Development of knowledge based body structure concept design model

The purpose of this study is to propose a concept design process for an automotive body structure using technical information on the major joints and members of vehicles. First, in order to collect the technical information on major joints and members, 17 vehicles were selected using benchmark data. The collected technical information for the selected vehicles was the cross sectional shapes of each joint and member which were used for the analysis of joint stiffness, crashworthiness and static stiffness of the member to make a database along with cross section properties. This study applied a ‘What If Study’ technique to perform a concept design of an automotive body using the analyzed information and selected cross section meeting the design objectives. The criteria for the selection of the cross section were defined by subdividing the defined design objectives of an automotive body structure and constraints into members and joints. In order to configure an analysis model of an automotive body structure using the selected cross section, a shape parametric model was used and static stiffness, dynamic stiffness and crashworthiness were assessed to evaluate the configured automotive body structure. The evaluation result showed that the crashworthiness and static/dynamic stiffness were improved compared to an existing body structure. In addition, the weight of the body structure was reduced. Through this study, the process that can rapidly and effectively derive and evaluate the concept design of an automotive body structure was defined. It is expected that, henceforth, this process will be helpful for the study of automotive body structures.     

Coupled heat and mass transfer analysis of NH<Subscript>3</Subscript>-H<Subscript>2</Subscript>O falling film absorption on inner tube surface with low solution flow rates

NH₃-H₂O falling film absorption usually takes place with low solution flow rate in real absorption refrigeration system. An experimental study of inner vertical absorption is carried out for the consideration of air-cooling absorber. Variable working conditions are tested to evaluate the heat and mass transfer performances.The traditional evaluation method based on log-mean-temperature（concentration） difference is criticized for its lack of theoretical basis while simultaneous heat and mass transfer process occurs. A new method proposed by Kim and Infante Ferreira is modified to evaluate the experimental results with reasonable assumptions. The method is based on the derivation of coupled heat and mass transfer differential equations of NH₃-H₂O absorption process.The analysis of the same experimental data shows that the new method realizes better consistency with smaller error, especially in heat transfer aspect. Heat and mass transfer performance is enhanced with the increase of solution Reynolds number. Sub-cooling of inlet weak solution also has positive influence on the absorption process,which should be evaluated by the new method correctly. Two correlations are developed to evaluate both Nusselt and Sherwood numbers for the design of air-cooling absorber.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Design and implementation of a UPnP-can gateway for automotive environments

In this paper, we propose a universal plug and play (UPnP) — controller area network (CAN) gateway system using UPnP middleware for interoperability between external smart devices and an in-vehicle network. The proposed gateway consists of a UPnP communication device, a CAN communication device, and a device translator layer. In-vehicle devices are not usually IP-based, so we implemented an in-vehicle device manager in the UPnP communication device which is in the gateway. We developed a vehicle simulator to produce real vehicular data for performance analysis. The CAN communication device transmits and receives real-time vehicle data between the real vehicular simulator and external devices through the UPnP. The device translator layer configures a message frame for enabling seamless data input and output between the CAN and UPnP protocols. After implementation, we generated an internal-external service request and tested the result. Finally, we confirmed the service request and operation between external devices and the internal vehicular device. Additionally, for a variety of external device numbers and communication environments, we demonstrated the gateway performance by measuring the round trip time (RTT) for overall service implementation.     

Development of an LIF image processing technique for measuring drop sizes in a pre-swirl spray

Depth of field effects in laser sheet imaging were considered for droplet sizing of a pre-swirl spray. A pre-swirl spray is formed before the hollow-cone type main-swirl spray from a D.I. gasoline injector, and shows transient characteristics with high axial velocity. A microscopic imaging technique was applied to obtain high spatial resolution LIF tomograms of the pre-swirl spray. A 1 mm thick Nd:YAG laser sheet was used as a light source to make the LIF tomograms that were imaged using a high-resolution CCD camera. The droplet sizing of the pre-swirl spray was carried out using an image processing technique. In the image processing procedure, the laser sheet-straddling large-sized droplets were carefully taken into account to remove the errors caused by the depth of field effects from the limited thickness and the energy distribution of the laser sheet. The mean intensity of the individual droplets and the line profile of the LIF signal around the droplet edge were inspected to screen the laser sheet-straddling large-sized droplets. In order to consider the effects of the size-dependent LIF signal intensity, the size-classified or ensemble-averaged mean intensity of the individual droplets was introduced. The mean droplet sizes such as AMD and SMD were calculated using only screened droplets, and they slightly increase before considering the depth of field effects.     

Vehicle interior noise model based on a power law

Identifying the components of a vehicle’s interior noise is important in many phases of the noise, vibration, and harshness (NVH) development process. Many test methods that have been widely used in the automobile industry to separate noise sources are based on system identification methods in the frequency domain. However, none of the frequency response function-based methods can directly estimate the wind noise component. In this article, an analytical model for the interior noise level based on a simple power law was developed. It was assumed that the mean squared acoustic pressure for the interior noise could be obtained by summing up those of the wind noise, road noise, and background noise. The wind noise and road noise were further assumed to depend only on wind speed and the vehicle’s driving speed, respectively, and to follow a simple power law. The resulting analytical model includes five parameters that can be optimized for the vehicle and the road. The validity of the model was verified by using data obtained from cruise tests performed on a proving ground for cruise speeds ranging from 40 km/h to 130 km/h. The model is applied to the overall and 1/3-octave bands of interior noise and is shown to describe the data trends fairly well. For the test vehicle used in the present work, the overall mean squared pressures for the wind and road noise components are shown to be proportional to the wind speed to the 5.8 power and to the driving speed to the 3.4 power, respectively.     

Brittleness analysis and important nodes discovery in large time-evolving complex networks

The brittleness analysis and important nodes detection have been a hot spot in the complex networks. How to get the overall feature of the whole network and how to find out some important nodes are requisites to solve these problems. In this paper, we adopt the trace of the adjacency matrix and the centrality of the complex networks to give a quantitative and qualitative analysis of networks being studied. Results show that the k-shell plays a more important role than the degree centrality and the betweenness in finding important nodes, and it can also be used to give direction on the immunization and maintenance of complex networks.     

Long-term tracking based on spatio-temporal context

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.     

Poisson noise removal based on nonlocal total variation with Euler’s elastica pre-processing

An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.     

Mathematical model validated by a crash test for studying the occupant’s kinematics and dynamics in a cars’ frontal collision

The aim of the paper was to determine the kinematic parameters that influence the occupant injury risk through a mathematical model. The developed model is a 2D model composed of 4 bodies (2 vehicles, thorax and head). The head and thorax are interconnected with a rotation joint and a torsion spring meant to stiffen the relative movement between the bodies. The thorax is connected with the vehicle body by a linear spring meant to simulate the seatbelt stiffness. The model was solved using Lagrange principle and the validation of the model was made through a crash test performed using the same initial conditions and comparing the obtained values of the displacement, velocity and acceleration parameters with the ones obtained with the mathematical model. The head and torso were chosen due to the fact that they are the common parts of the body that get injured, especially the head with the change of 80 % to cause fatal injury in car’s frontal collision. Once the model was validated, the stiffness of the seatbelt was modified in order to determine the behavior of the occupant in case of car frontal collisions. When the seatbelt stiffness was reduced, the occupant displacement and velocity increased, while by increasing the stiffness, these parameters decreased. The values of the developed model presented a high degree of similarity with the results obtained from the crash test with an error of 10 %. This model can be used by engineers to easily asses the occupant injury risk in case of vehicle frontal collisions.