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Computational Fluid Dynamics appears to be poised on the threshold of rapid advances powered by the recent developments in deep machine learning. Deep machine learning will be used to improve the speed, accuracy and, the user-friendliness of CFD software. The applications of CFD will expand beyond the usual aerospace and mechanical/thermal areas to include areas such as biomedical, sport, food processing, environmental, fire safety, buildings ventilation and energy efficiency, and a host of other areas of social relevance. Deep machine learning will be routinely used to generate digital twins/reduced order models which will have a profound impact on the way that CFD is utilized. Standardized interfaces will be developed to embed the digital twins into CAD/PLM software and even spreadsheets. This will enable engineers to rapidly assimilate these models into the product development process and thereby create optimal designs, without needing the services of a CFD expert. These models will also be used for optimal control. In addition, these models can be combined with experimental and field data using Internet-of-Things (IoT) to provide for real-time monitoring of the device and assessing the need for preventive maintenance, etc. This has profound implications for product safety in the field. The social benefits are obvious. In short, CFD will become ubiquitous but will be buried inside digital twins/reduced order models so that it is usable by engineers, whereas CFD experts will be more engaged in creating them using high fidelity computations and of course, in extending the application of CFD into diverse areas of human activity. |
