We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional numerical solvers, SimNet addresses a wide range of use cases -coupled forward simulations without any training data, inverse and data assimilation problems. SimNet offers fast turnaround time by enabling parameterized system representation that solves for multiple configurations simultaneously, as opposed to the traditional solvers that solve for one configuration at a time. SimNet is integrated with parameterized constructive solid geometry as well as STL modules to generate point clouds. Furthermore, it is customizable with APIs that enable user extensions to geometry, physics and network architecture. It has advanced network architectures that are optimized for high-performance GPU computing, and offers scalable performance for multi-GPU and multi-Node implementation with accelerated linear algebra as well as FP32, FP64 and TF32 computations. In this paper we review the neural network solver methodology, the SimNet architecture, and the various features that are needed for effective solution of the PDEs. We present real-world use cases that range from challenging forward multi-physics simulations with turbulence and complex 3D geometries, to industrial design optimization and inverse problems that are not addressed efficiently by the traditional solvers. Extensive comparisons of SimNet results with open source and commercial solvers show good correlation.
The generalizability of a convolutional encoder-decoder based model in predicting aerodynamic flow field across various flow regimes and geometric variation is assessed. A rich master dataset consisting of 11,000+ simulations including cambered, uncambered, thin and thick airfoils simulated at varying angles of attack is generated. The various Mach and Reynolds number (Re) chosen allows analysis across compressible, incompressible, low and high Re flow regimes. Multiple studies are carried out with the model trained on datasets that are categorized based on the above parameters. In each study, the loss of prediction accuracy by training the model on a larger dataset (generalizability), versus a smaller categorically sorted dataset, is evaluated. Largely disparate flow features across the Re range lead to a 25.56% loss, while the generalization across Mach range led to an average of 23.95% loss. However, flow-field changes induced due to geometric variation exhibited a better generalization potential, through an increased accuracy of 12.4%. The encoder-decoder architecture allows extraction of relevant geometric features from largely different geometries (geometric generalization) providing a better out-of-sample prediction accuracy in comparison to physics-based generalization. It is shown that, through user-informed choice of training data (removal of geometrically similar samples), computational costs incurred in generating training data can be reduced. This is important for the application of such methods in the design optimization of platforms and components that require analysis of the fluid flows.
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