Alfred J. Garrett

Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.

The fine details of overmarsh circulation remain largely unexplored and yet they are typically assumed to control many attributes of salt marsh material cycling, transport, and accretion. We characterized the spatial and temporal variability in overmarsh circulation at a 2 km² Georgia, USA, salt marsh using field observations, dye tracer, and numerical simulations. The marsh bathymetry was created with a high‐precision Global Positioning System survey that details the geomorphic structure of intertidal creeks and salt marsh platform features greater than about 1 m in width. We assessed flow path dynamics at four spatial scales ranging from 1 m to 1000 m. Results show the development and decay of simultaneous flow divergence and convergence, concentrated flow and large‐scale rotational flow, and strong differences between flood and ebb pathways. This current complexity is set by submergence and emergence of subtle salt marsh platform geomorphic structure, and it highlights the role of topography in system‐wide flow processes.

Laboratory experiments have demonstrated a correlation between the rate of heat loss q″ from an experimental fluid to the air above and the standard deviation σ of the thermal variability in images of the fluid surface. These experimental results imply that q″ can be derived directly from thermal imagery by computing σ. This paper analyses thermal imagery collected over two power plant cooling lakes to determine if the same relationship exists. Turbulent boundary layer theory predicts a linear relationship between q″ and σ when both forced (wind driven) and free (buoyancy driven) convection are present. Datasets derived from ground- and helicopter-based imagery collections had correlation coefficients between σ and q″ of 0.45 and 0.76, respectively. Values of q″ computed from a function of σ and friction velocity u_* derived from turbulent boundary layer theory had higher correlations with measured values of q″ (0.84 and 0.89). This research may be applicable to the problem of calculating losses of heat from the ocean to the atmosphere during high-latitude cold-air outbreaks because it does not require the information typically needed to compute sensible, evaporative, and thermal radiation energy losses to the atmosphere.

The Digital Imaging and Remote Sensing Laboratory (DIRS) at the Rochester Institute of Technology, along with the Savannah River National Laboratory is investigating passive methods to quantify vehicle loading. The research described in this paper investigates multiple vehicle indicators including brake temperature, tire temperature, engine temperature, acceleration and deceleration rates, engine acoustics, suspension response, tire deformation and vibrational response. Our investigation into these variables includes building and implementing a sensing system for data collection as well as multiple full-scale vehicle tests. The sensing system includes; infrared video cameras, triaxial accelerometers, microphones, video cameras and thermocouples. The full scale testing includes both a medium size dump truck and a tractor-trailer truck on closed courses with loads spanning the full range of the vehicle's capacity. Statistical analysis of the collected data is used to determine the effectiveness of each of the indicators for characterizing the weight of a vehicle. The final sensing system will monitor multiple load indicators and combine the results to achieve a more accurate measurement than any of the indicators could provide alone.

One year’s worth of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, sea level pressure) are validated for land-based stations over the entire planet for forecasts extending from 0 h into the future (an analysis) to 7 days. Approximately 12 000 surface stations worldwide were included in this analysis. Root-mean-square errors (RMSEs) increased as the forecast period increased from 0 to 36 h, but the initial RMSEs were almost as large as the 36-h forecast RMSEs for all variables. Typical RMSEs were 3°C for air temperature, 2–3 mb for sea level pressure, 3.5°C for dewpoint temperature, and 2.5 m s⁻¹ for wind speed. An analysis of the biases at each station shows that the biggest errors are associated with mountain ranges and other areas of steep topography, with land–sea contrasts also playing a role. When the error is decomposed into the bias, variance, and correlation terms, the large initial RMSEs for the 0-h forecasts are seen to be due to a large forecast bias (which persisted into the longer forecasts) with errors in forecast correlation also making a large contribution. A validation of two subdomains showed results similar to the global validation, but the dependence of the biases on the forecast time was clearer. Finally, the RMSE values climb as forecasts go out when validated out to a period of 7 days as the correlation error term grows.

The ALGE code is a hydrodynamic model developed by Savannah River National Laboratory (SRNL) to derive the power output levels of an electric generation facility from observing the associated cooling pond with an aerial imaging platform. Over the past two years work has been completed to extend the capabilities of the model to incorporate snow and ice as possible phenomena in the modeled environment. In order to validate the extension of the model, intensive ground truth data as well as high-resolution aerial infrared imagery were collected during the winters of 2008-2009 and 2009-2010, for a combined eight months of data collection. Due to the harsh and extreme environmental conditions automatic data collection instruments were designed and deployed. Based on experience gained during the first collection season and equipment design failures, overhauls in the design and operation of the automated data collection buoys were performed. In addition, a more thorough and robust twofold calibration technique was implemented within the aerial imaging chain to assess the accuracy of the retrieved surface temperatures. By design, the calibration method employed in this application uses ground collected, geolocated water surface temperatures and in-flight blackbody imagery to produce accurate temperature maps of the pond in interest. A sensitivity analysis was implemented within the data reduction technique to produce accurate sensor reaching temperature values using designed equipment and methods for temperature retrieval at the water's surface.

The Rochester Institute of Technology (RIT) collected visible, SWIR, MWIR and LWIR imagery of the Midland (Michigan) Cogeneration Ventures Plant from aircraft during the winter of 2008 - 2009. RIT also made ground-based measurements of lake water and ice temperatures, ice thickness and atmospheric variables. The Savannah River National Laboratory (SRNL) used the data collected by RIT and a 3-D hydrodynamic code to simulate the Midland cooling lake. The hydrodynamic code was able to reproduce the time distribution of ice coverage on the lake during the entire winter. The simulations and data show that the amount of ice coverage is almost linearly proportional to the rate at which heat is injected into the lake (Q). Very rapid melting of ice occurs when strong winds accelerate the movement of warm water underneath the ice. A snow layer on top of the ice acts as an insulator and decreases the rate of heat loss from the water below the ice to the atmosphere above. The simulated ice cover on the lake was not highly sensitive to the thickness of the snow layer. The simplicity of the relationship between ice cover and Q and the weak responses of ice cover to snow depth over the ice are probably attributable to the negative feedback loop that exists between ice cover and heat loss to the atmosphere.