Browse Topic: Noise

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ABSTRACT
Lehmann, JohannesMoorehead, StewartMuelaner, Jody E.
ABSTRACT
Beiker, SvenPorcel Magnusson, CristinaWaraniak, John
ABSTRACT
Coyner, KelleyBittner, JasonLambermont, SergeDe Boer, Niels
This specification covers runway deicing and anti-icing products in the form of a liquid. Unless otherwise stated, all specifications referenced herein are latest (current) revision.
G-12RDP Runway Deicing Product Committee
Abstract Predicting airbag deployment geometries is an important task for airbag and vehicle designers to meet safety standards based on biomechanical injury risk functions. This prediction is also an extraordinarily complex problem given the number of disciplines and their interactions. State-of-the-art airbag deployment geometry simulations (including time history) entail large, computationally expensive numerical methods such as finite element analysis (FEA) and computational fluid dynamics (CFD), among others. This complexity results in exceptionally large simulation times, making thorough exploration of the design space prohibitive. This paper proposes new parametric simulation models which drastically accelerate airbag deployment geometry predictions while maintaining the accuracy of the airbag deployment geometry at reasonable levels; these models, called herein machine learning (ML)-accelerated models, blend physical system modes with data-driven techniques to accomplish fast
Valenzuela del Rio, Jose E.Lancashire, RichardChatrath, KaranRitmeijer, PeterArvanitis, ElenaMirabella, Lucia
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