Brian D’entremont
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Brian D’entremont has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Christopher Sobecki
Savannah River National Laboratory (SRNL), Aiken, SC (United States)
2 shared publications - Alfred J. Garrett
Savannah River National Lab. (USA)
1 shared publication Christopher Katinas
Sandia National Laboratories/California , P.O. Box 969, Livermore, CA 94551-0969
1 shared publication- Larry Deschaine
Savannah River National Laboratory, Aiken, SC 29808, USA
1 shared publication - Michael J. Willis
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
1 shared publication - Ryan Connal
Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA
1 shared publication
Latest Publications
- Open Access
Multi-Modal Machine Learning to Predict the Energy Discharge Levels from a Multi-Cell Mechanical Draft Cooling Tower
An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about , but individual comparisons resulted in an of . Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to . This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations.Open AccessMulti-Modal Machine Learning to Predict the Energy Discharge Levels from a Multi-Cell Mechanical Draft Cooling Tower
An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about , but individual comparisons resulted in an of . Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to . This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations. - Open Access
- 21 April 2023
- Published by MDPI AG in Atmosphere
- Vol. 14 (4) , 754
Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes
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.
- 21 April 2023
- Published by MDPI AG in Atmosphere
- Vol. 14 (4) , 754
Open AccessAssessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes
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.
- 14 October 2022
- Published by ASME International in Journal of Thermal Science and Engineering Applications
- Vol. 15 (1)
Calculation of Nuclear Reactor Cooling Tower Performance With Limited Data Streams
Monitoring of cooling tower performance in a nuclear reactor facility is necessary to ensure safe operation; however, instrumentation for measuring performance characteristics can be difficult to install and may malfunction or break down over long duration experiments. This paper describes employing a thermodynamic approach to quantify cooling tower performance, the Merkel model, which requires only five parameters, namely, inlet water temperature, outlet water temperature, liquid mass flowrate, gas mass flowrate, and wet bulb temperature. Using this model, a general method to determine cooling tower operation for a nuclear reactor was developed in situations when neither the outlet water temperature nor gas mass flowrate are available, the former being a critical piece of information to bound the Merkel integral. Furthermore, when multiple cooling tower cells are used in parallel (as would be in the case of large-scale cooling operations), only the average outlet temperature of the cooling system is used as feedback for fan speed control, increasing the difficulty of obtaining the outlet water temperature for each cell. To address these shortcomings, this paper describes a method to obtain individual cell outlet water temperatures for mechanical forced-air cooling towers via parametric analysis and optimization. In this method, the outlet water temperature for an individual cooling tower cell is acquired as a function of the liquid-to-gas ratio (L/G). Leveraging the tight tolerance on the average outlet water temperature, an error function is generated to describe the deviation of the parameterized L/G to the highly controlled average outlet temperature. The method was able to determine the gas flowrate at rated conditions to be within 3.9% from that obtained from the manufacturer’s specification, while the average error for the four individual cooling cell outlet water temperatures were 1.6 °C, −0.5 °C, −1.0 °C, and 0.3 °C.
- 14 October 2022
- Published by ASME International in Journal of Thermal Science and Engineering Applications
- Vol. 15 (1)
Calculation of Nuclear Reactor Cooling Tower Performance With Limited Data Streams
Monitoring of cooling tower performance in a nuclear reactor facility is necessary to ensure safe operation; however, instrumentation for measuring performance characteristics can be difficult to install and may malfunction or break down over long duration experiments. This paper describes employing a thermodynamic approach to quantify cooling tower performance, the Merkel model, which requires only five parameters, namely, inlet water temperature, outlet water temperature, liquid mass flowrate, gas mass flowrate, and wet bulb temperature. Using this model, a general method to determine cooling tower operation for a nuclear reactor was developed in situations when neither the outlet water temperature nor gas mass flowrate are available, the former being a critical piece of information to bound the Merkel integral. Furthermore, when multiple cooling tower cells are used in parallel (as would be in the case of large-scale cooling operations), only the average outlet temperature of the cooling system is used as feedback for fan speed control, increasing the difficulty of obtaining the outlet water temperature for each cell. To address these shortcomings, this paper describes a method to obtain individual cell outlet water temperatures for mechanical forced-air cooling towers via parametric analysis and optimization. In this method, the outlet water temperature for an individual cooling tower cell is acquired as a function of the liquid-to-gas ratio (L/G). Leveraging the tight tolerance on the average outlet water temperature, an error function is generated to describe the deviation of the parameterized L/G to the highly controlled average outlet temperature. The method was able to determine the gas flowrate at rated conditions to be within 3.9% from that obtained from the manufacturer’s specification, while the average error for the four individual cooling cell outlet water temperatures were 1.6 °C, −0.5 °C, −1.0 °C, and 0.3 °C.