Improving vehicle aeroacoustics using machine learning |
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| Authors: | Damjan Kužnar Martin Možina Marina Giordanino Ivan Bratko |
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| Affiliation: | 1. University of Ljubljana, 1000 Ljubljana, Slovenia;2. Centro Ricerche Fiat S.C.p.A, 10043 Orbassano, Italy;1. Genesis Acoustics, Domaine du Petit Arbois, 13045 Aix-en-Provence, France;2. Soufflerie GIE S2A, 2 avenue Volta, 78180 Montigny-le-Bretonneux, France;1. Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China;2. State Key Laboratory of Turbulence, College of Engineering, Peking University, Beijing, 100871, China;1. Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UK;2. School of Manufacturing Engineering, Universiti Malaysia Perlis, 02600, Perlis, Malaysia;3. Embraer, São José dos Campos, 12227-901, Brazil |
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| Abstract: | This paper presents a new approach to improving the overall aeroacoustic comfort of a vehicle, an important feature of vehicle design. The traditional improvement process is extended to benefit extensively from machine learning, information retrieval and information extraction technologies to assist the wind tunnel engineers with difficult tasks. The paper first describes the general approach and then focuses on providing a detailed description of the most important task of assessing the degree of discomfort for a human caused by wind noise in a vehicle, when the noise spectrum is known. For this purpose a novel approach of learning linear regression models that are consistent with expert's domain knowledge is presented. The results of the end user evaluation of the entire system are also presented to reflect the strengths of this approach. |
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