Surrogate-based optimization of tidal turbine arrays: a case study for the Faro-Olhão inlet

This paper presents a study for estimating the size of a tidal turbine array for the Faro-Olhão Inlet (Potugal) using a surrogate optimization approach. The method compromises problem formulation, hydro-morphodynamic modelling, surrogate construction and validation, and constraint optimization. A total of 26 surrogates were built using linear RBFs as a function of two design variables: number of rows in the array and Tidal Energy Converters (TECs) per row. Surrogates describe array performance and environmental effects associated with hydrodynamic and morphological aspects of the multi inlet lagoon. After validation, surrogate models were used to formulate a constraint optimization model. Results evidence that the largest array size that satisfies performance and environmental constraints is made of 3 rows and 10 TECs per row.Eduardo González-Gorbeña has received funding for the OpTiCA project (http://msca-optica.eu/) from the Marie Skłodowska-Curie Actions of the European Union's H2020-MSCA-IF-EF-RI-2016 / GA#: 748747. The paper is a contribution to the SCORE pro-ject, funded by the Portuguese Foundation for Science and Technology (FCT–PTDC/AAG-TEC/1710/2014). André Pacheco was supported by the Portuguese Foun-dation for Science and Technology under the Portuguese Researchers’ Programme 2014 entitled “Exploring new concepts for extracting energy from tides” (IF/00286/2014/CP1234).info:eu-repo/semantics/publishedVersio

Search for a Metallic Dangling-Bond Wire on nn-doped H-passivated Semiconductor Surfaces

We have theoretically investigated the electronic properties of neutral and $n$-doped dangling bond (DB) quasi-one-dimensional structures (lines) in the Si(001):H and Ge(001):H substrates with the aim of identifying atomic-scale interconnects exhibiting metallic conduction for use in on-surface circuitry. Whether neutral or doped, DB lines are prone to suffer geometrical distortions or have magnetic ground-states that render them semiconducting. However, from our study we have identified one exception -- a dimer row fully stripped of hydrogen passivation. Such a DB-dimer line shows an electronic band structure which is remarkably insensitive to the doping level and, thus, it is possible to manipulate the position of the Fermi level, moving it away from the gap. Transport calculations demonstrate that the metallic conduction in the DB-dimer line can survive thermally induced disorder, but is more sensitive to imperfect patterning. In conclusion, the DB-dimer line shows remarkable stability to doping and could serve as a one-dimensional metallic conductor on $n$-doped samples.Comment: 8 pages, 5 figure

Optimal analog wavelet bases construction using hybrid optimization algorithm

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.Peer reviewedFinal Published versio

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

Automating reconfiguration chain generation for SRL-based run-time reconfiguration

Run-time reconfiguration (RTR) of FPGAs is mainly done using the configuration interface. However, for a certain group of designs, RTR using the shift register functionality of the LUTs is a much faster alternative than conventional RTR using the ICAP. This method requires the creation of reconfiguration chains connecting the run-time reconfigurable LUTs (SRL). In this paper, we develop and evaluate a method to generate these reconfiguration chains in an automated way so that their influence on the RTR design is minimised and the reconfiguration time is optimised. We do this by solving a constrained multiple travelling salesman problem (mTSP) based on the placement information of the run-time reconfigurable LUTs. An algorithm based on simulated annealing was developed to solve this new constrained mTSP. We show that using the proposed method, reconfiguration chains can be added with minimal influence on the clock frequency of the original design

Application of Artificial Neural Networks to Power System State Estimation

State estimation function is essential for effective and timely execution of power system automation and control systems, especially in modern active distribution systems where more intermittent renewable energy systems are integrated into the grid. Distribution system state estimation faces a lot of challenges including lack of monitoring devices and possible incorrect topology information. Developing efficient state estimation for distribution systems is thus of great interest. This paper presents results on utilizing artificial neural networks for this purpose. Artificial neural networks have been used in power distribution system state estimation. However, there is a lack of systematic analysis and study of which types of ANNs and what structures including parameters are most suitable for state estimation applications. When designing an ANN for a state estimator, trial and error approach has been common and there is no systematic method available to guide the process. The ultimate goal of the research is to examine the performance of various types of ANNs (e.g., Multi-Layer Perceptron (MLPs), Convolutional Neural Networks (CNNs) and Long-Short- Term-Memory Networks (LSTMs)) with different structures and also provide possible guidance on how to choose the different parameters, including model parameters such as number of hidden layers and number neurons in a layer, and algorithm parameters such as adjustable learning rate, for desired performance metrics. The paper presents preliminary results based on MLPs. IEEE standard 34-bus test system is used to illustrate the proposed methods and their effectiveness. The paper seeks to contribute to a more systematic approach to neural network and deep learning applied to power system state estimation, thus enhancing situational awareness, system resiliency and real-time monitoring and control of power distribution systems. Successful state estimation function will increase the ability of distribution systems to integrate more renewable energy based generations