34 research outputs found

    Beyond simulation: designing for uncertainty and robust solutions

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    Simulation is an increasingly essential tool in the design of our environment, but any model is only as good as the initial assumptions on which it is built. This paper aims to outline some of the limits and potential dangers of reliance on simulation, and suggests how to make our models, and our buildings, more robust with respect to the uncertainty we face in design. It argues that the single analyses provided by most simulations display too precise and too narrow a result to be maximally useful in design, and instead a broader description is required, as might be provided by many differing simulations. Increased computing power now allows this in many areas. Suggestions are made for the further development of simulation tools for design, in that these increased resources should be dedicated not simply to the accuracy of single solutions, but to a bigger picture that takes account of a design’s robustness to change, multiple phenomena that cannot be predicted, and the wider range of possible solutions. Methods for doing so, including statistical methods, adaptive modelling, machine learning and pattern recognition algorithms for identifying persistent structures in models, will be identified. We propose a number of avenues for future research and how these fit into design process, particularly in the case of the design of very large buildings

    Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment

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    An experiment was carried out to examine user preferences for light level using the method of adjustment. The study sought preferred illuminances under lighting from fluorescent lamps of different correlated colour temperature. It was hypothesised that the preferred illuminance would be influenced by variables inherent in the experimental design including the available stimulus range, the anchor (initial setting before adjustment) and adaptation time before onset of adjustment action. The experiment included three different stimulus ranges (21–482 lux, 38–906 lux and 72–1307 lux) and these lead to significantly different preferred illuminances (337 lux, 523 lux and 645 lux, respectively). The experimental results confirmed that stimulus range and anchor have significant effects on the outcome of the adjustment task, confirming the importance of considering and reporting these variables when determining user preference with this method

    The Bishopsgate Tower Case Study

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    This paper summarizes the ongoing research on the Bishopsgate Tower in the City of London designed by Kohn Pedersen Fox Associates. We present a pre-rational geometry computational solution targeting a constraint-aware exploration of the architectural design-space, while interactively optimizing building performance in terms of constructability and cost-efficiency. We document a novel approach in building metrics optimization supported by parametric technologies and embedded analytical algorithms. The process is indicative of how computational methods will develop in the future and help designers find solutions for increasingly complex spaces

    Computational Methods on Tall Buildings - the Bishopsgate Tower

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    This paper summarizes the ongoing research done on The Bishopsgate Tower in the City of London using parametric design methodologies. The process is indicative of how computational methods will develop in the future and help designers find solutions for increasingly complex spaces

    Inductive Aerodynamics

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    A novel approach is presented to predict wind pressure on tall buildings for early-stage generative design exploration and optimisation. The method provides instantaneous surface pressure data, reducing performance feedback time whilst maintaining accuracy. This is achieved through the use of a machine learning algorithm trained on procedurally generated towers and steady-state CFD simulation to evaluate the training set of models. Local shape features are then calculated for every vertex in each model, and a regression function is generated as a mapping between this shape description and wind pressure. We present a background literature review, general approach, and results for a number of cases of increasing complexity
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