bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of energy production and costs associated with a massive exploitation of wind power in the North Sea
Authors: Sørensen Jens N. , Larsen Gunner C. 
Published: May 2023

Abstract
A study to assess the potential of a massive exploitation of offshore wind power in the North Sea has been performed. The study includes both a prediction of the available wind resources and the costs associated with such a large-scale exploitation of the wind power in the North Sea. The main parameters of the model are wind turbine size, average distance between the turbines (determined from area and number of turbines), as well as wind resources (i.e. mean wind speed Weibull distribution) and water depths conditioned on specific locations within the North Sea. The cost model includes expressions for the most essential wind farm cost elements, such as costs of wind turbines, support structures, cables and electrical substations, as well as costs of operation and maintenance - all as function of wind climate, rotor size, interspatial distance between the turbines and water depth. A main outcome of the study, is that exploiting an area of 180.000 km2, corresponding to about 1/3 of the North Sea, covered with 60.000 20 MW wind turbines, may in average produce electricity corresponding to the consumption of Europe at a price of 6.2 €cents/kWh (in 2020 prices). Furthermore, more energy could actually be captured by installing the wind turbines closer to each other. As an example, doubling the average power by installing more wind turbines on the same area will increase the levelized cost of energy from 6.2 €cent/kWh to 10.1 €cent/kWh. Still, this may be relevant in case of scarcity of suitable sites for a massive exploration.
Local flow and loads estimation on wake-affected wind turbines using graph neural networks and PyWake
Authors: Duthé Gregory , Santos Francisco de Nolasco , Abdallah Imad , Réthore Pierre-Élouan , Weijtjens Wout , Chatzi Eleni , Devriendt Christof 
Published: May 2023

Abstract
The estimation of the flow conditions throughout a modern wind farm is a relevant albeit complex problem involving several different modelling scales. In this context, recent years have seen the rise of the demand - normally industry-driven - of quick, computationally inexpensive, wind farm flow field simulators upon which further calculations might be undertaken (e.g. power output, fatigue loads, etc.). Naturally, the trustworthiness of such models hinges upon their ability to accurately estimate wakes, under the evident trade-off between computational time and accuracy. One such simulator that has been recently gaining traction within the wind community is PyWake, a Python-based flow solver, with several possible add-ons and modularity options. Based on runs from PyWake, this contribution studies the applicability of graph neural networks (GNN) as a simultaneous wind turbine fleet, wind inflow and loads surrogate. As graphs are discrete mathematical sets of dependent objects, one can see how the layout-dependent interaction between wind turbines’ aerodynamics, from which wake arises, lends itself to be modelled as a graph. Thus, we introduce the use of GNNs for layout-agnostic joint modelling of rotor-averaged wind speed, turbulence intensity, power production and damage equivalent loads (DEL) on individual turbines of wind farms. To this end, probabilistic samples of inflow conditions from a Weibull distribution for wind speed, uniform wind direction and conditional normal distributions of wind shear and nacelle yaw angles are generated as main inflow properties in the numerical simulations. Additionally, arbitrary wind farm layouts are created based on varied geometric shapes with random parametrization (varying orientations, length/width ratio). Both the arbitrary layouts and the random inflow conditions are then used as inputs for PyWake. PyWake’s output, namely, rotor averaged wind speed, turbulence intensity, power production and DELs of each individual wind turbine in the wind farms, are used to train a general GNN model. We elect to implement an Encode-Process-Decode GNN as a graph learning model. We compare the performance of various approaches of the relational dependency representation (edge-forming techniques) amongst the nodes (wind turbines) on the graph, such as Delaunay triangulation, KNN, radius-based methods and fully connected scheme. In our analysis, we evaluate the accuracy of GNNs and its ability to generalize their joint predictions for unseen layouts and varying inflow conditions.
Simulating the helix wake within an actuator disk framework: verification against discrete-blade type simulations
Authors: Coquelet M. , Moens M. , Duponcheel M. , van Wingerden J.W. , Bricteux L. , Chatelain P. 
Published: May 2023

Abstract
Dynamic flow control strategies are raising interest for wake mitigation purposes. Among the different strategies, the so-called helix one relies on individual pitch control (IPC). The numerical simulation of the helix is thus readily performed by means of discrete-blade capturing methods. Yet, if this control strategy is considered at the scale of wind farms, the resolution required by such methods becomes prohibitive and actuator disk (AD) models should be envisioned. It is however not trivial to translate IPC strategies to an AD framework which by definition considers rotor-averaged effects. This work assesses the ability of an AD method to simulate the helix strategy by comparing it to a higher fidelity approach relying on a discrete-blade capturing model. Results show that the disk-type approach supplemented with a disk-adapted IPC scheme is able to capture both the forced motion of the wake at low turbulence and the faster wake recovery at moderate turbulence. From a quantitative perspective, the disk-type approach predicts bigger power gains, compared to those foreseen by the discrete-blade type approach, for a downstream turbine in the wake of a helix-operated one.
RANS-AD based ANN surrogate model for wind turbine wake deficits
Authors: Schøler J. P. , Riva R. , Andersen S. J. , Murcia Leon J. P. , van der Laan M. P. , Criado Risco J. , Réthoré P.-E. 
Published: May 2023

Abstract
Inside wind farms, wake effects are the primary source of turbine interactions, and as such, they constitute one of the most important aspects of wind farm operations. The two most widespread methods for calculating the wind farm wake flow are computational fluid dynamics (CFD) methods and engineering models. Both methods have drawbacks; CFD methods can be very accurate but are computationally expensive. Vice versa, engineering models sacrifice accuracy by simplifying the physics, thereby improving computational efficiency.
In cases where many evaluations of the flow are needed, this trade-off is a hindrance. One such case is wind farm layout optimization problems. It has been shown that the estimation of wake flows can be improved by surrogate modeling. Recently, Artificial Neural Networks (ANN) have been demonstrated to predict accurate wakes over a mesh. In this work, a new mesh-free ANN-based wake model is proposed. This new model can predict the flow everywhere in the domain, and as it employs smooth activation functions, it is suitable for gradient-based optimization.
Two ANNs were trained with data generated by Reynolds-Averaged Navier-Stokes with Actuator Disc simulations for several yaw angles. The first ANN predicts streamwise wake velocity induction/deficit, the second ANN predicts added wake turbulence intensity. Both ANNs predict with a low error.
Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs
Authors: Ávila Francisco Jara , Verstraeten Timothy , Vratsinis Konstantinos , Nowé Ann , Helsen Jan 
Published: May 2023

Abstract
Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a higher granularity of data. In this work, a naive methodology based on Multi-Task Gaussian Process Regression is presented, in order to show how spatiotemporal modeling benefits power estimation. Using sparsity properties a model for possible power prediction is proposed. The model proposed performs better than the power curves provided by the manufacturer.
A non-symmetric Gaussian wake model for lateral wake-to-wake interactions
Authors: Vad A , Tamaro S , Bottasso C L 
Published: May 2023

Abstract
In this paper, we propose a new non-symmetric Gaussian wake model, which allows for different lateral expansions on the two sides of a wake to account for its interaction with neighbouring wakes. The proposed model is formulated following classical speed-deficit assumptions and momentum conservation. Departing from the existing literature, a non-symmetric Gaussian function is used to represent the velocity deficit in the wake. Accordingly, different wake expansions are assumed on the two sides of the wake, each expressed as a function of the locally prevailing turbulence intensity. The model considers that wake-added turbulence changes with downstream distance; hence, the turbulence intensity on a wake-immersed side of the wake is location dependent. The new model is compared to LES-ALM numerical simulations of three turbines in partial wake overlap. The free parameters of the model describing the wake development are tuned based on the CFD results. Results indicate that the new model provides for a very good agreement of the velocity profiles at different downstream positions, generating an improved representation of merging wakes and their downstream development.
Estimation of wind turbine wakes with generative-adversarial networks
Authors: Bove M , López B , Toutouh J , Nesmachnow S , Draper M 
Published: May 2023

Abstract
The interaction of the atmospheric boundary layer with wind turbines and among wind turbines in a wind farm is a complex phenomenon. Its analysis is fundamental for wind energy development. In this work, a generative adversarial network has been developed to predict the mean streamwise velocity component at hub height in the wake of a wind turbine based on the mean streamwise velocity two rotor diameters upstream. The dataset used to train and test the model is obtained from Large Eddy Simulations (LES) of a wind farm comprising 15 wind turbines under different inlet conditions. The method is able to predict accurately the mean streamwise velocity in the wake of one wind turbine. When the model is used to predict the mean streamwise velocity at hub height of the whole wind farm based on the inlet condition, the difference between the model predictions and LES results are larger as looking downstream.
Performance Research of Dual Power Flow Continuous Differential Steering System for Tracked Vehicle
Authors: Liu Jiang , Liu Weiwei 
Published: May 2023

Abstract
The tracked vehicle has good passability and adaptability for different complex environments, and also can maintain a stable posture with the ability to regulate flexible posture. Therefore, it is necessary to design a steering system with continuous and accurate steering radius and high transmission efficiency. The dynamic model of differential steering system was established including input rotate speed and output rotate speed. The dynamic model between input rotate speed and steering radius was obtained according to the relationship of output rotate speed and steering radius. The dynamic model of steady steering including the length, gauge, ground instantaneous center offset and so on of tracked vehicle was built, which could be used to predict the steady state steering characteristic of tracked vehicle.
Design and Analysis of EAST Predictive Control System for Hot Nitrogen Baking Process
Authors: Fei Duheng , Zhu Lili , Bao Li Wei 
Published: May 2023

Abstract
To realize the automatic temperature closed-loop control of EAST (Experimental advanced super-conducting tokamak) baking system, the transfer function of EAST baking system is obtained by the step response method after being familiar with the mechanism structure of the baking system. According to the transfer function of the system, two control methods, conventional PI control and PI control based on Smith prediction are novelty proposed. MATLAB simulation analysis shows that PI control based on Smith prediction does not produce an overshoot compared with conventional PI control, and the final error given by tracking slope can be reduced by selecting appropriate PI parameters. Generally, PI control based on Smith prediction has better stability and dynamic performance. The novelty here lies in the introduction of PI control based on Smith prediction to solve the problem of large overshoot in conventional PI control.
Speech Signal Processing of Industrial Speech Recognition
Authors: Dong Min , Peng Lihu , Nie Qilei , Li Wei 
Published: May 2023

Abstract
The noise in the industrial production environment is more complex than the application scenario in the home or office environment, and the most of the noise loudness is much larger. Automatic speech recognition(ASR) system will fail when the front-end speech signal processing SNR is too poor. At this time the front-end speech signal processing ability is particularly important. The product solution mentioned in this article involves multiple point. It includes omnidirectional Microphones Array scheme selection, selection of Mic monomer, sampling, digitization, noise reduction and echo processing, so as to improve the overall SNR and improve the speech recognition rate under noise,therefore, it has become an AR head-mounted computer for industrial intelligent speech recognition accepted by many business worldwide.
Automatic Site Selection Method for Stretch Field with Multi-factor Coupling Constraints
Authors: Yang Jian , Qiang Wang , Jing Wenchuan , Wang Tao , Li Gang 
Published: May 2023

Abstract
In the construction of extra high voltage and ultra-high voltage paying-off, the national code requires the unified use of “tension stringing” technology. The site selection of stretch field is an important part of tension stringing. The traditional site selection of the stretch field is mainly determined by the experience of the designer, which has many disadvantages. This paper presents a method of automatic site selection for stretch field. First, two-dimensional and three-dimensional map data are fused in order to obtain the geographic information data on the line; secondly, according to the geographic information data, combined with the line segmentation rules, the segmentation of the entire line is realized to obtain the preliminary site location; finally, according to the factors affecting the establishment of the site, such as the length of the segment, the type of towers, the number of block, buildings, vegetation, road conditions, as well as site area, etc., a mathematical model for optimal site selection of the stretch field is established , with the goal of minimizing the cost of construction, automatic iterative optimization of the model is conducted, to find the optimal site location The proposed method of automatic site selection of the stretch field combines accurate geographic information data, considers various influencing factors, avoids various problems caused by manual experience site selection, improves site selection efficiency and construction safety, and saves construction cost. Experimental results show that this method can get better position of the stretch field.
Adaptive and Efficient Data Transmission Method Based on New Satellite Network
Authors: Wang Qinyu , Mao Zhijie , Zhou Lin 
Published: May 2023

Abstract
For the satellite to ground data transmission in the space-based satellite network, the worst channel conditions shall be considered, that is, the link budget shall be carried out according to the lowest elevation angle and the farthest transmission distance. It is extremely urgent to make full use of the limited link power resources. An efficient adaptive data transmission method is proposed to make the satellite data transmission rate increase with the increase of elevation angle, so as to maximize the amount of data transmitted from the satellite transit window, the reliability and throughput of information transmission are improved. And the effectiveness of this method is verified by computer simulation.
Speech Emotion Recognition Based on Swin-Transformer
Authors: Liao Zirou , Shen Shaoping 
Published: May 2023

Abstract
The ability of machines to understand human subjective emotions is an essential link to realize artificial intelligence. How to extract and utilize information from audio signals is still a challenging task. By transforming acoustic signals into time-domain information represented by spectrograms, advanced algorithms in the field of computer vision can be applied to the field of acoustics. In this paper, we propose a Speech Emotion Recognition(SER) system based on Swin-Transformer(Swin). In addition to verifying the feasibility of Swin in SER task, we also compared the effectiveness of various spectrum maps under the same model parameters. Our model is validated on the IEMOCAP dataset and achieves competitive performance.
Development of low-cost electrospun carbon nanofibers using asphaltene precursor
Authors: Hussain Atif , Bahi Addie , Ko Frank , Abdin Yasmine 
Published: May 2023

Abstract
This study reports for the first time the development of low-cost nano-scale carbon fibers from petroleum asphaltene precursors using the simple and cost-effective electrospinning technique. Firstly, the green nanofiber nonwoven mats were electrospun without any prior treatment of the asphaltene feedstock. The nanomats were then heat treated at different temperatures to optimise their thermostabilisation and carbonisation conditions. Based on extensive mechanical and analytical (DSC, TGA, ATR-FTIR) characterisations, it was found that thermostabilisation of fibers at 250 °C in air followed by carbonisation at 1000 °C in nitrogen resulted in nanofibers with the best tensile properties (∼340 MPa) and good morphology as also confirmed by SEM micrographs. Moreover, the nanofiber mats exhibited relatively good electrical conductivity (5.88 ± 0.43 S cm−1), which could be useful for energy storage applications. The average diameter of the carbonised nanofibers was found to be 700 nm, and the carbonised nanofiber mats were flexible and showed good handleability despite the inherent brittleness of asphaltenes. Overall, the present work opens new avenues for the valorisation of asphaltene waste into high-value nanofiber materials with the potential for multi-functional applications in the composite and energy industries.
The effect and mechanism for doping concentration of Mg-Hf on the piezoelectric properties for AlN
Authors: Lv Qiaoya , Qiu Jian , Zhang Haomiao , Wen Quan , Yu Jiabing 
Published: May 2023

Abstract
The piezoelectric coefficient of intrinsic AlN is not meeting the demand from some high-performance applications in MEMS compared with the commercialized piezoelectric ceramics. Therefore, we conducted a first principles calculation investigation of the piezoelectric properties of Mg-Hf co-doped AlN structures with the same doping proportion (Mg
x/2Hf
x/2Al1−x
N) to further improve the performance of AlN materials. The analysis results suggested the co-doped structures show decreased band gap values with a non-symmetrical charges assignation. Consequently, both the elastic constant C
33 and Young’s modulus are largely reduced. Furthermore, the co-doped structure shows a drastically improved piezoelectric coefficient d
33 compared with intrinsic AlN.
The impact of long Covid on people’s capacity to work
Authors: Gallegos Miguel , Morgan Melissa L , Burgos-Videla Carmen , Caycho-Rodríguez Tomás , Martino Pablo , Cervigni Mauricio 
Published: May 2023

Abstract
This commentary addresses the post-COVID-19 syndrome and its implications for workers’ health. Post-COVID-19 syndrome consists of a set of physiological and psychological symptoms resulting from SARS-CoV-2 (COVID-19) infection, which occur continuously for several weeks or months. Therefore, it is an affectation that has multiple consequences for the recovery of people’s health, and compromises the ability to perform daily activities, including work, whether in person or remotely. Although several studies have been published so far, and several long-term consequences on people’s health have been demonstrated, most have not adequately delved into the implications for the health of workers, their families, and the socioeconomic cost for governments. The aim of this paper is to highlight this public health issue and to encourage more specialized research.
On the representativeness and stability of a set of EFMs
Authors: Guil Francisco , Hidalgo José F , García José M 
Published: May 2023

Abstract

Motivation
Elementary flux modes are a well-known tool for analyzing metabolic networks. The whole set of elementary flux modes (EFMs) cannot be computed in most genome-scale networks due to their large cardinality. Therefore, different methods have been proposed to compute a smaller subset of EFMs that can be used for studying the structure of the network. These latter methods pose the problem of studying the representativeness of the calculated subset. In this article, we present a methodology to tackle this problem.

Results
We have introduced the concept of stability for a particular network parameter and its relation to the representativeness of the EFM extraction method studied. We have also defined several metrics to study and compare the EFM biases. We have applied these techniques to compare the relative behavior of previously proposed methods in two case studies. Furthermore, we have presented a new method for the EFM computation (PiEFM), which is more stable (less biased) than previous ones, has suitable representativeness measures, and exhibits better variability in the extracted EFMs.

Availability and implementation
Software and additional material are freely available at https://github.com/biogacop/PiEFM.