This paper discusses the structural integrity and creep-fatigue life assessment of a commercial size molten salt solar central receiver. The life evaluation is based on criteria that are a modified version of ASME Code Case N-47. These criteria are deemed conservative enough to provide a reasonable level of safety and reliability, and yet not so conservative as to impose severe economic penalties on the receiver. The justification for these criteria and their application to the receiver are discussed in detail.

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Tube arrays exposed to air, gas or liquid cross-flow can vibrate due to vortex-shedding, turbulence, or fluidelastic instability. The major emphasis of this paper is on the phenomenon of fluidelastic instability (or fluidelastic vibration). A numerical model is applied to the simulation of fluidelastic vibration of representative tubes in a tube bundle, based on S. S. Chen’s unsteady flow theory. The results are validated against published data based on linear cases. The model is then applied to a nonlinear structure of a U-bend tube bundle with clearances at supports, and the computed results compared to those obtained by experimental testing. The numerical studies were performed using the ABAQUS-EPGEN finite element code using a special subroutine incorporating fluidelastic forces. It is shown that the results of both the linear and nonlinear modeling are in good agreement with experimental data.

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In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.