Single sensor boost converter-based maximum power point tracking algorithms

Two new maximum power point tracking algorithms are presented: the input voltage sensor, and duty ratio maximum power point tracking algorithm (ViSD algorithm); and the output voltage sensor, and duty ratio maximum power point tracking algorithm (VoSD algorithm). The ViSD and VoSD algorithms have the features, characteristics and advantages of the incremental conductance algorithm (INC); but, unlike the incremental conductance algorithm which requires two sensors (the voltage sensor and current sensor), the two algorithms are more desirable because they require only one sensor: the voltage sensor. Moreover, the VoSD technique is less complex; hence, it requires less computational processing. Both the ViSD and the VoSD techniques operate by maximising power at the converter output, instead of the input. The ViSD algorithm uses a voltage sensor placed at the input of a boost converter, while the VoSD algorithm uses a voltage sensor placed at the output of a boost converter. © 2011 IEEE.

Poroviscoelastic characterization of particle-reinforced gelatin gels using indentation and homogenization.

Hydrogels are promising materials for bioengineering applications, and are good model materials for the study of hydrated biological tissues. As these materials often have a structural function, the measurement of their mechanical properties is of fundamental importance. In the present study gelatin gels reinforced with ceramic microspheres are produced and their poroviscoelastic response in spherical indentation is studied. The constitutive responses of unreinforced gels are determined using inverse finite element modeling in combination with analytical estimates of material parameters. The behavior of composite gels is assessed by both analytical and numerical homogenization. The results of the identification of the constitutive parameters of unreinforced gels show that it is possible to obtain representative poroviscoelastic parameters by spherical indentation without the need for additional mechanical tests. The agreement between experimental results on composite gelatin and the predictions from homogenization modeling show that the adopted modeling tools are capable of providing estimates of the poroviscoelastic response of particle-reinforced hydrogels.

Comparing autonomous underwater vehicle (AUV) and vessel-based tracking performance for locating acoustically tagged fish

Autonomous underwater vehicles (AUV’s) are increasingly used to collect physical, chemical, and biological information in the marine environment. Recent efforts include merging AUV technology with acoustic telemetry to provide information on the distribution and movements of marine fish. We compared surface vessel and AUV tracking capabilities under rigorous conditions in coastal waters near Juneau, Alaska. Tracking surveys were conducted with a REMUS 100 AUV equipped with an integrated acoustic receiver and hydrophone. The AUV was programmed to navigate along predetermined routes to detect both reference transmitters at 20–500 m depths and tagged fish and crabs in situ. Comparable boat surveys were also conducted. Transmitter depth had a major impact on tracking performance. The AUV was equally effective or better than the boat at detecting reference transmitters in shallow water, and significantly better for transmitters at deeper depths. Similar results were observed for tagged animals. Red king crab, Paralithodes camtschaticus, at moderate depths were recorded by both tracking methods, while only the AUV detected Sablefish, Anoplopoma fimbria, at depths exceeding 500 m. Strong currents and deep depths caused problems with AUV navigation, position estimation, and operational performance, but reflect problems encountered by other AUV applications that will likely diminish with future advances, enhanced methods, and increased use.

Acoustic tracking of reef fishes to elucidate habitat utilization patterns and residence times inside and outside marine protected areas around the island of St. John, USVI

This technical memorandum describes a developing project under the direction of NOAA’s Biogeography Branch in consultation with the National Park Service and US Geological Survey to understand and quantify spatial patterns and habitat affinities of reef fishes in the US Virgin Islands. The purpose of this report is to describe and disseminate the initial results from the project and to share information on the location of acoustic receivers and species electronic tag ID codes. The Virgin Islands Coral Reef National Monument (VICRNM), adjacent to Virgin Islands National Park (VIIS), was established by Executive Order in 2000, but resources within the monument are poorly documented and the degree of connectivity to VIIS is unknown. Whereas, VICRNM was established with full protection from resource exploitation, VIIS has incurred resource harvest by fishers since 1956 as allowed in its enabling legislation. Large changes in local reef communities have occurred over the past several decades, in part due to overexploitation. In order to better understand the habitat utilization patterns and movement of fishes among management regimes and areas open to fishing around St, John, an array of hydroacoustic receivers was deployed while a variety of reef fish species were acoustically tagged. In July 2006, nine receivers with a detection range of ca. 350 m were deployed in Lameshur Bay on the south shore of St. John, within VIIS. Receivers were located adjacent to reefs and in seagrass beds, inshore and offshore of these reefs. It was found that lane snappers and bluestriped grunts showed diel movement from reef habitats during daytime hours to offshore seagrass bed at night. Timing of migrations was highly predictable and coincided with changes in sunrise and sunset over the course of the year. Fish associated with reefs that did not have adjacent seagrass beds made more extensive movements than those fishes associated with reefs that had adjacent seagrass habitats. In April 2007, 21 additional receivers were deployed along much of the south shore of St. John (ca. 20 km of shoreline). This current array will address broader-scale movement among management units and examine the potential benefits of the VICRNM to provide adult “spillover” into VIIS and adjacent harvested areas. The results from this work will aid in defining fine to moderate spatial scales of reef fish habitat affinities and in designing and evaluating marine protected areas.

Preliminary results from the Caribbean Acoustic Tracking Network (CATn): a data sharing partnership for acoustic tracking and movement ecology of marine animals in the Caribbean Sea

We have recently exchanged and integrated into a single database tag detections for conch, teleost and elasmobranch fish from four separately maintained arrays in the U.S. Virgin Islands including the NMFS queen conch array (St. John nearshore), NOAA’s Biogeography Branch array (St. John nearshore & midshelf reef); UVI shelf edge arrays (Marine Conservation District, Grammanik & other shelf edge); NOAA NMFS Apex Predator array COASTSPAN (St. John nearshore). The integrated database has over 7.5 million hits. Data is shared only with consent of partners and full acknowledgements. Thus, the summary of integrated data here uses data from NOAA and UVI arrays under a cooperative agreement. The benefits of combining and sharing data have included increasing the total area of detection resulting in an understanding of broader scale connectivity than would have been possible with a single array. Partnering has also been cost-effectiveness through sharing of field work, staff time and equipment and exchanges of knowledge and experience across the network. Use of multiple arrays has also helped in optimizing the design of arrays when additional receivers are deployed. The combined arrays have made the USVI network one of the most extensive acoustic arrays in the world with a total of 150+ receivers available, although not necessarily all deployed at all times. Currently, two UVI graduate student projects are using acoustic array data.