Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries

Graening, Lars; Menzel, Stefan; Hasenjäger, Martina; Bihrer, Thomas; Olhofer, Markus; Sendhoff, Bernhard

doi:10.1007/s10852-008-9094-9

Cited by 21 publications

(16 citation statements)

References 20 publications

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“…In one such case, a large model set of turbine blades is used with a decision tree to analyse the relationship between point deformation of models and their change in surface pressure [13,14]. Areas of high sensitivity can then be mapped onto a pre-defined base geometry and used to focus subsequent analysis.…”

Section: Solution Approximationmentioning

confidence: 99%

Reduced-order urban wind interference

2015

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A novel approach is demonstrated to approximate the effects of complex urban interference on the wind-induced surface pressure of tall buildings. This is achieved by decomposition of the domain into two components: the obstruction model (OM) of the static large-scale urban context, for which a single computational fluid dynamics (CFD) simulation is run; and the principal model (PM) of the isolated tall building under design, for which repeatable reduced-order model (ROM) predictions can be made. The ROM is generated with an artificial neural network (ANN), using a set of feature vectors comprising an input of local shape descriptors and a range of wind speeds from a training geometry, and an output response of pressure. For testing, the OM CFD simulation provides the flow boundary condition wind speeds to the PM ROM prediction. The result is vertexresolution surface pressure data for the PM mesh, intended for use within generative design exploration and optimisation. It is found that the mean absolute prediction error is around 5.0% (σ:7.8%) with an on-line process time of 390s, 27-times faster than conventional CFD simulation; considering full process time, only 3.2 design iterations are required for the ROM time to match CFD. Existing work in the literature focuses solely on creating generalised rules relating global configuration parameters and a global interference factor (IF). The work presented here is therefore a significantly alternative approach, with the advantages of increased geometric flexibility, output resolution, speed, and accuracy.

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Section: Solution Approximationmentioning

confidence: 99%

Reduced-order urban wind interference

2015

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“…In one such approach, a large model set of turbine blades is used with a decision tree to analyse the relationship between point deformation of models and their change in surface pressure [8,9]. Areas of high sensitivity can then be mapped onto a pre-defined base geometry and used to focus subsequent analysis.…”

Section: Solution Approximationmentioning

confidence: 99%

Approximating Computational Fluid Dynamics for Generative Tall Building Design

Wilkinson

Hanna

2014

International Journal of Architectural Computing

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Background literature review, methodology, results, and analysis are presented for a novel approach to approximating wind pressure on tall buildings for the application of generative design exploration and optimisation. The predictions are approximations of time-averaged computational fluid dynamics (CFD) data with the aim of maintaining simulation accuracy but with improved speed. This is achieved through the use of a back-propagation artificial neural network (ANN) with vertex-based shape features as input and pressure as output. The training set consists of 600 procedurally generated tall building models, and the test set of 10 real building models; for all models in both sets, a feature vector is calculated for every vertex. Over the test set, mean absolute errors against the basis CFD are 1.99–4.44% (σ:2. 10–5.09%) with an on-line process time of 14.72–809.98s (0.028s/sample). Studies are also included on feature sensitivity, training set size, and comparison of CFD against prediction times. Results indicate that prediction time is only dependent on the number of test model vertices, and is therefore invariant to basis CFD time.

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“…Additionally, and with the aim of reducing the associated computational cost, Dellnitz et al [101] applied in a first phase of the MOP a relaxed thrust control law for finding Pareto feasible trajectories, which were further improved by the use of a local search operator and with a more accurate thrust control law. For example, Gräning et al [118] successfully applied this type of approach. In this sense, and for reducing the computational cost in MOO space mission design problems, a possible research path is the adoption of (probably local) surrogate/approximation models for the selection of promising solutions, specially for distinguishing between feasible and infeasible solutions.…”

Section: Future Research Trendsmentioning

confidence: 99%