Module Details

Module Code: COMP9063
Title: Computer Simulation & Analysis
Long Title: Computer Simulation & Analysis
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 1 programme(s)
Module Description: Computer simulation is widely used as a tool to design, implement, and analyse models of real-world systems in a computer. Computer simulation is used if an analytical (mathematical) analysis of the system model is too complicated or requires a level of abstraction that simplifies the model too much. It is also used if building the real system is not feasible or too expensive but an analysis of its behaviour or performance is required before real-world implementation. This module introduces the student to the principles of computer simulation and analysis. A specific emphasis is put on stochastic simulation approaches, that is computer simulation driven by random numbers. These occur in many real-world use cases such as computer and communication networks, traffic and transportation systems, thermodynamic systems and many others.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically evaluate the applications of computer simulation and modelling techniques and approaches used in computer simulation.
LO2 Appraise random number generation techniques available for the stochastic simulation of a particular system model.
LO3 Evaluate and apply a computer simulation modelling technique with the aim of modelling a real world system.
LO4 Design a computer simulation and select the most appropriate tool to implement the computer simulation.
LO5 Analyse the behaviour or performance of a computer simulation using statistical evaluation techniques.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction
Why computer simulation; modelling approaches used in computer simulation; simulation techniques and approaches; overview of simulation tools and languages; use case examples.
Random Number generation
Techniques for pseudo random number generation; testing randomness of pseudo random number generation; selecting random distributions and processes to model system properties; techniques for generation of random variates;
Analysis
Simulation output data analysis; Statistical evaluation methods; output data analysis of a single system; comparison of multiple systems; reliability testing, significance sampling, variance reduction techniques
Computer simulation modelling techniques
Description and discussion of stochastic simulation techniques; Monte Carlo simulation and examples; numerical and dynamic simulation modelling and examples. Discrete event and agent based simulation modelling; stochastic discrete event simulation. Building valid, credible and appropriately detailed simulation models; model verification approaches.
Simulation languages and tools
Detailed review, discussion and application of of selected computer simulation tools/languages; Choice of SimPy, CNCL, OpenModelica, Matlab, Omnett++, Arena, ns3, netsim, SUMO, and others; Example implementations of use cases in one or more of the selected tools
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 40
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
In this assignment the student may be expected to apply their understanding of random number generation and statistical evaluation and use an appropriate simulation tool to model and simulate a simple real world problem. Students may also be required to write a report evaluating and justifying their choice of techniques used.
Assessment Type Project % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
In this project students may be expected to model a real world problem or set of problems and design, implement and evaluate their performance in a computer simulation. Students may also be required to write a report evaluating and justifying their choice of technique used.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes of module. Every Week 2.00 2
Lab Contact Lab to support theoretical content delivered in class. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes of module. Every Week 2.00 2
Lab Contact Lab to support theoretical content delivered in class. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Averill M. Law. (2015), Simulation Modeling and Analysis, 5th. McGraw Hill Intl, [ISBN: 9781259254383].
Supplementary Book Resources
  • Sheldon M. Ross. (2006), Simulation, 4th. Elsevier Science, [ISBN: 9780125980630].
  • V. P. Singh. (2008), System Modeling and Simulation, New Age International, [ISBN: 9788122423860].
  • B. K. Choi, D.H. Kang. (2013), Modeling and Simulation of Discrete Event Systems, Wiley, [ISBN: 9781118386996].
Recommended Article/Paper Resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 1 Elective