A Dynamical Systems Theory of Thermodynamics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
CHAOS and DYNAMICAL SYSTEMS Spring 2021 Lectures: Monday, Wednesday, 11:00–12:15PM ET Recitation: Friday, 12:30–1:45PM ET
MATH-UA 264.001 – CHAOS AND DYNAMICAL SYSTEMS Spring 2021 Lectures: Monday, Wednesday, 11:00–12:15PM ET Recitation: Friday, 12:30–1:45PM ET Objectives Dynamical systems theory is the branch of mathematics that studies the properties of iterated action of maps on spaces. It provides a mathematical framework to characterize a great variety of time-evolving phenomena in areas such as physics, ecology, and finance, among many other disciplines. In this class we will study dynamical systems that evolve in discrete time, as well as continuous-time dynamical systems described by ordinary di↵erential equations. Of particular focus will be to explore and understand the qualitative properties of dynamics, such as the existence of attractors, periodic orbits and chaos. We will do this by means of mathematical analysis, as well as simple numerical experiments. List of topics • One- and two-dimensional maps. • Linearization, stable and unstable manifolds. • Attractors, chaotic behavior of maps. • Linear and nonlinear continuous-time systems. • Limit sets, periodic orbits. • Chaos in di↵erential equations, Lyapunov exponents. • Bifurcations. Contact and office hours • Instructor: Dimitris Giannakis, [email protected]. Office hours: Monday 5:00-6:00PM and Wednesday 4:30–5:30PM ET. • Recitation Leader: Wenjing Dong, [email protected]. Office hours: Tuesday 4:00-5:00PM ET. Textbooks • Required: – Alligood, Sauer, Yorke, Chaos: An Introduction to Dynamical Systems, Springer. Available online at https://link.springer.com/book/10.1007%2Fb97589. • Recommended: – Strogatz, Nonlinear Dynamics and Chaos. – Hirsch, Smale, Devaney, Di↵erential Equations, Dynamical Systems, and an Introduction to Chaos. Available online at https://www.sciencedirect.com/science/book/9780123820105. -
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods Yi Ding Panos Toulis University of Chicago University of Chicago Department of Computer Science Booth School of Business Abstract In this paper, we adopt results in nonlinear time series analysis for causal inference in dynamical settings. Our motivation is policy analysis with panel data, particularly through the use of “synthetic control” methods. These methods regress pre-intervention outcomes of the treated unit to outcomes from a pool of control units, and then use the fitted regres- sion model to estimate causal effects post- intervention. In this setting, we propose to screen out control units that have a weak dy- Figure 1: A Lorenz attractor plotted in 3D. namical relationship to the treated unit. In simulations, we show that this method can mitigate bias from “cherry-picking” of control However, in many real-world settings, different vari- units, which is usually an important concern. ables exhibit dynamic interdependence, sometimes We illustrate on real-world applications, in- showing positive correlation and sometimes negative. cluding the tobacco legislation example of Such ephemeral correlations can be illustrated with Abadie et al.(2010), and Brexit. a popular dynamical system shown in Figure1, the Lorenz system (Lorenz, 1963). The trajectory resem- bles a butterfly shape indicating varying correlations 1 Introduction at different times: in one wing of the shape, variables X and Y appear to be positively correlated, and in the In causal inference, we compare outcomes of units who other they are negatively correlated. Such dynamics received the treatment with outcomes from units who present new methodological challenges for causal in- did not. -
Thermodynamics Notes
Thermodynamics Notes Steven K. Krueger Department of Atmospheric Sciences, University of Utah August 2020 Contents 1 Introduction 1 1.1 What is thermodynamics? . .1 1.2 The atmosphere . .1 2 The Equation of State 1 2.1 State variables . .1 2.2 Charles' Law and absolute temperature . .2 2.3 Boyle's Law . .3 2.4 Equation of state of an ideal gas . .3 2.5 Mixtures of gases . .4 2.6 Ideal gas law: molecular viewpoint . .6 3 Conservation of Energy 8 3.1 Conservation of energy in mechanics . .8 3.2 Conservation of energy: A system of point masses . .8 3.3 Kinetic energy exchange in molecular collisions . .9 3.4 Working and Heating . .9 4 The Principles of Thermodynamics 11 4.1 Conservation of energy and the first law of thermodynamics . 11 4.1.1 Conservation of energy . 11 4.1.2 The first law of thermodynamics . 11 4.1.3 Work . 12 4.1.4 Energy transferred by heating . 13 4.2 Quantity of energy transferred by heating . 14 4.3 The first law of thermodynamics for an ideal gas . 15 4.4 Applications of the first law . 16 4.4.1 Isothermal process . 16 4.4.2 Isobaric process . 17 4.4.3 Isosteric process . 18 4.5 Adiabatic processes . 18 5 The Thermodynamics of Water Vapor and Moist Air 21 5.1 Thermal properties of water substance . 21 5.2 Equation of state of moist air . 21 5.3 Mixing ratio . 22 5.4 Moisture variables . 22 5.5 Changes of phase and latent heats . -
Equilibrium Thermodynamics
Equilibrium Thermodynamics Instructor: - Clare Yu (e-mail [email protected], phone: 824-6216) - office hours: Wed from 2:30 pm to 3:30 pm in Rowland Hall 210E Textbooks: - primary: Herbert Callen “Thermodynamics and an Introduction to Thermostatistics” - secondary: Frederick Reif “Statistical and Thermal Physics” - secondary: Kittel and Kroemer “Thermal Physics” - secondary: Enrico Fermi “Thermodynamics” Grading: - weekly homework: 25% - discussion problems: 10% - midterm exam: 30% - final exam: 35% Equilibrium Thermodynamics Material Covered: Equilibrium thermodynamics, phase transitions, critical phenomena (~ 10 first chapters of Callen’s textbook) Homework: - Homework assignments posted on course website Exams: - One midterm, 80 minutes, Tuesday, May 8 - Final, 2 hours, Tuesday, June 12, 10:30 am - 12:20 pm - All exams are in this room 210M RH Course website is at http://eiffel.ps.uci.edu/cyu/p115B/class.html The Subject of Thermodynamics Thermodynamics describes average properties of macroscopic matter in equilibrium. - Macroscopic matter: large objects that consist of many atoms and molecules. - Average properties: properties (such as volume, pressure, temperature etc) that do not depend on the detailed positions and velocities of atoms and molecules of macroscopic matter. Such quantities are called thermodynamic coordinates, variables or parameters. - Equilibrium: state of a macroscopic system in which all average properties do not change with time. (System is not driven by external driving force.) Why Study Thermodynamics ? - Thermodynamics predicts that the average macroscopic properties of a system in equilibrium are not independent from each other. Therefore, if we measure a subset of these properties, we can calculate the rest of them using thermodynamic relations. - Thermodynamics not only gives the exact description of the state of equilibrium but also provides an approximate description (to a very high degree of precision!) of relatively slow processes. -
Bluff Your Way in the Second Law of Thermodynamics
Bluff your way in the Second Law of Thermodynamics Jos Uffink Department of History and Foundations of Science Utrecht University, P.O.Box 80.000, 3508 TA Utrecht, The Netherlands e-mail: uffi[email protected] 5th July 2001 ABSTRACT The aim of this article is to analyse the relation between the second law of thermodynamics and the so-called arrow of time. For this purpose, a number of different aspects in this arrow of time are distinguished, in particular those of time-(a)symmetry and of (ir)reversibility. Next I review versions of the second law in the work of Carnot, Clausius, Kelvin, Planck, Gibbs, Caratheodory´ and Lieb and Yngvason, and investigate their connection with these aspects of the arrow of time. It is shown that this connection varies a great deal along with these formulations of the second law. According to the famous formulation by Planck, the second law expresses the irreversibility of natural processes. But in many other formulations irreversibility or even time-asymmetry plays no role. I therefore argue for the view that the second law has nothing to do with the arrow of time. KEY WORDS: Thermodynamics, Second Law, Irreversibility, Time-asymmetry, Arrow of Time. 1 INTRODUCTION There is a famous lecture by the British physicist/novelist C. P. Snow about the cul- tural abyss between two types of intellectuals: those who have been educated in literary arts and those in the exact sciences. This lecture, the Two Cultures (1959), characterises the lack of mutual respect between them in a passage: A good many times I have been present at gatherings of people who, by the standards of the traditional culture, are thought highly educated and who have 1 with considerable gusto been expressing their incredulity at the illiteracy of sci- entists. -
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music. Talk given at PoRT workshop, Glasgow, July 2012 Fred Cummins, University College Dublin [1] Dynamical Systems Theory (DST) is the lingua franca of Physics (both Newtonian and modern), Biology, Chemistry, and many other sciences and non-sciences, such as Economics. To employ the tools of DST is to take an explanatory stance with respect to observed phenomena. DST is thus not just another tool in the box. Its use is a different way of doing science. DST is increasingly used in non-computational, non-representational, non-cognitivist approaches to understanding behavior (and perhaps brains). (Embodied, embedded, ecological, enactive theories within cognitive science.) [2] DST originates in the science of mechanics, developed by the (co-)inventor of the calculus: Isaac Newton. This revolutionary science gave us the seductive concept of the mechanism. Mechanics seeks to provide a deterministic account of the relation between the motions of massive bodies and the forces that act upon them. A dynamical system comprises • A state description that indexes the components at time t, and • A dynamic, which is a rule governing state change over time The choice of variables defines the state space. The dynamic associates an instantaneous rate of change with each point in the state space. Any specific instance of a dynamical system will trace out a single trajectory in state space. (This is often, misleadingly, called a solution to the underlying equations.) Description of a specific system therefore also requires specification of the initial conditions. In the domain of mechanics, where we seek to account for the motion of massive bodies, we know which variables to choose (position and velocity). -
Thermodynamic State Variables Gunt
Fundamentals of thermodynamics 1 Thermodynamic state variables gunt Basic knowledge Thermodynamic state variables Thermodynamic systems and principles Change of state of gases In physics, an idealised model of a real gas was introduced to Equation of state for ideal gases: State variables are the measurable properties of a system. To make it easier to explain the behaviour of gases. This model is a p × V = m × Rs × T describe the state of a system at least two independent state system boundaries highly simplifi ed representation of the real states and is known · m: mass variables must be given. surroundings as an “ideal gas”. Many thermodynamic processes in gases in · Rs: spec. gas constant of the corresponding gas particular can be explained and described mathematically with State variables are e.g.: the help of this model. system • pressure (p) state process • temperature (T) • volume (V) Changes of state of an ideal gas • amount of substance (n) Change of state isochoric isobaric isothermal isentropic Condition V = constant p = constant T = constant S = constant The state functions can be derived from the state variables: Result dV = 0 dp = 0 dT = 0 dS = 0 • internal energy (U): the thermal energy of a static, closed Law p/T = constant V/T = constant p×V = constant p×Vκ = constant system. When external energy is added, processes result κ =isentropic in a change of the internal energy. exponent ∆U = Q+W · Q: thermal energy added to the system · W: mechanical work done on the system that results in an addition of heat An increase in the internal energy of the system using a pressure cooker as an example. -
A Cell Dynamical System Model for Simulation of Continuum Dynamics of Turbulent Fluid Flows A
A Cell Dynamical System Model for Simulation of Continuum Dynamics of Turbulent Fluid Flows A. M. Selvam and S. Fadnavis Email: [email protected] Website: http://www.geocities.com/amselvam Trends in Continuum Physics, TRECOP ’98; Proceedings of the International Symposium on Trends in Continuum Physics, Poznan, Poland, August 17-20, 1998. Edited by Bogdan T. Maruszewski, Wolfgang Muschik, and Andrzej Radowicz. Singapore, World Scientific, 1999, 334(12). 1. INTRODUCTION Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power-law form for power spectra of temporal fluctuations of all scales ranging from turbulence (millimeters-seconds) to climate (thousands of kilometers-years) (Tessier et. al., 1996) Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality (Bak et. al., 1988) Standard models for turbulent fluid flows in meteorological theory cannot explain satisfactorily the observed multifractal (space-time) structures in atmospheric flows. Numerical models for simulation and prediction of atmospheric flows are subject to deterministic chaos and give unrealistic solutions. Deterministic chaos is a direct consequence of round-off error growth in iterative computations. Round-off error of finite precision computations doubles on an average at each step of iterative computations (Mary Selvam, 1993). Round- off error will propagate to the mainstream computation and give unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. A recently developed non-deterministic cell dynamical system model for atmospheric flows (Mary Selvam, 1990; Mary Selvam et. -
Isolated Systems with Wind Power an Implementation Guideline
RisB-R=1257(EN) Isolated Systems with Wind Power An Implementation Guideline Niels-Erik Clausen, Henrik Bindner, Sten Frandsen, Jens Carsten Hansen, Lars Henrik Hansen, Per Lundsager Risa National Laboratory, Roskilde, Denmark June 2001 Abstract The overall objective of this research project is to study the devel- opment of methods and guidelines rather than “universal solutions” for the use of wind energy in isolated communities. So far most studies of isolated systems with wind power have been case-oriented and it has proven difficult to extend results from one project to another, not least due to the strong individuality that has characterised such systems in design and implementation. In the present report a unified and generally applicable approach is attempted in order to support a fair assessment of the technical and economical feasibility of isolated power supply systems with wind energy. General guidelines and checklists on which facts and data are needed to carry out a project feasibility analysis are presented as well as guidelines how to carry out the project feasibility study and the environmental analysis. The report outlines the results of the project as a set of proposed guidelines to be applied when developing a project containing an application of wind in an isolated power system. It is the author’s hope that this will facilitate the devel- opment of projects and enhance electrification of small rural communities in developing countries. ISBN 87-550-2860-8; ISBN 87-550 -2861-6 (internet) ISSN 0106-2840 Print: Danka Services -
REVIVING TEE SPIRIT in the PRACTICE of PEDAGOGY: a Scientg?IC PERSPECTIVE on INTERCONNECTMTY AS FOUNIBATION for SPIRITUALITP in EDUCATION
REVIVING TEE SPIRIT IN THE PRACTICE OF PEDAGOGY: A SCIENTg?IC PERSPECTIVE ON INTERCONNECTMTY AS FOUNIBATION FOR SPIRITUALITP IN EDUCATION Jeffrey Golf Department of Culture and Values in Education McGi University, Montreal July, 1999 A thesis subrnitîed to the Faculty of Graduate Studies and Research in partial fulfilment of the requirements for the Degree of Master of Arts in Education O EFFREY GOLF 1999 National Library Bibliothèque nationale 1+1 ofmada du Canada Acquisitions and Acquisitions et Bibliographic Sewices services bibliographiques 395 Wellington Street 395. rue Wellington Ottawa ON K1A ON4 OttawaON K1AON4 Canada Canada Your file Vam référence Our fi& Notre rékirence The author has granted a non- L'auteur a accorde une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loaq distribute or seU reproduire, prêter, distribuer ou copies of this thesis in microfom, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fkom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Thiç thesis is a response to the fragmentation prevalent in the practice of contemporary Western pedagogy. The mechanistic paradip set in place by the advance of classical science has contributed to an ideology that places the human being in a world that is objective, antiseptic, atomistic and diçjointed. -
Chapter 5. Flow Maps and Dynamical Systems
Chapter 5 Flow Maps and Dynamical Systems Main concepts: In this chapter we introduce the concepts of continuous and discrete dynamical systems on phase space. Keywords are: classical mechanics, phase space, vector field, linear systems, flow maps, dynamical systems Figure 5.1: A solar system is well modelled by classical mechanics. (source: Wikimedia Commons) In Chapter 1 we encountered a variety of ODEs in one or several variables. Only in a few cases could we write out the solution in a simple closed form. In most cases, the solutions will only be obtainable in some approximate sense, either from an asymptotic expansion or a numerical computation. Yet, as discussed in Chapter 3, many smooth systems of differential equations are known to have solutions, even globally defined ones, and so in principle we can suppose the existence of a trajectory through each point of phase space (or through “almost all” initial points). For such systems we will use the existence of such a solution to define a map called the flow map that takes points forward t units in time from their initial points. The concept of the flow map is very helpful in exploring the qualitative behavior of numerical methods, since it is often possible to think of numerical methods as approximations of the flow map and to evaluate methods by comparing the properties of the map associated to the numerical scheme to those of the flow map. 25 26 CHAPTER 5. FLOW MAPS AND DYNAMICAL SYSTEMS In this chapter we will address autonomous ODEs (recall 1.13) only: 0 d y = f(y), y, f ∈ R (5.1) 5.1 Classical mechanics In Chapter 1 we introduced models from population dynamics. -
Restricted Agents in Thermodynamics and Quantum Information Theory
Research Collection Doctoral Thesis Restricted agents in thermodynamics and quantum information theory Author(s): Krämer Gabriel, Lea Philomena Publication Date: 2016 Permanent Link: https://doi.org/10.3929/ethz-a-010858172 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Diss. ETH No. 23972 Restricted agents in thermodynamics and quantum information theory A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by Lea Philomena Kr¨amer Gabriel MPhysPhil, University of Oxford born on 18th July 1990 citizen of Germany accepted on the recommendation of Renato Renner, examiner Giulio Chiribella, co-examiner Jakob Yngvason, co-examiner 2016 To my family Acknowledgements First and foremost, I would like to thank my thesis supervisor, Prof. Renato Renner, for placing his trust in me from the beginning, and giving me the opportunity to work in his group. I am grateful for his continuous support and guidance, and I have always benefited greatly from the discussions we had | Renato without doubt has a clear vision, a powerful intuition, and a deep understanding of physics and information theory. Perhaps even more importantly, he has an exceptional gift for explaining complex subjects in a simple and understandable way. I would also like to thank my co-examiners Giulio Chiribella and Jakob Yngvason for agreeing to be part of my thesis committee, and for their input and critical questions in the discussions and conversations we had.