COMP SCI 7077 - Solving Engineering Models
North Terrace Campus - Semester 1 - 2017
General Course Information
Course Code COMP SCI 7077 Course Solving Engineering Models Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Assumed Knowledge basic level of proficiency in some programming language & engineering mathematics Course Description This course concerns techniques for the modelling and simulation of complex systems using a variety of methods and software tools. Students are introduced to the package Matlab and are taken through a study of the techniques used in sophisticated modelling packages to solve common engineering problems. The Matlab programming language is used extensively and students learn to program their own solutions for these common engineering problems. In addition to studying the equations for these models and their solutions, students study the stability, accuracy and reliability of the solution methods.
Course Coordinator: Professor David Suter
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. To demonstrate an understanding of the methods used to solve certain simulation problems that are common in engineering.
2. To demonstrate a proficiency in the programming language Matlab such as is necessary to solve certain simulation problems that are common in engineering.
3. To demonstrate an ability to write efficient and robust programs which solve certain simulation problems that are common in engineering and to demonstrate an ability to recognize aberrant behaviours of the numerical methods used to solve them.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-3 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-3 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
A comprehensive set of lecture notes will be given to each student at the start of the course. Students will be required to program extensively with the Matlab programming package. Matlab is available in many of the computing laboratories used by students. Tutorial and assignment questions will be posted on the MyUni course website
There is no set text for this course but the following references may be useful. Some of these references will be discussed in lectures. K.E. Atkinson. An introduction to numerical analysis. Wiley, 1978. R.L. Burden and J.D. Faires. Numerical Analysis. PWS-Kent, Boston, 1993. 5th Edition. S.C. Chapra and R.P. Canale. Numerical methods for Engineers. McGraw-Hill, New York, 1989. 4th edition. W. Cheney and D. Kincaid. Numerical mathematics and computing. Brookes/Cole, 2nd edition, 1985. S.D. Conte and C. de Boor. Elementary numerical analysis. McGraw-Hill, 3rd edition, 1980. G. Dahlquist and A. Bjork. Numerical methods. Prentice-Hall, 1974. L. Fausett. Numerical Methods: Algorithms and Applications. Prentice-Hall, New Jersey, 2003. G.E. Forsythe, M. Malcolm, and C.B. Moler. Computer methods for mathematical computations.Prentice-Hall, 1977. G.E. Forsythe and C.B. Moler. Computer solution of linear algebraic systems. Prentice-Hall, 1967. C. Gerald and P. Wheatley. Applied numerical analysis. Addison-Wesley, 4th edition, 1989. W. Hager. Applied numerical linear algebra. Prentice-Hall, 1988. E. Isaacson and H.B. Keller. Analysis of numerical methods. Wiley, 1966. R.L. Johnston. Numerical methods: A software approach. Wiley, 1982. D. Kincaid and W. Cheney. Numerical Mathematics. Brookes/Cole, New York, 1996. 2nd Edition. G. Linfield and J. Penny. Numerical methods using Matlab. Ellis-Horwood, 1995. J.H. Matthews. Numerical methods. Prentice-Hall, 1987. A. Ralston and P. Rabinowitz. A first course in numerical analysis. McGraw-Hill, 2nd edition,1978. G. Strang. Linear algebra and its applications. HBJ, 3rd edition, 1988. A. Gilat. MATLAB An Introduction with Applications. . 2nd Ed, John Wiley & Sons, 2005.
Tutorial and assignment questions will be posted on the MyUni course website
Tutorial problem questions will be posted about one week before the tutorial session where the problems will be discussed. Students will be expected to attempt the problems and to engage in their discussion during the tutorial session. An announcement will be made in lectures before each assignment is posted. Students will need to submit their assignment work via the MyUni forum website
Students can use the MyUni forum to exchange ideas with other students in the course and ask questions about the course.
Learning & Teaching Activities
Learning & Teaching ModesThe course will be taught by lectures supported by problem-solving tutorials developing material covered in the lectures. There will be a strong emphasis on programming in Matlab.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.Students should expect to have to spend about 12 hours/week on reviewing lecture material, solving tutorial and assignment problems. Tutorials will start in the second week of the semester.
Learning Activities SummaryA brief introduction to Matlab
Examples of simple applications that lead to linear systems of equations Numrical solution of ODEs in 1D
Initial value problems
Examples of engineering systems that lead to ODEs
Review of the numerical solution of first order ODEs
Adaptive step-size control
Numerical solution of systems of simultaneous first order ODEs
Numerical solution of second and higher order ODEs
Boundary value problems
An explicit finite difference method for the 1D wave equation
Finite difference solution of the potential equation
Systems of linear equations
Review of solution of linear systems of equations
Methods for special matrices arising from engineering problems
Iterative methods for large sparse systems
The ADI method for the 2D heat equation
Specific Course RequirementsStudents who have Matlab software installed on their own computers can do all their programming on those machines. Other students will be able to use any of the computing laboratories openly provided to students in the FECMS. Students who have their own Matlab software can do all their programming on their own computers. Other students will be able to use any of the computing laboratories openly provided to students in the FECMS.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryAttendance at all tutorials is compulsory. There will be a written exam for this course. It will count for 70% of the final assessment (learning outcomes 1-3). There will be two assignments, each of which will count for 15% of the final assessment for the course (learning outcomes 1-3). The first assignment will be handed out before the mid-semester break. There are no joint or collaborative assessment tasks for this course.
Assessment DetailThere will be two compulsory assignment tasks set during the course. Students will be required to submit their Matlab program work via MyUni. Assignments which are submitted late will incur a penalty which caps the maximum mark obtainable by 25% for each day late. Thus, submissions which are
1 day late - mark capped at 75%
2 days late - mark capped at 50%
3 days late - mark capped at 25%
more than 3 days late - no marks available.
Students may wish to submit their work even if no marks are available in order to get some feedback about the quality of their work from the markers of the assignments.
SubmissionStudents will be required to submit their Matlab program work via Moodle and their accompanying handwritten work via submission boxes in the foyer (near Reception on Level 4) of the School of Computer Science.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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