Engineering Science

Project 

In terms of Discrete Event Simulation (DES) a conceptual model is a formal representation of the system being modelled, and acts as an intermediary step between the real world situation and the computer model. Hierarchical Control Conceptual Modelling (HCCM) is a framework that defines a strict standard that conceptual models should adhere to, which improves the ability to communicate conceptual models unambiguously.

Currently developing a conceptual model that adheres to the HCCM standard is a difficult, error-prone, and time-consuming task. All of the components of the system, and the relationships between them need to be specified precisely.

Role

This project would investigate the use of a software tool (or tools) to automate parts of the conceptual model development. The aim is for users to be able to construct the activity diagrams of the entities in the system. Then the tool creates all of the necessary structural and relational information from the activity diagram. Users would then only need to add any additional details or make minor modifications.

Project 

Space in urban centres is limited and valuable. There is a limited number of car parks available in central cities. An hourly cost is associated with using these car parks. How much should be charged for a public parking space? What if a car sharing service takes advantage of public car parks? Should they be charged for using public paid car parking spaces, and what is a fair charge given car sharing vehicles only occasionally occupy public car parking spaces?

Role

While a car sharing vehicle may occupy a car park, thus reducing the availability of parking spaces to the wider community, we will analyse whether there is also a value to the community of providing this space to a car share vehicle.

We will develop models of car parking in urban centres in NZ and use them to better understand the marginal value of having free parking spaces available. These could be Operations Research / Optimisation models or simulation models.

Requirements

This project requires a student who is a confident programmer with a background in Operations Research. Code could be written in python or Julia. Students need to have an interest in transport and be enthusiastic to find, work with and adapt available transport data for this project sourced online (such as network data, traffic data, etc).

Project 

Approximately ten percent of the earth's land area is covered with glacial ice, with about 10% of that in the Greenland ice cap, the remainder in Antarctica. Human activity has led to conditions in which many glaciers are now rapidly melting, retreating on land and undergoing iceberg calving, i.e. large icebergs and chunks of ice falling from glaciers (and the ice shelf) into the ocean, some over a kilometre in height. This is resulting in a rise in ocean sea level, and a decrease in the earth's ability to reflect the sun's heat back into space. It is essential to understand the interrelationship between anthropomorphic global warming and iceberg calving and glacier melting, in order to reliably predict their consequence.

A number of factors are known to affect iceberg calving and glacier melting, including temperature, glacier thickness, ice density and crystal structure, base roughness and friction, and water pressure. These (typically uncertain) parameters are related to measurable quantities (such as the velocity of the ice on top of the glacier) through mathematical and computational models.

A common approach to enable forecasts/predictions, such as future sea level change, is to first estimate or infer these uncertain parameters and then run the predictive models. The Bayesian framework provides a natural framework to consider the estimation problem as it allows for incorporation and quantification of various sources of uncertainty. However, due to the complexity of the mathematical ice sheet models, solving the inference problem is computationally prohibitive and in some cases completely infeasible.

Role

The goal of this project is to investigate and compare the accuracy, applicability and efficiency of several approximate approaches to the inference problem.

Requirements

Good programming skills are essential. Mathematical modelling skills and some exposure to Bayesian statistics such as STATS331 will be useful. An interest in climate change, environment, ecology is desirable but not necessary.