The thermal energy of the sun can be used for electricity generation. Solar thermal energy can be applied with fossil fuels or independently in order to reduce the cost of generated electricity and CO2 emission. Several cycles are introduced to extract solar thermal energy to be used in power plants. Brayton cycles, Rankine‐Brayton cycles, and supercritical Brayton cycles are among the most conventional ones. Based on the reviewed researches, using solar energy in addition to fossil fuels results in lower carbon dioxide emission and lower levelized cost of the generated electricity. Moreover, thermodynamic and economic analyses of the cycles revealed that heat recovery leads to higher efficiency while increase capital cost. The efficiency of solar‐assisted gas turbines depend on various parameters including pressure ratio, turbine inlet temperature, heat absorber geometry and the performance of the components. The enhancement in the efficiency of the cycles by applying each method depends on the configuration, operating condition. For instance, results have shown that 10% increase in turbine efficiency can led to 6%‐12% improvement in the efficiency of a closed‐Brayton cycle.

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Thermal conductivity of nanofluids depends on several parameters including temperature, concentration, and size of nanoparticles. Most of the proposed models utilized concentration and temperature as influential factors in their modeling. In this study, group method of data handling (GMDH) artificial neural networks is applied in order to model the dependency of thermal conductivity on the mentioned factors. Firstly, temperature and concentration considered as inputs and a model is represented. Afterwards, the size of nanoparticles is added to the input variables and the results are compared. Based on obtained results, GMDH is an appropriate method to predict thermal conductivity of the nanofluids. In addition, it is necessary to consider size of nanoparticles in order to have a more precise model.

In the present study, efforts have been made to theoretically study the diffraction of plane harmonic compressional waves by a spherical nano-inclusion based on the Gurtin-Murdoch surface/interface elasticity theory in which the interface between the nano-inclusion and the matrix is considered as the material surface which has their own mechanical properties. Furthermore, a nano-composite has been considered in order to assess the size effect on the wave propagation characteristics of a plane compressional elastic wave containing the randomly distributed spherical nano-inclusions. Also, the phase velocities and attenuations of P and SV elastic waves along with the related dynamic effective elastic properties have been investigated for a wide variety of frequencies and volume fractions.