and fuel-lean e ames by combining a positive rate of temperature with positive velocity divergence, for rates of pressure of up to 25 atm/s. At higher rates of pressure or temperature, increased initial pressure or temperature, respectively, also assists in e ame stabilization.

A model for fuel ‐air mixing in diffusion e ames is presented and applied to study the mixing and quenching of methane‐air e ames. The model is based on the ideal gas law, the energy equation, the equation of continuity, and Arrhenius form of rate equation and is, therefore, strictly valid for mixtures having low density, that is, for lowpressurecombustors.Intheabsenceofpreferentialdiffusion,chemicalreactionscauseanunbalancedconsumption offuelandoxygeninnonstoichiometrice ames.Untilthedesiredequivalenceratioisachieved,enhancedpreferential diffusion ofoxygen orfuel isrequired in fuel-rich orfuel-lean e ames, respectively. Aftera desiredequivalenceratio is achieved, preferential diffusion of oxygen or fuel should bereduced to the exact level required to compensate for the unbalanced consumption of fuel and air. In the absence of these conditions, e ame chemistry cannot be strictly controlled. In addition, unless the desired equivalence ratio is at a position of stable equilibrium over an extended range of operational conditions, the e ame may be quenched. Net transport of fuel or oxygen due to diffusion is correlated with distributions of pressure,temperature, velocity, mass fraction of species,and heat transfer through radiation and conduction. Results show that negative rates of pressure (or positive rates of temperature ) and positive rates of pressure (or negative rates of temperature ) can enhance preferential diffusion of oxygen and fuel, respectively. Negative velocity divergence also enhances the diffusion of oxygen, whereas positive velocity divergenceenhances thediffusion offuel. Recirculation of burntgasesimproves thestability ofall e ames. Forrates of pressure of less than 1 atm/s, heat addition through conduction or radiation can provide a position of stable equilibrium for fuel-rich e ames. A position of stable equilibrium can be provided for both fuel-rich and fuel-lean e ames by combining a positive rate of temperature with positive velocity divergence, for rates of pressure of up to 25 atm/s. At higher rates of pressure or temperature, increased initial pressure or temperature, respectively, also assists in e ame stabilization.

In this chapter, we describe the problem to be solved: elliptic partial differential equation of secondorder with suitable boundary conditions. In particular, we are interested in diffusion problem in 2-d (two dimensions) with variable and possibly discontinuous coefficients. We also describe several discretization methods, including the finite difference, finite volume, and finite element discretization methods. These discretization methods produce large sparse linear systems of equations, whose solution is the subject of this book.

Magnetic refrigerators have been used for more than fifty years to attain millikelvin temperatures and provide microwatts of cooling power. In the last decade, numerous groups have begun development of magnetic refrigerators for applications above 1 K. The initial perception of many people was that magnetic devices would only be useful for small cooling loads of a watt or less at temperatures near liquid helium temperatures. However, calculations in the last several years indicate that magnetic refrigerators are attractive at much higher cooling powers at a variety of temperatures, e.g., 100W at 1.8 K or 50 kW at 270 K. Some areas where magnetic refrigeration does not apply are also being identified, e.g., providing a few watts of cooling at 80 K can be done much better with gas-cycle units than with magnetic devices because of the need for cooling of superconducting magnets to near 4K. In general, magnetic refrigerators do not miniaturize because of the high magnetic field requirements. In a desire to know where magnetic refrigerators best apply, detailed thermal models of magnetic refrigerators have been developed to predict cooling power, input power and efficiency as a function of temperature and frequency. These models show that different mechanisms limit the performance of magnetic devices at different design conditions. A combination of these limits produces a thermal operational envelope which is the primary topic of this paper.

As modern field effect transistors (FETs) scale down into the sub-10-nm regime, the approaching end of CMOS scaling becomes evident. Recently, we have performed a series of three-dimensional, fully quantum-mechanical, charge-self-consistent transport simulations and optimizations to show [1] that all FETs, irrespective of their channel material, will reach a fundamental downscaling limit around 4-5 nm technology node, due to unacceptably high levels of thermally induced errors. To investigate alternatives to FET/CMOS scaling, many emerging devices and technologies have been under active research in recent years. Among them, oxide-based memristors have become one of the strongest candidates to replace flash, and possibly DRAM and SRAM in the near future [2]. Moreover, memristors have a high potential to enable beyond-CMOS technology advances in novel architectures for high performance computing [3]. However, despite the spectacular progress in experimental demonstration of oxide memristors, their microscopic transport theory and even some key operational principles, essential to advance experimental progress, remain uncertain [4]. To facilitate the understanding of physical switching mechanisms and accelerate experimental development in oxide memristors, we have developed a 3D fully-coupled electrical and thermal transport model [5] that solves simultaneously the time-dependent continuity equations for electrons, holes, and oxygen vacancies, the time-dependent heat equation including Joule heating sources, and the Poisson equation for all the charged species. The model captures all the most important processes that drive memristive switching, including field drift due to the electrical potential gradient, Fick diffusion due to the concentration gradient, and the Soret effect due to the temperature gradient. To properly simulate the movement of oxygen vacancies, the rigid point ion model [5] is used, in which the vacancy velocity exponentially depends on the temperature and the electric field when the field exceeds a critical value. The fully-coupled equations and relevant physical models are implemented in CHARON, a multi-dimensional parallel TCAD code developed at Sandia National Labs. CHARONis built upon the open-source Trilinos packages [6], which support finite element and finite volume discretization methods, state-of-the-art nonlinear and linear solvers, MPI parallel capability, and many more. It can simulate arbitrary device geometry and supports multi-physics capability (i.e., allowing for solving different equations in different regions). The coupled model has been applied to simulate the switching process in a 3D filamentary tantalum oxide memristor shown in Fig. 1(a). The layer material and thickness come from an experimental device published in Ref. [7]. Making use of the multi-physics capability in CHARON, we solve the coupled Poisson, electron, oxygen vacancy, and heat equations in the active Ta and conduction filament (CF) regions, with vacancies confined in Ta and CF regions, while in the Pt and Ta2O5 regions, due to absence of mobile vacancies, the coupled Poisson, electron, and heat equations are solved, with Pt treated as a highly doped n-type semiconductor using immobile dopants, and Ta2O5 as an undoped semiconductor. Extensive simulations show that both field drift and thermal processes play crucial roles in determining the switching dynamics of the bipolar TaOx device. Specifically, (i) during OFF switching, when a sufficient negative voltage is applied to the top Pt electrode, Joule heating causes the increase of temperature with the hottest spot located in the CF, and consequently, oxygen vacancies move away from CF into Ta under the influence of both heating and field, which leads to a vacancy density gap and possible depletion of vacancies in the entire CF at high negative voltage, resulting in the OFF state; (ii) during ON switching, a sufficient positive voltage causes Joule heating and the increase of temperature, and increased temperature causes vacancies moving from Ta into CF to refill the vacancy gap, resulting in the ON state. Interestingly, we show that the thermally activated field-dominated switching in the considered devices can lead to more than three orders of magnitude speed-up in switching time, with only 50% voltage/current increase. Simulated resistances during OFF and ON switching as a function of time and voltage show very good qualitative agreement with experimental measurement in Ref. [7]. Furthermore, under a triangular voltage sweep, the simulated current-voltage hysteresis curve is shown in Fig. 1(b), which also shows good qualitative agreement with the measured data in Ref. [8]. The generality of the proposed model allows to simulate and investigate the underlying physical switching mechanisms in a wide spectrum of oxide memristors ranging from field-dominant, field-thermal-driven to thermal-dominant memristive devices. Figure 1

Hydrogen production for fuel cell application via thermo-chemical technique: An analytical evaluation

Image processing techniques have been used in processing of potential fields data in order to delineate the edges of the anomalous sources. In this research, firstly the 2D Gabor filter was introduced. Then its operation was investigated on the three magnetized prisms which were at the three different depths and orientations. The effects of body wavelength, standard deviation and body orientation in the Gabor filter on the detection of edge bodies were studied. Investigations showed that the Gabor filter is a low pass filter. Afterwards, the Gabor filter was applied on the aeromagnetic data of Khoram Abad region in the southwest and ground magnetic data from Kashmar region in the northeast of Iran. The results show that the trend of geological structures is NW-SE which is in concordance to those obtained from other filters as well as geological map of the region. 1 2D Gabor filter The boundary of the geological structures can be determined by processing of potential field spectrums .Most filters which have been used for edge detection are based on the different degree of derivatives. The derivatives cause noises and signals amplify together which decrease resolution of edges. Therefore, using filters without any derivative terms is a prime objective. The Gabor filter has not any derivative terms in its mathematical structure. Denis Gabor first introduced the filter for image processing. This filter is linear and has been used for analyzing details of an image or its texture. The characteristics of the Gabor filter is to obtain special frequencies in a certain point or a region. Mostly this filter has been used for analyzing human vision system. The Gabor filter has two parts, real and imaginary in exponential forms which are multiplied with a Gaussian function. The real part has been used for data filtering. The Gabor filter response in spatial domain is defined by convolution of data in the Gabor matrix. The Gabor filter is a low pass filter which attenuates noises while it detects the edges of the deep anomalies in a certain direction. The Gabor filter in mathematics is in the group of filter transformation. On this basis, this filter can process images in different directions and frequencies. The Gabor filter has many applications in image processing such as texture analysis, fingerprint detection, edge detection, document processing and so on. The 2D Gabor filter was also used for identification of the edges of the geological structures and faults. For determination of the geological trends and faults, we must apply the Gabor filter in different directions and frequencies and standard deviations. In this article, we first introduce the Gabor filter and its characteristics. Afterwards this filter is applied on the aeromagnetic data from Khoram Abad and ground magnetic from an iron mine at Kashmar regions in the the southwest and northeast of Iran respectively. 2 The real data We prepared the following maps from the real data in Khoram Abad region: the map of the total magnetic intensity, the reduction to the pole of aeromagnetic map and the map resulted from the application of the Gabor filter on the residual aeromagnetic map of the region. From the latter map, the trend and edges of the geological structures were identified. The other trends may be detected by changing the various parameters of the filter specially its direction. The trend of the geological structures which is revealed by the Gabor filter is northwest- southeast. If the Gabor filter is applied with the other edge filters such as derivative of tilt angle, analytic signal filters, we can identify the boundaries better. The Gabor filter on the data from Kashmar region, delineates the boundary of the iron ore body at the studied area.

Adjoint-based techniques can provide the sensitivity of an objective function to any number of parameters of a simulation inexpensively at roughly the cost of a single additional flow calculation. This information can be used to perform sensitivity analyses, aerodynamic shape optimization, and to estimate the error in the objective function due to numerical discretization. Existing approaches to derive the numerically discretized adjoint equations involve the so-called discrete and continuous methods, which differ in the order at which discretization and linearization steps are performed. The effect of these contrasting approaches is that they have both strengths and weaknesses over each other in the form of complexity of the formulation and computational expense of the solution. In this paper, we further develop the hybrid approach of Taylor et al. (2012) that combines elements of the continuous and discrete methods with the intention of capturing both their advantages: reducing the time spent on mathematical derivation of the continuous adjoint equations, lowering the computational requirements of the discrete adjoint equations, and generally improving the quality of the adjoint solution. The specific approach investigated in this paper treats the flow conservation equations in a continuous manner and the turbulence transport equations discretely. The framework is designed such that additional transport equations for any turbulence model can be seamlessly included in a discrete fashion for coupling with the mean flow equations. The methodology is demonstrated in an optimization problem of lift-constrained drag minimization of an airfoil in transonic turbulent flow.

Solid-state fuel ignition was given by Chu and Bobin according to the hydrodynamic theory at x = 0 qualitatively. A high threshold energy flux density, i.e., E* = 4.3 × 1012 J/m2, has been reached. Recently, fast ignition by employing clean petawatt—picosecond laser pulses was performed. The anomalous phenomena were observed to be based on suppression of prepulses. The accelerated plasma block was used to ignite deuterium—tritium fuel at solid-state density. The detailed analysis of the thermonuclear wave propagation was investigated. Also the fusion conditions at x ≠ 0 layers were clarified by exactly solving hydrodynamic equations for plasma block ignition. In this paper, the applied physical mechanisms are determined for nonlinear force laser driven plasma blocks, thermonuclear reaction, heat transfer, electron—ion equilibration, stopping power of alpha particles, bremsstrahlung, expansion, density dependence, and fluid dynamics. New ignition conditions may be obtained by using temperature equations, including the density profile that is obtained by the continuity equation and expansion velocity. The density is only a function of x and independent of time. The ignition energy flux density, E*t, for the x ≠ 0 layers is 1.95 × 1012 J/m2. Thus threshold ignition energy in comparison with that at x = 0 layers would be reduced to less than 50 percent.

Adaptive gridding methods are of fundamental importance both for industry and academia. As one of the computing methods, the Boundary Element Method (BEM) is used to simulate problems whose fundamental solutions are available. The method is usually characterised as constant elements BEM or linear elements BEM depending on the type of interpolation used at the elements. Its popularity is steadily growing because of its advantages over other numerical methods most important of which being that it only involves obtaining data at the boundary and computation of the solution in the domain is merely a case of post processing through the use of an identity. This results in the reduction of the problem dimension by unity. Although there is a reduction in dimension when we use BEM, the method usually results in full matrices which can be expensive to solve. This makes the method costly for problems that require very fine grids like those with hot spots as in the example of impressed current cathodic protection systems (ICCP). The BEM is a global method in nature in that the solution in one node depends on the solutions in all the other nodes of the grid. Hence an error in one node can pollute the solution in all the other nodes. In this thesis, we first focus our attention on defining and studying both the local and global errors for the BEM. This is not a completely new study as the literature suggests, however, our approach is different. We use the basic foundations of the the method to define the errors. Since the method is a global method, first we use the interpolation error on each element to define what we have called a sublocal error. Then using the sublocal error we have defined the local error. Understanding the local errors enabled us study the global error. Theoretical and numerical results show that these errors are second order in grid size for both the constant and linear element cases. Then, having explored errors, we study a method for adaptive grid refinement for the BEM. Rather than using a truly nonuniform grid, we present a method called local defect correction (LDC) that is based on local uniform grid refinement. This method is already developed and documented for other numerical methods such as finite difference and finite volume methods but not for BEM. In the LDC method, the discretization on a composite grid is based on a combination of standard discretizations on several uniform grids with different grid sizes that cover different parts of the domain. At least one grid, the uniform global coarse grid, should cover the entire boundary. The size of the global coarse grid is chosen in agreement with the relatively smooth behaviour of the solution outside the hot spots. Then several uniform local fine grids each of which covers only a (small) part of the boundary are used in the hot spots. The grid sizes of the local grids are chosen in agreement with the behaviour of the continuous solution in that part of the boundary. The LDC method is an iterative process whereby a basic global discretisation is improved by local discretisations defined in subdomains. The update of the coarse grid solution is achieved by adding a defect correction term to the right hand side of the coarse grid problem. At each iteration step, the process yields a discrete approximation of the continuous solution on the corresponding composite grid. We have shown how this discretisation can be achieved for the BEM. We apply the discretisation to an academic example to demonstrate its implementation and later show how to use it for an application as the ICCP system. The results show that it is a cheaper method than solving on a truly composite grid and converges in a single step.