Achieving the goals of the U.S. Department of Energy (DOE) Sunshot initiative requires 1) higher operating temperatures for concentrating solar power (CSP) plants to increase theoretical efficiency, and 2) effective thermal energy storage (TES) strategies to ensure dispatchability. Current inorganic salt-based TES systems in large-scale CSP plants generally employ molten nitrate salts for energy storage, but nitrate salts are limited in application to lower temperatures-generally, below 600°C. These materials are sufficient for parabolic trough power plants, but they are inadequate for use at higher temperatures. At the higher operating temperatures achievable in solar power tower-type CSP plants, chloride salts are promising candidates for application as TES materials, owing to their thermal stability and generally lower cost compared to nitrate salts. In light of this, a recent study was conducted, which included a preliminary survey of chloride salts and binary eutectic systems that show promise as high temperature TES media. This study provided some basic information about the salts, including phase equilibria data and estimates of latent heat of fusion for

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The standard methods of calculating the fluid friction factor, the ColebrookWhite and Haaland equations, require iterative solution of an implicit, transcendental function which entails high computational costs for large-scale piping networks while introducing as much as 15% error. This study applies the group method of data handling to the development of an artificial neural network optimized by multiobjective genetic algorithms to find an explicit polynomial model for friction factor. We developed a relatively simple and explicit model for friction factor that performs well over the entire range of applicability of the Colebrook-White equation: Reynolds number from 4,000 to 10 8 with relative roughness ranging from 5 × 10 −6 to 0.05. For a network with only two hidden layers and a total of five neurons, this model was found to have a mean relative error of only 3.4% in comparison with the Colebrook-White equation; a determination coefficient (R 2 ) over the range of input data was calculated to be 0.9954. The accuracy and simplicity of this model may make it preferable to traditional, transcendental representations of fluid friction factor. Further, this method of model development can be applied to any pertinent data-set-that is to say, the model can be tuned to the physical situation and input data range of interest.

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