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Field inversion machine learning (FIML) has the advantages of model consistency and low data dependency and has been used to augment imperfect turbulence models. However, the solver-intrusive field inversion has a high entry bar, and existing FIML studies focused on improving only steady-state or time-averaged periodic flow predictions. To break this limit, this paper develops an open-source FIML framework for time-accurate unsteady flow, where both spatial and temporal variations of flow are of interest. We augment a Reynolds-Averaged Navier–Stokes (RANS) turbulence model's production term with a scalar field. We then integrate a neural network (NN) model into the flow solver to compute the above augmentation scalar field based on local flow features at each time step. Finally, we optimize the weights and biases of the built-in NN model to minimize the regulated spatial-temporal prediction error between the augmented flow solver and reference data. We consider the spatial-temporal evolution of unsteady flow over a 45° ramp and use only the surface pressure as the training data. The unsteady-FIML-trained model accurately predicts the spatial-temporal variations of unsteady flow fields. In addition, the trained model exhibits reasonably good prediction accuracy for various ramp angles, Reynolds numbers, and flow variables (e.g., velocity fields) that are not used in training, highlighting its generalizability. The FIML capability has been integrated into our open-source framework DAFoam. It has the potential to train more accurate RANS turbulence models for other unsteady flow phenomena, such as wind gust response, bubbly flow, and particle dispersion in the atmosphere.

 

This paper develops a control co-design (CCD) framework to simultaneously optimize the spacecraft’s trajectory and onboard system (rocket engine) and quantify its benefit. An open-loop optimal control problem (two-finite burn Mars missions) is used as the benchmark, and the engine design considers the combustion equilibrium and nozzle geometry. The objective function is the fuel burn. The design variables are the trajectory control parameters (such as burn times, burn directions, and time of flight), initial fuel mass, and engine design parameters (such as throat area, mixture ratio, and chamber pressure). The constraints include the final velocities and positions of spacecraft. Single-point optimizations are conducted for three departure dates in May, July, and September 2020. A multipoint optimization is also performed to balance the engine performance for these dates with 49 design variables and 20 constraints. It is found that the CCD optimizations exhibit 22–28% more fuel burn reduction than the trajectory-only optimization with fixed engine parameters and 16–20% more fuel burn reduction than the decoupled trajectory-engine optimization. The proposed CCD optimization framework can be extended to more spacecraft trajectory control parameters and onboard systems and has the potential to design more efficient spacecraft missions.