Government review of the Mod-2 wind turbine (as-built)

The findings and recommendations of the Government committee formed to conduct an as-built review of the three Mod-2 wind turbine units at Goldendale, Washington are given. The purpose of the review was to identify any critical deficiencies in machine components that could result in failure, and to recommend any necessary corrective action before resuming safe machine operation. The review concluded that one of the deficiencies identified would preclude planned attended or unattended operation, provided that certain corrective actions were implemented

MOD-0A 200 kW wind turbine generator design and analysis report

The design, analysis, and initial performance of the MOD-OA 200 kW wind turbine generator at Clayton, NM is documented. The MOD-OA was designed and built to obtain operation and performance data and experience in utility environments. The project requirements, approach, system description, design requirements, design, analysis, system tests, installation, safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the wind turbine are discussed. The design and analysis of the rotor, drive train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electricl system, and control systems are presented. The rotor includes the blades, hub, and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control are discussed. Systems analyses on dynamic loads and fatigue are presented

Frequency domain reflectometry for irrigation scheduling of cover crops.

Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.A well-managed irrigation scheduling system needs a rapid, preCIse, simple, costeffective and non-destructive soil water content sensor. The PRl profile probe and Diviner 2000 were used to determine the timing and amount of irrigation of three cover crops (Avena sativa L., Secale cereale L. and Lolium multiflonlm Lam.), which were planted at Cedara, KwaZulu-Natal. The PRl profile probe was first calibrated in the field and also compared with the Diviner 2000. For the calibration of the PRl profile probe the factory-supplied parameters (aJ = 8.4 and ao = 1.6) showed good correlation· compared to the soil-estimated parameters (aJ = 11.04 and ao = 1.02). The factorysupplied parameters gave a linear regression coefficient (r2 ) of 0.822 and root mean square error (RMSE) of 0.062. The soil-estimated parameter showed a linear regression coefficient of 0.820 with RMSE of 0.085. The comparison between the soil water content measured using the PR1 profile probe and Diviner 2000 showed a linear regression coefficient of 0.947 to 0.964 with a range of RMSE of 0.070 to 0.109 respectively for the first 100 to 300 mm soil depths. The deeper depths (400, 600 and 1000 mm) showed a linear regression coefficient ofO.716to 0.810 with a range of 0.058 to 0.150 RMSE. These differences between the shallow and deeper depths could be due to soil variability or lack of good contact between the access tube and the surrounding soil. To undertake irrigation scheduling using the PRl profile probe and Diviner 2000, the soil water content limits were determined using field, laboratory and regression equations. The field method was done by measuring simultaneously the soil water content using the PR1 profile probe and soil water potential using a Watermark sensor and tensiometers at three depths (100, 300 and 600 mm) from a 1 m2 bare plot, while the soil dries after being completely saturated. The retentivity function was developed from these measurements and the drained upper limit was estimated to be 0.355 m3 m-3 when the drainage from the pre-wetted surface was negligible. The lower limit was calculated at -1500 kPa and it was estimated to be 0.316 m3m,3. The available soil water content, which is the difference between the upper and lower limit, was equal to 0.039 m3 m,3. In the laboratory the soil water content and matric potential were measured from the undisturbed soil samples taken from the edge of the 1 m2 bare plot before the sensors were installed. Undisturbed soil samples were taken using a core sampler from 100 to 1000 mm soil depth in three replications in 100 mm increments. These undisturbed soil samples were saturated and subjected to different matric potentials between -1 to -1500 kPa. In the laboratory, the pressure was increased after the cores attained equilibrium and weighed before being subjecting to the next matric potential. The retentivity function was then developed from these measurements. The laboratory method moved the drained upper limit to be 0.390 m3 m,3 at -33 kPa and the lower limit be 0.312 m3m-3 at -1500 kPa. The regression equation, which uses the bulk density, clay and silt percentage to calculate the soil water content at a given soil water potential, estimated the drained upper limit to be 0.295 m3m-3at -33 kPa and the lower limit 0.210 m3 m,3 at -1500 kPa. Comparison was made between the three methods using the soil water content measured at the same soil water potential. The fieldmeasured soil water content was not statistically the same with the laboratory and estimated soil water content. This was shown from the paired-t test, where the probability level (P) for the laboratory and estimated methods were 0.011 and 0.0005 respectively at 95 % level of significance. However, it showed a linear regression coefficient of 0.975 with RMSE of 0.064 when the field method was compared with the laboratory method. The field method showed a linear regression coefficient of 0.995 with RMSE of 0.035 when compared with the estimated method. The timing and amount of irrigation was determined using the PR1 profile probe and Diviner 2000. The laboratory measured retentivity function was used to define the fill (0.39 m3 m-3 ) and high refill point (0.34 m3 m-3 ). The soil water content was measured using both sensors two to three times per week starting from May 29 (149 day of year, 2002) 50 days after planting until September 20 (263 day of year) 11 days before harvesting. There were five irrigations and twenty rainfall events. The next date of irrigation was predicted graphically using, the PRl profile probe measurements, to be on 3 September (246 day of year) after the last rainfall event on 29 August (241 day of year) with 8 mm. When the Diviner 2000 was used, it predicted two days after the PRl profile probe predicted date. This difference appeared since the Diviner 2000-measured soil water content at the rooting depth was slightly higher than the PRl profile probe measurements. The amount of irrigation was estimated using two comparable methods (graphic and mathematical method). The amount of irrigation that should have been applied on 20, September (263 day of year) to bring the soil water content to field capacity was estimated to be 4.5 hand 23 mm graphically and 5.23 hand 20 mm mathematically. The difference between these two methods was caused due to the error encountered while plotting the correct line to represent the average variation in soil water content and cumulative irrigation as a function of time. More research is needed to find the cause for the very low soil water content measurements of the PRI profile probe at some depths. The research should be focused on the factors, which could affect the measurement of the PRl profile probe and Diviner 2000 like salinity, temperature, bulk density and electrical conductivity. Further research is also needed to extend the non-linear relationship between the electrical resistance of the sensor and soil water potential up to -200 kPa. This non-linear equation of the Watermark is only applicable within the range of soil water potential between -10 and -100 kPa

Modelling, Monitoring and Validation of Plant Phenology Products

Phänologie, die Lehre der periodisch wiederkehrenden Entwicklungserscheinungen in der Natur, hat sich in den letzten Jahrzehnten zu einem wichtigen Teilgebiet der Klimaforschung entwickelt. Einer der Haupteffekte der globalen Erwärmung ist die Veränderung der Wachstumsmuster und Fortpflanzungsgewohnheiten von Pflanzen, und somit veränderte Phänologie. Um die Auswirkungen der Klimaveränderung auf wildwachsende sowie Kulturpflanzen vorherzusagen, werden phänologische Modelle angewendet, verbessert und validiert. Dabei ist Wissen über den aktuellen Stand der Vegetation notwendig, welches aus Beobachtungen und fernerkundliche Messungen gewonnen wird. Die hier präsentierte Arbeit befasst sich mit dem Verständnis der Zusammenhänge zwischen fernerkundlichen Messungen und phänologischen Stadien und somit den Herausforderungen der modernen phänologischen Forschung: Der Vorhersage der Phänologie durch Modellierungsansätze, der Beobachtung der Phänologie mit optischen boden- und satellitengestützten Sensoren und der Validierung phänologischer Produkte.Phenology, the study of recurring life cycle events of plants and animals has emerged as an important part of climate change research within the last decades. One of the main effects of global warming on vegetation is altered phenology, since plants have to modify their growth patterns and reproduction habits as reaction to changing environmental conditions. Forecasting phenology, thus phenological modelling, is a timely challenge given the necessity to predict the impact of global warming on wild-growing species and agricultural crops. However, assessing the present state of vegetation, thus phenological monitoring, is essential to update and validate model results. An improved comprehension of the relationships between plant phenology and remotely sensed products is crucial to interpret these results. Consequently, the presented thesis deals with the main challenges faced in modern phenology research, covering phenological forecasting with a modelling approach, satellite-based phenology extraction, and near-surface long-term monitoring of phenology

Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks

The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network

Water quality monitor

The preprototype water quality monitor (WQM) subsystem was designed based on a breadboard monitor for pH, specific conductance, and total organic carbon (TOC). The breadboard equipment demonstrated the feasibility of continuous on-line analysis of potable water for a spacecraft. The WQM subsystem incorporated these breadboard features and, in addition, measures ammonia and includes a failure detection system. The sample, reagent, and standard solutions are delivered to the WQM sensing manifold where chemical operations and measurements are performed using flow through sensors for conductance, pH, TOC, and NH3. Fault monitoring flow detection is also accomplished in this manifold assembly. The WQM is designed to operate automatically using a hardwired electronic controller. In addition, automatic shutdown is incorporated which is keyed to four flow sensors strategically located within the fluid system

In situ sensors for measurements in the global trosposphere

Current techniques available for the in situ measurement of ambient trace gas species, particulate composition, and particulate size distribution are reviewed. The operational specifications of the various techniques are described. Most of the techniques described are those that have been used in airborne applications or show promise of being adaptable to airborne applications. Some of the instruments described are specialty items that are not commercially-available. In situ measurement techniques for several meteorological parameters important in the study of the distribution and transport of ambient air pollutants are discussed. Some remote measurement techniques for meteorological parameters are also discussed. State-of-the-art measurement capabilities are compared with a list of capabilities and specifications desired by NASA for ambient measurements in the global troposphere

Cost-effective autonomous sensor for the long-term monitoring of water electrical conductivity of crop fields

Salinity is a key parameter determining water quality. In agriculture, irrigation water with high salinity levels impacts plant growth and yield negatively hence there is a need to monitor it and irrigate with fresh water whenever salinity surpasses the tolerance threshold of a cultivar. Electrical conductivity (EC) of water is usually measured periodically instead of salinity because of its practicality. This work describes a low-cost low-power (and yet inexpensive) autonomous sensor prototype that is able to compute continuously the EC of water from complex electrical impedance measurements based on synchronous sampling, a technique that lowers cost and power consumption. The sensor is easy to assemble and has been verified in the lab for an EC range from 0.35 dS m-1 to 6.18 dS m-1 , showing a maximal deviation of ± 0.03 dS m-1 from the readings of a commercial reference EC meter. The sensor has also been installed in a rice paddy and left unattended for a whole cultivation season (107 days). The maximum deviation observed during this field test is ± 0.14 dS m-1 , which is good enough to detect high salinity levels.Peer ReviewedPostprint (published version