Design, modelling and measurement of hybrid powerplant for unmanned aerial vehicles (UAVs)

The success or effectiveness for any aircraft design is a function of many trade-offs. Over the last 100 years of aircraft design these trade-offs have been optimized and dominant aircraft design philosophies have emerged. Pilotless aircraft (or uninhabited airborne systems, UAS) present new challenges in the optimization of their configuration. Recent developments in battery and motor technology have seen an upsurge in the utility and performance of electric powered aircraft. Thus, the opportunity to explore hybrid-electric aircraft powerplant configurations is compelling. This thesis considers the design of such a configuration from an overall propulsive, and energy efficiency perspective. A prototype system was constructed using a representative small UAS internal combustion engine (10cc methanol two-stroke) and a 600W brushless Direct current (BLDC) motor. These components were chosen to be representative of those that would be found on typical small UAS. The system was tested on a dynamometer in a wind-tunnel and the results show an improvement in overall propulsive efficiency of 17% when compared to a non-hybrid powerplant. In this case, the improvement results from the utilization of a larger propeller that the hybrid solution allows, which shows that general efficiency improvements are possible using hybrid configurations for aircraft propulsion. Additionally this approach provides new improvements in operational and mission flexibility (such as the provision of self-starting) which are outlined in the thesis. Specifically, the opportunity to use the windmilling propeller for energy regeneration was explored. It was found (in the prototype configuration) that significant power (60W) is recoverable in a steep dive, and although the efficiency of regeneration is low, the capability can allow several options for improved mission viability. The thesis concludes with the general statement that a hybrid powerplant improves the overall mission effectiveness and propulsive efficiency of small UAS.

Cell migration and proliferation on homogeneous and non-homogeneous domains : modelling on the scale of individuals and populations

Cell migration is a behaviour critical to many key biological effects, including wound healing, cancerous cell invasion and morphogenesis, the development of an organism from an embryo. However, given that each of these situations is distinctly different and cells are extremely complicated biological objects, interest lies in more basic experiments which seek to remove conflating factors and present a less complex environment within which cell migration can be experimentally examined. These include in vitro studies like the scratch assay or circle migration assay, and ex vivo studies like the colonisation of the hindgut by neural crest cells. The reduced complexity of these experiments also makes them much more enticing as problems to mathematically model, like done here. The primary goal of the mathematical models used in this thesis is to shed light on which cellular behaviours work to generate the travelling waves of invasion observed in these experiments, and to explore how variations in these behaviours can potentially predict differences in this invasive pattern which are experimentally observed when cell types or chemical environment are changed. Relevant literature has already identified the difficulty of distinguishing between these behaviours when using traditional mathematical biology techniques operating on a macroscopic scale, and so here a sophisticated individual-cell-level model, an extension of the Cellular Potts Model (CPM), is been constructed and used to model a scratch assay experiment. This model includes a novel mechanism for dealing with cell proliferations that allowed for the differing properties of quiescent and proliferative cells to be implemented into their behaviour. This model is considered both for its predictive power and used to make comparisons with the travelling waves which result in more traditional macroscopic simulations. These comparisons demonstrate a surprising amount of agreement between the two modelling frameworks, and suggest further novel modifications to the CPM that would allow it to better model cell migration. Considerations of the model’s behaviour are used to argue that the dominant effect governing cell migration (random motility or signal-driven taxis) likely depends on the sort of invasion demonstrated by cells, as easily seen by microscopic photography. Additionally, a scratch assay simulated on a non-homogeneous domain consisting of a ’fast’ and ’slow’ region is also used to further differentiate between these different potential cell motility behaviours. A heterogeneous domain is a novel situation which has not been considered mathematically in this context, nor has it been constructed experimentally to the best of the candidate’s knowledge. Thus this problem serves as a thought experiment used to test the conclusions arising from the simulations on homogeneous domains, and to suggest what might be observed should this non-homogeneous assay situation be experimentally realised. Non-intuitive cell invasion patterns are predicted for diffusely-invading cells which respond to a cell-consumed signal or nutrient, contrasted with rather expected behaviour in the case of random-motility-driven invasion. The potential experimental observation of these behaviours is demonstrated by the individual-cell-level model used in this thesis, which does agree with the PDE model in predicting these unexpected invasion patterns. In the interest of examining such a case of a non-homogeneous domain experimentally, some brief suggestion is made as to how this could be achieved.

Mathematical modelling of tumour growth and interaction with host tissue and the immune system

In this thesis, three mathematical models describing the growth of solid tumour incorporating the host tissue and the immune system response are developed and investigated. The initial model describes the dynamics of the growing tumour and immune response before being extended in the second model by introducing a time-varying dendritic cell-based treatment strategy. Finally, in the third model, we present a mathematical model of a growing tumour using a hybrid cellular automata. These models can provide information to pre-experimental work to assist in designing more effective and efficient laboratory experiments related to tumour growth and interactions with the immune system and immunotherapy.

Combining elicited expert knowledge into Bayesian priors

Information that is elicited from experts can be treated as `data', so can be analysed using a Bayesian statistical model, to formulate a prior model. Typically methods for encoding a single expert's knowledge have been parametric, constrained by the extent of an expert's knowledge and energy regarding a target parameter. Interestingly these methods have often been deterministic, in that all elicited information is treated at `face value', without error. Here we sought a parametric and statistical approach for encoding assessments from multiple experts. Our recent work proposed and demonstrated the use of a flexible hierarchical model for this purpose. In contrast to previous mathematical approaches like linear or geometric pooling, our new approach accounts for several sources of variation: elicitation error, encoding error and expert diversity. Of interest are the practical, mathematical and philosophical interpretations of this form of hierarchical pooling (which is both statistical and parametric), and how it fits within the subjective Bayesian paradigm. Case studies from a bioassay and project management (on PhDs) are used to illustrate the approach.

On structured workflow modelling

While there are many similarities between the languages of the various workflow management systems, there are also significant differences. One particular area of differences is caused by the fact that different systems impose different syntactic restrictions. In such cases, business analysts have to choose between either conforming to the language in their specifications or transforming these specifications afterwards. The latter option is preferable as this allows for a separation of concerns. In this paper we investigate to what extent such transformations are possible in the context of various syntactical restrictions (the most restrictive of which will be referred to as structured workflows). We also provide a deep insight into the consequences, particularly in terms of expressive power, of imposing such restrictions.

Modelling the failure of thin layered mortar joints in masonry

This paper deals with the failure of high adhesive, low compressive strength, thin layered polymer mortar joints in masonry through a contact modelling in finite element framework. Failure due to combined shear, tensile and compressive stresses are considered through a constitutive damaging contact model that incorporates traction–separation as a function of displacement discontinuity. The modelling method is verified using single and multiple contact analyses of thin mortar layered masonry specimens under shear, tensile and compressive stresses and their combinations. Using this verified method, the failure of thin mortar layered masonry under a range of shear to tension ratios and shear to compression ratios has been examined. Finally, this model is applied to thin bed masonry wallettes for their behaviour under biaxial tension–tension and compression–tension loadings perpendicular and parallel to the bed joints.

Modelling the information seeking user by the decision they make

The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive science showing that decision theory based on quantum probabilities is superior to modelling human judgements than standard probability models [2, 1]. By quantum probabilities, we mean decision event space is modelled as vector space rather than the usual Boolean algebra of sets. In this way,incompatible perspectives around a decision can be modelled leading to an interference term which modifies the law of total probability. The interference term is crucial in modifying the probability judgements made by current probabilistic systems so they align better with human judgement. The goal of this article is thus to model the information seeker user as a decision maker. For this purpose, signal detection models will be sketched which are in principle applicable in a wide variety of information seeking scenarios.

Using natural user interfaces for collaborative process modelling in virtual environments

Modelling business processes for analysis or redesign usually requires the collaboration of many stakeholders. These stakeholders may be spread across locations or even companies, making co-located collaboration costly and difficult to organize. Modern process modelling technologies support remote collaboration but lack support for visual cues used in co-located collaboration. Previously we presented a prototype 3D virtual world process modelling tool that supports a number of visual cues to facilitate remote collaborative process model creation and validation. However, the added complexity of having to navigate a virtual environment and using an avatar for communication made the tool difficult to use for novice users. We now present an evolved version of the technology that addresses these issues by providing natural user interfaces for non-verbal communication, navigation and model manipulation.

In-cylinder pressure and inter-cycle variability analysis for a compression ignition engine : Bayesian approaches

This thesis introduced Bayesian statistics as an analysis technique to isolate resonant frequency information in in-cylinder pressure signals taken from internal combustion engines. Applications of these techniques are relevant to engine design (performance and noise), energy conservation (fuel consumption) and alternative fuel evaluation. The use of Bayesian statistics, over traditional techniques, allowed for a more in-depth investigation into previously difficult to isolate engine parameters on a cycle-by-cycle basis. Specifically, these techniques facilitated the determination of the start of pre-mixed and diffusion combustion and for the in-cylinder temperature profile to be resolved on individual consecutive engine cycles. Dr Bodisco further showed the utility of the Bayesian analysis techniques by applying them to in-cylinder pressure signals taken from a compression ignition engine run with fumigated ethanol.

Mechanical testing and modelling of a bone-implant construct

Finite Element modelling of bone fracture fixation systems allows computational investigation of the deformation response of the bone to load. Once validated, these models can be easily adapted to explore changes in design or configuration of a fixator. The deformation of the tissue within the fracture gap determines its healing and is often summarised as the stiffness of the construct. FE models capable of reproducing this behaviour would provide valuable insight into the healing potential of different fixation systems. Current model validation techniques lack depth in 6D load and deformation measurements. Other aspects of the FE model creation such as the definition of interfaces between components have also not been explored. This project investigated the mechanical testing and FE modelling of a bone– plate construct for the determination of stiffness. In depth 6D measurement and analysis of the generated forces, moments and movements showed large out of plane behaviours which had not previously been characterised. Stiffness calculated from the interfragmentary movement was found to be an unsuitable summary parameter as the error propagation is too large. Current FE modelling techniques were applied in compression and torsion mimicking the experimental setup. Compressive stiffness was well replicated, though torsional stiffness was not. The out of plane behaviours prevalent in the experimental work were not replicated in the model. The interfaces between the components were investigated experimentally and through modification to the FE model. Incorporation of the interface modelling techniques into the full construct models had no effect in compression but did act to reduce torsional stiffness bringing it closer to that of the experiment. The interface definitions had no effect on out of plane behaviours, which were still not replicated. Neither current nor novel FE modelling techniques were able to replicate the out of plane behaviours evident in the experimental work. New techniques for modelling loads and boundary conditions need to be developed to mimic the effects of the entire experimental system.

Using natural user interfaces for collaborative process modelling in virtual environments

A demo video showing the BPMVM prototype using several natural user interfaces, such as multi-touch input, full-body tracking and virtual reality.

Modelling risk at low exposure railway level crossings : supporting an argument for low-cost level crossing warning devices with lower levels of safety integrity

This paper describes a risk model for estimating the likelihood of collisions at low-exposure railway level crossings, demonstrating the effect that differences in safety integrity can have on the likelihood of a collision. The model facilitates the comparison of safety benefits between level crossings with passive controls (stop or give-way signs) and level crossings that have been hypothetically upgraded with conventional or low-cost warning devices. The scenario presented illustrates how treatment of a cross-section of level crossings with low cost devices can provide a greater safety benefit compared to treatment with conventional warning devices for the same budget.

Homeostatic epistemology : reliability, coherence and coordination in a Bayesian virtue epistemology

How do agents with limited cognitive capacities flourish in informationally impoverished or unexpected circumstances? Aristotle argued that human flourishing emerged from knowing about the world and our place within it. If he is right, then the virtuous processes that produce knowledge, best explain flourishing. Influenced by Aristotle, virtue epistemology defends an analysis of knowledge where beliefs are evaluated for their truth and the intellectual virtue or competences relied on in their creation. However, human flourishing may emerge from how degrees of ignorance are managed in an uncertain world. Perhaps decision-making in the shadow of knowledge best explains human wellbeing—a Bayesian approach? In this dissertation I argue that a hybrid of virtue and Bayesian epistemologies explains human flourishing—what I term homeostatic epistemology. Homeostatic epistemology supposes that an agent has a rational credence p when p is the product of reliable processes aligned with the norms of probability theory; whereas an agent knows that p when a rational credence p is the product of reliable processes such that: 1) p meets some relevant threshold for belief (such that the agent acts as though p were true and indeed p is true), 2) p coheres with a satisficing set of relevant beliefs and, 3) the relevant set of beliefs is coordinated appropriately to meet the integrated aims of the agent. Homeostatic epistemology recognizes that justificatory relationships between beliefs are constantly changing to combat uncertainties and to take advantage of predictable circumstances. Contrary to holism, justification is built up and broken down across limited sets like the anabolic and catabolic processes that maintain homeostasis in the cells, organs and systems of the body. It is the coordination of choristic sets of reliably produced beliefs that create the greatest flourishing given the limitations inherent in the situated agent.

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Econometric Modelling with Time Series : Specification, Estimation and Testing

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.