Browsing by Author "Nouri, Arash"

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    Characterization of Structure-Borne Tire Noise Using Virtual Sensing
    Nouri, Arash (Virginia Tech, 2021-01-27)
    Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction.
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    Correlation-Based Detection and Classification of Rail Wheel Defects using Air-coupled Ultrasonic Acoustic Emissions
    Nouri, Arash (Virginia Tech, 2016-04-29)
    Defected wheel are one the major reasons endangered state of railroad vehicles safety statue, due to vehicle derailment and worsen the quality of freight and passenger transportation. Therefore, timely defect detection for monitoring and detecting the state of defects is highly critical. This thesis presents a passive non-contact acoustic structural health monitoring approach using ultrasonic acoustic emissions (UAE) to detect certain defects on different structures, as well as, classifying the type of the defect on them. The acoustic emission signals used in this study are in the ultrasonic range (18-120 kHz), which is significantly higher than the majority of the research in this area thus far. For the proposed method, an impulse excitation, such as a hammer strike, is applied to the structure. In addition, ultrasound techniques have higher sensitivity to both surface and subsurface defects, which make the defect detection more accurate. Three structures considered for this study are: 1) a longitudinal beam, 2) a lifting weight, 3) an actual rail-wheel. A longitudinal beam was used at the first step for a better understanding of physics of the ultrasound propagation from the defect, as well, develop a method for extracting the signature response of the defect. Besides, the inherent directionality of the ultrasound microphone increases the signal to noise ratio (SNR) and could be useful in the noisy areas. Next, by considering the ultimate goal of the project, lifting weight was chosen, due to its similarity to the ultimate goal of this project that is a rail-wheel. A detection method and metric were developed by using the lifting weight and two type of synthetic defects were classified on this structure. Also, by using same extracted features, the same types of defects were detected and classified on an actual rail-wheel.
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    Experimental Analysis of a Novel Double Damper System with Semi-Active Control
    Gorantiwar, Anish; Nalawade, Rajvardhan; Nouri, Arash; Taheri, Saied (MDPI, 2020-09-17)
    An experimental study was conducted to compare the performance of an in-house built novel double semi-active damper against a conventional semi-active single damper. Different performance metrics were analyzed, and the performance of the two dampers was evaluated based on these metrics. A Hybrid Skyhook–Groundhook control algorithm was developed and implemented on the variable orifice double damper. The semi-active single damper is governed via two separate control strategies, namely—Skyhook and Groundhook control, respectively. The effectiveness of each algorithm is better understood by adding a normal load on top of the Shock Dyno, thus modifying it to act as a quarter car test rig. The sprung and unsprung acceleration data are collected via the accelerometers mounted on the Shock Dyno through a Data Acquisition System. The results obtained from this experiment provide a strong basis that the semi-active double damper performs better in terms of the comfort cost than that of the commercial semi-active single dampers.
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    Suspension Controls and Parameter Estimation Using Accelerometer Based Intelligent Tires
    Nalawade, Rajvardhan Prashant (Virginia Tech, 2021-05-14)
    This thesis aims at estimating vital vehicle states and developing control algorithms for automotive suspensions and vehicle stability. A parametric model of an automotive monotube damper is developed and several control algorithms for semi-active suspensions have been developed. An extensive comparison of different control algorithms has been done. Skyhook, Groundhook, Hybrid, Acceleration-driven, Power-driven, Groundhook-linear, Linear Quadratic Regulator (LQR) optimal, Genetic algorithm optimized Linear Quadratic Regulator optimal, Model-reference adaptive, H∞ robust, µ-synthesis, fuzzy-logic based, and Deep Reinforcement learning based control algorithms have been developed and simulated. A shock dyno is instrumented and skyhook and groundhook control algorithms have been implemented as well. In addition to this, a semi-active suspension switching based control algorithm is developed for reducing the effort of a direct moment yaw rate controller, and improve stability of a vehicle when turning. Accelerometer based intelligent tires have been used to estimate vehicle states like vertical load on tire, velocity of the vehicle, unsprung mass acceleration, and forces on a tire. All these estimations would be helpful in observing various parameters of a vehicle using data from only a tri-axis accelerometer inside the tire. Data was collected in an instrumented Volkswagen Jetta and a Trailer setup as well. The test vehicle was instrumented with a tri-axis accelerometer inside the tire, encoder, Inertial Measurement Unit (IMU), and VBOX Racelogic Global Positioning System (GPS) based velocity measurement unit. For payload estimation, the data collected by the in-tire accelerometer was converted into frequency domain using Welch's method of averaging, followed by feature extraction. The extracted features were fed to a trained bagged trees model. Root mean squared error of 11% was observed on the test dataset. For velocity estimation, the data collected by the accelerometer was fed to a variational mode decomposition process. The extracted mode was converted to time-frequency domain using Hilbert transform and features for machine learning were extracted. A root mean squared error of 1.02kmph was observed on the trained dataset. A Gaussian process model was trained for this application. For unsprung mass acceleration estimation, the test vehicle was instrumented with an accelerometer near the wheel spindle as well. For this estimation problem, Convolutional neural networks (CNN) were used. The time-frequency spectrogram of x, y, and z axis data of the in-tire accelerometer were considered as the three color channels of an image. With this, an image of 224 x 224 x 3 dimensions was generated, which represented the time and frequency variation of data. These images were used for training the CNN and a 96.8% coefficient of correlation was obtained for this regression task. For the last wheel force estimation problem, the concept of training the images generated by overlapping time-frequency matrices was used and an accuracy of 90.1% was achieved. With these estimation of vehicle states, better control algorithms can be developed and deployed for better handling, safety and comfort of vehicles using data from only tri-axis accelerometer in the tire.