There is a demand for a whole field velocimetry technique which offers the capability of rapid characterisation of complex engineering flow fields. This thesis describes a research programme aimed at the development of a reliable Doppler global velocimeter, suited to the measurement of such flows. The programme of work undertaken is discussed with reference both to research undertaken elsewhere and to previous work on the system at Oxford. While much of the underlying technology required for the construction of an accurate and reliable velocimeter has already been studied in Oxford and elsewhere, little attention has been paid by previous workers to the examination of the impact of data processing techniques on attainable flow measurement accuracy. In the present work, a number of image processing methods have been utilised for Doppler global velocimetry data processing. Those methods are described here, together with a theoretical analysis of their expected performance when applied to Doppler global velocimetry data. The expected error resulting from image processing considerations and also from the physical characteristics of the Doppler global velocimetry hardware are quantified in such a way that error estimates may be computed for real measured data frames. The results of the application of the velocimeter to the simple test case of measuring a velocity component of a rotating disc are presented. The velocimeter was subsequently applied to the measurement of a free jet flow and to a transonic flow field in a convergent-divergent nozzle. Correlations with accepted velocity field values were undertaken, and compared to the expected error previously determined. The choice of image processing algorithms was found to be of great importance in terms of Doppler global velocimetry measurement accuracy.

It is essential that all countries operate a form of blood banking service, where blood is collected at donation sessions, stored and then distributed to local healthcare providers. It is imperative that these services are efficiently managed to ensure a safe supply of blood and that costs and wastages are kept minimal. Previous works in the area of blood management have focussed primarily on the perishable inventory problem and on routing blood deliveries to hospitals; there has been relatively little work focusing on scheduling blood donation sessions. The primary aim of this research is to provide a tool that allows the National Blood Service (the English and Welsh blood service) to schedule donation sessions so that collection targets are met in such a way that costs are minimised (the Blood Scheduling Problem). As secondary aims, the research identifies the key types of data that blood services should be collecting for this type of problem. Finally, various what-if scenarios are considered, specifically improv- ing donor attendance through paying donors and the proposed changes to the inter-donation times for male and female donors. The Blood Scheduling Problem is formulated as a Mixed Integer Linear Programming (MILP) problem and solved using a variable bound heuristic. Data from the South East of England is used to create a collection schedule, with all further analysis also being carried out on this data set. It was possible to make improvements to the number of units under collected in the current schedule, moreover the number of venues and panels operated could be reduced. Further- more, it was found that paying donors to donate was uneconomical. Finally, changing the inter-donation times could lead to a reduction in the number of shortfalls, even when demand was increased by as much as 20%. Though the model is specific to England and Wales, it can easily be adapted to other countries’ blood services. It is hoped that this model will provide blood services with a model to help them better schedule donation sessions and allow them to identify the data necessary to better understand their performance.

Open innovation that Henry Chesbrough introduced in 2003 promotes the usage of the input of outsiders to strengthen internal innovation processes and the search for outside commercialization opportunities for what is developed internally. Open innovation has enabled both academics and practitioners to design innovation strategies based on the reality of our connected world. Although the literature has identified and explored a variety of benefits and costs, to the best of our knowledge, no study has reviewed the benefits and costs of open innovation in terms of their importance for strategic performance. To conduct such a study, we need to take into account two main issues. First, the number of benefits and costs of open innovation are multifold; so, to have a comprehensive comparison, a large number of benefits and costs must be compared. Second, to have a fair comparison, benefits and costs must be compared in terms of different performance criteria, including financial and non-financial. Concerning the issues above, we will face a complex process of exploring benefits and costs. In this regard, we use multiple criterion decision-making (MCDM) methods that have shown promising solutions to complex exploratory problems. In particular, we present how using a stochastic multi-criteria clustering algorithm that is one of the recently introduced MCDM methods can bring promising results when it comes to exploring the strategic importance of benefits and costs of open innovation. Since there is no comprehensive understanding of the nature of the benefits and costs of open innovation, the proposed model aims to cluster them into hierarchical groups to help researchers identify the most crucial benefits and costs concerning different dimensions of performance. In addition, the model is able to deal with uncertainties related to technical parameters such as criteria weights and preference thresholds. We apply the model in the context of open innovation for universities concerning their research collaboration with industries. An online survey was conducted to collect experts' opinions on the open-innovation benefits and costs of university-industry research collaboration, given different performance dimensions. The results obtained through the cluster analysis specify that university researchers collaborate with industry mainly because of knowledge-related and research-related reasons rather than economic reasons. This research also indicates that the most important benefits of university-industry research collaboration for universities are implementing the learnings, increased know-how, accessing specialized infrastructures, accessing a greater idea and knowledge base, sensing and seizing new technological trends, and keeping the employees engaged. In addition, the results show that the most important costs are the lack of necessary resources to monitor activities between university and industry, an increased resistance to change among employees, conflict of interest (different missions), an increased employees' tendency to avoid using the knowledge that they do not create themselves, paying time costs associated with bureaucracy rules, and loss of focus. The research's findings enable researchers to analyze open innovation's related issues for universities more effectively and define their research projects on these issues in line with the priorities of universities.