J. Zhou

H. Pahlavani, M. Amani, M. Cruz Saldívar et al. · tencent-ai

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials within those geometries. Varying the spatial distribution of multiple materials gives rise to many interesting and potentially unique combinations of anisotropic elastic properties. While the availability of a design approach to cover a large portion of all possible combinations of elastic properties is interesting in itself, it is even more important to find the extremely rare designs that lead to highly unusual combinations of material properties (e.g., double-auxeticity and high elastic moduli). Here, we used a random distribution of a hard phase and a soft phase within a regular lattice to study the resulting anisotropic mechanical properties of the network in general and the abovementioned rare designs in particular. The primary challenge to take up concerns the huge number of design parameters and the extreme rarity of such designs. We, therefore, used computational models and deep learning algorithms to create a mapping from the space of design parameters to the space of mechanical properties, thereby (i) reducing the computational time required for evaluating each designand (ii) making the process of evaluating the different designs highly parallelizable. Furthermore, we selected ten designs to be fabricated using polyjet multi-material 3D printing techniques, mechanically tested them, and characterized their behavior using digital image correlation (DIC, 3 designs) to validate the accuracy of our computational models. The results of our simulations show that deep learning-based algorithms can accurately predict the mechanical properties of the different designs, which match the various deformation mechanisms observed in the experiments.

S. Tsianikas, N. Yousefi, J. Zhou et al.

In the wake of the highly electrified future ahead of us, the role of energy storage is crucial wherever distributed generation is abundant, such as in microgrid settings. Given the variety of storage options that are becoming more and more economical, determining which type of storage technology to invest in, along with the appropriate timing and capacity becomes a critical research question. It is inevitable that these problems will continue to become increasingly relevant in the future and require strategic planning and holistic and modern frameworks in order to be solved. Reinforcement Learning algorithms have already proven to be successful in problems where sequential decision-making is inherent. In the operations planning area, these algorithms are already used but mostly in short-term problems with well-defined constraints. On the contrary, we expand and tailor these techniques to long-term planning by utilizing model-free algorithms combined with simulation-based models. A model and expansion plan have been developed to optimally determine microgrid designs as they evolve to dynamically react to changing conditions and to exploit energy storage capabilities. We show that it is possible to derive better engineering solutions that would point to the types of energy storage units which could be at the core of future microgrid applications. Another key finding is that the optimal storage capacity threshold for a system depends heavily on the price movements of the available storage units. By utilizing the proposed approaches, it is possible to model inherent problem uncertainties and optimize the whole streamline of sequential investment decision-making.