Topology optimization for heat transfer enhancement in Latent Heat Thermal Energy Storage

Abstract

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Maximization of performance in multi-tube latent heat storage – Optimization of fins topology, effect of materials selection and flow arrangements

“…The recent advances of Additive Manufacturing (AM) technologies are giving new momentum to this design methodology which is rapidly expanding in the energy field, for the design of e.g. thermoelectric components [31], Li-Ion batteries [32] and LHTES units [15,23].…”

“…However, the design approaches commonly adopted lack generality as well as design freedom. Recently [15,23], we demonstrated the use of topology optimization as a systematic approach to find configurations of finned LHTES systems capable to deliver optimized time evolution of charging/discharging processes. Although these studies demonstrated high potential for design-driven innovations in the LHTES technology, they considered single-tube configurations, which are popular only for lab-scale or small facilities.…”

“…This paper covers all these literature gaps and deals with the optimal design of shell-and-tubes multi-tube LHTES systems with different flow arrangements, phase change materials (PCMs) and high conductivity fin materials. The previous key design questions are answered with matchless design freedom, drawing uniquely from topology optimization method for TES [15,23]. This approach yields: (i) fit for purpose shell-and-tubes LHTESs that surpass conventional LHTES designs and (ii) useful guidelines for design engineers.…”

Topology optimization of a pseudo 3D thermofluid heat sink model

“…The thermal integration of a thermoelectric cooler in a robotic downhole intervention tool using topology optimization to distribute conducting and insulating material in a 3D domain as well as fabrication and experimental validation of the prototype is presented in [19]. Pizzolato et al [20] apply density-based topology optimization to the design of conducting fins in a phase change material (PCM) storage tank modeling the solidification of the PCM as transient thermal diffusion problem both in 2D and 3D.…”

Improving the performance of finned latent heat thermal storage devices using a Cartesian grid solver and machine-learning optimization techniques

“…Sciacovelli et al [132] optimized tree-shaped fins for a double pipe heat exchanger by using numerical simulations to create a response surface in a 4-dimensional parameter space. Pizzolato et al [133] identified an optimal fin design for an annular heat exchanger with a topological optimization approach. More recently, Alayil et al [134] used an artificial neural network to find an optimal melting solution for a finned rectangular LHTSD with four independent parameters.…”