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Modeling of elastohydrodynamic lubrication phenomena for the spiral bevel gears is performed in the present study. The damping and the friction coefficient generated from the lubricated contact area will have profound effects on the dynamics of spiral bevel gears. Thus the damping value generated from this friction model will be time varying. This makes the use of constant and empirical damping value in the dynamics of spiral bevel gears questionable. The input geometric and kinematic data required for the elastohydrodynamic lubrication (EHL) simulations are obtained using Tooth Contact Analysis. A full numerical elastohydrodynamic lubrication simulations are carried out using asymmetric integrated control volume (AICV) algorithm to compute the contact pressures. The fast Fourier transform is used to calculate the elastic deformations on the gear surfaces due to contact load.

This paper describes an active sound tuning (AST) system for vehicle powertrain response. Instead of simply aiming to attenuate cabin interior noise, AST system is capable of reshaping the powertrain response based on predetermined vehicle sound quality criteria. However, conventional AST systems cannot yield a balanced result over the broad frequency range when applied to powertrain noise. It is due to the fact that existing systems are typically configured with the filtered-x least mean square (FXLMS) algorithm or its modified versions, which has inherent frequency dependent convergence behavior due to large dynamic range of secondary path (the electro-acoustic path from the control speaker to the error microphone). Therefore, fast convergence can only be reached at the resonant frequencies.

Current powertrain active noise control (ANC) systems are not sufficient enough to track the fast engine speed variations, and yield consistent convergence speeds for individual engine order such that a balanced noise reduction performance can be achieved over a broad frequency range. This is because most of these ANC systems are configured with the standard filtered-x least mean squares (FxLMS) algorithm, which has an inherent limitation in the frequency-dependent convergence behavior due to the existence of secondary path model (electro-acoustic path from the input of control loudspeaker to the output of monitoring error microphone) in the reference signal path. In this paper, an overview is given first to compare several recently modified FxLMS algorithms to improve the convergence speed for harmonic responses such as eigenvalue equalization FxLMS (EE-FXLMS) and normalized reference LMS (NX-LMS) algorithms.

Active noise control systems have been gaining popularity in the last couple of decades, due to the deficiencies in passive noise abatement techniques. In the future, a novel combination of passive and active noise control techniques may be applied more widely, to better control the interior sound quality of vehicles. In order to maximize the effectiveness of this combined approach, smarter algorithms will be needed for active noise control systems. These algorithms will have to be computationally efficient, with high stability and convergence rates. This will be necessary in order to accurately predict and control the interior noise response of a vehicle. In this study, a critical review of the filtered-x least mean square (FXLMS) algorithm and several other newly proposed algorithms for the active control of vehicle powertrain noise, is performed. The analysis examines the salient features of each algorithm, and compares their system performance.

This paper discusses an enhanced active vibration control concept to suppress the dynamic response associated with gear mesh frequencies. In active control application, the control of dynamic gear mesh tonal response is essentially the rejection or suppression of periodical disturbance. Our active control experimental work shows that the existence of un-controlled harmonic result in the increase at these harmonics when applying direct control to the target mesh frequencies. To address this problem, the effect of the existence of un-correlated harmonic components in error signal when applying active control to suppress the target gear mesh harmonics is examined. The proposed adaptive controller that is designed specifically for tackling gear mesh frequency vibrations is based on an enhanced filtered-x least mean square algorithm (FXLMS) with frequency estimation to synthesize the required reference signal.

Automobile brake squeal has been experimentally studied in many ways over the past 65 years. A large body of published research and a substantial amount of unpublished work have attempted to experimentally define the variables involved with and describe the system dynamics initiating the friction-induced self-excited vibration. Much of this work has centered on pin on disk type test rigs used to characterize the contact mechanics and/or friction laws without considering the brake system influence. This paper describes a dynamometer designed and constructed to study brake squeal on a system level.

This paper provides several examples of silicon “micromachined” semiconductor sensors with which the authors are involved for aerospace condition monitoring. This and related work in MEMS (Micro Electro Mechanical Systems) has the potential to revolutionize condition monitoring in aerospace condition and “health monitoring” by (1) moving “smart” electronics out to the sensor chip itself and (2) combining a vast quantity and types of, not only electronic, but micromechanical sensing schemes into the silicon chip . Precisely formed cantilevers, gears, valves, microplumbing and even micro motors of the cross-section of a human hair can be fabricated on a single silicon microchip. Silicon is an excellent mechanical material with a yield strength several times that of stainless steel. Also silicon has excellent thermal properties , whereas compatible silicon dioxide (which we typically use in connection with silicon microelectronics patterning) is virtually a thermal insulator.

In this work, the goal of enhanced reliability through redundancy is explored. Two levels of fusion have been defined: the first is a fusion of sensors, redundant in both number and type, and the second is a statistical fusion of the resulting data at a software level. An intermediate preprocessing level is required to connect both fusions. The various types of sensors which are included are bulk micromachined flow, pressure and hydrogen sensors and a thin film poly-crystalline silicon temperature sensor. Individual sensors have been fabricated and packaged in arrays. Associated preprocessing has been designed to be able to handle all of the signals coming from each sensor and prepare them for statistical analysis. Data fusion algorithms have been written and tested.

The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

Efforts have been made to utilize Al constructs to identify flaws in the Space Shuttle Main Engine (SSME) faceplate regions. In order to expand the applicability of these algorithms to a larger problem domain, the automatic visual inspection system(AVIS) has been modified to enable a user with little or no image processing background to define a system capable of identifying flaws on a given set of imagery. This system requires the user to simply identify flawed regions and the selection of processing and feature descriptors is performed automatically. This paper explicates the motivations, definitions, and performance issues associated with the AVIS paradigm.

A manual task can be performed based on alternative movement techniques. Ergonomic human motion simulation requires consideration of alternative movement techniques, because they could bring different biomechanical, physiological, and psychophysical consequences. A method for identifying movement techniques from existing motion data was developed. The method is based on a JCV (Joint Contribution Vector) index and statistical clustering. A JCV quantifies a motion's underlying movement technique by computing contributions of individual body joint DOFs (degree-of-freedom) to the achievement of the task goal. Given a set of motions (motion capture data) achieving the same or similar task goals, alternative movement techniques can be identified by 1) representing the motions in terms of JCV and 2) performing a statistical clustering analysis. Performance of this movement technique identification method was evaluated based on a set of stoop and squat lifting motions.

Squeak and rattle (S&R) noises are undesirable noises caused by friction-induced vibration or impact between surfaces. While several computer programs have been developed to automatically detect and rate S&R events over the years, no reported work has been found that can detect squeak and rattle noises and distinguish them. Because the causes of squeak noises and rattle noises are different, knowing if it is a squeak noise or rattle noise will be very helpful for automotive engineers to choose an appropriate measure to solve the problem. The authors have developed a new algorithm to differentiate squeak noises and rattle noises, and added it to the S&R detection algorithm they had developed previously. The new algorithm utilizes a combination of sound quality metrics, specifically sharpness, roughness, and fluctuation strength.

Squeak and rattle (S&R) problems in body structure and trim parts have become serious issues for automakers because of their influence on the initial quality perception of consumers. In this study, various CAE and experimental methods developed by Hyundai Motors for squeak and rattle analysis of door systems are reported. Friction-induced vibration and noise generation mechanisms of a door system are studied by an intelligent combination of experimental and numerical methods. It is shown that the effect of degradation of plastics used in door trims can be estimated by a numerical model using the properties obtained experimentally. Effects of changes in material properties such as Young's modulus and loss factor due to the material degradation as well as statistical variations are predicted for several door system configurations. As a new concept, the rattle and squeak index is proposed, which can be used to guide the design.

Cycloid drives are widely used in the in-wheel motor for electric vehicles due to the advantages of large ratio, compact size and light weight. To improve the transmission efficiency and the load capability and reduce the manufacturing cost, a novel cycloid drive with non-pin design for the application in the in-wheel motor is proposed. Firstly, the generation of the gear pair is presented based on the gearing of theory. Secondly, the meshing characteristics, such as the contact zones, curvature difference, contact ratio and sliding coefficients are derived for performance evaluation. Then, the loaded tooth contact analysis (LTCA) is performed by establishing a mathematical model based on the Hertz contact theory to calculate the contact stress and deformation.

Active noise control (ANC) technique with the filtered-x least mean square (FXLMS) algorithm has proven its efficiency and drawn increasingly interests in vehicle noise control applications. However, many vehicle interior and/or exterior noises are exhibiting non-Gaussian type with impulsive characteristic, such as diesel knocking noise, injector ticking, impulsive crank-train noise, gear rattle, and road bumps, etc. Therefore, the conventional FXLMS algorithm that is based on the assumption of deterministic and/or Gaussian signal may not be appropriate for tackling this type of impulsive noise. In this paper, an ANC system configured with modified FXLMS (MFXLMS) algorithm by adding thresholds on reference and error signal paths is proposed for impulsive noise control. To demonstrate the effectiveness of the proposed algorithm, an experimental study is conducted in the laboratory.

An enhanced, frequency domain filtered-x least mean square (LMS) algorithm is proposed as the basis for an active control system for treating powertrain noise. There are primarily three advantages of this approach: (i) saving of computing time especially for long controller’s filter length; (ii) more accurate estimation of the gradient due to the sample averaging of the whole data block; and (iii) capacity for rapid convergence when the adaptation parameter is correctly adjusted for each frequency bin. Unlike traditional active noise control techniques for suppressing response, the proposed frequency domain FXLMS algorithm is targeted at tuning vehicle interior response in order to achieve a desirable sound quality. The proposed control algorithm is studied numerically by applying the analysis to treat vehicle interior noise represented by either measured or predicted cavity acoustic transfer functions.

The compressor surge line of automotive turbochargers can limit the low-end torque of an engine. In order to determine how close the compressor operates to its surge limit, the Hurst exponent of the pressure signal has recently been proposed as a criterion. The Hurst exponent quantifies the fractal properties of a time series and its long-term memory. This paper evaluates the outcome of applying Hurst exponent based criterion on time-resolved pressure signals, measured simultaneously at different locations in the compression system. Experiments were performed using a truck-sized turbocharger on a cold gas stand at the University of Cincinnati. The pressure sensors were flush-mounted at different circumferential positions at the inlet of the compressor, in the diffuser and volute, as well as downstream of the compressor.

A new algorithm to detect and to quantify the seriousness of the detected squeak and rattle (S&R) events was developed. A T-F analysis technique called AWT, the Zwicker loudness model and leaky integration are employed to define new concepts we called transient specific loudness time histories and perceived transient loudness time history. The detection threshold of the perceived transient loudness was identified by a clever interpretation of jury test results. The proposed algorithm showed a good promise producing results that are well correlated with the jury tests. The new algorithm developed in this work will be able to automate detection and rating of the S&R events with good accuracy and with minimum possibility of false alarm under normal operating conditions

Actuator and roller screw mechanism are key components of electromechanical brake (EMB) system in automotive and aerospace industry. The inverted planetary roller screw mechanism (IPRSM) is particularly competitive due to its high load-carrying capacity and small assembly size. For such systems, friction characteristic and friction torque generated from rolling/sliding contacts can be an important factor that affects the dynamic performance as well as vibration behavior. This paper investigates the modeling and simulation of the EMB system in early design stage with special attention to friction torque modelling of IPRSM. Firstly, a step-by-step system model development is established, which includes the controller, servo motor, planetary gear train and roller screw mechanism to describe the dynamic behavior of the EMB system.