Inverse transfer path analysis, a different approach to shorten time in NVH assessments

This paper presents the design and implementation of a simplified method, based on the transmissibility concept, for a noise path assessment, which allows a rapid and accurate analysis. The Inverse Transfer Path Analysis aims to assess, and determine, the contribution of the critical paths, which are transmitting structure-borne noises and vibrations, from the vehicle’s vibration sources to the driver’s ear. The cabin noise transfer function, from the involved attachment points and directions, can be simultaneously obtained by applying an impulsive noise source inside the cabin. This approach avoids the use of other time consuming classic procedures. The proposed methodology includes two types of tests, static condition tests in a semi-anechoic chamber and operational tests on a roller bench. The results assessment comprises the analysis of the noise contribution of each path, depending on the frequency and the vehicle speed range. This publication introduces a novel NVH method proposed to study and identify noise transfer paths in a car structure. The theoretical approach of the methodology, practical implementation, and obtained results, are described in this work, as well as a methodology validation, to evidence the suitability of the proposed method

An alternative close-proximity test to evaluate sound power level emitted by a rolling tyre

The noise emission of a rolling tyre is produced by different physical mechanisms generated during the tyre-road interaction, being the main noise source of a vehicle when driving at high speeds. Diverse measurement methods can be found in the literature to assess the rolling noise emission. In that sense, the close-proximity (CPX) method allows to evaluate tyre/road sound level with at least two microphones operating in the close field of the test tyre. This paper presents a new methodology, based on the CPX method, which allows assessing the sound power level of the rolling tyre by introducing some changes in the traditional close-proximity test. The methodology (named A-CPX) has been analytically and experimentally validated, and is finally used to obtain the total tyre/road sound power level emitted by the whole set of tyres of a vehicle

Gear sound model for an approach of a Mechanical Acoustic Vehicle Alerting System (MAVAS) to increase EV’s detectability

Hybrid-electric and electric vehicles significantly reduce noise road emissions. This noise mitigation also causes a reduction in the sound detectability and therefore it increases the potential of causing accidents. A suitable solution arises with the Acoustic Vehicle Alerting Systems (AVAS) emitting a warning sound to alert pedestrians about the presence of a silent vehicle. This paper details an acoustic prediction model capable of simulating the sound produced by a pair of spur dry gears used as a Mechanical Acoustic Vehicle Alerting System (MAVAS). This proposal that tries to reproduce a sound closer to the mechanical sound of a conventional vehicle would be used as an alternative to existing systems. The prediction model developed is validated and consists in two consecutive parts: first, a dynamic model studies the rattle of the gears, then, an analytical model reproduces the sound of each impact of the gear teeth. This sound model makes it possible to characterize a proposed gear combination of the MAVAS, verifying its compliance with the European legislation

A methodology for the extrapolation of coast-by noise of tyres from sound power level measurements

Traffic noise is one of the most predominant noise sources that affect citizens’ quality of life in urban areas. The increasing presence of alternative powered vehicles, such as electric or hybrid vehicles, could provide an improvement of such a situation due to the absence of internal combustion engines. However, tyre/road noise is independent of the vehicle type and still exists in alternative powered vehicles. Hence, efforts should focus also on reducing noise emission by means of new tyre designs. The tyre/road noise emission of newly produced tyres is currently evaluated by the Coast-By method, and as a result the rolling sound pressure level at the measuring distance, located 7.5 m away from the test vehicle is obtained. Such an acoustic index provides a very representative data of the annoyance that a pedestrian located at such distance could suffer. However, this value could be affected by external factors, such as environmental conditions. For that reason, this paper presents a methodology for extrapolating the sound pressure levels that are obtained in a Coast-By test, by means of the sound power level emitted by the specific tyre/road combination evaluated. This methodology could serve as the basis for defining a universal model to evaluate a tyre when rolling on a road, by using its sound power emission and predicting the Coast-By sound pressure level

Assessing the Impact of Attendance Modality on the Learning Performance of a Course on Machines and Mechanisms Theory

University education approaches related to the field of science, technology, engineering and mathematics (STEM), have generally particularized on teaching activity and learning programs which are commonly understood as reoriented lessons that fuse theoretic concepts interweaved with practical activities. In this context, team work has been widely acknowledged as a means to conduct practical and hands-on lessons, and has been revealed to be successful in the achievement of exercise resolution and design tasks. Besides this, methodologies sustained by ICT resources such as online or blended approaches, have also reported numerous benefits for students’ active learning. However, such benefits have to be fully validated within the particular teaching context, which may facilitate student achievement to a greater or lesser extent. In this work, we analyze the impact of attendance modalities on the learning performance of a STEM-related course on “Machines and Mechanisms Theory”, in which practical lessons are tackled through a team work approach. The validity of the results is reinforced by group testing and statistical tests with a sample of 128 participants. Students were arranged in a test group (online attendance) and in a control group (face-to-face attendance) to proceed with team work during the practical lessons. Thus, the efficacy of distance and in situ methodologies is compared. Moreover, additional variables have also been compared according to the historical record of the course, in regards to previous academic years. Finally, students’ insights about the collaborative side of this program, self-knowledge and satisfaction with the proposal have also been reported by a custom questionnaire. The results demonstrate greater performance and satisfaction amongst participants in the face-to-face modality. Such a modality is prooven to be statistically significant for the final achievement of students in detriment to online attendance

Numerical sound prediction model to study tyre impact noise

Impact noise is one of the mechanisms of vibratory origin that constitutes tyre/road interaction noise. When assessing a vehicle as a noise source, the impact sound mechanism is especially significant when obstacles are present on the driving surface. This document aims to enhance understanding of the impact noise phenomenon by presenting a two-step numerical model for studying the sound propagation of an accelerated tyre impacting a flat, rigid, and reflective surface: Firstly, a dynamic analysis of the contact is performed using the Finite Element Method. Then, the Boundary Element Method is used to perform an acoustic analysis with the vibration of the tyre surface as the sound source. The model has been successfully validated through a drop-test, where a tyre/rim assembly is dropped onto a ground surface. The validation was determined by comparing the predicted Sound Pressure Level measurements to those obtained from a circular microphone structure at various points during the drop-test

QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease

Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.Fil: Sebastián Pérez, Víctor. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Gil, Carmen. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, Ana. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin