ENERGY, ENTROPY AND NUMERICAL PHYSICS - 2022/3
Module code: PHY2063
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
This module considers develops both the thermodynamic and statistical descriptions of energy and entropy. In addition it builds on the introductory Level FHEQ 4 computing modules to develop the skills needed for computational physics. The module will explore various meanings and definitions of entropy. Knowledge of thermodynamics will then be applied to problem solving. The module will build upon the knowledge obtained of the laws of thermodynamics introduced in Properties of Matter at Level FHEQ 4. It will introduce additional thermodynamic theory and by show how statistical physics allows us to calculate thermodynamic functions such as the entropy. The computational physics component will develop the student’s skills in solving both ordinary and partial differential equations, in the context of both quantum and thermal physics.
ERKAL Denis (Physics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: F300
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 69
Lecture Hours: 11
Tutorial Hours: 22
Guided Learning: 21
Captured Content: 27
Prerequisites / Co-requisites
Indicative content includes:
The module will build on the Level HE1 module “Properties of Matter” by developing our understanding of entropy within thermodynamics, and by showing how statistical physics allows us to calculate the properties of matter, such as the entropy, by averaging huge numbers of states of the matter’s constituent atoms.
The statistical nature of the 2nd Law of Thermodynamics will be shown.
The concept of a free energy and the Helmholtz and Gibb’s free energy functions will be covered. The statistical physics part of the course will introduce Shannon’s expression for the entropy, the partition function at constant temperature, the Boltzmann weight of a state at constant temperature, and also the weight of a state at constant chemical potential/Fermi level.
Fluctuations will be studied and the Central Limit Theorem will be introduced. The relationship between fluctuations and thermodynamic quantities such as heat capacities will be shown.
An application to a simple system: a two-level system at fixed temperature, will be described in detail.
Classical statistical mechanics will be introduced, with the simple example of the partition function of a simple classical particle, as well as the equipartition theorem.
Microscopic models of Phase Transitions, and phenomenological models (Landau theory) will be introduced at the end of the course.
The computational part of the module will include:
Euler’s method for the solution of ordinary differential equations.
Applications of this method to: a single first-order differential equation; two coupled first-order differential equations; and a single second-order equation, expressed as a pair of coupled first-order equations.
The treatment of boundary conditions.
Elementary discussion of finite difference methods for the solution of partial differential equations: application to the solution of partial differential equations in two spatial dimensions, and in one spatial dimension plus time.
Introduction to the Monte Carlo numerical calculation technique.
Introduction to the use of Bayes' theorem.
|Assessment type||Unit of assessment||Weighting|
|Coursework||ENERGY AND ENTROPY COURSEWORK||10|
|Coursework||NUMERICAL PHYSICS COURSEWORK||30|
|Examination Online||ONLINE (OPEN BOOK) EXAM||60|
The assessment strategy is designed to provide students with the opportunity to demonstrate
recall of subject knowledge
ability to apply subject knowledge to unseen problems in mathematics and physics
ability to solve mathematical problems by writing computer programs
Thus, the summative assessment for this module consists of:
Two computing coursework assignments: In the first the computer program (only) is assessed (10% contribution to module mark), while for the second (20% of module mark) a page-limited report plus a program is assessed.
a final 2 hour exam with section A consisting of compulsory questions, worth a total of 20 marks, and section B consisting of a choice of 2/3 questions for a total of 40 marks.
Formative assessment and feedback
Students receive verbal feedback in tutorials and in the supervised computational classes. Written feedback is given on the computational assignments, with feedback on each being given before the next is due.
- introduce thermodynamic and statistical descriptions of entropy in a coherent way
- introduce the basic statistical physics ideas and tools needed to understand and to calculate the properties of matter
- develop computational and problem solving skills.
|001||Recall both statistical and thermodynamic descriptions of entropy and be able to assess how entropy is related to uncertainty as to the state of the system, the direction of time, and heat flow.||KC|
|002||Compare the statistical and thermodynamic definitions of entropy.||K|
|003||Solve problems by applying the thermodynamic method.||C|
|004||Explain how the state variables (pressure, volume and temperature) and bulk properties (modulus and thermal expansivity) are inter-related.||K|
|005||State Gibb's expression for the entropy and the partition function at constant temperature,||K|
|006||Derive both the Boltzmann weight of a state at constant temperature, and also the weight of a state at constant chemical potential/Fermi level.||KC|
|007||Assess why a statistical approach is required in the study of matter such as gases, liquids and solids.||C|
|008||Explain the role of fluctuations, and estimate their size in a range of contexts.||C|
|009||Calculate the properties of the two-level system||KC|
|010||Explain how fluctuations are related to thermodynamic functions, such as the heat capacities.||C|
|011||Recall both the partition function for a simple classical particle and the equipartition theorem, and judge which one is required for a given system.||K|
|012||Analyse phase transitions such as the ferromagnetic phase transition using statistical physics methods||KC|
|013||Solve ordinary differential equations numerically using simple finite difference algorithms||KCT|
|014||Solve simple partial differential equations by discretising space and solving the differential equation on a grid. In both cases, the student will be able to assess the accuracy of the solutions, judge what accuracy is required and be able to plan simple computational approaches to relevant problems in physics. Use the solution to show understanding of a simple physical system.||KCT|
|015||Solve a simple problem using the Monte Carlo algorithm, and perform a basic analysis on the results. Apply Bayes' theorem to analyse a simple data set. Use the solution to show understanding of a simple physical system, or or to analyse a simple data set.||KCT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
equip students with subject knowledge
develop skills in applying subject knowledge to physical situations and to solve mathematical problems
develop skills in writing computer programs to solve problems in mathematics and physics
The learning and teaching methods include:
- 32h of lectures and 6h of tutorials
- 33h of supervised computational laboratory as 3h/week x 11 weeks
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: PHY2063
Programmes this module appears in
|Physics with Astronomy BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics with Quantum Technologies BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics with Nuclear Astrophysics MPhys||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics with Astronomy MPhys||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics with Nuclear Astrophysics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics with Quantum Technologies MPhys||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Physics MPhys||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.