The effectiveness of a thermal energy storage (TES) system is typically characterized with the help of thermal stratification or temperature gradients along the direction of heat injection, which is typically the flow direction of heat transfer fluid. The steepness of temperature gradients are a direct indicator of the effectiveness or efficiency of the heat storage or dispatch process. The temperature gradient evolution along the packed bed of ceramic particles upon saturated steam injection is presented in this work. Distributed temperature sensing based on optical frequency domain reflectometry was deployed in a packed bed of ceramic particles to capture the thermal front evolution in the axial direction. The physical processes accompanying steam injection in packed beds are complex due to phase change, transitioning two-phase flow, and changes in condensate accumulation. Therefore, the variation of thermal response of the TES system for various steam injection flow rates was experimentally studied using a high-resolution distributed temperature sensing system in a chemically inert alumina particle-packed bed. Distinct zones of different heat transfer modes were observed during the steam injection experiments. A distinct conduction zone, evident from diffuse thermal fronts, was observed at low flow rates, and these thermal gradients became sharper as the flow rate increased. The diffuse thermal fronts in the heat storage media suggest a low exergy efficiency of the TES system, as energy losses started initiating before a significant fraction of the bed was saturated with steam.

Turbulent flows can be simulated using direct numerical simulations (DNS), but DNS is computationally expensive. Reduced-order models implemented into Reynolds-averaged Navier–Stokes and large eddy simulations (LES) can reduce the computational cost, but need to account for subgrid-scale (SGS) turbulence through closure relations. Turbulence modeling has presented a significant challenge due to the non-linearities in the flow and multi-scale behavior. Well-established features of the turbulent energy cascade can be leveraged through statistical mechanics to provide a characterization of turbulence. This paper presents a physics-based data-driven SGS model for LES using the concepts of statistical mechanics. The SGS model is implemented and tested using the stochastic Burgers equation. DNS data are used to calculate Kramers–Moyal (KM) coefficients, which are then implemented as an SGS closure model. The presented data-driven KM method outperforms traditional methods in capturing the multi-scale behavior of Burgers turbulence.