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Luo Ji-Wang, Yaji Kentaro, Chen Li, et al. Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage. Applied Energy 377, 124596 (2024); https://doi.org/10.1016/j.apenergy.2024.124596. ; This work develops a data-driven multi-fidelity topology design (MFTD) method for designing fins in a latent heat thermal energy storage tube. The high-fidelity simulation resolves the actual solid-liquid phase change process using the enthalpy method, while the low-fidelity topology optimization (TO) simply considers the natural convection with Darcy flow. The above MFTD method is integrated into the framework of evolutional algorithm, and the variational autoencoder is introduced to generate new offspring. Fins for accelerating the melting and solidification processes at different Grashof number (Gr) are designed. It is found that when the fin volume fraction is low, the melt designs exhibit strong heterogeneity due to the strong convection, while the solidification designs are almost isotropic. Along with the increase of the fin volume fraction, the fins are first getting longer, then having more branches or sub-branches and finally becoming thicker. The superiority of the present data-driven MFTD method to the gradient-based direct TO method for solving optimization problems with strong multimodality has been demonstrated in this work. Results find that compared with the designs from direct TO, the MFTD melt design can further reduce the melting time by at least 27 % and 20 % at Gr = 3.3 × 103 and Gr = 3.3 × 104 respectively, and the MFTD solidification design can further shorten the solidification time by at least 9 %.

Luo Ji-Wang, Yaji Kentaro, Chen Li, et al. Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage. Applied Energy 377, 124596 (2024); https://doi.org/10.1016/j.apenergy.2024.124596. ; This work develops a data-driven multi-fidelity topology design (MFTD) method for designing fins in a latent heat thermal energy storage tube. The high-fidelity simulation resolves the actual solid-liquid phase change process using the enthalpy method, while the low-fidelity topology optimization (TO) simply considers the natural convection with Darcy flow. The above MFTD method is integrated into the framework of evolutional algorithm, and the variational autoencoder is introduced to generate new offspring. Fins for accelerating the melting and solidification processes at different Grashof number (Gr) are designed. It is found that when the fin volume fraction is low, the melt designs exhibit strong heterogeneity due to the strong convection, while the solidification designs are almost isotropic. Along with the increase of the fin volume fraction, the fins are first getting longer, then having more branches or sub-branches and finally becoming thicker. The superiority of the present data-driven MFTD method to the gradient-based direct TO method for solving optimization problems with strong multimodality has been demonstrated in this work. Results find that compared with the designs from direct TO, the MFTD melt design can further reduce the melting time by at least 27 % and 20 % at Gr = 3.3 × 103 and Gr = 3.3 × 104 respectively, and the MFTD solidification design can further shorten the solidification time by at least 9 %.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.