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This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints) differ across instances. Some are deterministic, others are stochastic with possibilities to execute several replications to control stochasticity. Most instances offer variable fidelity surrogates, two are biobjective and one is unconstrained. The solar plant model takes into account various subsystems: a heliostats field, a central cavity receiver (the receiver), a molten salt thermal energy storage, a steam generator and an idealized power block. Several numerical methods are implemented throughout the solar code and most of the executions are time-consuming. Great care was applied to guarantee reproducibility across platforms. The solar tool encompasses most of the characteristics that can be found in industrial and real-life blackbox optimization problems, all in an open-source and stand-alone code.

Among all the properties required for the design of the next generation of PCM (density, heat capacity, thermal expansion, latent energy, volume change upon melting, corrosion rate, etc.) the thermal transport properties are by far the least known, especially for molten salt mixtures and solid solutions. We present in this paper a theoretical framework for accurate predictions of thermal conductivity of multicomponent salt-based PCM, from 273.15 K up to above melting temperature. The solid phase is considered as a microstructure with its proper temperature dependent parameters: phase volume fraction, grain size distribution, porosity, etc. As case studies, five new potential PCMs for CSP applications are considered. Their thermal conductivity is estimated as a function of temperature, from room temperature to 200 K above their melting point. The predictive capability of the proposed framework is discussed based on a comparison with available experimental data. The effect of equilibrium and non-equilibrium microstructural parameters (i.e. phase fraction, phase composition, average grain size, inter-grain, and intra-grain porosity) on the effective thermal conductivity of the solid states of the promising chloride PCMs is discussed. Lastly, recommendations for the design of next generations of PCM materials are suggested in order to improve their thermal transport properties.

In aluminium electrolysis cells, a ledge of frozen electrolyte is formed, attached to the sides of the cell. The control of the side ledge thickness is essential in ensuring a reasonable lifetime for the cells. Numerical modelling of the side ledge thickness requires an accurate knowledge of the thermal transport properties as a function of temperature. Unfortunately, there is a considerable lack of experimental data for the large majority of the phases constituting the side ledge. The aim of this work is to provide, for each phase possibly present in the side ledge, a formulation of the thermal conductivity as a function of both temperature and size. To achieve this, we consider reliable physical models linking the density of the lattice vibration energy and the phonon mean free path to key parameters: the high temperature limit of the Debye temperature and the Gruneisen constant. These model parameters can be obtained by simultaneous fitting of (i) the heat capacity, (ii) the thermal expansion tensor coefficient and (iii) the adiabatic elastic constants, on relevant physical models. Where data is missing, first principles (ab initio) calculations are used to determine directly the model parameters. For compounds for which data is available, the model's predictions are found to be in very good agreement with the reported experimental data.