Experimental study on an airfoil equipped with an active flow control element

This paper addresses the torque ripple problem of vertical axis wind turbines (VAWT) by applying an oscillating bump which was previously optimised with a numerical model. Since the element primarily affects the drag of an airfoil, a low-speed wind-tunnel experiment using the wake survey method was conducted in order to highlight the effect on the drag of a symmetrical airfoil. Measurements were taken along the centreline of the foil to compare with numerically simulated results on a 2D foil. This study provides an experimental data set for an active flow control device. When the stationary results were compared against numerical simulation they showed an overall good agreement. The moving bump increased the drag coefficient for higher frequencies of oscillation and induced less drag compared to the steady maximum distortion. Uncertainties in the experiments were primarily caused by fluctuations in the test section and the data reduction error. Future work should include the measurement of lift in order to determine the influence of the element on the torque.

Experimental test of the sample reduction random error formula

Introduction. An immediate estimate of the accidental error of sample reduction can only be made if the measurement method of execution is zero. This can be achieved by imitating the grains of a useful component with markers fully extracted from the reduced sample. The markers can be larger than 44 "Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal". No. 4. 2021 ISSN 0536-1028 the maximum size of the sample material and are extracted from the sample using screens. Markers whose granulometric composition matches the sample composition should be extracted completely from the reduced sample using a hand magnet in the case. Methodology. A small number of markers of the correct shape imitates forgeable gold grains or d99 platinum. A much larger number of free-form markers simulate the granulometric composition of a sample in the –1+0.5 mm class. It is necessary to find a form factor linking the particles real volume with the cube volume. For magnetite markers, the form factor is 0.4. Results and analysis. The samples have been reduced with regular shaped and free-form markers, which makes it possible to experimentally determine the error of reduction. Theoretical formulas found errors for the conditions of experiments. For experiments with the regular shaped markers, a 54.77– 58.43% relative reduction error has been obtained according to 480 parallel measurements. Theoretically determined 57.64% relative error falls into this range. Similar relative reduction errors with free-form markers are 8.82–10.00% and 9.15%. Conclusion. The fairness of the reduction error analytical formula has been directly evaluated for the first time. The reduction error analytical formula can be applied when analyzing the schemes of sample preparation.

Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions

Metabolic modeling has been particularly efficient to understand the conditions affecting the metabolism of an organism. But so far, metabolic models have mainly considered static situations, assuming balanced growth. Some organisms are always far from equilibrium, and metabolic modeling must account for their dynamics. This leads to high-dimensional models in which metabolic fluxes are no more constant but vary depending on the intracellular concentrations. Such metabolic models must be reduced and simplified so that they can be calibrated and analyzed. Reducing these models of large dimension down to a model of smaller dimension is very challenging, specially, when dealing with nonlinear metabolic rates. Here, we propose a rigorous approach to reduce metabolic models using quasi-steady-state reduction based on Tikhonov’s theorem, with a characterized and bounded reduction error. We assume that the metabolic network can be represented with Michaelis-Menten enzymatic reactions that evolve at different time scales. In this simplest approach, some metabolites can accumulate. We consider the case with a continuous varying input in the model, such as light for microalgae, so that the system is never at a steady state. Furthermore, our analysis proves that metabolites in the slow part of the metabolic system reach higher concentrations (by one order of magnitude) than metabolites in the fast part under some flux conditions. A simple example illustrates our approach and the resulting accuracy of the reduction method.

Peningkatan Kualitas Citra Stego pada Adaptive Pixel Block Grouping Reduction Error Expansion dengan Variasi Model Scanning pada Pembentukan Kelompok Piksel

<p class="Abstrak">ebutuhan komunikasi yang terus bertambah dan ditandai dengan meningkatnya jumlah <em>IP traffic</em> dari 744 EB menjadi 1.164 EB menjadikan keamanan sebagai salah satu kebutuhan utama dalam menjaga kerahasiaan data. <em>Adaptive Pixel Block Grouping Reduction Error Expansion (APBG-REE)</em> sebagai salah satu metode data hiding dapat diterapkan untuk memenuhi kebutuhan tersebut. Metode ini akan membagi citra carrier menjadi blok-blok dan membentuknya menjadi kelompok-kelompok piksel. Hasil dari proses ini akan dimanfaatkan untuk menyembunyikan data rahasia. Namun, metode ini memiliki kekurangan, yaitu belum diketahuinya metode <em>scanning</em> terbaik dalam pembentukan kelompok piksel untuk menciptakan citra <em>stego</em> dengan kualitas tinggi. Untuk mengatasi masalah ini, kami mengusulkan 4 mode (cara) <em>scanning</em> berdasarkan arah <em>scanning</em> tersebut. Mode <em>scanning</em> tersebut memberikan hasil yang berbeda-beda untuk masing-masing citra <em>stego</em> yang diujikan. Namun berdasarkan hasil uji coba, setiap mode <em>scanning</em> mampu menjaga kualitas citra stego diatas 57,5 dB. Hasil ini akan meningkat seiring dengan berkurangnya jumlah <em>shifted pixel</em> yang terbentuk.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>The need of communication has increased continously which is represented by the rise of number of IP traffic, from 744 EB to 1.164 EB. This has made data security one of the main requirements in terms of securing secret data. Adaptive Pixel Block Grouping Reduction Error Expansion (APBG-REE) as one of data hiding methods can be implemented to meet that requirement. It divides the carrier image into blocks which are then used as pixel groups. The result of this process is to be a space for secret data. However, this method has a problem in the scanning when creating pixel groups to generate a high quality stego image. To handle this problem, we propose four scanning models base on its direction. This means that the scanning can be done row-by-row or column-by-column. Base on the experiment, we find that those modes deliver various results and each of them is able to maintain the stego quality of more than 57,5 dB. This result increases along with the decreasing the number of shifted pixels.</em></p>

Mixed-Dimensional Model Analysis Under Dimension Reduction Error Control

In order to conduct engineering analysis efficiently, complex CAD model is generally idealized by dimension reduction of its local thin regions into mid-surfaces, which results in a mixed-dimensional model. However, such dimension reduction inevitably induces analysis errors when plate or shell theory applied to the mixed-dimensional model. In this paper, an evaluation indicator is proposed for estimating analysis error induced by dimension reduction of a original model into mixed-dimensional model and used to control the analysis results of the mixed-dimensional model with given accuracy. The evaluation indicator is defined as the stress difference on the coupling interface between the mixed-dimensional model and the original model. When the mixed-dimensional model is analyzed, p-version solid elements were generated by offsetting the shell nodes in the thickness direction. Moreover, element stiffness matrix, boundary conditions and material properties can be extracted from the analysis results and reused for the indicator computation. Displacements of the mixed-dimensional model are input as initial value to iterative solver to accelerate the computation. When the indicator is below the accuracy, final analysis can be proceeded with p-adaptivity in the thin regions. The hierarchical shape function for p-version solid elements ensures the efficiency of the error estimation and the reliability of the final analysis. The robustness of the evaluation indicator and computational efficiency for final analysis are illustrated by experiments on engineering models.