Particle-to-supercritical carbon dioxide (sCO₂) heat exchanger is a critical component in next-generation concentrating solar power (CSP) plants. The inherently low heat transfer between falling particles and sCO₂ imposes a challenge towards economic justification of levelized cost of electricity produced through solar energy. Introduction of integrated porous media with the walls bounding particle flow has the potential to enhance the overall particle-to-sCO₂ heat exchanger performance. This paper presents an experimental study on heat transfer characterization of additively manufactured lattice frame material based on Octet-shaped unit cell with particles and air as working fluids. The lattice structures were additively manufactured in Stainless Steel (SS) 316L and SS420 (with 40% bronze infiltration) via Binder jetting process, where the lattice porosities were varied between 0.75 and 0.9. The mean particle diameters were varied from 266-966 μm. The effective thermal conductivity and averaged heat transfer coefficient were determined through steady-state experiments. It was found that the presence of lattice enhances the effective thermal conductivity by 2-4 times when compared to packed bed of particles alone. Furthermore, for gravity-assisted particle flow through lattice panel, significantly high convective heat transfer coefficients ranging from 200-400 W/m2K were obtained for the range of particle diameters tested. The superior thermal transport properties of Octet-shape-based lattice frame for particle flow makes it a very promising candidate for particle-to-sCO₂ heat exchanger for CSP application.

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. The presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

Particle-based thermochemical energy storage (TCES) through metal oxide redox cycling is advantageous compared to traditional sensible and latent heat storage (SHS and LHS) due to its higher operating temperature and energy density, and the capability for long-duration storage. However, overall system performance also depends on the efficiency of the particle-to-working fluid heat exchangers (HXs). Moving packed-bed particle-to-supercritical CO₂ (sCO₂) HXs have been extensively studied in SHS systems. Integrating the oxidation reactor (OR) for discharging with a particle-to-sCO₂ HX is a natural choice, for which detailed analysis is needed for OR/HX design and operation. In this work, a 2D continuum heat and mass transfer model coupling transport phenomena and reaction kinetics is developed for a shell-and-plate moving-bed OR/HX. For the baseline design, the model predicted ∼75% particle bed extent of oxidation at the channel exit, yielding a total heat transfer rate of 16.71 kW for 1.0 m² heat transfer area per channel, while the same design with inert particles (SHS only) gives only 4.62 kW. A parametric study was also conducted to evaluate the effects of particle, air, and sCO₂ flow rates, channel height and width, and average particle diameters. It is found that the respective heat transfer rate and sCO₂ outlet temperature can approach ∼25 kW and >1000°C for optimized designs for the OR/HX. The present model will be valuable for further OR/HX design, scale-up, and optimization of operating conditions.

Joule-heated electric glass melters are highly energy-intensive and operate under stringent throughput, quality, and refractory-protection constraints, yet existing optimization approaches either rely on offline high-fidelity CFD—which is too slow for day-to-day operating decisions—or on purely data-driven energy models that do not enforce physics-based glass-quality constraints. This work presents a simulation-informed supervisory optimization framework that closes this gap by integrating (i) a high-fidelity CFD design of experiments on a cold-top electric glass melter, (ii) interpretable linear reduced-order models (ROMs) for furnace temperatures and glass-process indices (residence time, sand dissolution, mixing, and melting indices), and (iii) a convex linear-program supervisory optimizer that minimizes electrical power subject to throughput, glass-quality, and refractory-protection constraints. Sixteen CFD operating points spanning power inputs of 900–1671 kW, pull rates of 0.12–0.36 kg/s, and cullet fractions of 0–30% are used to fit and cross-validate the ROMs. The novelty of this contribution is the unification of CFD-derived multi-KPI quality constraints with a fast, interpretable supervisory optimizer suitable for advisory deployment on industrial electric melters. For a representative 1350 kW baseline, the optimizer recommends a reduced operating power of 1256 kW (~7% reduction) while satisfying all quality, throughput, and refractory constraints—demonstrating a practical ROM-enabled digital-twin pathway for energy-efficient electric glass melting.