6 resultados para School building

em Cambridge University Engineering Department Publications Database


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Social and political concerns are frequently reflected in the design of school buildings, often in turn leading to the development of technical innovations. One example is a recurrent concern about the physical health of the nation, which has at several points over the last century prompted new design approaches to natural light and ventilation. The most critical concern of the current era is the global, rather than the indoor, environment. The resultant political focus on mitigating climate change has resulted in new regulations, and in turn considerable technical changes in building design and construction. The vanguard of this movement has again been in school buildings, set the highest targets for reducing operational carbon by the previous Government. The current austerity measures have moved the focus to the refurbishment and retrofit of existing buildings, in order to bring them up to the exacting new standards. Meanwhile there is little doubt that climate change is happening already, and that the impacts will be considerable. Climate scientists have increasing confidence in their predictions for the future; if today’s buildings are to be resilient to these changes, building designers will need to understand and design for the predicted climates in order to continue to provide comfortable and healthy spaces through the lifetimes of the buildings. This paper describes the decision processes, and the planned design measures, for adapting an existing school for future climates. The project is at St Faith’s School in Cambridge, and focuses on three separate buildings: a large Victorian block built as a substantial domestic dwelling in 1885, a smaller single storey 1970s block with a new extension, and an as-yet unbuilt single storey block designed to passivhaus principles and using environmentally friendly materials. The implications of climate change have been considered for the three particular issues of comfort, construction, and water, as set out in the report on Design for Future Climate: opportunities for adaptation in the built environment (Gething, 2010). The adaptation designs aim to ensure each of the three very different buildings remains fit for purpose throughout the 21st century, continuing to provide a healthy environment for the children. A forth issue, the reduction of carbon and the mitigation of other negative environmental impacts of the construction work, is also a fundamental aim for the school and the project team. Detailed modelling of both the operational and embodied energy and carbon of the design options is therefore being carried out, in order that the whole life carbon costs of the adaptation design options may be minimised. The project has been funded by the Technology Strategy Board as part of the Design for Future Climates programme; the interdisciplinary team includes the designers working on the current school building projects and the school bursar, supported by researchers from the University of Cambridge Centre for Sustainable Development. It is hoped that lessons from the design process, as well as the solutions themselves, will be transferable to other buildings in similar climatic regions.

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The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

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This paper presents the development of a new building physics and energy supply systems simulation platform. It has been adapted from both existing commercial models and empirical works, but designed to provide expedient exhaustive simulation of all salient types of energy- and carbon-reducing retrofit options. These options may include any combination of behavioural measures, building fabric and equipment upgrades, improved HVAC control strategies, or novel low-carbon energy supply technologies. We provide a methodological description of the proposed model, followed by two illustrative case studies of the tool when used to investigate retrofit options of a mixed-use office building and primary school in the UK. It is not the intention of this paper, nor would it be feasible, to provide a complete engineering decomposition of the proposed model, describing all calculation processes in detail. Instead, this paper concentrates on presenting the particular engineering aspects of the model which steer away from conventional practise. © 2011 Elsevier Ltd.

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The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.