•Western Interior Seaway oceanography is reconstructed for the first time using GIS.•Foraminiferal occurrences are tied to water depth, silt and carbonate contents.•The area of north-south water mixing may be larger than previously thought.•Fuzzy sets and DS theory are viable, quantitative methods for paleontological data.•New regions are identified for meaningful data collection to understand the seaway.

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multicriteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site selection in eastern Ohio. One of the main features of this SDSS is to emphasize potential trade-offs between important factors of environmental and economic ramifications from hydraulic fracturing activities using a weighted linear combination (WLC) method. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype also facilitates nonexpert participation capabilities using a mapping module, decision-making tool, group decision module, and social media sharing tools. The logical flow of successively presented forms and standardized criteria maps is used to generate visualization of trade-off scenarios and alternative solutions tailored to individual user's preferences that are graphed for subsequent decision-making.

Particle filters (PFs) have been used for the nonlinear estimation for a number of years. However, they suffer from the impoverishment phenomenon. It is brought by resampling which intends to prevent particle degradation, and therefore becomes the inherent weakness of this technique. To solve the problem of sample impoverishment and to improve the performance of the standard particle filter we propose a modification to this method by adding a sampling mechanism inspired by optimisation techniques, namely, the pattern search, particle swarm optimisation, differential evolution and Nelder-Mead algorithms. In the proposed methods, the true state of the target can be better expressed by the optimised particle set and the number of meaningful particles can be grown significantly. The efficiency of the proposed particle filters is supported by a truck-trailer problem. Simulations show that the hybridised particle filter with Nelder-Mead search is better than other optimisation approaches in terms of particle diversity.

A multisensor data fusion approach via acoustics, infrared camera, and marine radar is proposed and described in the application of avian monitoring. The ultimate scope of avian monitoring is to preserve the population of birds and bats, especially those listed in the endangered list, by observing their activity and behavior over the migration period. With the significant attention toward the construction of off-shore/on-shore wind farms in recent decades, the wind turbines are more threatening the avian life with increasing the risk of birds/bats collision with turbine blades. In order to address this problem, this paper proposes a fuzzy Bayesian-based multisensory data fusion approach to provide the activity information regarding the targets in the application of avian monitoring. The developed technique is used to process the Spring and Fall 2011 migration period.

Chiropean monitoring has become an important public concern given that wind turbines pose the threat of injury or death to bats through direct impact or barotraumas. Such monitoring therefore requires robust methodology to assess the local density, temporal activity, and diversity of bats. This work develops machine learning based monitoring approach' for nocturnal Hight activity of bats. it consists of Ultrasound Acoustic Monitoring System (LAMS) and thermal-Infrared Imaging Monitoring System (HMS), the former for identification of bat echolocation calls and the latter for assessing flight characteristics. Supervised and unsupervised machine learning techniques were used for U AMS and 11MS; respectively. The proposed methodology was tested with real data collected during 2011 spring and fall migration around Lake Erie in Ohio. The research will be helpful for biologists and decision makers to rapidly but effectively asses bat density, activity, and diversity within natural areas or proposed wind development sites.

The Coastal Ohio Wind Project intends to address problems that impede deployment of wind turbines in the coastal and offshore regions of Northern Ohio. The project evaluates different wind turbine designs and the potential impact of offshore turbines on migratory and resident birds by developing multidisciplinary research, which involves wildlife biology, electrical and mechanical engineering, and geospatial science. Firstly, the project conducts cost and performance studies of two- and three-blade wind turbines using a turbine design suited for the Great Lakes. The numerical studies comprised an analysis and evaluation of the annual energy production of two- and three-blade wind turbines to determine the levelized cost of energy. This task also involved wind tunnel studies of model wind turbines to quantify the wake flow field of upwind and downwind wind turbine-tower arrangements. The experimental work included a study of a scaled model of an offshore wind turbine platform in a water tunnel. The levelized cost of energy work consisted of the development and application of a cost model to predict the cost of energy produced by a wind turbine system placed offshore. The analysis found that a floating two-blade wind turbine presents the most cost effective alternative for the Great Lakes. The load effects studies showed that the two-blade wind turbine model experiences less torque under all IEC Standard design load cases considered. Other load effects did not show this trend and depending on the design load cases, the two-bladed wind turbine showed higher or lower load effects. The experimental studies of the wake were conducted using smoke flow visualization and hot wire anemometry. Flow visualization studies showed that in the downwind turbine configuration the wake flow was insensitive to the presence of the blade and was very similar to that of the tower alone. On the other hand, in the upwind turbine configuration, increasing the rotor blade angle of attack reduced the wake size and enhanced the vortices in the flow downstream of the turbine-tower compared with the tower alone case. Mean and rms velocity distributions from hot wire anemometer data confirmed that in a downwind configuration, the wake of the tower dominates the flow, thus the flow fields of a tower alone and tower-turbine combinations are nearly the same. For the upwind configuration, the mean velocity shows a narrowing of the wake compared with the tower alone case. The downwind configuration wake persisted longer than that of an upwind configuration; however, it was not possible to quantify this difference because of the size limitation of the wind tunnel downstream of the test section. The water tunnel studies demonstrated that the scale model studies could be used to adequately produce accurate motions to model the motions of a wind turbine platform subject to large waves. It was found that the important factors that affect the platform is whether the platform is submerged or surface piercing. In the former, the loads on the platform will be relatively reduced whereas in the latter case, the structure pierces the wave free surface and gains stiffness and stability. The other important element that affects the movement of the platform is depth of the sea in which the wind turbine will be installed. Furthermore, the wildlife biology component evaluated migratory patterns by different monitoring systems consisting of marine radar, thermal IR camera and acoustic recorders. The types of radar used in the project are weather surveillance radar and marine radar. The weather surveillance radar (1988 Doppler), also known as Next Generation Radar (NEXRAD), provides a network of weather stations in the US. Data generated from this network were used to understand general migratory patterns, migratory stopover habitats, and other patterns caused by the effects of weather conditions. At a local scale our marine radar was used to complement the datasets from NEXRAD and to collect additional monitoring parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species. Our work focused on the design and development of custom built marine radar that used t-bar and parabolic dish antennas. The marine radar used in the project was Furuno (XANK250) which was coupled with a XIR3000B digitizing card from Russell Technologies for collection of the radar data. The radar data was processed by open source radR processing software using different computational techniques and methods. Additional data from thermal IR imaging cameras were collected to detect heat emitted from objects and provide information on movements of birds and bats, data which we used for different animal flight behavior analysis. Lastly, the data from the acoustic recorders were used to provide the number of bird calls for assessing patterns and peak passage rates during migration. The development of the geospatial database included collection of different data sources that are used to support offshore wind turbine development. Many different data sets were collected and organized using initial version of web-based repository software tools that can accommodate distribution of rectified pertinent data sets such as the lake depth, lake bottom engineering parameters, extent of ice, navigation pathways, wind speed, important bird habitats, fish efforts and other layers that are relevant for supporting robust offshore wind turbine developments. Additional geospatial products developed during the project included few different prototypes for offshore wind farm suitability which can involve different stakeholders and participants for solving complex planning problems and building consensus. Some of the prototypes include spatial decision support system (SDSS) for collaborative decision making, a web-based Participatory Geographic Information System (PGIS) framework for evaluating importance of different decision alternatives using different evaluation criteria, and an Android application for collection of field data using mobile and tablet devices . In summary, the simulations of two- and three-blade wind turbines suggested that two-bladed machines could produce comparable annual energy as the three-blade wind turbines but have a lighter tower top weight, which leads to lower cost of energy. In addition, the two-blade rotor configuration potentially costs 20% less than a three blade configuration that produces the same power at the same site. The cost model analysis predicted a potential cost savings of approximately 15% for offshore two-blade wind turbines. The foundation design for a wind turbine in Lake Erie is likely to be driven by ice loads based on the currently available ice data and ice mechanics models. Hence, for Lake Eire, the cost savings will be somewhat smaller than the other lakes in the Great Lakes. Considering the size of cranes and vessels currently available in the Great Lakes, the cost optimal wind turbine size should be 3 MW, not larger. The surveillance data from different monitoring systems suggested that bird and bat passage rates per hour were comparable during heavy migrations in both spring and fall seasons while passage rates were significantly correlated to wind directions and wind speeds. The altitude of migration was higher during heavy migrations and higher over water relative to over land. Notable portions of migration on some spring nights occurred parallel the shoreline, often moving perpendicular to southern winds. The birds approaching the Western basin have a higher propensity to cross than birds approaching the Central basin of Lake Erie and as such offshore turbine development might be a better option further east towards Cleveland than in the Western basin. The high stopover density was more strongly associated with migration volume the following night rather than the preceding night. The processed mean scalar wind speeds with temporal resolutions as fine as 10-minute intervals near turbine height showed that August is the month with the weakest winds while December is the month, which typically has the strongest winds. The ice data suggests that shallow western basin of Lake Erie has higher ice cover duration many times exceeding 90 days during some winters.

Nocturnally migratory birds and bats are at higher risk of colliding with wind turbines. It is important to gather scientific data in an area which have potential of wind farm development. An IR camera recording and its analysis can provide necessary information to wildlife biologists involve with interaction of birds/bats with wind turbines. An efficient IR video processing algorithm has been developed. The proposed algorithm consists of background and consecutive frame subtraction, frame selection, 3-D region labeling and breakpoint recovery. It is then used to process spring 2011 bird migration data that has been collected in Ottawa National Wildlife Refuge in Ohio. Results from this study will be useful for wildlife biologists to make intelligent decision for siting of wind turbines. It will also help policy makers to develop an appropriate public policy for wind farm development in an area with extensive avian activity.

The Coastal Ohio Wind Project was created to establish the viability of wind turbines on the coastal and offshore regions of Northern Ohio. The project’s main goal was to improve operational unit strategies used for environmental impact assessment of offshore turbines on lake wildlife by optimizing and fusing data from the multi-instrument surveillance system and providing an engineering analysis of potential design/operational alternatives for offshore wind turbines. The project also developed a general economic model for offshore WTG deployment to quantify potential revenue losses due to wind turbine shutdown related to ice and avian issues. In a previous phase of this project (Award Number: DE-FG36-06GO86096), we developed a surveillance system that was used to collect different parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species, movements of birds and bats, and bird calls for assessing patterns and peak passage rates during migration. To derive such parameters we used thermal IR imaging cameras, acoustic recorders, and marine radar Furuno (XANK250), which was coupled with a XIR3000B digitizing card from Russell Technologies and open source radR processing software. The integration yielded a development of different computational techniques and methods, which we further developed and optimized as a combined surveillance system. To accomplish this task we implemented marine radar calibration, optimization of processing parameters, and fusion of the multi-sensor data in order to make inferences about the potential avian targets. The main goal of the data fusion from the multi-sensor environment was aimed at reduction of uncertainties while providing acceptable confidence levels with detailed information about the migration patterns. Another component comprised of an assessment of wind resources in a near lake environment and an investigation of the effectiveness of ice coating materials to mitigate adverse effects of ice formation on wind turbine structures. Firstly, a Zephir LiDAR system was acquired and installed at Woodlands School in Huron, Ohio, which is located near Lake Erie. Wind resource data were obtained at ten measurement heights, 200m, 150m, 100m, 80m, 60m, 40m, 38m, 30m, 20m, and 10m. The Woodlands School’s wind turbine anemometer also measured the wind speed at the hub height. These data were collected for approximately one year. The hub anemometer data correlated well with the LiDAR wind speed measurements at the same height. The data also showed that on several days different power levels were recorded by the turbine at the same wind speed as indicated by the hub anemometer. The corresponding LiDAR data showed that this difference can be attributed to variability in the wind over the turbine rotor swept area, which the hub anemometer could not detect. The observation suggests that single point hub wind velocity measurements are inadequate to accurately estimate the power generated by a turbine at all times since the hub wind speed is not a good indicator of the wind speed over the turbine rotor swept area when winds are changing rapidly. To assess the effectiveness of ice coatings to mitigate the impact of ice on turbine structures, a closed-loop icing research tunnel (IRT) was designed and constructed. By controlling the temperature, air speed, water content and liquid droplet size, the tunnel enabled consistent and repeatable ice accretion under a variety of conditions with temperatures between approximately 0°C and -20°C and wind speeds up to 40 miles per hour in the tunnel’s test section. The tunnel’s cooling unit maintained the tunnel temperature within ±0.2°C. The coatings evaluated in the study were Boyd Coatings Research Company’s CRC6040R3, MicroPhase Coatings Inc.’s PhaseBreak TP, ESL and Flex coatings. Similar overall performance was observed in all coatings tested in that water droplets form on the test articles beginning at the stagnation region and spreading in the downstream direction in time. When comparing ice accumulation characteristics for the four coatings tested, for ice thickness during accumulation the CRC6040R3 had the least, followed by the ESL, Flex, and TP. However, when comparing the coatings’ ability to reduce ice adhesion, the Flex showed the highest adhesion reduction, followed by the ESL, TP and CRC 6040R3 coatings. The ice accumulated on the Flex coated surface shed under gravity when rotated 90 degrees following the tests while the other coatings required application of varying degrees of force to remove the ice. In conclusion, the ice coatings tested were not sufficient in preventing ice accumulation on all surfaces. However, Flex coating shows promise in mitigating ice on the rotor blades under the gravitational and centrifugal forces. Only the effect of gravity in shedding the ice was considered in this study. Further research will be needed to evaluate this coating on rotating blades in the icing tunnel to characterize its effectiveness. Lastly, the development of economic feasibility models used existing approaches adapted for offshore deployment in marine settings to one more suitable for Lake Erie deployment. Two different wind turbine models were tested and dynamic return on investment (ROI) model scenarios were generated. For the purpose of estimating power generation three bladed wind turbines of 3 MW capacity were selected including Model1- Leitwind LTW101-3.000-kW and Model2-Vostro V90-3.0 MW. The analysis were based on the revenue aspect of decision making of deploying wind turbines in the Ohio coastal region. The installation cost, maintenance and operational aspects were disregarded due to unavailability of data. The adjusted varying price (residential and industrial sector) and projected future price of electricity in different years suggested that the Leitwind model could generate $32.4 million of revenue in 25 years if the supply electricity is in the residential sector, while it would be $14.7million if the supply is in the industrial sector. For the Vostro model these figures are $28.6 million for residential sector and $12.9 million for industrial sector for 25 years.

A radar monitoring system is implemented to evaluate the birds activity and their quantification in the western basin of lake Erie in Ohio. This location is a habitat stopover for migratory birds and also it is a putative area for constructing wind farm. The radar monitoring is performed in three steps consist of data acquisition, blip detections, and target tracking. Blip detection is performed in radR platform and tracking is performed using developed particle filter in MATLAB. The radar monitoring system is used to process data collected in migration period of fall and spring 2011.

There are large number of reports regarding bird and bat mortality due to strikes with turbine blades in wind farm applications. This issue is threatening the avian life especially migratory birds and bats. Avian monitoring techniques can be used to detect bird and bats, assess their activity, and make intelligent decision for construction of wind farms. In this paper, an IR monitoring approach is used for monitoring birds and bats in an area potential for future construction of wind turbines. As there is no a priori database of images for local birds/bats, clustering is used as an effective technique to group different avian categories. Fuzzy C Means is developed in this application to cluster the targets into birds, bats, and insects groups. The results are then compared with a hard Ant Clustering Algorithm. The number of created clusters and number of individuals in each cluster are then compared.

There is an increase in the bird and bat mortality near wind farms. It is desirable to document and quantify behavior and activity of birds/bats near wind farms. Infrared Imaging is a useful monitoring method for this purpose. However, IR images do not provide information as whether the target is a bird, bat or insect. A logistic regression classifier is used to provide category information of the targets. There are no priori known database of images available for birds, and bats. Features of targets are extracted and used for classification purpose. A database of labeled category based on the features has been created.

A bird and bat monitoring system has been developed that uses marine radar, IR camera and acoustic recorders for wind farm applications. IR video recording is used to monitor birds and bats activity which will be useful for wildlife biologists in developing mitigation techniques to minimize impact of wind turbines on birds and bats. In order to process nocturnal migration data that is recorded from one hour after sun set to one hour before the sun rise requires high speed computations. IR video processing is computationally intensive. A parallel processing approach and use of GPU is proposed to process IR video data that will meet real time requirements. This paper examines the parallel implementation of the IR video processing on GPU.We achieved the real time requirements and the necessary performance for analyzing IR with size 704×480.

Wind Energy is considered as the green and free energy. It can be harvested any time wherever there are high winds. One of the concerns with turbine is with birds and bats. There are reports of birds and bats fatalities around wind turbine. One way to investigate this issue is to monitor wind turbine areas. Researchers have used marine radars for this purpose. Weather radars commonly known as NEXRAD have also been used for monitoring purposes. The advantage of NEXRAD is that it can provide bird migration movements over a broad geographical scale and can be accessed free of charge. This work proposes image processing based algorithm for retrieval of biological information from NEXRAD data. The method is limited to the radar range up to 80 km. It will be useful to wildlife biologists, wind farm developers and policy makers.

Avian monitoring system is developed based on data fusion of thermal/Infrared camera (IR) and marine radar. First data were processed separately using video/image processing and radar signal processing techniques and features of the targets are obtained by each sensor. Data fusion of radar and IR then is implemented to achieve feature vectors of the target. IR camera provides the coordinate information of the targets as well as some features such as flight's straightness index, direction, and target's heat, while the radar provides altitude information (z-coordinates) which is not provided by IR. So the data fusion of the IR and radar provide more detail and reliable information possible of the avian targets and their activity. Data was collected near Lake Erie in Ohio during 2011 spring and fall migration periods. Data analysis was performed in accordance to needs of wildlife biologists.

There are many reports of bird and bat mortality in vicinity of wind turbines [1]. It is important to quantify numbers and species of birds and bats in a given area which is targeted for wind farm development. It is also necessary to assess the behavior of birds and bats in wind farm areas. Acoustic monitoring techniques have been developed in this work for monitoring of birds and bats. Spectrogram-based Image Frequency Statistics (SIFS) is used for feature extraction and Evolutionary Neural Network (ENN) is used for classification purposes. Data was collected near Lake Erie in Ohio during 2011 spring and fall migration periods. Data analysis was performed in accordance to needs of wildlife biologists.

Acoustic monitoring of birds in the vicinity of wind turbines is becoming an important public policy issue. Acoustic monitoring involves preprocessing, feature extraction and classification. A novel Spectrogram-based Image Frequency Statistics (SIFS) feature extraction algorithm has been developed. Features extracted from proposed algorithms were then combined with various classification algorithms such as k-NN, Multilayer Perceptron (MLP) and Hidden Markov Models (HMM) and Evolutionary Neural Network (ENN). SIFS and MMS algorithms, combined with ENN, provided the most accurate results. Proposed algorithms were tested with real data collected during spring migration around Lake Erie in Ohio.

There are reports that large number of bat fatalities occur near wind turbines. Acoustic characteristics can be employed for bat call recognition to better understand the effects of turbines on different bat species. Acoustic features of bat echolocation calls are extracted based on three different techniques: Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC) and Discrete Wavelet Transform (DWT). These features are fed into an Evolutionary Neural Network (ENN) for their classification at the species level using acoustic features. Results from different feature extraction techniques are compared based on classification accuracy. The technique can identify bats and will contribute towards developing mitigation procedures for reducing bat fatalities.

A computerized morphometric classification technique based on latent factors reveals major protrusion classes: factors 4, 5, and 7. Previous work showed that factor 4 represented filopodia, 5 the distribution of lamellar cytoplasm, and 7 a blunt protrusion. We explore the relationship of focal contact (FC) characteristics and their integrated actin cables to factors values. The results show that FC maturation/cytoskeletal integration affects factor 5, because FC elongation/integration was correlated with its values. On the contrary, 7 values decreased with maturation, so cable or FC size or their integration must be restricted to form these protrusions. Where integration did occur, the cables showed distinctive size and orientation, as indicated by correlation of 7 values with FC shape. Results obtained with myosin inhibitors support the interpretation that a central, isometric, contractile network puts constraints on both factor 5 and 7 protrusions. We conclude that cells establish functional domains by rearranging the cytoskeleton.