In this article, we present a deep learning-based reduced order model (DL-ROM) for predicting the fluid forces and unsteady vortex shedding patterns. We consider the flow past a sphere to examine the accuracy of our DL-ROM predictions. The proposed DL-ROM methodology relies on a three-dimensional convolutional recurrent autoencoder network (3D CRAN) to extract the low-dimensional flow features from the full-order snapshots in an unsupervised manner. The low-dimensional features are evolved in time using a long short-term memory-based recurrent neural network and reconstructed back to the full-order as flow voxels. These flow voxels are introduced as static and uniform query probes in the point cloud domain to reduce the unstructured mesh complexity while providing convenience in the 3D CRAN training. We introduce a novel procedure to recover the interface description and the instantaneous force quantities from these 3D flow voxels. To evaluate the 3D flow reconstruction and inference, the 3D CRAN methodology is first applied to an external flow past a static sphere at the single Reynolds number of Re = 300. We provide an assessment of the computing requirements in terms of the memory usage, training, and testing cost of the 3D CRAN framework. Subsequently, variable Re-based flow information is infused in one 3D CRAN to learn a symmetry-breaking flow regime (280 Re 460) for the flow past a sphere. Effects of transfer learning are analyzed for training this complex 3D flow regime on a relatively smaller time series dataset. The 3D CRAN framework learns the flow regime nearly 20 times faster than the parallel full-order model and predicts this flow regime in time with a reasonable accuracy. Based on the predicted flow fields, the network demonstrates an accuracy of 98.58% for the drag and 76.43% for the lift over the sphere in this flow regime. The proposed framework aligns with the development of a digital twin for 3D unsteady flow field and instantaneous force predictions with variable Re-based effects.
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