Costs of tidal stream energy generation are anticipated to fall considerably with array expansion and time. This is due to both economies of volume, where arrays comprising of large numbers of turbines can split fixed...
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Costs of tidal stream energy generation are anticipated to fall considerably with array expansion and time. This is due to both economies of volume, where arrays comprising of large numbers of turbines can split fixed costs over a greater number of devices, and learning rates, where the industry matures and so arrays of the same size become cheaper due to lessons learned from previous installations. This paper investigates how tidal energy arrays can be designed to minimize the levelized cost of energy (LCOE), by optimizing not only the location but also the number of devices, to find a suitable balance between decreased costs due to economies of volume and diminishing returns due to global blockage effects. It focuses on the Alderney Race as a case study site due to the high velocities found there, making it a highly suitable site for large-scale arrays. It is demonstrated that between 1 and 2GW could be feasibly extracted as costs in the tidal industry fall, with the LCOE depending greatly on the assumed costs. A Monte-Carlo analysis is undertaken to account for variability in capital and operational cost data used as inputs to the array optimization. Once optimized, the estimated P50 LCOE of an 80MW array is 110/MWh. This estimate aligns closely with the level of subsidy considered for tidal stream projects in the Alderney Race in the past. This article is part of the theme issue 'New insights on tidal dynamics and tidal energy harvesting in the Alderney Race'.
A method for optimizing the design of heterogeneous gas chemiresistor arrays to maximize classification accuracy is presented. The features used for classification include coefficients from a Fast Fourier Transform, p...
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A method for optimizing the design of heterogeneous gas chemiresistor arrays to maximize classification accuracy is presented. The features used for classification include coefficients from a Fast Fourier Transform, properties of a piecewise representation of the response shape, and features characterizing the response time. The novel response time features are designed to be insensitive to noise and sensor drift. This work introduces a novel approach for leveraging experimental data to create large synthetic datasets for training classifiers. Pairs of time-series sensor response measurements are randomly combined with a noise model to create synthetic sensor responses, which are then combined to create synthetic array responses. J48 decision trees are used to classify species and support vector machine regression models are used to determine the concentration once the species is known. The results demonstrate the value of array optimization, as the highest classification accuracy is achieved using a subset of the available sensor designs. J48 decision trees proved to be efficient for use in optimization and achieved high accuracy. Separating the tasks of classifying species and identifying concentration also proved to be effective. The techniques were applied to the design of an array for classifying H-2, H2S, NH3, and NO2. The optimal array achieved 95.7 % accuracy at classifying species and an average correlation coefficient of 0.92 for determining concentration.
We formulate resolution enhancement as a modified Backus-Gilbert inverse problem and determine the optimal complex weights that improve focusing of waves in space and time. The optimization corrects for receiver geome...
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We formulate resolution enhancement as a modified Backus-Gilbert inverse problem and determine the optimal complex weights that improve focusing of waves in space and time. The optimization corrects for receiver geometry. If we accurately know the location of a control point in the subsurface we can use the corresponding optimal weights to achieve enhanced focusing in a prescribed target zone surrounding the control point. Errors in the back propagation velocity and noisy data degrade the quality of focusing. The optimal wave field shows a blow-up behavior outside the optimization area. We show different measures of resolution to estimate the compression of the focal spot. The optimized weights amplify the high frequencies, but the algorithm also improves the focusing for monochromatic waves. At all frequencies our algorithm improves the resolution of the focal spot. We also show that for a uniformly sampled line array and a homogeneous medium, the weights used to enhance resolution have a negligible imaginary part and that they are oscillatory across the array used. To fully test the robustness of our algorithm, we also consider focusing in a heterogeneous medium with embedded scatterers and an irregular receiver line, and show that in this scenario we are also able to attain focusing improvement. (C) 2021 Elsevier B.V. All rights reserved.
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