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The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

As a new type of energy, free-piston linear generator (FPLG) attracts more research on its stable operation and power performance, while less on its combustion and emission performance. So, in this paper, the emission characteristics of FPLG in two different modes are studied through a port fuel injection (PFI) mode which was verified by the experiment and a gasoline direct injection (GDI) mode. The results showed that: both the GDI mode and the PFI mode produced large amounts of nitrogen oxide (NOx) during the working process. But the GDI mode produced before the PFI mode and it produced nearly 2 times than the PFI mode. However, the formation rate of NOx in GDI mode is much lower than that in PFI mode. Meanwhile, in both modes, 90% of NOX was generated in the cylinder at the temperature higher than 1750K, and only about 10% of NOX was generated at a temperature lower than 1750K.

The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

This research examined the focused design, elastic design, energy decoupling, and torque roll axis (TRA) decoupling methods for mount optimization design. Requiring some assumptions, these methods are invalid for some load conditions and constraints. The linearity assumption is advantageous and simplifies both design and optimization analysis, facilitating engineering applications. However, the linearity is rarely seen in real-world applications, and there is no practical method to directly measure the reaction forces in the three locally orthogonal directions, preventing validation of existing methods by experimental results. For nonlinear system identification, there are additional challenges such as unobservable internal variables and the uncertainty of measured data.

To meet the strict emission regulations for diesel engines, an advanced processing device such as a Urea-SCR (selective catalytic reduction) system is used to reduce NOx emissions. The Real Driving Emissions (RDE) test, which is implemented in the European Union, will expand the range of conditions under which the engine has to operate [1], which will lead to the construction of a Urea-SCR system capable of reducing NOx emissions at lower and higher temperature conditions, and at higher space velocity conditions than existing systems. Simulations are useful in improving the performance of the urea-SCR system. However, it is necessary to construct a reliable NOx reduction model to use for system design, which covers the expanded engine operation conditions. In the urea-SCR system, the mechanism of ammonia (NH3) formation from injected aqueous urea solution is not clear. Thus, it is important to clarify this mechanism to improve the NOx reduction model.

This study addresses the virtual optimization of the technical specifications for a recently developed Advanced Pedestrian Legform Impactor (aPLI). The aPLI incorporates a number of enhancements for improved lower limb injury predictability with respect to its predecessor, the FlexPLI. It also incorporates an attached Simplified Upper Body Part (SUBP) that enables the impactor’s applicability to evaluate pedestrian’s lower limb injury risk also with high-bumper cars. The response surface methodology was applied to optimize both the aPLI’s lower limb and SUBP specifications, while imposing a total mass upper limit of 25 kg that complies with international standards for maximum weight lifting allowed for a single operator in the laboratory setting. All parameters were virtually optimized considering variable interaction, which proved critical to avoid misleading specifications.

In the urea SCR system, urea solution is injected by injector installed in the front stage of the SCR catalyst, and NOx can be purified on the SCR catalyst by using NH3 generated by the chemical reaction of urea. NH3 is produced by thermolysis of urea and hydrolysis of isocyanic acid after evaporation of water in the urea solution. But, biuret and cyanuric acid which may cause deposit are sometimes generated by the chemical reactions without generating NH3. Spray behavior and chemical reaction of urea solution injected into the tail-pipe are complicated. The purpose of this study is to reveal the spray behavior and NH3 generation process in the tail-pipe, and to construct the model capable of predicting those accurately. In this report, the impingement spray behavior is clarified by scattered light method in high temperature flow field. Liquid film adhering to the wall and deposit generated after evaporation of water from the liquid film are photographed by the digital camera.

The vehicle driveline suffers low frequency torsional vibration due to the abrupt change of input torque and torque fluctuation under variable frequency. This problem can be solved by model based control, so building a control oriented driveline model is extremely important. In this paper, an on-line recursive identification method is proposed for control oriented model and validated based on an electric car. First of all, the control oriented driveline model is simplified into a six-parameter model with double inertia. Secondly, based on stability analysis, motor torque and motor speed are chosen as input signal for on-line model identification. A recursive identification algorithm is designed and implemented based on Simulink. Meanwhile a detail model of the vehicle which considering driveline parameter variation is built based on ADAMS. Thirdly, on-line identification is conducted by using co-simulation of ADAMS and Simulink.

In this paper, a novel driver model is proposed to track vehicle speed in MIL (Model-in-the-Loop) test system, which has structural consistency with HIL (Hardware-in-the-Loop) test system. First, the MIL test system which contains models of driver, vehicle and test bench is established. Second, according to the connections of the established models in Matlab/Simulink environment, the vehicle speed is calculated in vehicle model. Emphatically, through the deviation between driving cycle speed and calculated vehicle speed, PI controller in driver model adjusts the vehicle speed to ideal point through sending the torque command to drive motor, the ILC (Iterative Learning Control) controller modifies and stores P value of PI controller. Then, in order to obtain the better modification of PI controller, iterative learning control algorithm is deeply researched in term of types and parameters.

For applying ISO 26262 to motorcycles, controllability classification (C class evaluation) by expert riders is considered an appropriate technique. Expert riders have evaluated commercial product development for years and can appropriately conduct vehicle tests while observing safety restrictions (such as avoiding the risk of falling). Moreover, expert riders can ride safely and can stably evaluate motorcycle performance even if the test conditions are close to the limits of vehicle performance. This study aims to construct a motorcycle C class evaluation method based on an expert rider’s subjective evaluation. On the premise that expert riders can rate the C class, we improved a test procedure that used a subjective evaluation sheet as the concrete C class evaluation method for an actual hazardous event.

The ever-growing number of interacting electronic vehicle control systems requires new control algorithms to manage the increasing system complexity. As a result, torque-based control architecture has been popular for its easy extension as the torque demand variable is the only interface between the engine control algorithms and other vehicle control systems. Under the torque-based control architecture, the engine and AT coordinated control for upshift process is investigated. Based on the dynamics analysis, quantitative relationship between the turbine torque of HTC and output shaft torque of AT has been obtained. Then the coordinated control strategy has been developed to smooth the torque trajectory of AT output shaft. The designed control strategy is tested on a powertrain simulation model in MATLAB/Simulink and a test bench. Through simulation, the shift time range in which the engine coordinated control strategy is effective is acquired.

Within the hydraulic shifting circuit of power-shift steering transmission, the performance of filter is generally characterized by the theoretical filtration ratio. However in practical work, the actual filtration ratio is far less than the theoretical ratio. On the basis of investigation on the structural characteristics, the oil flowing distribution and the filter mechanisms, the re-filtering rate ω and recontaminative rate θ are defined to simulate the actual filtering process. Therefore, the dynamic filtration ratio is modelled and simulated in MATLAB/Simulink to investigate that how the filtering rate ω and θ influence the dynamic filtration ratio and the deviation between the dynamic ratio and theoretical ratio. Afterwards, the variation of dynamic filtration ratio is tested through a filtration experiment under the circumstances of various flow rate, temperature and pressure.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

A CAE method has been developed to address engine tonal noise and whine due to the excitation from a gerotor oil pump. The method involves a multidisciplinary approach including CFD, frequency-response structural analysis and acoustic analysis. The results from the application of the method applied to a couple of pumps with different designs are discussed. Engine tonal noise improvement through reduction in the excitation source from the pump and also stiffening the excitation path from the pump to the engine are studied. The effect of component modal alignment with oil pump orders is addressed as well.

Lightweighting of vehicle panels enclosing vehicle cabin causes NVH degradation since engine, road, and wind noise acoustic sources propagate to the vehicle interior through these panels. In order to reduce this NVH degradation, there is a need to develop new NVH sound package materials and designs for use in lightweight vehicle design. Statistical Energy Analysis (SEA) model can be an effective CAE design tool to develop NVH sound packages for use in lightweight vehicle design. Using SEA can help engineers recover the NVH deficiency created due to sheet metal lightweighting actions. Full vehicle SEA model was developed to evaluate the high frequency NVH performance of “Vehicle A” in the frequency range from 200 Hz to 10 kHz. This correlated SEA model was used for the vehicle sound package optimization studies. Full vehicle level NVH laboratory tests for engine and tire patch noise reduction were also conducted to demonstrate the performance of sound package designs on “Vehicle A”.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

Magnesium die-cast alloys are known to have a layered microstructure composed of: (1) An outer skin layer characterized by a refined microstructure that is relatively defect-free; and (2) A “core” (interior) layer with a coarser microstructure having a higher concentration of features such as porosity and externally solidified grains (ESGs). Because of the difference in microstructural features, it has been long suggested that removal of the surface layer by machining could result in reduced mechanical properties in tested tensile samples. To examine the influence of the skin layer on the mechanical properties, a series of round tensile bars of varying diameters were die-cast in a specially-designed mold using the AM60 Mg alloy. A select number of the samples were machined to different final diameters. Subsequently, all of the samples (as-cast as well as machined) were tested in tension.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.