Improving vehicle aeroacoustics using machine learning

Authors:
Damjan Kunar;Martin Moina;Marina Giordanino;Ivan Bratko
Affiliations:
University of Ljubljana, 1000 Ljubljana, Slovenia;University of Ljubljana, 1000 Ljubljana, Slovenia;Centro Ricerche Fiat S.C.p.A, 10043 Orbassano, Italy;University of Ljubljana, 1000 Ljubljana, Slovenia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

Citing 6
Cited 0

Qualitative process theory

Artificial Intelligence - Special volume on qualitative reasoning about physical systems
A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on some of the Variables

SIAM Journal on Optimization
When Is ''Nearest Neighbor'' Meaningful?

ICDT '99 Proceedings of the 7th International Conference on Database Theory
Qualitatively faithful quantitative prediction

Artificial Intelligence
Principles of Computational Fluid Dynamics

Principles of Computational Fluid Dynamics
Learning qualitative models from numerical data

Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.