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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Movement of the Melt Metal Layer under Conditions Typical of Transient Events in ITER

During the operation of ITER, protective coatings of the divertor and the first wall will be exposed to significant plasma heat loads which may cause a huge erosion. One of the major failure mechanisms of metallic armor is diminution of their thickness due to the melt layer displacement. New experimental data are required in order to develop and validate physical models of the melt layer movement. The paper presents the experiments where metal targets were irradiated by a plasma stream at the quasi-stationary high-current plasma accelerator QSPA-T. The obtained data allow one to determine the velocity and acceleration of the melt layer at various distances from the plasma stream axis. The force causing the radial movement of the melt layer is shown to create an acceleration whose order of magnitude is 1000g. The pressure gradient is not responsible for creating this large acceleration. To investigate the melt layer movement under a known force, the experiment with a rotating target was carried out. The influence of centrifugal and Coriolis forces led to appearance of curved elongated waves on the surface. The surface profile changed: there is no hill in the central part of the erosion crater in contrast to the stationary target. The experimental data clarify the trends in the melt motion that are required for development of theoretical models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Identification of slip surface location by TLS-GPS datafor landslide mitigation case study: Ciloto-Puncak, West Java

Landslide can prevented by understanding the direction of movement to the safety evacuation track or slip surface location to hold avalanches. Slip surface is separating between stable soil and unstable soil in the slope. The slip surface location gives information about stable material depth. The information can be utilize to mitigate technical step, such as pile installation to keep construction or settlement safe from avalanches.There are two kinds landslide indicators which are visualization and calculation. By visualization, landslide identified from soil crack or scarp. Scarp is a scar of exposed soil on the landslide. That identification can be done by Terrestrial Laser Scanner (TLS) Image. Shape of scarp shows type of slip surface, translation or rotational. By calculation, kinematic and dynamic mathematic model will give vector, velocity and acceleration of material movement. In this calculation need velocity trend line at GPS point from five GPS data campaign. From intersection of trend lines it will create curves or lines of slip surface location. The number of slip surface can be known from material movement direction in landslide zone.Ciloto landslide zone have complicated phenomenon because that zone have influence from many direction of ground water level pressure. The pressure is causes generating several slip surface in Ciloto zone. Types of Ciloto slip surface have mix between translational and rotational type.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
NASA NTRS1999

Head and Trunk Movement Control During Locomotion After Long-Duration Spaceflight

Exposure to the microgravity environment encountered during spaceflight induces adaptive alteration in sensorimotor function that leads to postflight disturbances in locomotor control. Head and trunk movement control plays a central role in maintaining gaze stability and in providing a stable reference system to permit spatial navigation in a complex and constantly varying environment. The goal of the present study was to investigate the effects of long-duration spaceflight (3-6 months) on head and trunk movement control during postflight terrestrial locomotion. Before and after spaceflight, subjects walked on a motorized treadmill while performing a challenging gaze stabilization task requiring number recognition. Head and trunk kinematic data were collected with a video-based motion analysis system. Analysis of roll, pitch and yaw head and trunk movements during treadmill walking revealed postflight alterations in head and trunk movement control in all three planes of motion. Subjects also experienced oscillopsia during postflight walking which led to impairment in performance of the number recognition task. These data indicate that exposure to long-duration space flight causes alteration in head and trunk movement control during postflight locomotion. These changes have implications for the control of gaze and maintenance of dynamic stability during walking after long-duration spaceflight.

Bloomberg, Jacob J.↗
NASA NTRS1980

Investigation of tidal displacements of the Earth's surface by laser ranging to GEOS-3

An analysis of laser ranging data from three stations was carried out in an attempt to measure the geometric Earth tide. Two different approaches to the problem were investigated. The dynamic method computes pass to pass apparent movements in stations height relative to short arcs fitted to several passes of data from the same station by the program GEODYNE. The quasi-geometric method reduces the dependence on unmodelled satellite dynamics to a knowledge of only the radial position of the satellite by considering two station simultaneous ranging at the precise time that the satellite passes through the plane defined by two stations and the center of mass of the Earth.

Bower, D. R.↗
DOE OSTI2020

Assessing the exposure of three diving bird species to offshore wind areas on the U.S. Atlantic Outer Continental Shelf using satellite telemetry

Abstract Aim The United States Atlantic Outer Continental Shelf (OCS) has considerable offshore wind energy potential. Capturing that resource is part of a broader effort to reduce CO 2 emissions. While few turbines have been constructed in U.S. waters, over a dozen currently planned offshore wind projects have the potential to displace marine birds, potentially leading to effective habitat loss. We focused on three diving birds identified in Europe to be vulnerable to displacement. Our research aimed to determine their potential exposure to areas designated or proposed for offshore wind development along the Atlantic OCS. Methods Satellite tracking technology was used to determine the spatial and temporal use and movement patterns of Surf Scoters ( Melanitta perspicillata ), Red‐throated Loons ( Gavia stellata ) and Northern Gannets ( Morus bassanus ), and calculate their exposure to each offshore wind area. We tagged 236 adults in 2012–2015 on the Atlantic OCS from New Jersey to North Carolina; an additional 147 birds tagged in previous tracking studies were integrated into our analyses. Tracking data were analysed in two‐week intervals using dynamic Brownian bridge movement models to develop composite spatial utilization distributions. For each species, these distributions were then used to calculate the spatio‐temporal exposure to each offshore wind area. Results Surf Scoters and Red‐throated Loons were exposed to offshore wind areas almost exclusively during migration because these species were distributed among coastal and inshore waters during winter months. In contrast, Northern Gannets ranged over a much larger area, reaching farther offshore and south in winter, thus exhibited the greatest exposure to extant offshore wind areas. Conclusions Results of this study provide better understanding of how diving birds use current and potential future offshore wind areas on the Atlantic OCS, and can inform permitting, risk assessment and pre‐ and post‐construction impact assessments of offshore energy infrastructure.

Stenhouse, Iain J.↗
DOE OSTI2018

A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases

In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

McClay, Wilbert↗
DOE OSTI2018

Unsupervised Parsing of Gaze Data with a Beta-process Vector Auto-regressive Hidden Markov Model

The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as “if the dispersion of gaze samples is lower than the threshold, then code as a fixation; otherwise code as a saccade.” More recent approaches incorporate additional eye-movement categories in automated parsing algorithms, particularly glissades, by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye-movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson, Larsson, Holmqvist, Stridh, and Nyström (2016) and to low-sampling rate data from the DIEM project (Mital, Smith, Hill, & Henderson, 2011). Driven by the data properties, the BP-AR-HMM identified clusters of samples resembling saccades, fixations and smooth pursuit, and a category that approximately corresponds to glissades (as well as blinks, errors, etc.). The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.

eye tracking, fixation, fixation, saccade, smooth ↗
NASA NTRS1978

Beach erosion control study at Pass Christian

The methods of measuring the existence of erosion and the effects of sand stabilization control systems are described. The mechanics of sand movement, the nature of sand erosion, and the use of satellite data to measure these factors and their surrogates are discussed using the locational and control aspects of aeolian and litoral erosion zones along the sand beach of the Mississippi coast. The aeolian erosion is highlighted due to the redeposition of the sand which causes high cleanup costs, property damage, and safety and health hazards. The areas of differential erosion and the patterns of beach sand movement are illustrated and the use of remote sensing methods to identify the areas of erosion are evaluated.

Source record↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Leaf temperature raw data, 2015 - 2017, at Manaus, Brazil

This data package contains raw leaf surface temperature data from the NGEE Tropics K34 tower site on a plateau near Manaus, Brazil, including automated and manually collected time series data on leaf surface temperature (thermocouples, IR radiometer, thermocamera). Leaf surface temperature (LST) was determined throughout diurnal periods using individual type T thermocouples (Omega Engineering) on 8 individual leaves of a single tree. Mature leaves at the same height and general area near the tower were selected. LST measurements were taken every 15 s and recorded on a field portable self-powered data logger (OM-CP-OCTTEMP-A, Omega Engineering). Measurements were stopped in the case of a storm or rain which can remove the attached thermocouple wires from the leaf due to large wind-driven branch movements. Included in the attached zip file are six folders: three with LST data in Excel and CSV file formats from both the K34 and B34 towers, one folder with data logger configuration programs that were used for data collection, a fifth folder with PDF files of manuals for instruments used and a final folder with two Excel metadata files. See dataset references for associated metadata package. This dataset replaces the leaf surface temperature data of two retired packages, http://dx.doi.org/10.15486/ngt/1507764 and http://dx.doi.org/10.15486/ngt/1507767

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1973

ERTS Applications in earthquake research and mineral exploration in California

Examples that ERTS imagery can be effectively utilized to identify, locate, and map faults which show geomorphic evidence of geologically recent breakage are presented. Several important faults not previously known have been identified. By plotting epicenters of historic earthquakes in parts of California, Sonora, Mexico, Arizona, and Nevada, we found that areas known for historic seismicity are often characterized by abundant evidence of recent fault and crustal movements. There are many examples of seismically quiet areas where outstanding evidence of recent fault movements is observed. One application is clear: ERTS-1 imagery could be effectively utilized to delineate areas susceptible to earthquake recurrence which, on the basis of seismic data alone, may be misleadingly considered safe. ERTS data can also be utilized in planning new sites in the geophysical network of fault movement monitoring and strain and tilt measurements.

Abdel-Gawad, M.↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2015

Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to Côte d’Ivoire

The widespread adoption of mobile devices that record the communications, social relations, and movements of billions of individuals in great detail presents unique opportunities for the study of social structures and human dynamics at very large scales. This is particularly the case for developing countries where social and economic data can be hard to obtain and is often too sparse for real-time analytics. In this paper, we leverage mobile call log data from Côte d’Ivoire to analyze the relations between its nation-wide communications network and the socio-economic dynamics of its regional economies. We introduce the CallRank indicator to quantify the relative importance of an area on the basis of call records, and show that a region’s ratio of in- and out-going calls can predict its income level. We detect a communication divide between rich and poor regions of Côte d’Ivoire, which corresponds to existing socio-economic data. Our results demonstrate the potential of mobile communication data to monitor the economic development and social dynamics of low-income developing countries in the absence of extensive econometric and social data. Finally, our work may support efforts to stimulate sustainable economic development and to reduce poverty and inequality.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Flight Demonstration of Integrated Airport Surface Technologies for Increased Capacity and Safety

A flight demonstration was conducted to address airport surface movement area capacity and safety issues by providing pilots with enhanced situational awareness information. The demonstration presented an integration of several technologies to government and industry representatives. These technologies consisted of an electronic moving map display in the cockpit, a Differential Global Positioning system (DGPS) receiver, a high speed very high frequency (VHF) data link, an Airport Surface Detection Equipment (ASDE-3) radar, and the Airport Movement Area Safety System (AMASS). Aircraft identification was presented to an air traffic controller on an AMASS display. The onboard electronic map included the display of taxi routes, hold instructions, and clearances, which were sent to the aircraft via data link by the controller. The map also displayed the positions of other traffic and warning information, which were sent to the aircraft automatically from the ASDE-3/AMASS system. This paper describes the flight demonstration in detail, along with test results.

Jones, Denise R.↗
NASA NTRS1996

Geotail MCA Plasma Wave Investigation Data Analysis

The goals of this program include identifying, studying, and understanding the source, movement, and dissipation of plasma mass, momentum, and energy between the Sun and Earth. The GEOTAIL spacecraft was built by the Japanese Institute of Space and Aeronautical Science and has provided extensive measurements of entry, storage, acceleration, and transport in the geomagnetic tail. Due to the GEOTAIL trajectory, which kept the spacecraft passing into the deep tail, GEOTAIL also made 'magnetopause skimming passes' which allowed measurements in the outer magnetosphere, magnetopause, bow shock, and upstream solar wind regions as well as in the lobe, magnetosheath, boundary layers, and central plasma sheet regions of the tail. In late 1994, after spending nearly 30 months primarily traversing the deep tail region, GEOTAIL began its near Earth phase where apogee was reduced first to about 50 Re and later to 30 Re and perigee was decreased to about 10 Re. The WIND spacecraft was launched on November 1, 1994, and the POLAR spacecraft was launched on February 24, 1996. These successful launches have dramatically increased the opportunities for GEOTAIL and the GGS spacecraft to conduct global research.

Anderson, Roger R.↗
DOE OSTI2016

Smooth Particle Hydrodynamics-based Wind Representation

As a result of the 2011 accident at the Fukushima Dai-ichi NPP and other operational NPP experience, there is an identified need to better characterize and evaluate the potential impacts of externally generated hazards on NPP safety. Due to the ubiquitous occurrence of high winds around the world and the possible extreme magnitude of the hazard that has been observed, the assessment of the impact of the high-winds hazard has been identified as an important activity by both NPP owner-operators and regulatory authorities. However, recent experience obtained from the conduct of high-winds risk assessments indicates that such activities have been both labor-intensive and expensive to perform. Additionally, the existing suite of methods and tools to conduct such assessments (which were developed decades ago) do not make use of modern computational architectures (e.g., parallel processing, object-oriented programming techniques, or simple user interfaces) or methods (e.g., efficient and robust numerical-solution schemes). As a result, the current suite of methods and tools will rapidly become obsolete. Physics-based 3D simulation methods can provide information to assist in the RISMC PRA methodology. This research is intended to determine what benefits SPH methods could bring to high-winds simulations for the purposes of assessing their potential impact on NPP safety. The initial investigation has determined that SPH can simulate key areas of high-wind events with reasonable accuracy, compared to other methods. Some problems, such as simulation voids, need to be addressed, but possible solutions have been identified and will be tested with continued work. This work also demonstrated that SPH simulations can provide a means for simulating debris movement; however, further investigations into the capability to determine the impact of high winds and the impacts of wind-driven debris that lead to SSC failures need to be done. SPH simulations alone would be limited in size and computation time. An advanced method of combing results from grid-based methods with SPH through a data-driven model is proposed. This method could allow for more accurate simulation of particle movement near rigid bodies even with larger SPH particle sizes. If successful, the data-driven model would eliminate the need for a SPH turbulence model and increase the simulation domain size. Continued research beyond the scope of this project will be needed in order to determine the viability of a data-driven model.

17 WIND ENERGY↗