An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design of wind energy projects.Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models.In this paper, we develop an optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations are carried out to obtain the corresponding AEPs.This wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset.Such adaptive update of wind farm layout dataset continues until the algorithm converges.To evaluate the performance of the proposed SBO framework, we apply it to three representative wind farm cases.Compared to the conventional staggered layout, the optimized wind farm produces significantly higher total AEP.In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method.Further analysis on the velocity fields show that the optimization framework attempts to locate the downstream turbines away from the the wakes of upstream ones.The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects

Accelerating Spatial Data Processing with MapReduce

Abstract—MapReduce is a key-value based programming model and an associated implementation for processing large data sets. It has been adopted in various scenarios and seems promising. However, when spatial computation is expressed straightforward by this key-value based model, difficulties arise due to unfit features and performance degradation. In this paper, we present methods as follows: 1) a splitting method for balancing workload, 2) pending file structure and redundant data partition dealing with relation between spatial objects, 3) a strip-based two-direction plane sweep-ing algorithm for computation accelerating. Based on these methods, ANN(All nearest neighbors) query and astronomical cross-certification are developed. Performance evaluation shows that the MapReduce-based spatial applications outperform the traditional one on DBMS

Convolutional Embedding for Edit Distance

Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment. However, computing edit distance is known to have high complexity, which makes string similarity search challenging for large datasets. In this paper, we propose a deep learning pipeline (called CNN-ED) that embeds edit distance into Euclidean distance for fast approximate similarity search. A convolutional neural network (CNN) is used to generate fixed-length vector embeddings for a dataset of strings and the loss function is a combination of the triplet loss and the approximation error. To justify our choice of using CNN instead of other structures (e.g., RNN) as the model, theoretical analysis is conducted to show that some basic operations in our CNN model preserve edit distance. Experimental results show that CNN-ED outperforms data-independent CGK embedding and RNN-based GRU embedding in terms of both accuracy and efficiency by a large margin. We also show that string similarity search can be significantly accelerated using CNN-based embeddings, sometimes by orders of magnitude.Comment: Accepted by the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 202

Fatigue assessment on local components of a semi-submersible platform subjected to wind and wave loads

The objective in this work is to assess fatigue damages on local components of a semi-submersible platform under combined actions of wind and wave loads in time domain. Some improvements are provided in the present study to improve the efficiency and accuracy of the whole evaluating process. Firstly, a combined wind and wave relationship as well as an innovative mixture simulation method are used to generate time series of random wind and waves. Moreover, an m-block division method is proposed to compress the number of the whole short-term sea states in the wind-wave scatter diagram. Then, with an improved multiple interpolation sub-model method, the structural stress responses of the local structural components are calculated as is in the whole model analysis. Finally, a modified rain-flow counting method is provided and validated to count the stress cycles efficiently and accurately. Thus, the short- and long-term fatigue damages are computed based on the S-N curve approach and the cumulative fatigue damage rule. In relative agreement with the numerical results by the traditional time-domain method and existing experimental data, these proposed improved methods are demonstrated to be applicable and efficient methods for fatigue damage analyses. All the fatigue damages on local components satisfy the specification requirements and the minimum value appears under the up wind-wave state, which is the proper working condition for a semi-submersible platform

Flow characteristics and dynamic responses of a rear circular cylinder behind the square cylinder with different side lengths

Wake-induced vibrations of a 2-DOF circular cylinder which is placed behind a stationary square cylinder with tandem arrangement are numerically investigated at low Reynolds numbers by using semi-implicit Characteristics-based split (CBS) finite element algorithm in this study. Numerical results demonstrate that the side length of the upstream square cylinder can significantly affect the characteristics of flow patterns, oscillation frequency, maximum amplitudes, X-Y trajectories, and hydrodynamic coefficients of the rear circular cylinder. The predominant vortex shedding patterns are 2S, 2P and 2T mode. In addition to the figure “8”, “raindrop” and figure “dual-8” are observed in the X-Y vibrating trajectories for the circular cylinder. Finally, the interactions between cylinders are revealed according to the phase portrait of fluid force to displacement and the power spectral densities (PSD) features of the vibration amplitude on the rear circular cylinder and the instantaneous flow field, together with the wake-induced vibration (WIV) mechanism underlying the oscillation characteristics of the circular cylinder behind a stationary square cylinder with different sizes

Flow characteristics and dynamic responses of a parked straight‐bladed vertical axis wind turbine

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