The role of virtual reality in built environment education

This study builds upon previous research on the integration of Virtual Reality (VR) within the built environment curriculum and aims to investigate the role of VR and three-dimensional (3D) computer modelling on learning and teaching in a school of the built environment. In order to achieve this aim, a number of academic experiences were analysed to explore the applicability and viability of 3D computer modelling and VR into built environment subject areas. Although two-dimensional (2D) representations have been greatly accepted by built environment professions and education, 3D computer representations and VR applications, offering interactivity and immersiveness, are not yet widely accepted. The study attempts to understand the values and challenges of integrating visualisation technologies into built environment teaching and investigates tutors’ perceptions, opinions and concerns with respect to these technologies. The study reports on the integration process and considers how 3D computer modelling and VR technologies can combine with, and extend, the existing range of learning and teaching methods appropriate to different disciplines and programme areas

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

An artificial neural network for dimensions and cost modelling of internal micro-channels fabricated in PMMA using Nd:YVO4 laser

For micro-channel fabrication using laser micro-machining processing, estimation techniques are normally utilised to develop an approach for the system behaviour evaluation. Design of Experiments (DOE) and the Artificial Neural Networks (ANN) are two methodologies that can be used as estimation techniques. These techniques help in finding a set of laser processing parameters that provides the required micro-channel dimensions and in finding the optimal solutions in terms reducing the product development time, power consumption and of least cost. In this work, an integrated methodology is presented in which the ANN training experiments were obtained by the statistical software DoE to improve the developed models in ANN. A 33 factorial design of experiments (DoE) was used to get the experimental set. Laser power, P; pulse repetition frequency, PRF; and sample translation speed, U were the ANN inputs. The channel width and the produced micro-channel operating cost per metre were the measured responses. Four Artificial Neural Networks (ANNs) models were developed to be applied to internal micro-channels machined in PMMA using a Nd:YVO4 laser. These models were varied in terms of the selection and the quantity of training data set and constructed using a multi-layered, feed-forward structure with a the back-propagation algorithm. The responses were adequately estimated by the ANN models within the set micro-machining parameters limits. Moreover the effect of changing the selection and the quantity of training data on the approximation capability of the developed ANN model was discussed

Simulated design strategies for SPECT collimators to reduce the eddy currents induced by MRI gradient fields

Combining single photon emission computed tomography (SPECT) with magnetic resonance imaging (MRI) requires the insertion of highly conductive SPECT collimators inside the MRI scanner, resulting in an induced eddy current disturbing the combined system. We reduced the eddy currents due to the insert of a novel tungsten collimator inside transverse and longitudinal gradient coils. The collimator was produced with metal additive manufacturing, that is part of a microSPECT insert for a preclinical SPECT/MRI scanner. We characterized the induced magnetic field due to the gradient field and adapted the collimators to reduce the induced eddy currents. We modeled the x-, y-, and z-gradient coil and the different collimator designs and simulated them with FEKO, a three-dimensional method of moments / finite element methods (MoM/FEM) full-wave simulation tool. We used a time analysis approach to generate the pulsed magnetic field gradient. Simulation results show that the maximum induced field can be reduced by 50.82% in the final design bringing the maximum induced magnetic field to less than 2% of the applied gradient for all the gradient coils. The numerical model was validated with measurements and was proposed as a tool for studying the effect of a SPECT collimator within the MRI gradient coils

Short and slim nacelle design for ultra-high BPR engines

An optimisation method consisting of the non-dominated sorting genetic algorithm (NSGA-II) and computational fluid dynamics of aero-engine nacelles is outlined. The method is applied to three nacelle lengths to determine the relative performance of different ultra-high bypass ratio engine nacelles. The optimal designs at each nacelle length are optimised for three objective functions: cruise drag, drag rise Mach number and change in spillage drag from mid to end of cruise. The Pareto sets generated from these optimisation computations demonstrate that the design space for short nacelles is much narrower in terms of these performace metrics and there are significant penalties in the off design conditions compared to the longer nacelle. Specifically the minimum spillage drag coefficient attainable, for a nacelle with a drag rise Mach number above 0.87, was 0.0040 for the shortest nacelle compared to 0.0005 for a nacelle which was 23% longer

Evaluation of the effect of ND:YVO4 laser parameters on internal micro-channel fabrication in polycarbonate

This paper presents the development of Artificial Neural Network (ANN) models for the prediction of laser machined internal micro-channels’ dimensions and production costs. In this work, a pulsed Nd:YVO4 laser was used for machining micro-channels in polycarbonate material. Six ANN multi-layered, feed-forward, back-propagation models are presented which were developed on three different training data sets. The analysed data was obtained from a 33 factorial design of experiments (DoE). The controlled parameters were laser power, P; pulse repetition frequency, PRF; and sample translation speed; U. Measured responses were the micro-channel width and the micro-machining operating cost per metre of produced microchannel. The responses were sufficiently predicted within the set micro-machining parameters limits. Three carefully selected statistical criteria were used for comparing the performance of the ANN predictive models. The comparison showed that model which had the largest amount of training data provided the highest degree of predictability. However, in cases where only a limited amount of ANN training data was available, then training data taken from a Face Centred Cubic (FCC) model design provided the highest level of predictability compared with the other examined training data set

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Reverse engineering applied to a lumbar vertebra

Bone studies can be made in vivo or in vitro. However, disadvantages of both traditional techniques call for a compromise between the two. Reverse engineering allows in vitro bone samples to be simulated and analysed in a virtual in vivo environment thus offering a middle ground solution and a sound foundation on which biomechanical studies of bone could develop.peer-reviewe