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Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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November 13–17, 2022
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Nuclear Science and Engineering
Fusion Science and Technology
Next for nuclear: Energy arbitrage
Can nuclear power plants prosper in the grid of 2030 or 2035, when new wind and solar farms will make electricity prices even more volatile? Can plants install energy storage that will help them keep running at full power, 24/7, to ride out times of surplus and sell their energy only when prices are high?
Dan G. Cacuci, Federico Di Rocco
Nuclear Science and Engineering | Volume 185 | Number 3 | March 2017 | Pages 484-548
Technical Paper | dx.doi.org/10.1080/00295639.2017.1279940
Articles are hosted by Taylor and Francis Online.
A cooling tower discharges waste heat produced by an industrial plant to the external environment. The amount of thermal energy discharged into the environment can be determined by measurements of quantities representing the external conditions, such as outlet air temperature, outlet water temperature, and outlet air relative humidity, in conjunction with computational models that simulate numerically the cooling tower’s behavior. Variations in the model’s parameters (e.g., material properties, model correlations, boundary conditions) cause variations in the model’s response. The functional derivatives of the model response with respect to the model parameters (called “sensitivities”) are needed to quantify such response variations changes. In this work, the comprehensive adjoint sensitivity analysis methodology for nonlinear systems is applied to compute the cooling tower’s response sensitivities to all of its model parameters. These sensitivities are used in this work for (1) ranking the model parameters according to the magnitude of their contribution to response uncertainties; (2) propagating the uncertainties in the model’s parameters to quantify the uncertainties in the model’s responses. In an accompanying work, these sensitivities are subsequently used for predictive modeling, combining computational and experimental information, including the respective uncertainties, to obtain optimally predicted best-estimate nominal values for the model’s parameters and responses, with reduced predicted uncertainties.