9,222 research outputs found
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
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Cooling load forecasting-based predictive optimisation for chiller plants
Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced
Non-linear Convolution Filters for CNN-based Learning
During the last years, Convolutional Neural Networks (CNNs) have achieved
state-of-the-art performance in image classification. Their architectures have
largely drawn inspiration by models of the primate visual system. However,
while recent research results of neuroscience prove the existence of non-linear
operations in the response of complex visual cells, little effort has been
devoted to extend the convolution technique to non-linear forms. Typical
convolutional layers are linear systems, hence their expressiveness is limited.
To overcome this, various non-linearities have been used as activation
functions inside CNNs, while also many pooling strategies have been applied. We
address the issue of developing a convolution method in the context of a
computational model of the visual cortex, exploring quadratic forms through the
Volterra kernels. Such forms, constituting a more rich function space, are used
as approximations of the response profile of visual cells. Our proposed
second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a
network which combines linear and non-linear filters in its convolutional
layers, can outperform networks that use standard linear filters with the same
architecture, yielding results competitive with the state-of-the-art on these
datasets.Comment: 9 pages, 5 figures, code link, ICCV 201
Grey-box model identification via evolutionary computing
This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on
a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox
models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building
dominant structural identification with local parametric tuning without the need of a differentiable performance index in the
presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of
accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the
model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two
practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better
modelling accuracy
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