Engineering Science

Building a generic toolbox for simulating piling operations

Supervisor

Michael O'Sullivan

Faculty of Engineering

Project code: ENG040

The ORUA research group has an initial simulation model for piling operations. There is interest from the construction industry for more models of other piling operations, but more work needs to be done into order to provide a generic modelling tool box for any piling operation.

This project will work towards the realisation of that tool box. Students will need knowledge of Java and knowledge of Python would be preferred, but is not essential.

Adding Multi-criteria Optimisation to OpenSolver

Supervisor

Andrew Mason, Andrea Raith

Faculty of Engineering

Project code: ENG041

This project will extend the popular open source optimiser, OpenSolver, so that users can model and solve optimisation problems with more than one objective using Excel. Solving these problems requires showing users a set of possible solutions, and letting them easily understand the trade-offs in these solutions. We will need to develop good solution algorithms and new visualisation tools to let the user explore the solution set.

The student needs good skills in programming (preferably with some VBA experience), experience using OpenSolver, and an understanding of linear and integer programming.

Developing an online SolverStudio for Google Sheets: A New Online Optimisation Tool

Supervisor

Andrew Mason

Faculty of Engineering

Project code: ENG042

SolverStudio is an Excel add-in that allows optimisation models to be built using Excel and modern modelling languages such as the AMPL clone GMPL. This project will build on our experience moving OpenSolver to the online Google Sheets framework to create a new add-in that allows GMPL models to be built and solved using Google Sheets.

The student will need experience programming in JavaScript and developing web sites with client-side code. Experience with the Google API’s would be a bonus.

Enhancement of SolverStudio for Excel

Supervisor

Andrew Mason

Faculty of Engineering

Project code: ENG043

SolverStudio is an Excel add-in that allows optimisation models to be built using Excel and modern modelling languages such as PuLP and JuMP. This project will extend SolverStudio by adding support for more modelling languages (including SCIP), adding new visualisation capabilities, and creating a new interface for creating and editing OpenSolver models within SolverStudio.

The student will need good programming skills, and will ideally have experience with Python and perhaps Visual Basic. Experience in C# would give more scope for the range of improvements that could be implemented, but is not essential.

OpenSolver optimisation for Libre Office

Supervisor

Andrew Mason

Faculty of Engineering

Project code: ENG044

OpenSolver is a popular open source optimisation tool that is currently only available for Excel. This makes it inaccessible to those who choose to use open source spreadsheets such as Calc in Libre Office. This project will develop a first release of OpenSolver for Libre Office, written either using Python or OpenOffice Basic (https://en.wikipedia.org/wiki/OpenOffice_Basic).

The student will need experience with Linux, good programming skills, and a willingness to learn much more about the inner workings of Libre Office than is known by their supervisor!

Advanced non-linear optimisation for OpenSolver – the open source optimiser for Excel

Supervisor

Andrew Mason

Faculty of Engineering

Project code: ENG045

This project will develop a new enhanced non-linear tool for OpenSolver – a popular open source optimiser for Excel that has been downloaded over 335,000 times. OpenSolver currently solves non-linear models by translating the Excel spreadsheet formula into various internal formats before writing out a file suitable for a non-linear solver. This leads to many problems, including memory overflows, crashes, poor error messages, and slow solving. We are seeking a student with good VBA (and/or C#) skills to create a new approach for handling non-linear problems. These changes will address the most common complaints from our OpenSolver community, and make a real difference to our tens of thousands of users.

Barriers and enablers of commuter cycling: The role of the weather in mode choice

Supervisor

Andrea Raith, Kim Dirks

Faculty of Engineering

Project code: ENG046

We seek to better understand the connection between commuter cyclist trips and the weather in this project. We will analyse available meterological data including temperature, rainfall, wind, sunshine hours as well as cycling data to understand the connection between commuter cyclist trips and weather. Cyclist data is either obtained from counters alongside popular cycle paths, such as the northwestern shared path, or from websites such as Strava.com, where members can log their trips giving information about the exact route travelled. Initial results indicate that cyclist numbers on the northwestern shared path are significantly higher during working days, indicating that this is indeed a commuter route. Cyclist numbers also appear to be correlated with different weather variables. We will extend the analysis to include other environmental variables such as whether it was light or dark at the time of the journey, and analyse combinations of variables to answer questions such as “are people happier to ride in the rain if their commute is in the light”? We will also analyse cyclist routes from Strava and seek to understand trade-off between directness of routes and available cycling infrastructure, as well as other environmental parameters such as traffic, green space.

For instance there is evidence elsewhere that cyclists do take detours in order to travel on cycle paths that are separated from routes. We may also be able to analyse gender differences in this context. Ultimately this analysis could help better model uptake of cycling. The analysis of cyclist routes can feed into route choice (shortest path) models that can help optimise the location of new infrastructure when planning the extension of cycling networks.

Skills: R will be used for data manipulation statistical analysis, and a GIS system such as ArcGIS or qGIS will be used to extract cycle route information. Any student who is confident in R programming, and willing to independently learn to use GIS tools should be able to work on this project.

Software Development for orbital radar

Supervisor

John Cater, Andrew Austin, Nicholas Rattenbury

Faculty of Engineering

Project code: ENG047

This project will use start of the art Arena miniaturized radar system to create an unfocused synthetic aperture radar system for use in space. Some programming experience is required.

CubeSat Antenna Testing

Supervisor

John Cater, Andrew Austin, Nicholas Rattenbury

Faculty of Engineering

Project code: ENG048

This project will investigate and determine the antenna radiation pattern from a CubeSat, under different solar panel deployment scenarios. Antenna design or radio physics experience desirable.

Life in our Solar System

Supervisor

John Cater, Nicholas Rattenbury

Faculty of Engineering

Project code: ENG049

This project will scope and design a mission to neighbouring planets and moons to look for evidence of life off-Earth within our solar system using micro-fluidic technology, such as the lab-on-a-chip. Knowledge of life processes is desirable for this project.

Synthetic Aperture Radar data processing

Supervisor

John Cater, Andrew Austin, Nicholas Rattenbury

Faculty of Engineering

Project code: ENG050

This project will use existing radar data from the ESA Copernicus system to analyse markers of environmental pollution in New Zealand. Software programming or GIS experience is desirable.

Monitoring the Hauraki Gulf

Supervisor

John Cater, Andrew Austin, Nicholas Rattenbury

Faculty of Engineering

Project code: ENG051

We need to develop a sensor suite for monitoring oceanic conditions in the Hauraki Gulf, suitable for drone or aircraft fly-over missions. Optics or image analysis is preferred.

Modelling of 3D and 4D Freeform Additive Printing

Supervisor

Mark Battley

Faculty of Engineering

Project code: ENG052

The aim of this project is to develop and validate analysis methods that can be used to accurately predict the deformation and failure of 3D printed lattice-type structures from bio-based materials, and use the models to explore opportunities for 3D and 4D behaviour, where the structure responds to its environment such as by changing shape, stiffness or other functionalities.

Desired outcomes from the project include developing and validating analysis methodologies that can predict expected properties based on constituent material and printer performance inputs, and understanding how the geometry and material choice can be tailored to achieve required functionalities. This will include analytical and numerical (e.g. FEA) modelling of the material and structural behaviour, and undertaking experimental testing to measure the performance under different loading and environmental conditions.

The student should have a good understanding of mechanics of materials and structures, and an interest in learning more about how to apply various analysis methods to real-world problems.