Concrete casting, a cornerstone of construction, relies on formwork to shape structures and has been used to create topology-optimized lightweight designs. The interplay between cast structures and formwork necessitates balancing casting constraints, particularly filling efficiency in topological channels, to avoid defects that compromise performance. However, filling efficiency is often empirically addressed, limiting optimization potential. Traditional methods require extensive post-processing to improve filling efficiency, increasing costs and design time. This work introduces a process simulation-informed reverse topology optimization framework, integrating casting constraints into the design process. The framework combines topology optimization, Discrete Element Method (DEM) simulation, and filling ratio identification. Its effectiveness is demonstrated through 2D numerical examples, experimental validation, and a preliminary 3D extension. Results show that the optimized structures improve filling efficiency and allow customizable casting positions. This approach offers a novel strategy for formwork optimization, enhancing efficiency and reducing costs in the building industry.

A challenging topic arising in dynamic wind turbine wake is modeling, especially the low-order approximation. The central problem is the fact that it has high-dimensional and nonlinear wake characteristics. In this paper, a Koopman-linear flow estimator is designed according to the Koopman operator theory. Different from the conventional flow reconstruction with the linear stochastic estimation method, a dynamic state-space model with physical states is constructed. The wake dynamics are approximated using a limited number of measurable physical parameters by the dynamic part; then, the full wake flow is reconstructed from the low-order states by the estimation part. The flow estimator is designed into three different forms following Extended Dynamic Mode Decomposition (EDMD) method. Each form has its unique advantages. Precisely, probe sensors are placed in the studied space and provide direct information of the wake, and a few in-directly physical parameters are also included. Nonlinear integer programming is further adopted using a heuristic optimization algorithm, by which the sensor configurations are optimized. Comparisons with the standard Dynamic Mode Decomposition (DMD)-based wake model are adopted in time domain and frequency domain to verify the effectiveness of the proposed flow estimators. The results show acceptable accuracy in typical modeling cases and maintain good estimation accuracy when the measurement noises are involved. Finally, the proposed Koopman-linear flow estimator is compared with related stochastic estimation methods, in which the connections of the proposed estimator with stochastic ones are also discussed.