Towards the Evolution of Novel Vertical-Axis Wind Turbines

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.Comment: 14 pages, 11 figure

Student preferences towards law becoming a tertiary entrance score subject

In Western Australian schools, student subject selection during Year 10 determines the future career path to which students aspire. Subjects offered in Year 11 and Year 12 Upper School studies are classified as Tertiary Entrance score Subjects or certificate of Secondary Education subjects, and students are given the opportunity to select six subjects which may comprise all Tertiary Entrance score Subjects, all Certificate of Secondary Education Subjects, or a combination of both. Law is classified as a Certificate of Secondary Education subject. It is the student\u27s intention to study Law that is the focus of this research project. The major objective is to examine the influence of the factor Law as a Tertiary Entrance Score subject on student selection of Law, and to assess the extent of influence other subjective norms may exert on student subject selection of Law. The project required the establishment of a pilot study group in School A to develop and refine an instrument based upon Likert\u27s Summated Ratings Scale. The questionnaire was formulated through student/research interview trialled within the pilot school. Results of the instrument were examined, further interviews conducted and changes to the questionnaire made in preparation for the major study in School B. The major study involved 275 respondents and utilised the SAS computer programme for analysis. The questionnaire instrument has been evaluated for validity and reliability The results of the study indicate students support a change to the subject classification of Law from Certificate of Secondary Education Subject to Tertiary Entrance Score Subject. The subjective norms elicited and displayed in the subject selection behaviours of students in this study have special significance for school administrators. The data collected was prior to the introduction of the Andrich Report ( 1989). In 1991 this report will ensure those students seeking tertiary entrance select a minimum of 4 Tertiary Entrance Score Subjects out of a maximum of 6 subjects. The tertiary entrance requirement during this study was 3 Tertiary Entrance Score Subjects. The impact of students selecting subjects for tertiary entrance is evident in this study in relation to selecting the subject Law. The full impact of ·the effect of student subject selections on Law and other Certificate of Secondary Education subjects in the future as a consequence of new requirements is yet to be evaluated

On Design Mining: Coevolution and Surrogate Models

© 2017 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines

Evolutionary n-level hypergraph partitioning with adaptive coarsening

Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel approach wherein an initial partitioning is performed after compressing the hypergraph to a predetermined level. This level is typically chosen to produce very coarse hypergraphs in which heuristic algorithms are fast and effective. This article presents a novel memetic algorithm which remains effective on larger initial hypergraphs. This enables the exploitation of information that can be lost during coarsening and results in improved final solution quality. We use this algorithm to present an empirical analysis of the space of possible initial hypergraphs in terms of its searchability at different levels of coarsening. We find that the best results arise at coarsening levels unique to each hypergraph. Based on this, we introduce an adaptive scheme that stops coarsening when the rate of information loss in a hypergraph becomes non-linear and show that this produces further improvements. The results show that we have identified a valuable role for evolutionary algorithms within the current state-of-the-art hypergraph partitioning framework