Statistical investigation on the coupling mode characteristics of a blade-disk-shaft unit

Author(s):  
Houxin She ◽  
Chaofeng Li ◽  
Guobin Zhang ◽  
Qiansheng Tang
1988 ◽  
Vol 24 (13) ◽  
pp. 811 ◽  
Author(s):  
M.R. Jones ◽  
H.D. Griffiths

2021 ◽  
pp. 107754632110131
Author(s):  
Somaye Mohammadi ◽  
Abdolreza Ohadi ◽  
Mostafa Irannejad-Parizi

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.


Author(s):  
Adam F. Werner ◽  
Jamie C. Gorman

Objective This study examines visual, auditory, and the combination of both (bimodal) coupling modes in the performance of a two-person perceptual-motor task, in which one person provides the perceptual inputs and the other the motor inputs. Background Parking a plane or landing a helicopter on a mountain top requires one person to provide motor inputs while another person provides perceptual inputs. Perceptual inputs are communicated either visually, auditorily, or through both cues. Methods One participant drove a remote-controlled car around an obstacle and through a target, while another participant provided auditory, visual, or bimodal cues for steering and acceleration. Difficulty was manipulated using target size. Performance (trial time, path variability), cue rate, and spatial ability were measured. Results Visual coupling outperformed auditory coupling. Bimodal performance was best in the most difficult task condition but also high in the easiest condition. Cue rate predicted performance in all coupling modes. Drivers with lower spatial ability required a faster auditory cue rate, whereas drivers with higher ability performed best with a lower rate. Conclusion Visual cues result in better performance when only one coupling mode is available. As predicted by multiple resource theory, when both cues are available, performance depends more on auditory cueing. In particular, drivers must be able to transform auditory cues into spatial actions. Application Spotters should be trained to provide an appropriate cue rate to match the spatial ability of the driver or pilot. Auditory cues can enhance visual communication when the interpersonal task is visual with spatial outputs.


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