Chapter 4 Fluid Description of Plasma
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Glossary Physics (I-Introduction)
1 Glossary Physics (I-introduction) - Efficiency: The percent of the work put into a machine that is converted into useful work output; = work done / energy used [-]. = eta In machines: The work output of any machine cannot exceed the work input (<=100%); in an ideal machine, where no energy is transformed into heat: work(input) = work(output), =100%. Energy: The property of a system that enables it to do work. Conservation o. E.: Energy cannot be created or destroyed; it may be transformed from one form into another, but the total amount of energy never changes. Equilibrium: The state of an object when not acted upon by a net force or net torque; an object in equilibrium may be at rest or moving at uniform velocity - not accelerating. Mechanical E.: The state of an object or system of objects for which any impressed forces cancels to zero and no acceleration occurs. Dynamic E.: Object is moving without experiencing acceleration. Static E.: Object is at rest.F Force: The influence that can cause an object to be accelerated or retarded; is always in the direction of the net force, hence a vector quantity; the four elementary forces are: Electromagnetic F.: Is an attraction or repulsion G, gravit. const.6.672E-11[Nm2/kg2] between electric charges: d, distance [m] 2 2 2 2 F = 1/(40) (q1q2/d ) [(CC/m )(Nm /C )] = [N] m,M, mass [kg] Gravitational F.: Is a mutual attraction between all masses: q, charge [As] [C] 2 2 2 2 F = GmM/d [Nm /kg kg 1/m ] = [N] 0, dielectric constant Strong F.: (nuclear force) Acts within the nuclei of atoms: 8.854E-12 [C2/Nm2] [F/m] 2 2 2 2 2 F = 1/(40) (e /d ) [(CC/m )(Nm /C )] = [N] , 3.14 [-] Weak F.: Manifests itself in special reactions among elementary e, 1.60210 E-19 [As] [C] particles, such as the reaction that occur in radioactive decay. -
Plasma Physics I APPH E6101x Columbia University Fall, 2016
Lecture 1: Plasma Physics I APPH E6101x Columbia University Fall, 2016 1 Syllabus and Class Website http://sites.apam.columbia.edu/courses/apph6101x/ 2 Textbook “Plasma Physics offers a broad and modern introduction to the many aspects of plasma science … . A curious student or interested researcher could track down laboratory notes, older monographs, and obscure papers … . with an extensive list of more than 300 references and, in particular, its excellent overview of the various techniques to generate plasma in a laboratory, Plasma Physics is an excellent entree for students into this rapidly growing field. It’s also a useful reference for professional low-temperature plasma researchers.” (Michael Brown, Physics Today, June, 2011) 3 Grading • Weekly homework • Two in-class quizzes (25%) • Final exam (50%) 4 https://www.nasa.gov/mission_pages/sdo/overview/ Launched 11 Feb 2010 5 http://www.nasa.gov/mission_pages/sdo/news/sdo-year2.html#.VerqQLRgyxI 6 http://www.ccfe.ac.uk/MAST.aspx http://www.ccfe.ac.uk/mast_upgrade_project.aspx 7 https://youtu.be/svrMsZQuZrs 8 9 10 ITER: The International Burning Plasma Experiment Important fusion science experiment, but without low-activation fusion materials, tritium breeding, … ~ 500 MW 10 minute pulses 23,000 tonne 51 GJ >30B $US (?) DIII-D ⇒ ITER ÷ 3.7 (50 times smaller volume) (400 times smaller energy) 11 Prof. Robert Gross Columbia University Fusion Energy (1984) “Fusion has proved to be a very difficult challenge. The early question was—Can fusion be done, and, if so how? … Now, the challenge lies in whether fusion can be done in a reliable, an economical, and socially acceptable way…” 12 http://lasco-www.nrl.navy.mil 13 14 Plasmasphere (Image EUV) 15 https://youtu.be/TaPgSWdcYtY 16 17 LETTER doi:10.1038/nature14476 Small particles dominate Saturn’s Phoebe ring to surprisingly large distances Douglas P. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
Plasma Physics and Advanced Fluid Dynamics Jean-Marcel Rax (Univ. Paris-Saclay, Ecole Polytechnique), Christophe Gissinger (LPEN
Plasma Physics and advanced fluid dynamics Jean-Marcel Rax (Univ. Paris-Saclay, Ecole Polytechnique), Christophe Gissinger (LPENS)) I- Plasma Physics : principles, structures and dynamics, waves and instabilities Besides ordinary temperature usual (i) solid state, (ii) liquid state and (iii) gaseous state, at very low and very high temperatures new exotic states appear : (iv) quantum fluids and (v) ionized gases. These states display a variety of specific and new physical phenomena : (i) at low temperature quantum coherence, correlation and indiscernibility lead to superfluidity, su- perconductivity and Bose-Einstein condensation ; (ii) at high temperature ionization provides a significant fraction of free charges responsible for in- stabilities, nonlinear, chaotic and turbulent behaviors characteristic of the \plasma state". This set of lectures provides an introduction to the basic tools, main results and advanced methods of Plasma Physics. • Plasma Physics : History, orders of magnitudes, Bogoliubov's hi- erarchy, fluid and kinetic reductions. Electric screening : Langmuir frequency, Maxwell time, Debye length, hybrid frequencies. Magnetic screening : London and Kelvin lengths. Alfven and Bohm velocities. • Charged particles dynamics : adiabaticity and stochasticity. Alfven and Ehrenfest adiabatic invariants. Electric and magnetic drifts. Pon- deromotive force. Wave/particle interactions : Chirikov criterion and chaotic dynamics. Quasilinear kinetic equation for Landau resonance. • Structure and self-organization : Debye and Child-Langmuir sheath, Chapman-Ferraro boundary layer. Brillouin and basic MHD flow. Flux conservation : Alfven's theorem. Magnetic topology : magnetic helicity conservation. Grad-Shafranov and basic MHD equilibrium. • Waves and instabilities : Fluid and kinetic dispersions : resonance and cut-off. Landau and cyclotron absorption. Bernstein's modes. Fluid instabilities : drift, interchange and kink. Alfvenic and geometric coupling. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
Plasma Descriptions I: Kinetic, Two-Fluid 1
CHAPTER 5. PLASMA DESCRIPTIONS I: KINETIC, TWO-FLUID 1 Chapter 5 Plasma Descriptions I: Kinetic, Two-Fluid Descriptions of plasmas are obtained from extensions of the kinetic theory of gases and the hydrodynamics of neutral uids (see Sections A.4 and A.6). They are much more complex than descriptions of charge-neutral uids because of the complicating eects of electric and magnetic elds on the motion of charged particles in the plasma, and because the electric and magnetic elds in the plasma must be calculated self-consistently with the plasma responses to them. Additionally, magnetized plasmas respond very anisotropically to perturbations — because charged particles in them ow almost freely along magnetic eld lines, gyrate about the magnetic eld, and drift slowly perpendicular to the magnetic eld. The electric and magnetic elds in a plasma are governed by the Maxwell equations (see Section A.2). Most calculations in plasma physics assume that the constituent charged particles are moving in a vacuum; thus, the micro- scopic, “free space” Maxwell equations given in (??) are appropriate. For some applications the electric and magnetic susceptibilities (and hence dielectric and magnetization responses) of plasmas are derived (see for example Sections 1.3, 1.4 and 1.6); then, the macroscopic Maxwell equations are used. Plasma eects enter the Maxwell equations through the charge density and current “sources” produced by the response of a plasma to electric and magnetic elds: X X q = nsqs, J = nsqsVs, plasma charge, current densities. (5.1) s s Here, the subscript s indicates the charged particle species (s = e, i for electrons, 3 ions), ns is the density (#/m ) of species s, qs the charge (Coulombs) on the species s particles, and Vs the species ow velocity (m/s). -
Rheology of Human Blood Plasma: Viscoelastic Versus Newtonian Behavior
week ending PRL 110, 078305 (2013) PHYSICAL REVIEW LETTERS 15 FEBRUARY 2013 Rheology of Human Blood Plasma: Viscoelastic Versus Newtonian Behavior M. Brust,1 C. Schaefer,1 R. Doerr,1 L. Pan,2 M. Garcia,2 P.E. Arratia,2 and C. Wagner1,* 1Experimentalphysik, Universita¨t des Saarlandes, Postfach 151150, 66041 Saarbru¨cken, Germany 2Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA (Received 16 August 2012; revised manuscript received 5 December 2012; published 15 February 2013) We investigate the rheological characteristics of human blood plasma in shear and elongational flows. While we can confirm a Newtonian behavior in shear flow within experimental resolution, we find a viscoelastic behavior of blood plasma in the pure extensional flow of a capillary breakup rheometer. The influence of the viscoelasticity of blood plasma on capillary blood flow is tested in a microfluidic device with a contraction-expansion geometry. Differential pressure measurements revealed that the plasma has a pronounced flow resistance compared to that of pure water. Supplementary measurements indicate that the viscoelasticity of the plasma might even lead to viscoelastic instabilities under certain conditions. Our findings show that the viscoelastic properties of plasma should not be ignored in future studies on blood flow. DOI: 10.1103/PhysRevLett.110.078305 PACS numbers: 83.50.Jf, 83.60.Wc, 87.19.UÀ Blood is a complex fluid that consists of a suspension of microfluidic devices. The two investigated blood replace- blood cells in a liquid plasma which contains mostly water ment solutions with the same shear but different elonga- as well as proteins, mineral ions, hormones, and glucose. -
The Divergence of a Vector Field.Doc 1/8
9/16/2005 The Divergence of a Vector Field.doc 1/8 The Divergence of a Vector Field The mathematical definition of divergence is: w∫∫ A(r )⋅ds ∇⋅A()rlim = S ∆→v 0 ∆v where the surface S is a closed surface that completely surrounds a very small volume ∆v at point r , and where ds points outward from the closed surface. From the definition of surface integral, we see that divergence basically indicates the amount of vector field A ()r that is converging to, or diverging from, a given point. For example, consider these vector fields in the region of a specific point: ∆ v ∆v ∇⋅A ()r0 < ∇ ⋅>A (r0) Jim Stiles The Univ. of Kansas Dept. of EECS 9/16/2005 The Divergence of a Vector Field.doc 2/8 The field on the left is converging to a point, and therefore the divergence of the vector field at that point is negative. Conversely, the vector field on the right is diverging from a point. As a result, the divergence of the vector field at that point is greater than zero. Consider some other vector fields in the region of a specific point: ∇⋅A ()r0 = ∇ ⋅=A (r0) For each of these vector fields, the surface integral is zero. Over some portions of the surface, the normal component is positive, whereas on other portions, the normal component is negative. However, integration over the entire surface is equal to zero—the divergence of the vector field at this point is zero. * Generally, the divergence of a vector field results in a scalar field (divergence) that is positive in some regions in space, negative other regions, and zero elsewhere. -
In Vitro Demonstration of Cancer Inhibiting Properties from Stratified Self-Organized Plasma-Liquid Interface
www.nature.com/scientificreports OPEN In vitro Demonstration of Cancer Inhibiting Properties from Stratifed Self-Organized Plasma-Liquid Received: 2 March 2017 Accepted: 11 September 2017 Interface Published: xx xx xxxx Zhitong Chen1, Shiqiang Zhang1, Igor Levchenko2,3, Isak I. Beilis4 & Michael Keidar1 Experiments on plasma-liquid interaction and formation of thinly stratifed self-organized patterns at plasma-liquid interface have revealed a nontrivial cancer-inhibiting capability of liquid media treated at self-organized interfacial patterns. A pronounced cancer suppressing activity towards at least two cancer cells, breast cancer MDA-MB-231 and human glioblastoma U87 cancer lines, was demonstrated in vitro. After a short treatment at the thinly stratifed self-organized plasma-liquid interface pattern, the cancer inhibiting media demonstrate pronounced suppressing and apoptotic activities towards tumor cells. Importantly, this would have been impossible without interfacial stratifcation of plasma jet to thin (of several µm) current flaments, which plays a pivotal role in building up the cancer inhibition properties. Furthermore, thinly stratifed, self-organized interfacial discharge is capable to efciently control the ROS and RNS concentrations in the cancer-inhibiting media. In particular, abnormal ROS/ RNS ratios are not achievable in discharges since they do not form stratifed thin-flament patterns. Our fndings could be tremendously important for understanding the cancer proliferation problem and hence, the potential of this approach in tackling the challenges of high cancer-induced mortality should be explored. Despite tremendous eforts undertaken, cancer and cancer-related diseases still remain among the most dan- gerous and mortiferous abnormalities responsible for about 13% of human death cases, totally accounting for more than 7 million per year1. -
8.09(F14) Chapter 6: Fluid Mechanics
Chapter 6 Fluid Mechanics So far, our examples of mechanical systems have all been discrete, with some number of masses acted upon by forces. Even our study of rigid bodies (with a continuous mass distri- bution) treated those bodies as single objects. In this chapter, we will treat an important continuous system, which is that of fluids. Fluids include both liquids and gases. (It also includes plasmas, but for them a proper treatment requires the inclusion of electromagnetic effects, which will not be discussed here.) For our purposes, a fluid is a material which can be treated as continuous, which has the ability to flow, and which has very little resistance to deformation (that is, it has only a small support for shear stress, which refers to forces parallel to an applied area). Applica- tions include meteorology, oceanography, astrophysics, biophysics, condensed matter physics, geophysics, medicine, aerodynamics, plumbing, cosmology, heavy-ion collisions, and so on. The treatment of fluids is an example of classical field theory, with continuous field variables as the generalized coordinates, as opposed to the discrete set of variables qi that we have treated so far. Therefore the first step we have to take is understanding how to make the transition from discrete to continuum. 6.1 Transitioning from Discrete Particles to the Con- tinuum Rather than starting with fluids, lets instead consider a somewhat simpler system, that of an infinite one dimensional chain of masses m connected by springs with spring constant k, which we take as an approximation for an infinite continuous elastic one-dimensional rod. -
Generalized Stokes' Theorem
Chapter 4 Generalized Stokes’ Theorem “It is very difficult for us, placed as we have been from earliest childhood in a condition of training, to say what would have been our feelings had such training never taken place.” Sir George Stokes, 1st Baronet 4.1. Manifolds with Boundary We have seen in the Chapter 3 that Green’s, Stokes’ and Divergence Theorem in Multivariable Calculus can be unified together using the language of differential forms. In this chapter, we will generalize Stokes’ Theorem to higher dimensional and abstract manifolds. These classic theorems and their generalizations concern about an integral over a manifold with an integral over its boundary. In this section, we will first rigorously define the notion of a boundary for abstract manifolds. Heuristically, an interior point of a manifold locally looks like a ball in Euclidean space, whereas a boundary point locally looks like an upper-half space. n 4.1.1. Smooth Functions on Upper-Half Spaces. From now on, we denote R+ := n n f(u1, ... , un) 2 R : un ≥ 0g which is the upper-half space of R . Under the subspace n n n topology, we say a subset V ⊂ R+ is open in R+ if there exists a set Ve ⊂ R open in n n n R such that V = Ve \ R+. It is intuitively clear that if V ⊂ R+ is disjoint from the n n n subspace fun = 0g of R , then V is open in R+ if and only if V is open in R . n n Now consider a set V ⊂ R+ which is open in R+ and that V \ fun = 0g 6= Æ. -
Metrics Defined by Bregman Divergences †
1 METRICS DEFINED BY BREGMAN DIVERGENCES y PENGWEN CHEN z AND YUNMEI CHEN, MURALI RAOx Abstract. Bregman divergences are generalizations of the well known Kullback Leibler diver- gence. They are based on convex functions and have recently received great attention. We present a class of \squared root metrics" based on Bregman divergences. They can be regarded as natural generalization of Euclidean distance. We provide necessary and sufficient conditions for a convex function so that the square root of its associated average Bregman divergence is a metric. Key words. Metrics, Bregman divergence, Convexity subject classifications. Analysis of noise-corrupted data, is difficult without interpreting the data to have been randomly drawn from some unknown distribution with unknown parameters. The most common assumption on noise is Gaussian distribution. However, it may be inappropriate if data is binary-valued or integer-valued or nonnegative. Gaussian is a member of the exponential family. Other members of this family for example the Poisson and the Bernoulli are better suited for integer and binary data. Exponential families and Bregman divergences ( Definition 1.1 ) have a very intimate relationship. There exists a unique Bregman divergence corresponding to every regular exponential family [13][3]. More precisely, the log-likelihood of an exponential family distribution can be represented by a sum of a Bregman divergence and a parameter unrelated term. Hence, Bregman divergence provides a likelihood distance for exponential family in some sense. This property has been used in generalizing principal component analysis to the Exponential family [7]. The Bregman divergence however is not a metric, because it is not symmetric, and does not satisfy the triangle inequality.