bluuebunny/crossref_metadata_2025_split huggingface dataset:
Entrainment model for wind turbine wakes considering blockage effect
Authors: Li Ning , Liu Yongqian , Li Li , Meng Hang 
Published: May 2023

Abstract
The entrainment model applied firstly by P. Luzzatto-Fegiz can predict the streamwise velocity of the far-wake more accurately than the traditional models (e.g., Jensen model, Frandsen model). The current model, however, neglects the wind speed drop approaching the rotor caused by the blockage effect, the phenomenon of which cannot be considered in the velocity recovery of the far-wake region, further affecting the precision of wake prediction. Hence, this paper proposes an entrainment model considering the blockage effect. We adopt several numerical simulation results to analyze the performance with the vortex cylinder model for the rotor vicinity. The initial entrainment parameter Einitial calculates, according to the formula of the breakdown position of tip vortices proposed by J.N. Sørensen. The Enear-wake can obtain with the length of the near-wake region, defined by P. E. J. Vermeulen. The linearized entrainment-parameterized expression we develop combines the linearization hypothesis with the above two positions and their entrainment values. The proposed model consists of two main components: the modified entrainment model and the vortex cylinder model, validated against the advanced Gaussian model of Bastankhah and Porté-Agel (BPA) and several LES cases. The results demonstrate that the proposed model significantly improves the streamwise wake distribution.
Simulating wake losses of the Danish Energy Island wind farm cluster
Authors: van der Laan M. P. , García-Santiago O. , Sørensen N. N. , Troldborg N. , Criado Risco J. , Badger J. 
Published: May 2023

Abstract
The increase in the number of installed offshore wind farms has led to clustering, where wind farm interaction can cause energy losses. The Danish government is planning an Energy Island in the North Sea consisting of ten 1 GW wind farms. The initial wind farm layout design from a consultant company (COWI) has reported a 5% annual energy production (AEP) wake loss based on a low-fidelity engineering wake model that is known to underestimate wind farm interaction. The present work employs higher-fidelity wake models based on Reynolds-averaged Navier-Stokes (RANS) and a mesoscale model, which predict higher AEP losses due to wind farm interaction; namely, between 8.6-10.1%. In addition, we investigate how a microscale model as RANS can be employed to simulate a large wind farm cluster efficiently, and we compare its results with mesoscale model simulations.
A baseline for ensemble-based, time-resolved inflow reconstruction for a single turbine using large-eddy simulations and latent diffusion models
Authors: Rybchuk A , Martínez-Tossas L A , Hamilton N , Doubrawa P , Vijayakumar G , Hassanaly M , Kuhn M B , Zalkind D S 
Published: May 2023

Abstract
We are interested in reconstructing winds flowing through a turbine on a second-by-second basis over a 10 min window. Previously, we developed a machine learning algorithm that takes in a snapshot of wind speed measurements and generates ensembles of three-dimensional wind field estimates. Here, we use these estimates as initial conditions in large-eddy simulations and reconstruct atmospheric and turbine response dynamic quantities in a synthetic field campaign. In doing so, we establish a baseline for model validation that future time-aware data assimilation techniques will be compared to. In turbine-free case studies, ground truth wind speeds consistently fall within our estimated wind speed distribution for the first 100 s after the simulation start. In simulations with turbines, the wind estimates show a small bias of 0.10 m s−1 and good correlation of 0.80 during the first 100 s. During this window, our estimates of the Blade 1 bending moment and generator power typically span the ground truth, with the estimate of the former performing better overall. In summary, this approach shows promise as a stand-alone technique for reconstructing real-world inflow and turbine dynamics in 1−2 min windows and as a foundation for future time-aware data assimilation techniques.
Analytical Descriptions of Swirling Wake Profiles
Authors: Schutz William , Naughton Jonathan 
Published: May 2023

Abstract
Wake behavior significantly affects the performance and lifetime of wind turbines on which it impacts. This study considers wake profile fits applied to velocity data gathered in a swirling turbulent wake. Wake rotation or swirl has been found to impact wake growth and evolution, and thus both wake deficit and swirl profiles are fit. Specifically, Gaussian and double Gaussian functions are applied to the wake deficit profiles, and Lamb-Oseen and Taylor distributions are used for the tangential velocity profiles. Fits that accurately represent the wake profiles promise to improve wake representations that can be used, for example, in stability theory analyses and wake modeling. The double Gaussian and Taylor vortex distributions were found to best represent the distributions, but Taylor vortex distribution failed to capture the true distribution at some radial locations. An example use of these fits to quantify the impact of swirl on wake evolution is provided.
Comparing scanning lidar configurations for wake measurements based on the reduction of associated measurement uncertainties
Authors: Hung Lin-Ya , Santos Pedro , Gottschall Julia 
Published: May 2023

Abstract
Scanning lidars are increasingly being used for new measurement applications, such as wake measurements. This type of measurement requires an uncertainty quantification, and the setup of such instruments should ideally be planned to minimize the overall measurement uncertainty. Based on a previous experimental study, we quantify the overall uncertainty for a scanning lidar wake measurement campaign by analyzing the pointing accuracy for distinct configuration scenarios. The selected scenarios are based on a single ground-based scanning lidar measuring the downstream flow, a scanning lidar mounted on the turbine’s nacelle and, finally, two scanning lidars measuring dual-Doppler points within the wake. The results for each configuration are presented in terms of the overall uncertainty and measurement outputs provided from these setups. In a case study using the experimental dataset to compare ground- and nacelle-based configurations, we find that – under slightly stable conditions at 5D downstream – the overall radial wind speed uncertainty, normalized by reference wind speed without counting the sample size uncertainty, decreases on average from 2.78% for the ground-based lidar to 1.33% for the nacelle-based setup. Another advantage of the nacelle-based setup is the potentially larger sample size, as it can measure multiple downstream distances at once. The dual-lidar configuration has advantages in providing horizontal wind speed and direction instead of radial wind speed components only, hence reducing uncertainties associated with wind direction. However, this comes with the cost of fewer measured points to be compared with an engineering wake model and less flexibility to position the lidars, since their relative spatial position impacts the uncertainty. In summary, the presented methodology can benefit the best selection of scanning lidar configuration for a particular application, here demonstrated for wake measurements, proving the user with an estimation of the overall measurement uncertainty and, most importantly, the sensitivity of each parameter.
Numerical investigation of rotor asymmetry to promote wake recovery
Authors: Abraham Aliza , Ramos-García Néstor , Sørensen Jens Nørkær , Leweke Thomas 
Published: May 2023

Abstract
Tip vortices in the wakes of wind turbines are known to have detrimental effects on downstream turbines such as reduced performance and increased fatigue loading. Rotor asymmetry is investigated as a passive method for mitigating these effects by triggering the helical vortex pairing instability. The study is conducted using MIRAS, a multi-fidelity vortex solver, to compare the wakes of the standard NREL 5MW turbine and a modified asymmetric version where one blade is extended radially relative to the other two. The asymmetric rotor is shown to successfully trigger the vortex instability, increasing the wake average velocity by a maximum of 3.5% and the power available to a downstream turbine by up to 11%. The turbulence in the wake of the asymmetric rotor is also modified, exhibiting enhanced mixing. Using the available power gains from the simulations and operational data from the Lillgrund wind farm, the total impact of rotor asymmetry on wind farm efficiency is estimated, showing increases > 2% under certain wind conditions. The findings of this study indicate that rotor asymmetry has strong potential as a wake control method and would benefit from further investigation to understand the effects of inflow turbulence and the impacts on rotor loading.
High-fidelity simulations of wake-to-wake interaction in an atmospheric boundary layer over a complex terrain
Authors: Jané-Ippel C. , Bempedelis N. , Palacios R. , Laizet S. 
Published: May 2023

Abstract
In this work, the wind farm simulator WInc3D is employed to perform large-eddy simulations (LES) of the flow over a complex terrain. The methodology used is validated through comparison with previously published wind tunnel experiments and numerical data for the flow over a cosine-squared hill with and without wind turbines. The study focuses on the wake-to-wake interaction between two wind turbines, with one on the hilltop and the other 2.5 or 5 turbine diameters downstream of the hilltop. The results indicate that the power output of the downstream turbine can be increased by a factor of three when a wind turbine is placed on the hilltop. This is explained by the accelerated flow that results from the favourable pressure gradient produced by the first half of the hill and the blockage of the hilltop turbine. It is also found that this effect is not sufficient to enhance the power of the downstream turbine if it is placed too far away from the upstream turbine.
Large-Eddy Simulation of airborne wind energy systems wakes
Authors: Crismer J-B , Trigaux F , Duponcheel M , Winckelmans G 
Published: May 2023

Abstract
Wind energy is usually harnessed using wind turbines but it is not the only way of collecting this energy. In this paper, Airborne Wind Energy Systems (AWES) are considered. The understanding of the wake shed by such devices is of primary importance when considering farms, yet it has not been much studied so far, and even less so when considering kites with soft curved wing. In this work, LES is used to analyse the wake of AWES. Two types of wings are considered: a straight wing, modelling the reference rigid wing AWES and a curved wing accounting for leading edge inflatable kites. Both types of wings are modelled using actuator lines. The wake analysis reveals that the wake of curved wings tends to be stronger and to recover faster. For the case of the tilted trajectory, asymmetries in the wake are also noted. The difference is explained by the azimuthal variation of the circulation distribution on the wing. It also relates to some wakes characteristics for tilted wind turbines. The wake of the curved wing is also more sensitive to the variation of the loading when it flies tilted trajectory, due to the varying loading on its tips.
Graph machine learning for predicting wake interaction losses based on SCADA data
Authors: Hammer Florian , Helbig Nora , Losinger Thomas , Barber Sarah 
Published: May 2023

Abstract
Six wind turbine generators (WTGs) within a larger wind farm were chosen to model wake interaction losses by means of graph machine learning. 10-minute averaged measurement data values at each turbine over a period of two years were available. Based on the measured power output and ”free-stream” (undisturbed) wind conditions, determined at an upstream WTG unaffected by wakes, a data-driven free-stream power curve model was built. The cluster of WTGs was then represented by a graph data structure in the form of an adjacency matrix, where the turbines are nodes, which in turn are connected by edges. The edges are represented by weightings, indicating the strength of wake interactions between connected turbines. To learn the weightings, the predicted power outputs of the free-stream power curve model were compared to the measured power outputs and then optimised to account for these differences, which are attributed to wake interaction losses. After that a new data-driven model was trained, with free-stream wind speed, direction and yaw misalignment as features and the optimised weightings as targets. It was shown that the model was able to predict power curves that show clear patterns due to wake effects and reduced the root mean squared error, based on the measured and predicted values, by 16.29% compared to the free-stream power curve model.
Comparison of three DWM-based wake models at above-rated wind speeds
Authors: Hanssen-Bauer Ø W , Doubrawa P , Aa Madsen H , Asmuth H , Jonkman J , Larsen G C , Ivanell S , Stenbro R 
Published: May 2023

Abstract
In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine’s rotational period (1P) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1P) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.
Research on Chemical Composition and Weathering of Ancient Glass Based on Grey Correlation
Authors: Wu Chi , Fu Xingyao , Shi Jinlong , Liu Quanwei 
Published: May 2023

Abstract
In this paper, the probability statistics method is used to convert the categorical values into quantitative values, and the TOPSIS method is used to obtain the weight of each index. After unification, the canonical correlation regression model is constructed to judge the correlation between glass weathering and its colour, type and decoration. Finally, the conclusion that the weathering of glass cultural relics is related to these attributes is drawn. In view of the chemical composition of glass cultural relics of unknown category, this paper uses grey correlation analysis to identify its type, and uses multi-factor sensitivity analysis to analyse its sensitivity. Finally, it is found that SiO2 has the highest sensitivity among all chemical components.
Simulation and Experimental Study on Anti-damage Performance of Gun & Ammunition Cabinet
Authors: He Yong-liang , Sun Chuan-ming 
Published: May 2023

Abstract
In order to meet the requirements that the gun & ammunition cabinets is carried and transported with the armed forces, it used the finite element analysis method to analyze the anti-damage performance by ANSYS software. Firstly, it established the analysis model of the finite element about gun & ammunition cabinet, and determined the stress distribution through static analysis. Secondly, it designs the dropping analysis test that in order to close to the real dropping in the finite element analysis. From three angles of left, right and vertical angle, the test is carried out that fall from a height of 1m. In the test, the cabinet contains three different materials thickness that are 1.5mm, 1.4mm and 1.3mm. Through the analysis of dropping, it knows that the cabinet can meet the requirements of carrying and transporting with armed forces, when the thickness is 1.5mm. Although the whole cabinet is seriously deformed, the stress in the key position is less than the fracture stress. Finally, the anti-damage experiment is designed that it verified by the anti-damage test. The experiment results show that the working time for damaging the cabinet should take 14min, and the damage area is not less than 38cm2. The results confirm that the gun & ammunition cabinet with a material thickness of 1.5mm meet the requirements of the army forces.
Rolling Bearing Fault Diagnosis Based on Multiscale Block Convolution Neural Network
Authors: Wang Peng , Luo Yuting , Gong Liming , Zhou Yiren 
Published: May 2023

Abstract
The working conditions of rolling bearings are always non-stationary, which will degrade the performance of the traditional intelligent fault diagnosis algorithm can achieves better results under constant working conditions. Aiming to the problems mentioned above, a novel fault diagnosis algorithm based on Multiscale Block Convolutional Neural Network (MBCNN) has been proposed in this paper. Compared with other intelligent fault diagnosis algorithms, the proposed algorithm achieves the reuse of effective features while simultaneously extracting global and local fault features end-to-end synchronously. Owing to the original one-dimensional vibration signal can effectively reveal the non-stationarity of bearing fault signal, it is selected as the input of MBCNN and the output of multiple fault categories. Finally, a experiment is conducted to verify the validity of the model, and the advantages of the model are analyzed through the visualization of features. The results show that compared with other methods, this method has higher prediction performance under variable working conditions.
Research on the Influence of the Parasitic Loop of the Roll-Pitch Seeker on the Stability of the Guidance System
Authors: Xiao Bowen , Zhou Hang , Yao Dongdong , Xia Qunli 
Published: May 2023

Abstract
Disturbance rejection rate is an important performance index of the seeker,the disturbance rejection rate affects the extraction accuracy of the line-of-sight angular velocity output by the seeker,and introduce an additional parasitic loop in the guidance loop,this loop affects the stability of the guidance system and reduces the accuracy of the missile guidance system. This paper analyzes the extraction principle of the line-of-sight angular velocity of the roll-pitch seeker,the parasitic loop model of the roll-pitch seeker caused by the scale deviation between the detector, the frame angle sensor and the angular rate gyroscope is established, and the stability and time domain response characteristics of the parasitic loop are analyzed. Then, based on the time-invariant theory, the influence of the parasitic loop of the roll-pitch seeker on the stability of the guidance system and the guidance parameters is analyzed.The simulation results show that the positive and negative feedback characteristics of the disturbance rejection rate parasitic loop have a great influence on the stability of the guidance system. It is necessary to design appropriate guidance parameters according to the characteristics of the parasitic loop to meet the guidance accuracy requirements.
Robust Control Design for Systems with Discrete Time Delay of 1,3-PD Fermentation
Authors: Li Xuan , Li Meixia , Zhu Xi 
Published: May 2023

Abstract
A disturbed model with discrete time delay for the continuous fermentation of glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae is presented in this paper. The biochemistry process cannot refrain from the disturbances caused by both time delay and exogenous disturbance. The above negative factors may restrict and undermine the accomplishment performance of the dynamic system, and even lead to unstable oscillations. In consideration of multiplicity phenomena, the stability behaviors of multiply equilibriums have been analyzed. In order to reduce the disturbance effect, we propose an H∞ controller through LMIs method. The simulation results indicate that the closed-loop system can be assured to be internal stable and to achieve the H∞ performance. The controller might be practicable for the continuous culture.
Gesture Recognition Algorithm Based on Computer Vision
Authors: Li Manhua , Xia Hongye , Li Peiyang , Lu Huacai 
Published: May 2023

Abstract
With the advent of the era of artificial intelligence, computer vision and machine learning are developing rapidly, and human-computer interaction technology has become one of the important research directions. This paper mainly combines computer vision and other related technologies to analyze and judge the gestures, and predict the corresponding models according to the different matching of gestures, so as to recognize the corresponding gesture information. By converting the image into HSV space, HSV channel separation is realized, and then filtering and skin color segmentation are carried out to carry out morphological operation. Finally, shape matching is carried out according to the contour information of the image to judge the information of gesture. The experimental results show that the gesture recognition algorithm proposed in this paper can effectively judge the gesture information contained in the image, has good operation effect and strong practicability.
Development and Application of Particle Transport Monte Carlo Simulation Modeling Toolkit
Authors: Shang Peng , Huang Liu-xing , Zhu Jin-hui , Niu Sheng-li , Liu Li 
Published: May 2023

Abstract
In particle transport Monte Carlo simulation, the manual modeling method is not suitable for complex geometric models. A Monte Carlo Simulation Modeling Toolkit (pyMCMT) is developed based on Python language. The toolkit can generate input card in several classical format such as MCNP and GDML format automatically. In this paper, two typical applications are presented based on the pyMCMT toolkit: 1, constructing a multilayer circular geometric model in complex density distribution by pyMCMT toolkit in order to satisfy the requirement of building large-space fine model with the influence of blast wave. A two-dimensional adaptive model is built for non-uniform atmospheric induced by blast wave. The number of the cells in this model is reduced vastly with the same model resolution; 2, producing the optimized weight window parameter using the mesh-based weight window variance reduction combined with density iteration method which is realized to solve the high statistical fluctuation and low counting efficiency problem in deep penetration process. The successful application of pyMCMT shows that pyMCMT is a modularized, parameterized, customizable and easy-to-debug toolkit for particle transport Monte Carlo simulation modeling, which can improve the modeling efficiency tremendously.