Satellite Component Layout Optimization (SCLO) is crucial in satellite system design. This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization (3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer, with both influencing each other. To address this issue, first, a Mixed Integer Programming (MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method (FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.

Deep learning has been widely applied in surrogate modeling for airfoil flow field prediction. The success of deep learning relies heavily on large-scale, high-quality labeled samples. However, acquiring labeled samples with complete annotations is prohibitively expensive, and the available annotations in practical engineering are often sparse due to limited observation. To leverage samples with sparse annotations, this paper proposes an uncertainty-based active transfer learning method. The most valuable positions in the flow field are selected based on uncertainty for annotation, effectively improving prediction accuracy and reducing annotation costs. Our method involves a novel active annotation based on synchronous quantile regression, which can mitigate the computational cost of query annotation. Besides, a novel quantile levels-based consistency regularization is proposed to constrain the remaining unlabeled regions and further improve the model performance. Experiments show that our method can significantly reduce prediction errors with only 1% extra annotations, and is a promising tool for achieving rapid and accurate flow field prediction.

This paper reports a numerical research on MEMS (microelectromechanical system) micronozzles through multiphysics coupling simulation along with design optimization based on simulation results. The micronozzle, which is a core component of the electrothermal microthruster, features a micron-scale geometry, a 2-dimensional (2D) Laval configuration, a rectangular cross section, and a highly thermal conductive silicon wall due to MEMS fabrication. As a result, viscous loss in the flow field and heat transfer to the nozzle wall can strongly influence nozzle performance, namely, thrust force and specific impulse. To accurately understand the flow field inside the micronozzle and how the highly thermal conductive silicon wall interacts with gas flow, a numerical simulation that couples fluid dynamics field and solid heat transfer field is employed in the research. The influence of different structural parameters on micronozzle performance is then investigated to set a basis for design optimization. The optimum design of the linear expander micronozzle is obtained through constrained optimization by linear approximation. To further improve micronozzle performance, the bell-shaped expander is adapted. The optimization result shows that the bell-shaped expander is not suitable for micronozzle featuring 2D Laval configuration, and the reason behind the phenomenon is thoroughly discussed.

In the satellite overall design phase, it is a crucial step to perform satellite layout design to guarantee that the aggregation of electronic components can operate normally and stably in an appropriate temperature environment. In order to handle the satellite payload placement problem of the DongFangHong 4 (DFH-4) platform, the heat pipe-constrained component layout optimization (HCLO) problem is proposed with the HCLO model formulated. Through careful investigation, it can be divided into two optimization subproblems that can be solved subsequently. Based on the divide-and-conquer strategy, an integer linear programming- (ILP-) assisted two-stage layout optimization method is proposed. In stage one, component-heat pipe distribution optimization is performed using the ILP technique so that specific heat pipes occupied by each component can be determined and the horizontal movement range of components can be reduced. In stage two, the detailed component layout optimization is investigated to obtain the final positions of components. First, the sequence layout sampling (SeqLS) method is used to generate one nonoverlap initial layout. Next, swap operation between components is incorporated to reduce the centroid deviation. Finally, sequential quadratic programming (SQP) search is conducted based on the generated promising initial layout solutions. Therefore, the SeqLS-based heuristic layout search algorithm is proposed in the second stage. Two layout test cases, including 15 components and 90 components, respectively, are investigated to demonstrate the validity and efficacy of the proposed layout design method. Experimental results show that it is promising to apply such a two-stage approach for satellite payload placement in engineering.