COLOGNE, Germany - The path to climate-compatible flying involves energy-efficient aircraft, low-emission engines, and optimized flight paths. Digitalization can accelerate these developments, for example by simulating the airflow around an aircraft – requiring high-performance computers to process vast quantities of data. The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has now demonstrated how machine learning and artificial intelligence (AI) can provide critical support both in the generation and processing of this data, the German Aerospace Center (DLR) reports. Continue reading original article.
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6 November 2024 - "With our software, we can very accurately simulate the airflow for various situations encountered in flight operations and analyze it accordingly," explains Stefan Görtz from the DLR Institute of Aerodynamics and Flow Technology in Braunschweig. "Numerous flow conditions and aircraft configurations are relevant in the design and certification process. However, calculating each of these using conventional fluid mechanics is prohibitively costly. This is where deep artificial neural networks become invaluable, as they can process large quantities of unstructured data. We have specifically adapted these machine learning methods for use in aerodynamics, enabling us to make many rapid predictions."
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Jamie Whitney, Senior Editor
Military + Aerospace Electronics
