Part 3. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 3.) Practical application to the design and optimisation of UAV systems

These lecture notes highlight some of the recent applications of multi-objective and multidisciplinary design optimisation in aeronautical design using the framework and methodology described in References 8, 23, 24 and in Part 1 and 2 of the notes. A summary of the methodology is described and the treatment of uncertainties in flight conditions parameters by the HAPEAs software and game strategies is introduced. Several test cases dealing with detailed design and computed with the software are presented and results discussed in section 4 of these notes.

A large-scale computing infrastructure for design education

Most departmental computing infrastructure reflects the state of networking technology and available funds at the time of construction, which converge in a preconceived notion of homogeneity of network architecture and usage patterns. The DMAN (Digital Media Access Network) project, a large-scale server and network foundation for the Hong Kong Polytechnic University's School of Design was created as a platform that would support a highly complex academic environment while giving maximum freedom to students, faculty and researchers through simplicity and ease of use. As a centralized multi-user computation backbone, DMAN faces an extremely hetrogeneous user and application profile, exceeding implementation and maintenance challenges of typical enterprise, and even most academic server set-ups. This paper sumarizes the specification, implementation and application of the system while describing its significance for design education in a computational context.

Creating a therapeutic environment : a non-randomised controlled trial of a quiet time intervention for patients in acute care

Background: Noise is a significant barrier to sleep for acute care hospital patients, and sleep has been shown to be therapeutic for health, healing and recovery. Scheduled quiet time interventions to promote inpatient rest and sleep have been successfully trialled in critical care but not in acute care settings. Objectives: The study aim was to evaluate as cheduled quiet time intervention in an acute care setting. The study measured the effect of a scheduled quiet time on noise levels, inpatients’ rest and sleep behaviour, and wellbeing. The study also examined the impact of the intervention on patients’, visitors’ and health professionals’ satisfaction, and organisational functioning. Design: The study was a multi-centred non-randomised parallel group trial. Settings: The research was conducted in the acute orthopaedic wards of two major urban public hospitals in Brisbane, Australia. Participants: All patientsadmitted to the two wards in the5-month period of the study were invited to participate, withafinalsample of 299 participants recruited. This sample produced an effect size of 0.89 for an increase in the number of patients asleep during the quiet time. Methods: Demographic data were collected to enable comparison between groups. Data for noise level, sleep status, sleepiness and well being were collected using previously validated instruments: a Castle Model 824 digital sound level indicator; a three point sleep status scale; the Epworth Sleepiness Scale; and the SF12 V2 questionnaire. The staff, patient and visitor surveys on the experimental ward were adapted from published instruments. Results: Significant differences were found between the two groups in mean decibel level and numbers of patients awake and asleep. The difference in mean measured noise levels between the two environments corresponded to a ‘perceived’ difference of 2 to 1. There were significant correlations between average decibel level and number of patients awake and asleep in the experimental group, and between average decibel level and number of patients awake in the control group. Overall, patients, visitors and health professionals were satisfied with the quiet time intervention. Conclusions: The findings show that a quiet time intervention on an acute care hospital ward can affect noise level and patient sleep/wake patterns during the intervention period. The overall strongly positive response from surveys suggests that scheduled quiet time would be a positively perceived intervention with therapeutic benefit.

Work at hand : an exploration of gesture in the context of work and everyday life to inform the design of gestural input devices

This research explores gestures used in the context of activities in the workplace and in everyday life in order to understand requirements and devise concepts for the design of gestural information applicances. A collaborative method of video interaction analysis devised to suit design explorations, the Video Card Game, was used to capture and analyse how gesture is used in the context of six different domains: the dentist's office; PDA and mobile phone use; the experimental biologist's laboratory; a city ferry service; a video cassette player repair shop; and a factory flowmeter assembly station. Findings are presented in the form of gestural themes, derived from the tradition of qualitative analysis but bearing some similarity to Alexandrian patterns. Implications for the design of gestural devices are discussed.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Relentless patterns the immersive interior

What happens when patterns become all pervasive? When pattern contagiously corrupts and saturates adjacent objects, artefacts and surfaces; blurring internal and external environment and dissolving any single point of perspective or static conception of space. Mark Taylor ruminates on the possibilities of relentless patterning in interior space in both a historic and a contemporary context.

YAWL : power through patterns

Workflow Management Systems (WfMSs) enable the development and maintenance of workflow specifications at design time and their execution and monitoring at runtime. The open source WfMS YAWL supports the YAWL language – a formally defined language based on Petri nets which offers comprehensive support for control-flow and resource patterns. In addition, the YAWL system provides extensive support for process flexibility, in particular for process configuration, exception handling, dynamic workflow and declarative workflow. Due to its formal foundation, sophisticated verification support can also be achieved. This paper presents the YAWL system and its main applications.

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual Nash strategies in a CFD design environment

This paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables - aerofoil sections - supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.

A phenomenological study of contemplative experiences : implications for interior design

This research reports on a project concerned with the relationship between the person and the environment in the context of achieving a contemplative or existential state – a state which can be experienced either consciously or subconsciously. The need for such a study originated with the desire to contribute to the design of multicultural spaces which could be used for a range of activities within the public and the personal arena, activities including contemplation, meditation and prayer. The concept of ‘sacred’ is explored in the literature review and in primary interviews with the participants of this study. Given that the word ‘sacred’ is highly value-laden and potentially alienating for some people, it was decided to use the more accessible term ‘contemplative’. The outcomes of the study inform the practice of interior design and architecture which tends currently to neglect the potential for all spaces to be existentially meaningful. Informed by phenomenological methodology, data were collected from a diverse group of people, using photo-elicitation and interviews. The technique of photo-elicitation proved to be highly effective in helping people reveal their everyday lived experience of contemplative spaces. Reflective analysis (Van Manen 2000) was used to explore the data collected. The initial stage of analysis produced three categories of data: varying conceptions of contemplation, aspects of the person involved in the contemplation, and aspects of environment involved in contemplation. From this, it was found that achieving a state of contemplation involves both the person and the environment in a dialectic process of unfolding. The unfolding has various physical, psycho-social, and existential dimensions or qualities which operate sequentially and simultaneously. Two concepts emerged as being central to unfolding: ‘Cleansing’ and ‘Nothingness’. Unfolding is found to comprise the Core; Distinction; Manifestation; Cleansing; Creation; and Sharing. This has a parallel with Mircea Eliade’s (1959) definition of sacred as something that manifests itself as different from the profane. The power of design, re-contextualization through utility and purpose, and the existential engagements between the person and environment are used as a basis for establishing the potential contribution of the study to interior design. In this way, the study makes a contribution to our understanding of how space and its elements inspire, support and sustain person environment interaction – particularly at the existential level – as well as to our understanding of the multi-dimensional and holistic nature of this interaction. In addition, it points to the need for a phenomenological re-conceptualisation of the design/client relationship. In summary, the contributions of this research are: the exploration of contemplative experience as sacred experience; an understanding of the design of space as creating engagement between person and environment; a rationale for the introduction of a phenomenological approach to the relationship between designer and clients; and raising awareness of the spiritual in a holistic approach to design.

Theoretical investigation of thermal tweezers for parallel manipulation of atoms and nanoparticles on surfaces

A major focus of research in nanotechnology is the development of novel, high throughput techniques for fabrication of arbitrarily shaped surface nanostructures of sub 100 nm to atomic scale. A related pursuit is the development of simple and efficient means for parallel manipulation and redistribution of adsorbed atoms, molecules and nanoparticles on surfaces – adparticle manipulation. These techniques will be used for the manufacture of nanoscale surface supported functional devices in nanotechnologies such as quantum computing, molecular electronics and lab-on-achip, as well as for modifying surfaces to obtain novel optical, electronic, chemical, or mechanical properties. A favourable approach to formation of surface nanostructures is self-assembly. In self-assembly, nanostructures are grown by aggregation of individual adparticles that diffuse by thermally activated processes on the surface. The passive nature of this process means it is generally not suited to formation of arbitrarily shaped structures. The self-assembly of nanostructures at arbitrary positions has been demonstrated, though these have typically required a pre-patterning treatment of the surface using sophisticated techniques such as electron beam lithography. On the other hand, a parallel adparticle manipulation technique would be suited for directing the selfassembly process to occur at arbitrary positions, without the need for pre-patterning the surface. There is at present a lack of techniques for parallel manipulation and redistribution of adparticles to arbitrary positions on the surface. This is an issue that needs to be addressed since these techniques can play an important role in nanotechnology. In this thesis, we propose such a technique – thermal tweezers. In thermal tweezers, adparticles are redistributed by localised heating of the surface. This locally enhances surface diffusion of adparticles so that they rapidly diffuse away from the heated regions. Using this technique, the redistribution of adparticles to form a desired pattern is achieved by heating the surface at specific regions. In this project, we have focussed on the holographic implementation of this approach, where the surface is heated by holographic patterns of interfering pulsed laser beams. This implementation is suitable for the formation of arbitrarily shaped structures; the only condition is that the shape can be produced by holographic means. In the simplest case, the laser pulses are linearly polarised and intersect to form an interference pattern that is a modulation of intensity along a single direction. Strong optical absorption at the intensity maxima of the interference pattern results in approximately a sinusoidal variation of the surface temperature along one direction. The main aim of this research project is to investigate the feasibility of the holographic implementation of thermal tweezers as an adparticle manipulation technique. Firstly, we investigate theoretically the surface diffusion of adparticles in the presence of sinusoidal modulation of the surface temperature. Very strong redistribution of adparticles is predicted when there is strong interaction between the adparticle and the surface, and the amplitude of the temperature modulation is ~100 K. We have proposed a thin metallic film deposited on a glass substrate heated by interfering laser beams (optical wavelengths) as a means of generating very large amplitude of surface temperature modulation. Indeed, we predict theoretically by numerical solution of the thermal conduction equation that amplitude of the temperature modulation on the metallic film can be much greater than 100 K when heated by nanosecond pulses with an energy ~1 mJ. The formation of surface nanostructures of less than 100 nm in width is predicted at optical wavelengths in this implementation of thermal tweezers. Furthermore, we propose a simple extension to this technique where spatial phase shift of the temperature modulation effectively doubles or triples the resolution. At the same time, increased resolution is predicted by reducing the wavelength of the laser pulses. In addition, we present two distinctly different, computationally efficient numerical approaches for theoretical investigation of surface diffusion of interacting adparticles – the Monte Carlo Interaction Method (MCIM) and the random potential well method (RPWM). Using each of these approaches we have investigated thermal tweezers for redistribution of both strongly and weakly interacting adparticles. We have predicted that strong interactions between adparticles can increase the effectiveness of thermal tweezers, by demonstrating practically complete adparticle redistribution into the low temperature regions of the surface. This is promising from the point of view of thermal tweezers applied to directed self-assembly of nanostructures. Finally, we present a new and more efficient numerical approach to theoretical investigation of thermal tweezers of non-interacting adparticles. In this approach, the local diffusion coefficient is determined from solution of the Fokker-Planck equation. The diffusion equation is then solved numerically using the finite volume method (FVM) to directly obtain the probability density of adparticle position. We compare predictions of this approach to those of the Ermak algorithm solution of the Langevin equation, and relatively good agreement is shown at intermediate and high friction. In the low friction regime, we predict and investigate the phenomenon of ‘optimal’ friction and describe its occurrence due to very long jumps of adparticles as they diffuse from the hot regions of the surface. Future research directions, both theoretical and experimental are also discussed.

Object-centric process models and the design of flexible processes

Mainstream business process modelling techniques promote a design paradigm wherein the activities to be performed within a case, together with their usual execution order, form the backbone of a process model, on top of which other aspects are anchored. This paradigm, while eective in standardised and production-oriented domains, shows some limitations when confronted with processes where case-by-case variations and exceptions are the norm. In this thesis we develop the idea that the eective design of exible process models calls for an alternative modelling paradigm, one in which process models are modularised along key business objects, rather than along activity decompositions. The research follows a design science method, starting from the formulation of a research problem expressed in terms of requirements, and culminating in a set of artifacts that have been devised to satisfy these requirements. The main contributions of the thesis are: (i) a meta-model for object-centric process modelling incorporating constructs for capturing exible processes; (ii) a transformation from this meta-model to an existing activity-centric process modelling language, namely YAWL, showing the relation between object-centric and activity-centric process modelling approaches; and (iii) a Coloured Petri Net that captures the semantics of the proposed meta-model. The meta-model has been evaluated using a framework consisting of a set of work ow patterns. Moreover, the meta-model has been embodied in a modelling tool that has been used to capture two industrial scenarios.

Mining patterns and factors contributing to crash severity on road curves

Road curves are an important feature of road infrastructure and many serious crashes occur on road curves. In Queensland, the number of fatalities is twice as many on curves as that on straight roads. Therefore, there is a need to reduce drivers’ exposure to crash risk on road curves. Road crashes in Australia and in the Organisation for Economic Co-operation and Development(OECD) have plateaued in the last five years (2004 to 2008) and the road safety community is desperately seeking innovative interventions to reduce the number of crashes. However, designing an innovative and effective intervention may prove to be difficult as it relies on providing theoretical foundation, coherence, understanding, and structure to both the design and validation of the efficiency of the new intervention. Researchers from multiple disciplines have developed various models to determine the contributing factors for crashes on road curves with a view towards reducing the crash rate. However, most of the existing methods are based on statistical analysis of contributing factors described in government crash reports. In order to further explore the contributing factors related to crashes on road curves, this thesis designs a novel method to analyse and validate these contributing factors. The use of crash claim reports from an insurance company is proposed for analysis using data mining techniques. To the best of our knowledge, this is the first attempt to use data mining techniques to analyse crashes on road curves. Text mining technique is employed as the reports consist of thousands of textual descriptions and hence, text mining is able to identify the contributing factors. Besides identifying the contributing factors, limited studies to date have investigated the relationships between these factors, especially for crashes on road curves. Thus, this study proposed the use of the rough set analysis technique to determine these relationships. The results from this analysis are used to assess the effect of these contributing factors on crash severity. The findings obtained through the use of data mining techniques presented in this thesis, have been found to be consistent with existing identified contributing factors. Furthermore, this thesis has identified new contributing factors towards crashes and the relationships between them. A significant pattern related with crash severity is the time of the day where severe road crashes occur more frequently in the evening or night time. Tree collision is another common pattern where crashes that occur in the morning and involves hitting a tree are likely to have a higher crash severity. Another factor that influences crash severity is the age of the driver. Most age groups face a high crash severity except for drivers between 60 and 100 years old, who have the lowest crash severity. The significant relationship identified between contributing factors consists of the time of the crash, the manufactured year of the vehicle, the age of the driver and hitting a tree. Having identified new contributing factors and relationships, a validation process is carried out using a traffic simulator in order to determine their accuracy. The validation process indicates that the results are accurate. This demonstrates that data mining techniques are a powerful tool in road safety research, and can be usefully applied within the Intelligent Transport System (ITS) domain. The research presented in this thesis provides an insight into the complexity of crashes on road curves. The findings of this research have important implications for both practitioners and academics. For road safety practitioners, the results from this research illustrate practical benefits for the design of interventions for road curves that will potentially help in decreasing related injuries and fatalities. For academics, this research opens up a new research methodology to assess crash severity, related to road crashes on curves.

Active flow control bump design using hybrid Nash-game coupled to evolutionary algorithms

In this paper, the optimal design of an active flow control device; Shock Control Bump (SCB) on suction and pressure sides of transonic aerofoil to reduce transonic total drag is investigated. Two optimisation test cases are conducted using different advanced Evolutionary Algorithms (EAs); the first optimiser is the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPMOEA) based on canonical Evolutionary Strategies (ES). The second optimiser is the HAPMOEA is hybridised with one of well-known Game Strategies; Nash-Game. Numerical results show that SCB significantly reduces the drag by 30% when compared to the baseline design. In addition, the use of a Nash-Game strategy as a pre-conditioner of global control saves computational cost up to 90% when compared to the first optimiser HAPMOEA.