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Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

With the increasing penetration of wind power generation, the fluctuating and intermittent behavior of wind power poses huge challenges to the operation and planning of distribution networks. A popular way to mitigate these challenges is to provide a group of possible wind power forecasting scenarios instead of depending on deterministic point forecasting values, so that system operators can consider the uncertainties. This letter proposes a novel WindGMMN method for wind power scenario forecasting, in which necessary modifications are made on the generative moment matching network (GMMN), and an optimization strategy is designed to find a series of wind power scenarios with similar shapes, probability distributions, and temporal correlations as potential scenarios. Simulations and analyses were performed on a public dataset with 2190 wind power generation curves and their corresponding meteorological features. The results show that the proposed WindGMMN outperforms popular baselines (e.g., variational auto-encoders and generative adversarial networks) for scenario forecasting of wind power, without any restrictions on the time horizon (e.g., times ranging from 10 min to 24 h).

Resource shortages and environmental pollution of the world are becoming more and more serious, wind Turbine generation as a kind of clean energy power generation has attracted much attention. The difficulty of wind power generation lies in the randomness of natural wind energy, which leads to the instability of the output power of Wind Turbine Generators (WTGs). The traditional variable pitch controller cannot realize global precision control of the wind power system. To solve the problem, this paper combines Takagi-Sugeno (T-S) fuzzy model with optimal control theory to design a robust controller. The unique novelty of the controller is that when the wind speed is higher than the rated, no matter how much the wind speed is, even if it is far from the balance point of the system, the controller can make the output power at a constant value, that is, increase the global stability of the wind power system. Specifically, the wind power system was divided into several linear subsystems, and the state feedback controller of each subsystem was designed through optimal control. Then, the subsystem controllers were merged by superposing membership functions, forming a controller of the entire system. Finally, wind turbine generators (WTGs) were modeled and simulated in Matlab/Simulink. The results show that the proposed controller kept the output power of WTGs constant under the global step wind of 13–25 m/s and the stochastic wind of 14–15 m/s. Our design enables the precision control at any wind speed, and improves the stability of power output of WTGs.

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

The mechanisms that amplify orbitally-driven changes in insolation and drive the glacial cycles of the past 2.6 million years, the Pleistocene, are poorly understood. Previous studies indicate that cloud-phase feedbacks oppose ice sheet initiation when orbital configuration supports ice sheet growth. Cloud phase was observationally constrained in a recent study and provides evidence for a weaker negative cloud feedback in response to carbon dioxide doubling. We observationally constrain cloud phase in the Community Earth System Model and explore how changes in orbital configuration impact the climate response. Constraining cloud phase weakens the negative high latitude cloud phase feedback and unmasks positive water vapor and cloud feedbacks (amount and optical depth) that extend cooling to lower latitudes. Snowfall accumulation and ablation metrics also support ice sheet expansion as seen in proxy records. This indicates that well-known cloud and water vapor feedbacks are the mechanisms amplifying orbital climate forcing.

This work deals with the sensitivity to the plate roughness of Lamb waves. An experimental study is performed involving an air-coupling transducer system. Signal processing allows us to extract the Lamb waves characteristics: phase velocity and attenuation. Plate surface topographies are obtained by means of an optical surface profiler. The acoustic characteristics and the surface topographies are finally linked.

Natural ice accretion on the lifting surface of an aircraft is detrimental to its aerodynamic performance, as it changes the effective streamlined body. The main focus of this work considers the optimization design of airfoils under atmospheric icing conditions for the Unmanned Aerial Vehicle (UAV). The ice formation process is simulated by the Eulerian approach and the three-dimensional Myers model. A three-equation turbulence model is implemented to accurately predict the stall performance of the iced airfoil. In recognition of the real atmospheric variability in the icing parameters, the medium volume diameter of supercooled water droplets is treated as an uncertainty with an assumed probability density function. A technique of polynomial chaos expansion is used to propagate the input uncertainty through the deterministic system. The numerical results show that the multipoint/multiobjective optimization strategy can efficiently improve both the ice tolerance and the cruise performance of an airfoil. The reason for the focus on robust optimization is that the ice angle of the optimized airfoil becomes less critical to the incoming flow. The optimized airfoils are applied to a UAV platform, in which the performance improvement and the relevant key flow feature are both preserved.

This paper studies a novel gas cyclone with a cylindrical filter face installed in the center from the vortex finder to the bottom hopper. The experimental results show that this composite cyclone has a higher collection efficiency and a lower pressure drop than the original cyclone. The mechanisms for the improvement are analyzed by both physical experiments and numerical simulations. By measuring dust samples collected at different places it is revealed that the center filter can prevent fine particles from entering the inner vortex and escaping, which accounts for the increase of the collection efficiency. In addition, the flow field of the composite cyclone is simulated by computational fluid dynamics and compared with that of the original cyclone. The analysis shows that with the filter layer installed, the swirling flow disappears in the vortex finder, which decreases the kinetic energy dissipation and hence lowers the pressure drop.

Probabilistic wind power forecasting is an important input in the decision-making process in future electric power grids with large penetrations of renewable generation. Traditional probabilistic wind power forecasting models are trained offline and are then used to make predictions online. However, this strategy cannot make full use of the most recent information during the prediction process. In addition, ensemble learning is recognized as an effective approach for further improving forecasting performance by combining multiple forecasting models. This paper studies an online ensemble approach for probabilistic wind power forecasting by taking full advantage of the most recent information and leveraging the strengths of multiple forecasting models. The online ensemble approach is first formulated as an online convex optimization model. On this basis, a quantile passive-aggressive regression model is proposed to solve the online convex optimization model. Case studies and comparisons with other online learning methods are conducted on an open wind power data set from Belgium. Results show that the proposed method outperforms competing methods in terms of pinball loss and Winkler score with high reliability.

In order to study the propagation laws of acoustic wave of coal samples from the Upper Permian Xuanwei Formation in the east of Yunnan Province, China, under saturated water and dry conditions, the basic physical parameters, acoustic parameters and anisotropic parameters were obtained through the experiments. Based on FFT and wavelet analysis theory, the spectral characteristics of coal samples under different conditions were studied. The results show that physical parameters of coal samples in different directions have different values, that is, the anisotropy of coal samples is obvious. When the coal samples are saturated with water, the acoustic velocities and the attenuation coefficient increase, whereas the dominant frequency decreases. The signal amplitude of the frequency domain significantly decreases, that is, the internal structure of coal samples is damaged. The P-wave velocity and S-wave velocity increase with the increase of the confining pressure, whereas the anisotropy parameters decrease with the increase of the confining pressure. Overall, this study provides the basis to understand basic acoustic information and anisotropy characteristics of coal samples.

Optical communication has been employed in a wide range of applications, including the terrestrial, submarine, inter-satellite, and even space communication. It is particularly successful in fiber-based communication networks that vastly reshape modern life through the Internet. With the intensified activities such as undersea resource exploration, ecosystem monitoring, and recreation, underwater is an exciting new arena for optical wireless communication (OWC). In this paper, we review the recent progress of underwater and water-air OWC systems. Channel characterization, communication system design, and performance investigations are given. Critical limitations and effective mitigation methods to overcome the influence of bubbles and waves are presented. We also address the current issues for proper performance comparison under different wave conditions. With further research in channel modeling, device innovation, and system design optimization, practical and robust underwater and water-air OWC systems can be realized.

The clouds in the Atlantic trade-wind region are known to have an important role in the global climate system. Acquiring a comprehensive characterization of these clouds based on observations is a challenge, but it is a necessary piece of information for the evaluation of their representation in models. An exploration of how the macrophysical and microphysical cloud properties and organization of the cloud field impact the large-scale cloud radiative forcing is presented here. Direct measurements of the cloud radiative effects from the Broadband AirCrAft RaDiometer Instrumentation (BACARDI) on board the High Altitude and LOng Range Research Aircraft (HALO) and cloud observations from the GOES-16 satellite during the Elucidating the role of clouds-circulation coupling in climate (EUREC4A) campaign provide evidence to demonstrate what drives the cloud radiative effects in shallow trade-wind clouds. We find that the solar and terrestrial radiative effects of these clouds are largely driven by their macrophysical properties (cloud fraction and a scene-averaged liquid water path). However, we also conclude that the microphysical properties, cloud top height and the organization of the cloud field demonstrate an increasing relevance in determining the cloud radiative effects as the cloud fraction increases.

In this study, a filtering method based on the threshold value of normalized synthetic aperture radar (SAR) data is proposed to eliminate clutter in millimeter wave ground based synthetic aperture radar (GB-SAR) images. In the proposed method, first, stepped frequency continuous wave SAR data are reconstructed by using the back-projection algorithm and focused complex SAR data are obtained. Then, the amplitude values of the complex SAR data are normalized and the best threshold values to distinguish the target from clutter is determined by the OTSU’s thresholding method. Next, a filter mask is created that cancels all data below the computed threshold values. The mask matrix is finally multiplied with the resulted GB-SAR data to eliminate all clutter from the image. With the proposed technique, the best threshold value is determined automatically by directly processing the raw data without converting the SAR data into any RGB images. The proposed technique is validated through real GB-SAR experiments that were carried out in the frequency band of 78-81 GHz. In the experiments, challenging GB-SAR data are obtained using high cluttered background materials, and very successful filtering operations are performed with the proposed technique.

Electromagnetic formation flight (EMFF) uses the electromagnetic force to control the relative positions of multiple satellites without using conventional fuel-based propulsion. To compensate for t...

In this paper, the drivetrain identification problem of a horizontal axis gear-driven wind turbine has been considered. The identification problem leads to a precise model of the drivetrain of the wind turbines which plays a key role in the production and transmission of electrical energy. This process consists of two stages: First, offline identification which needs the input–output data from the drivetrain system. These data are obtained from the FAST code. FAST (Fatigue, Aerodynamics, Structures, and Turbulence) is a valid aeroelastic code in the simulation aeroelastic field of offshore and onshore wind turbines. In region 2 (wind velocity is between the cut-in and rated velocities), the generator torque is input, and rotor speed is output. It is supposed that the wind velocity is neglected in the identification process (the identification process is not considered in the presence of wind or done in the low wind velocities bellower than the cut-in wind velocity). In the second stage, after completion of the offline identification process, the drivetrain model of the wind turbine is obtained but the aerodynamic torque in the real performance of the wind turbine is still unknown. In this stage, to estimate the aerodynamic torque, high order sliding mode estimator is used and the unknown states are estimated by using the Extended Kalman Filter (EKF)

Lightning is frequently initiated within the convective regions of thunderstorms, and so flash rates tend to follow trends in updraft speed and volume. It has been suggested that lightning production is linked to the turbulent flow generated by updrafts as turbulent eddies organize charged hydrometeors into complex charge structures. These complex charge structures consist of local regions of increased charge magnitudes between which flash-initiating electric fields may be generated. How turbulent kinematics influences lightning production, however, remains unclear. In this study, lightning flashes produced in a multicell and two supercell storms simulated using The Collaborative Model for Multiscale Atmospheric Simulation (COMMAS) were examined to identify the kinematic flow structures within which they occurred. By relating the structures of updrafts to thermals, initiated lightning flashes were expected to be located where the rate of strain and rotational flow are equal, or between updraft and eddy flow features. Results showed that the average lightning flash is initiated in kinematic flow structures dominated by vortical flow patterns, similar to those of thermals, and the structures’ kinematics are characterized by horizontal vorticity and vertical shearing. These kinematic features were common across all cases and demonstrated that where flash-initiating electric fields are generated is along the periphery of updrafts where turbulent eddies are produced. Careful consideration of flow structures near initiated flashes is consistent with those of thermals rising through a storm.

This paper examines if wind might be an additional energy source for solar powered, crewed Mars missions during dusty days. Wind data from Viking Lander 2 is analyzed from a wind engineering point of view, as it is done on Earth, to evaluate wind turbine sites. It is assumed that wind energy will not be the primary energy source used on a crewed Mars mission but an additional option during times of global dust storms, when solar power is suffering from an increase of atmospheric optical depth, which is seen as one of the largest drawbacks of solar arrays. In order to evaluate if wind energy might be a reliable energy source during dust storms, when the energy yield of solar cells is reduced, it will be investigated whether wind strength has a positive correlation with increased atmospheric optical depth. This is, in case wind speed is generally capable of providing a significant amount of power, a further requirement if wind turbines should compensate the power loss of solar arrays during increased atmospheric optical depth. For a first estimate of the amount of wind turbines needed to maintain energy output during a storm a case study is done which regards power production during the 1977b global dust storm.

To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.

In the present work, the dust pollutants from a fully mechanized mining face under different situation were investigated for a typical coal roadway. The Euler-Lagrange model was adopted for dust movement process. After the validation, the varieties of the air curtain, the spatial and temporal distribution of dust pollutants under various conditions were analyzed. The present study revealed that the air curtain jet flow develops with time and space and significantly influences the surrounding flow field. The dust concentration was controlled for the front part of the roadway. The higher initial supply air velocities (

Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be applied to off-the-grid spatio-temporal data. This model has a substantial advantage over existing downscaling methods in that the trained model can be used to generate multisite predictions at an arbitrary set of locations, regardless of the availability of training data. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies, and motivates further research into applications of deep learning techniques in statistical downscaling., 26 pages, 12 figures

Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.

With the increasing requirements for miniaturization and high braking torque, it is now necessary to analyze the thermal characteristics to the same extent as the electromagnetic design for eddy current brakes. However, due to the lack of suitable convection heat transfer correlations for axial flux eddy current brakes, it brings difficulties to designers. To solve this problem, the correlations for an axial flux permanent magnet (PM) eddy current brake are derived using the computational fluid dynamics (CFD) and the least square method. The proposed correlations considered the changes in design parameters and temperatures of the eddy current brake, and the accuracy is verified by the CFD. Subsequently, convective heat transfer coefficients calculated through the correlations are assigned to a thermal network model of the eddy current brake, and the thermal network model is verified by experimental tests. The results show that the thermal network model can accurately predict temperatures of the eddy current brake. Since the time required to calculate convective heat transfer coefficients through the correlations is significantly less than that required through CFD, and the variation of the geometrical parameters and temperatures can be considered, these correlations are very suitable for use in the optimization process.

Wind tunnel oscillatory testing is a technique conventionally used for the measurement of dynamic aerodynamic loads on subscale aircraft models. Oscillatory testing is commonly used for identificat...

Pulse position modulation (PPM) is a key technology for deep space optical communication (DSOC) system to improve the data transmission rate. To reach optimum rate, the modulation parameters of PPM are required to be analyzed. The rate can be improved by choosing appropriate modulation orders and the initial pulse width. In this paper, the meteorological statistical data of our experimental location are analyzed. Then, the analysis formula of the cloud scattering and atmospheric turbulence influence on PPM communication rate are given. The cloud particles are measured through aircraft detector, simulation analysis is carried out by using the measured data of aircraft detector. Finally, the influence on the communication rate of different cloud physical thickness are analyzed.

Sea surface temperature (SST) can be measured from space using infrared sensors on Earth-observing satellites. However, the tradeoff between spatial resolution and swath size (and hence revisit time) means that SST products derived from remote sensing measurements commonly only have a moderate resolution (>1 km). In this article, we adapt the design of a super-resolution neural network architecture [specifically very deep super-resolution (VDSR)] to enhance the resolution of both top-of-atmosphere thermal images of sea regions and bottom-of-atmosphere SST images by a factor of 5. When tested on an unseen dataset, the trained neural network yields thermal images that have an RMSE 2-3x smaller than interpolation, with a 6-9 dB improvement in PSNR. A major contribution of the proposed neural network architecture is that it fuses optical and thermal images to propagate the high-resolution information present in the optical image to the restored thermal image. To illustrate the potential benefits of using super-resolution (SR) in the context of oceanography, we present super-resolved SST images of a gyre and an ocean front, revealing details and features otherwise poorly resolved by moderate resolution satellite images.

Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction.

Accurate wind power forecasting is a vital factor in day-ahead dispatch and increasing the level of penetration of renewable energy. The feature extraction of meteorological factors related to wind power output is a big challenge to improve forecasting accuracy, and selecting key meteorological factors based on experience will decrease the prediction accuracy. Therefore, a day-ahead wind power combined forecasting approach is innovatively proposed through key meteorological factors selection, data decomposition and reconstruction, combined forecasting model generation, and optimization strategy. Correlated variables namely variational mode decomposition and weighted permutation entropy (VMD-WPE) decomposed historical wind power and key meteorological factors are used as the inputs. A forecasting model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is used to forecast future wind power. Four optimizers with different optimization performances are used to find the best parameters of the forecasting model to obtain accurate prediction results. Multiple comparative experiments from regional wind farms in Ningxia and Jilin of China are utilized as case studies to evaluate the effectiveness of the proposed model. Results show that the proposed approach outperforms other benchmark prediction models, taking into account multiple-error metrics including error metrics, accuracy rate, and improvement percentages.

Using temporal and spatial correlations to predict wind speed is still one of the most challenging and least-researched fields in wind speed prediction. How to make full use of the spatial correlation of wind speed between adjacent wind turbines to improve the accuracy of wind speed prediction is significant. Therefore, to address the challenges of making multi-location, short-term wind speed predictions, we propose a multi-location wind speed prediction model based on spatiotemporal joint learning, which called Mixed-SqueezeNet-BiGRU. By designing the SqueezeNet model with a deformable convolutional network, the spatial correlation of a multi-location time series is learned by using the spatial wind speed matrix; by introducing the Channel Shuffle operation of ShuffleNet, the characteristics of multi-location and multi-channel are output to ensure the orderly flow of information among the channels. To describe the dynamic information of the time series, a time series dynamic network model based on the bi-directional gating cycle unit, BiGRU, is introduced to model the long-term and nonlinear dependence of the time series. Experiments were carried out on three wind farm's wind speed dataset to verify that the Mixed-SqueezeNet-BiGRU short-term wind speed prediction model has a better prediction performance than some advanced methods.

Ocean fronts have an important influence on global ocean-atmosphere interactions and marine fishery. Hence, it is of great significance to obtain the positions of the ocean fronts. However, current ocean front detection research confronts two challenges: scarcity of labeled data and limitations of ocean front detection algorithms. To address these two problems, we have collected and labeled an ocean front data set and proposed a new deep learning model for ocean front detection. For concreteness, due to the weak edge property of the ocean fronts, we formulate ocean front detection as a weak edge identification problem and propose the weak edge identification network (WEIN) for ocean front detection. WEIN consists of four convolutional blocks. Each block has a side output layer used to detect front edges at a specific image representation level. The side outputs are then fused to predict (detect) the locations of the ocean fronts. In this work, we adopt two metrics to measure the experimental results, i.e., the F₁-score and intersection over union (IoU). The experimental results with comparison to traditional and deep learning approaches demonstrate the superiority of WEIN for ocean front detection.

A water-assisted method is used to reduce heat accumulation during the ultrafast laser drilling of metals. To understand the complex material ablation mechanism, an underwater percussion drilling process using an ultrafast laser with pulse energy below the water breakdown threshold was investigated. The laser-induced plasma (LIP) was recorded using a high-speed camera, and the ablation morphology was characterized. The material removal mechanism in air and underwater was studied using metallurgical analysis. A numerical model including plasma formation by a single pulse and plasma evolution by multiple pulses was established to quantitatively describe the effects of LIP on the energy attenuation and beam refraction. This model can predict the hole radius and depth in underwater drilling and explain the difference in drilling performance in air and underwater.

In this article, the benchmark drift of micromachined thermal wind sensor due to the packaging asymmetry has been analyzed and compensated. For the ideal wind sensors with symmetric packaging, the benchmark does not vary with speed and direction. However, for the real sensors, especially for handmade ones, packaging asymmetry problems are usually inevitable. Consequently, benchmark drifts with wind because of different thermal distribution in each direction, which was verified by the proposed lumped parameters model. The real sensors were tested in a wind tunnel and results showed the benchmark drift as well. The contribution of this work lies in the establishment of the equation of benchmark dependent on wind speed, which can predict benchmark value under different wind speeds by calibration at zero wind speed. This compensation method is extremely simple and convenient for industrial production. After compensation, the relative wind speed errors are reduced from 46 to 8% up to 30 m/s, and the wind direction errors are reduced from 30° to 7° over the full range of 360°.

The aim of this study was to develop a prediction model and identifying relative important factors in the evaluation of moisture content of pharmaceutical granules using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques for the data sets of Addis Pharmaceutical Factory (APF). Optimal models of ANN and SVM models were developed and compared, utilizing Matlab16.0 as a software tool. The performance of the models is evaluated using a quantitative error metric; mean squared error (MSE), Regression(R) and Confusion Matrix (CM). This study reveals that the ANN model is an optimal model for predicting moisture content of pharmaceutical granules for the datasets of APF. The model of ANN, with MSE of 0.016941 and classification accuracy of 98.7% is built and accepted as optimal model for predicting moisture content of pharmaceutical granules. Temperature, Mixer time, Initial moisture, air flow rate and drying time respectively are the most important factors in determining the moisture content of the granules.

Wind energy technology is based on the ability to capture the energy contained in air motion. Wind power quantifies the rate of this kinetic energy extraction. Wind power is also the rate of kinetic energy flow carried by the moving air. Because the motion is both the source of the energy and the means of its transport, the efficiency of wind power extraction is a balance of slowing down the wind while maintaining a sufficient flow. This chapter quantifies these fundamental concepts and discusses the nature of wind.

The current numerical study aimed for analayzing heat transfer of a spherical water droplet placed in a rectangular duct. The problem particularly deals with air-cooling of the droplet. The computational fluid dynamic technique is used to treat the problem. In order to study the problem, the effect of evaporation on the droplet's heat transfer is taken into consideration to make the problem more realistic. Moreover, various parameters like the inlet velocity and the temperature of the upstream are numerically checked. It is found out that with involving evaporation, the mean temperature of the droplets increases in comparison with the case that there is no evaporation involved. The outcomes of the current study offer useful and deep insight into the evaporative cooling of a water droplet.

Ground-based sky imagers have become a valuable tool for cloud monitoring as they allow to observe the local sky in great detail due to their temporally and spatially high-resolution imagery. Set up in pairs, they can be used to estimate both cloud geometry and motion. While considerable effort is currently devoted to their use for cloud shadow forecasting and short-term prediction of solar irradiance for renewable energies, their potential for vertical wind profiling and cloud evolution monitoring has not yet received much attention. We evaluate the accuracy and reliability of such retrievals from two sky imagers in stereo mode by comparing derived 3D cloud motion vector fields with observations from a wind lidar. We find that the wind vectors derived from the detected motion of cumulus clouds and multiple cloud layers can be retrieved with a quality comparable to a wind lidar. Differences in wind speed and direction from both methods remain mostly below 1 to 3 ms−1 in wind speed and 6° to 20° in wind direction, depending on cloud type.

Solar air heater acts as one of the important components in utilization of solar energy. The air heater absorbs the irradiance and converts it into heat energy at the absorbing surface. The thermal energy is further use in heating flowing air through the duct. Solar air heaters are cost effective as well as simple in design. Solar air heater can be used in space heating, timber seasoning and agricultural drying. In spite of all these advantages the solar air heater has certain challenges such as the air has low heat transfer coefficient. The heat transfer rate from the heated absorber surface to the air is low. Hence in order to enhance the heat transfer coefficient the surface area either increases or the flow made to be turbulent. In order to do so the artificial roughened element must be incorporated on the heated surface. The use of artificial roughness is considered as an effective technique to enhance the heat transfer rate of fluid flowing through the duct of solar air heater. The heat transfer and friction characteristics of number of roughness geometries incorporated solar air heater have been investigated. In this paper an attempt has been made to review on element geometries used as artificial roughness in solar air heater in order to improve thermal and thermo hydraulic performance of solar air heater ducts.

For the wind turbines installed in the cold and humid regions, icing accretion on the blade surface seriously degrades its performance. The Straight-Bladed Vertical Axis Wind Turbine (SB-VAWT) is also troubled by the problem. For exploring the characteristics of icing, the icing wind tunnel tests have been carried out in this paper. An icing wind tunnel experimental system is designed and fabricated. The experimental parameters in the icing wind tunnel system are measured. A rotor test stand with two blades of NACA0018 airfoil is designed and amounted in the icing wind tunnel. The icing test is conducted under the different tip speed ratios and the rime ice condition. The experimental results show that the rotational speed of the blade has significant effects on the characteristics of icing. When the tip speed ratio is lower than 1, the ice layer covers in the whole blade surface and grows layer by layer. As the tip speed ratio is higher than 1, the ice layer mainly concentrates in the vicinity of the leading edge. Three factors, which are the relative angle of attack, the rotational position of blade and the centrifugal force, influencing on the characteristics of icing are analyzed and discussed.

High precision wind speed forecasting will maximize the utilization of wind power, which is essential for wind farm operation and energy system management. But the inherent instability and volatility of wind speed bring difficulties in the forecasting and operation processes. At present, experts and scholars have proposed many wind speed prediction methods. However, parts of studies ignored the importance of parameter optimization and data preprocessing, which made the results vulnerable to the instability of a single model. To fill this gap, a weighted combination model is obtained by an advanced swarm intelligence optimization algorithm to overcome limitations of the individual neural network. At the same time the denoising technology is implemented to reduce the noise in original speed sequences. Our empirical study and multi-angle evaluation results show that the advanced optimization algorithm we adopted is superior to other well-known meta-heuristic algorithms. And the experimental results show that the novel system owns strong stability and high forecasting accuracy. It can not only provide a new idea for the field of wind power forecasting, but also open an effective way for smart planning in the future.

A novel optical-tracking technique is presented for aerodynamic measurements on free-flying models in hypersonic wind tunnels. This technique is a generalization of the edge-fitting concept develop...

Millimeter-wave (MMW) radar sensors are considered key components of autonomous vehicles. Because of the performance degeneration of cameras and lidars under inclement weather conditions, robust autonomy must rely on radar sensors to perform target detection and classification. Unlike basic target classifier methods in literature that make use of target velocity, the proposed approach is far more comprehensive and can be applied to targets with zero-Doppler. Depending on the radar type and target range, this paper presents four target classification models based on four different types of radar data: statistical RCS, distributed (time-domain) RCS, range-azimuth angle radar images and 3D radar images. The classification models are implemented by machine learning approaches artificial neural network (ANN) and convolutional neural network (CNN) with a comprehensive simulated dataset. Good classification accuracies are demonstrated, and the proposed model is validated with measured data. Different radar target classification approaches are compared, which clearly reveals the trade-off between classification performance and system complexity. The proposed radar target classification methods can be applied effectively to both static and dynamic targets, at near or far ranges, using traditional or imaging radars, resulting in improved safety for autonomous vehicles in a wide variety of complex environments.

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks.

The large-span ice shell is a novel building style with the aid of inflatable formwork and water-spraying method. Ice material has strong temperature sensitivity, and the ice shell experiences complicated environmental conditions during the operation stage. A thermal-mechanical coupling analysis framework is presented to explore the thermal effect of the ice shell. The transient simulation method for the non-uniform temperature field is established. A thermoelastic damage model for fibre-reinforced ice is derived and the thermal damage analysis method is conducted. Subsequently, the non-uniform temperature field is mapped into the mechanical field of the ice shell structure to carry out the temperature effect and further evaluate the thermal damage. A large-span ice shell built in Harbin Ice and Snow World expounds the applicability of the proposed method in practice. The distribution of non-uniform temperature field on the ice shell is investigated and thermal damage is further studied.

Wind speed accurate forecasting plays an important role in preserving the stability of offshore wind power. Most of current predictions are based on a single wind node. It is difficult for this methods to capture wind high dimensional features and latent spatio-temporal dependencies. In this paper, we propose a general graph optimization neural network specifically for multi-node offshore wind speed prediction named spatio-temporal correlation graph neural network. The proposed model firstly employs graph convolution, which performs Laplace transforms instead of the traditional convolution, to capture the potential spatial dependencies from the nodes' relationship and historical time series better. The channel-wise attention makes the original concentrated weights within a region constructed by adjacency matrix disperse to all input nodes, which distinguishes the nodes’ contributions and generates high dimensional spatial features. Long short-term memory is applied to extract temporal correlation from the high dimensional spatial features. The model has ability to excavate the full potential of spatial-temporal dependencies from multiple wind nodes to the utmost. Experiments select 120 wind nodes in the China sea for prediction. The results show that the proposed model can be very competitive with state-of-the-art methods and holds great performance on multi-node and multi-step wind speed forecasting.

Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called nature run. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also global navigation satellite system/radio occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and ground instantaneous field of view (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons). Significance Statement An orbit simulator for satellite and near-space platforms for supporting observing system simulation experiments (OSSE) is developed in this study. It can compute spatial, temporal, and view geometry parameters of the new observations based on the specific hosting platform and onboard instrument characteristics. These parameters are important for the OSSE to extract atmospheric profiles and surface properties from nature run data and simulate new observations. In addition to simulating existing polar-orbiting and geostationary orbits, it is applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also radio occultation instruments.

An altimeter is a critical instrument in planetary missions, for both safe operations and science activities. We present required specifications and link budget calculations for light detection and ranging (LIDAR) onboard the Martian Moons Exploration (MMX) spacecraft. During the mission phase, this LIDAR will continuously measure the distance between the spacecraft and its target. The time-series distance provides important diagnostic information for safe spacecraft operations and important information for geomorphological studies. Because MMX is a sample return mission, its LIDAR must accommodate physical disturbances on the Martian satellite surface. This resulted in changes to the optical system design. Graphical abstract

As a renewable, clean and economical energy source, wind energy has rapidly infiltrated into the modern power grid system. Wind speed forecasting, the crucial technology of wind power grid connection, has attracted large amounts of scholars for research and modeling. However, a large number of models only focus on the point forecasts, which are far from meeting the requirements of risk control and evaluation of power system. To fill the gap, a novel forecasting model which combined the modified multi-objective tunicate algorithm, benchmark models, and Quantile regression is proposed for deterministic and probabilistic interval forecasts. Theoretical proof demonstrates that the proposed modified algorithm can combine the merits of all benchmark models and better solve the nonlinear characteristics of wind speed. Comparative experiments which include sixteen relevant models are performed on three datasets to validate the performance of the proposed model. Simulation results show that the proposed model is the most accurate in all datasets, and can also get the interval forecast results with relatively high coverage and the narrowest width. Therefore, this model can provide accurate point forecasting results and uncertainty information, which is beneficial to the real-time control of wind turbine and power grid dispatching.

Wind speed predictions are essential for wind power management and wind farm operation. However, due to the high volatility and nonstationarity of measured wind data, it is often difficult to achieve an accurate prediction. This study proposes a hybrid approach that consists of two stages, i.e., data preprocessing and wind speed predicting, to improve the accuracy of short-term wind speed prediction. A preprocessing algorithm for the transformation and standardization of hourly mean wind speed is utilized to remove the non-Gaussian distribution of wind data and diurnal nonstationarity. Several statistical models and artificial intelligence models are then adopted in the second stage of the prediction process, including a persistence model, autoregressive model, autoregressive moving average model and backpropagation neural network. The proposed approach is developed based on the weighted averaging of these models and error optimization theory. Finally, wind speed data for 12 months from two meteorological towers located in Yanan, China, are investigated to demonstrate the effectiveness and accuracy of the proposed approach for multistep wind speed predictions, and its performance is then compared with several existing prediction models. The results indicate that the prediction accuracy improves significantly after preprocessing with the proposed approach, outperforming all the existing aforementioned models.

The integration of wind power with traditional electricity grids is challenging because of the volatile and intermittent nature of wind speed. Consequently, wind speed forecasting is an essential tool in the energy system because it supports economic, regulation, and operational efficiency issues decisions. The development of systems for accurate wind speed forecasting is a difficult task because it presents linear and nonlinear temporal patterns, and is differently influenced by climate and environmental variables. In this paper, we propose a method to search for the best combination of linear, nonlinear forecasts, and exogenous variables , aiming to increase the accuracy of wind speed forecasting. The proposed method performs: (i) the choice of the linear statistical model to forecast the time series; (ii) the error series modeling using a Long Short-Term Memory model; and (iii) the evolutionary search for the most suitable nonlinear combination of linear and nonlinear forecasts with exogenous variables. The proposed method is assessed using hourly and monthly time series of three stations located in northeast Brazil. The evaluation of the results using two traditional measures shows that the proposed method has a better performance than statistical techniques, Machine Learning models, and state-of-the-art hybrid systems in most series.

The Doppler lidar (DL) is an active remote sensing instrument that provides range- and time-resolved measurements of radial velocity and attenuated backscatter. The principle of operation is similar to radar in that pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal. From the time delay between each outgoing transmitted pulse and the backscattered signal, the distance to the scatterer is inferred. The radial or line-of-sight velocity of the scatterers is determined from the Doppler frequency shift of the backscattered radiation. The DL uses a heterodyne detection technique in which the return signal is mixed with a reference laser beam (i.e., local oscillator) of known frequency. An onboard signal processing computer then determines the Doppler frequency shift from the spectra of the heterodyne signal. The energy content of the Doppler spectra can also be used to determine attenuated backscatter.