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Climate change needs an Operation Warp Speed
The government of the United States should throw its muscle behind ramping up a mammoth, rapid rollout of all forms of renewable energy through Operation Warp Speed, similar to what is being done with COVID-19, Clive Thompson writes in an Ideas column for Wired.
The rollout should include energy sources that we already know how to build—like solar and wind — but also experimental emerging sources such as geothermal and small nuclear, and cutting-edge forms of energy storage or transmission.
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.