The main goal of the current paper is to outline a low order modeling procedure of a heaving airfoil in still fluid using experimental measurements. Due to its relative simplicity, the proposed procedure is applicable for the analysis of flow fields within complex and unsteady geometries and it is suitable for analyzing the data obtained by experimentation. Currently, this procedure is used to model and predict the flow field evolution using small number of low profile load sensors and flow field measurements. A time delay neural network is used in order to estimate the flow field. The neural network estimates the amplitudes of the most energetic modes using four sensory inputs. The modes are calculated using proper orthogonal decomposition of the flow field data obtained experimentally by time-resolved, phase-locked particle imaging velocimetry. In order to permit the use of proper orthogonal decomposition, the measured flow field is mapped onto a stationary domain using volume preserving transformation. The analysis performed by the model showed good estimation quality within the parameter range used in the training procedure. However, the performance deteriorates for cases out of this range. This situation indicates that, in order to improve the robustness of the model, both the decomposition and the training data sets must be diverse in terms of input parameter space.
|State||Published - 2017|
|Event||57th Israel Annual Conference on Aerospace Sciences, IACAS 2017 - Tel Aviv and Haifa, Israel|
Duration: 15 Mar 2017 → 16 Mar 2017
|Conference||57th Israel Annual Conference on Aerospace Sciences, IACAS 2017|
|City||Tel Aviv and Haifa|
|Period||15/03/17 → 16/03/17|