Dengfeng Wang

The Effects of Various Additive Components on the Sound Absorption Performances of Polyurethane Foams

Flexible polyurethane (PU) foams comprising various additive components were synthesized to improve their acoustic performances. The purpose of this study was to investigate the effects of various additive components of the PU foams on the resultant sound absorption, which was characterized by the impedance tube technique to obtain the incident sound absorption coefficient and transmission loss. The maximum enhancement in the acoustic properties of the foams was obtained by adding fluorine-dichloroethane (141b) and triethanolamine. The results showed that the acoustic absorption properties of the PU foams were improved by adding 141b and triethanolamine and depended on the amount of the water, 141b, and triethanolamine.

Vehicle Ride Comfort Analysis and Optimization Using Design of Experiment

In this paper the fundamental of the component mode synthesis was reviewed. In order to optimize the ride comfort of the vehicle, a rigid-flexible coupling model of a car was established by using multibody system dynamics method and component mode synthesis technology. The flexibility of the car body, twist beam of the rear suspension, and stabilizer rod was considered. The ride comfort of the vehicle was calculated at different vehicle speeds with an interval of 10 km/h from 40 to 150 km/h. The ride comfort was optimized by using the design of experiment method. The optimal combination of suspension stiffness and damping parameters was determined, and the overall weighted acceleration RMS of the seat vibration was reduced by 16.96% at the upper surface of the driver seat on vehicle speed at 70 km/h. The ride comfort was obviously improved. Further simulation analysis shows that the ride comfort is effective improved at the other higher speed when applies optimal suspension parameter obtained.

Bionic Fan Optimization Based on Taguchi Method

Abstract In this research, the multi-response optimization of mass flow rate and A-weighted SPL using Taguchi quality loss function has been done for bionic fan performance. The factors of the geometric parameters selected in this investigation are blade number, boss ratio, and blade stagger angle. The contribution of each parameter factor to the performance of bionic fan has been determined. The blade stagger angle has been found as the most effective factor and its contribution is 52.58% on multiple quality characteristics. The verification test has also been performed based on the optimum conditions predicted. The values of the mass flow rate and the A-weighted SPL at the optimum levels of the fan parameters are 0.4518 kg/s and 61.47 dB(A), respectively against the initial fan of 0.3438 kg/s and 70.50 dB(A). The mass flow rate and the A-weighted SPL have been increased by 0.1080 kg/s and decreased by 9.03 dB(A), respectively. The optimization results demonstrate considerable improvement in both mass flow rate and A-weighted SPL with the multiple response optimization combination, as compared with those of the initial fan.

OPTIMIZATION OF LOW NOISE BIONIC FAN USING GRAY RELATIONAL ANALYSIS COUPLED WITH ENTROPY MEASUREMENT

This research presents a hybrid optimization method for the determination of the optimal geometry parameters which can minimize the A-weighted sound pressure level of bionic fan on condition that the mass flow rate of the bionic fan is as high as possible. A hybrid approach of gray relational analysis (GRA), Taguchi method, and entropy measurement has been used to obtain better acoustic performances with three basic geometry parameters, which are blade number, boss ratio, and blade stagger angle. A L9(34) orthogonal array has been used for conducting the experiment for optimization of flow and acoustic performances. The problem of multiple performance indices is simplified into single performance index by using gray relational grade. The designed experimental results are utilized in GRA, and the weights of the flow and acoustic performances are determined by using the entropy measurement method. Meanwhile, the optimal combination of bionic fan parameters is determined by using GRA method. Moreover, the validation tests show that the overall sound pressure level (SPL) reduces by 15.50% at the cost of mass flow rate reducing by 3.00%. The comparison of the A-weighted SPL for acoustic tests between original fan and optimized bionic fan demonstrates that the acoustic performance obviously improve from 20 Hz to 6300 Hz. Therefore, it is clearly shown that the proposed approach in this paper can be an useful tool to improve acoustic performance of bionic fan.

AUTOMOTIVE EXTERIOR NOISE OPTIMIZATION USING GREY RELATIONAL ANALYSIS COUPLED WITH PRINCIPAL COMPONENT ANALYSIS

This paper investigates optimization design of the thickness of the sound package performed on a passenger automobile. The major characteristics indexes for performance selected to evaluate the processes are the SPL of the exterior noise and the weight of the sound package, and the corresponding parameters of the sound package are the thickness of the glass wool with aluminum foil for the first layer, the thickness of the glass fiber for the second layer, and the thickness of the PE foam for the third layer. In this paper, the process is fundamentally with multiple performances, thus, the grey relational analysis that utilizes grey relational grade as performance index is especially employed to determine the optimal combination of the thickness of the different layers for the designed sound package. Additionally, in order to evaluate the weighting values corresponding to various performance characteristics, the principal component analysis is used to show their relative importance properly and objectively. The results of the confirmation experiments uncover that grey relational analysis coupled with principal analysis methods can successfully be applied to find the optimal combination of the thickness for each layer of the sound package material. Therefore, the presented method can be an effective tool to improve the vehicle exterior noise and lower the weight of the sound package. In addition, it will also be helpful for other applications in the automotive industry, such as the First Automobile Works in China, Changan Automobile in China, etc.

OPTIMIZATION OF CAR SOUND PACKAGE WITH STATISTICAL ENERGY ANALYSIS MODEL USING GREY RELATIONAL ANALYSIS AND TAGUCHI METHOD

Optimization design of the structures and thicknesses of the sound packages is performed for automotive interior acoustic performance and lightweight characteristics. A SEA model of the car was created, and its interior noise of the automobile was predicted and validated at speed of 120 km/h. The contributions of some major subsystems to interior noise were calculated at 120 km/h. Both of major characteristic indices including interior SPL and total weight of sound packages are selected to evaluate automotive interior acoustic performance and lightweight characteristics, and four selected optimization parameters are structures and thicknesses of the sound package of firewall and front floor. The problem of multiple performance indices is simplified into single performance index by using grey relational grade. The optimal combination of sound package parameters is determined by employing grey relational analysis method. Furthermore, the validation tests show that the values of interior SPL and overall weight of sound packages decrease by 1.31% and 60.36%, respectively. Also, the comparison of the overall SPL for automotive interior noise experiments between original and optimized structures and thicknesses of the sound packages shows that automotive interior acoustic performance is obviously improved from 200 Hz to 10,000 Hz. Therefore, it is demonstrated that it is an effective way to deal with optimization problems by using the presented approach in this research.

Research on prediction and control method of car exterior noise

In order to predict and control exterior noise, a new car exterior noise prediction method is presented based on Statistical Energy Analysis (SEA) principle and point sound source radiation formula. The Sound Pressure Level (SPL) of the car exterior structure-borne noise is predicted by using a SEA model. The exhaust noise and tyre noise are also considered. The prediction result was validated. The sound package schemes of the engine cabin for controlling car exterior noise are designed and analysed. A control measure of the exterior noise is chosen and put into practice for controlling car exterior noise.

SOUND QUALITY ANALYSIS AND PREDICTION OF VEHICLE INTERIOR NOISE BASED ON GREY SYSTEM THEORY

This paper presents a novel effective methodology for sound quality analysis and prediction of vehicle interior noise using gray system theory. Four objective psychoacoustic parameters selected to evaluate acoustic performance of vehicle interior environment are loudness, sharpness, roughness, and fluctuation. Subjective evaluation is presented using paired comparison method, and Bradley–Terry model is also created. The relationship between subjective evaluation and psychoacoustic parameters is determined by using gray relational analysis. Meanwhile, the correlation among psychoacoustic parameters is also revealed. Sound quality prediction models are created based on GM(0, N) and GM(1, N) model. The prediction results show that GM(1, N) model is more capable than GM(0, N) model for prediction of sound quality. Thus, the analysis and prediction results confirm that the proposed method in this study can be a useful tool to analyze and predict sound quality of the vehicle interior noise.

Multi-Objective Optimization of Vehicle Sound Package in Middle Frequency Using Gray Relational Analysis Coupled with Principal Component Analysis

This research studies optimization design of the thickness of sound packages for a passenger car. The major characteristics indexes for performance determined to evaluate the process are sound pressure level of the interior middle frequency noise and weight of the sound package. Three kinds of materials of sound packages are selected for the optimization process. The corresponding parameters of the sound packages are the thickness of the insulation plate for outer side of the firewall, thickness of the sound absorbing wool for inner side of the firewall, thickness of PU foam for the front floor, and thickness of PU foam for the rear floor, respectively. In this paper, the optimization procedure is a multi-objective optimization. Therefore, gray relational analysis (GRA) is applied to decide the optimal combination of sound package parameters. Furthermore, the principal component analysis (PCA) is used to calculate the weighting values which are corresponding to multiple performance characteristics. Then, the results of the confirmation tests uncover that GRA coupled with principal analysis methods can effectively be applied to find the optimal combination of the thickness of the sound packages at different positions for a passenger car. Thus, the proposed method can be a useful tool to improve the automotive interior middle frequency noise and lower the weight of the sound packages. Additionally, it will also be useful for automotive manufactures and designers in other fields.

Map Matching Algorithm Based on Mobile Phone Location

According to many shortcomings such as high cost, small coverage and small samples of methods based on GPS, license plate recognition or RFID for collecting traffic information, the methods based on mobile phone locations for collecting traffic information are more and more popular with the conditions of large number of users and advanced location technology about mobile phone. However, the map matching algorithms based on mobile phone locations can not meet the requirements for input data to traffic information collecting systems because of the larger sampling time interval and lower positioning accuracy of mobile phone locations than GPS locations. Consequently, a map matching algorithm based on mobile phone locations is designed considering the factors of distance, angle and travel expense. The verification results with measured data show that higher positioning accuracy can be acquired by the algorithm put forward above, which will provide reliable decision information for traffic management.