NUMERICAL AND STATISTICAL METHODS - 2020/1
Module code: ENG2106
Civil Engineers routinely make use of software tools for calculations on physical systems, ranging from structural analysis, to soil mechanics and fluid dynamics. This module provides an introduction to the numerical and statistical methods underlying many of these tools, including Finite Element and Finite Difference Methods as well as linear regression.
The module is hands-on: students will be introduced to MATLAB and learn to write their own programs to apply the methods encountered in the module.
Civil and Environmental Engineering
HAGEN-ZANKER Alex (Civl Env Eng)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: G300
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
The module sits within the core subject of mathematic and covers the following areas:
- Fundamental programming concepts: variables, functions, control structures, vector and matrix data structures
- Syntax, semantics and good programming practice.
- Using the MATLAB programming language and integrated development environment
- Solution of Ordinary Differential Equations by Runge-Kutta and associated methods
- Solution methods for simultaneous linear equations
- Principles and application of the Finite Difference Method for solving Ordinary and Partial Differential Equations
- Principles and application of the Finite Element Method applied on truss structures
- Principles and application of (multiple) linear regression, including confidence and significance
|Assessment type||Unit of assessment||Weighting|
|Coursework||ASSIGNMENT 1: PROGRAMMING FUNDAMENTALS||30|
|Coursework||ASSIGNMENT 2: PROGRAMMING APPLICATION||30|
|Examination||EXAMINATION - 2 HOURS||40|
The summative assessment for this module consists of two coursework assignment and an exam. In the first coursework assignment, students demonstrate fundamental programming skills in MATLAB, including the use of functions, variable, matrix and vector data structures, control structures and plotting (learning outcomes 003 and 005). In the second coursework, students apply their programming skills to implement a numerical method that they have independently researched (learning outcomes 004 and 005). The end-of-semester exam assesses the element of theoretical understanding of numerical and statistical methods (learning outcomes 001 and 002).
Formative assessment and feedback
During computer lab based tutorials students have an opportunity to receive verbal feedback on their work. Additionally, students can test their understanding and get immediate feedback through weekly multiple-choice tests.
- Knowledge and experience of the use of standard numerical and statistical methods to solve complex engineering problems
- Knowledge and experience of using computer programming as a tool to solve engineering problems
|001||Proficiently and critically use a range of numerical methods for the analysis and solution of engineering problems, including an understanding of alternative approaches and their limitations||KC||SM2B, SM2M, SM5M, EA3B, EA3B, P2B|
|002||Proficiently and critically use multiple linear regression for data analysis||KC||SM2B, SM2M, SM5M, EA3B, EA3B, P2B|
|003||Use MATLAB and programming as a tool to help solve engineering problems||KC||SM2B, SM4M, EA3B, P2B|
|004||Move towards independent research, application and analysis of numerical methods for engineering problems||KCT||P4, G1|
|005||Convey technical information in a written report to a professional standard||PT||D6|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 113
Lecture Hours: 22
Laboratory Hours: 15
Methods of Teaching / Learning
Lectures – and supporting notes – are used to introduce key concepts and theoretical background. The main learning however, will take place as students complete weekly exercises that bring the newly learned concepts and knowledge to practice. Weekly tutorial sessions are used to provide feedback and discuss these exercises. The first four weeks of the module are dedicated to programming fundamentals. In these weeks the tutorials are longer than usual (two hours instead of one) because of the importance of feedback in this stage of learning. The coursework provides further exercise in developing programming skills and the ability to use these skills to solve numerical problems. A staggered approach is followed, where the first coursework assignment covers fundamentals and the second requires independent inquiry and application of techniques
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.
Reading list for NUMERICAL AND STATISTICAL METHODS : http://aspire.surrey.ac.uk/modules/eng2106
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 2020/1 academic year.