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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.

An opposed piston two-stroke engine is more suitable for use in an unmanned aerial vehicle because of its small size, excellent self-balancing, stable operation, and low noise. Consequently, in this study, based on experimental data for a prototype opposed piston two-stroke engine, numerical simulation models were established using GT-POWER for 1D simulation and AVL-FIRE for 3D CFD simulation. The mesh grid and solver parameters for the numerical model of the CFD simulation were determined to guarantee the accuracy of the numerical simulation, before studying and optimizing the ventilation efficiency of the engine with different dip angles. Furthermore, the fuel spray and combustion were analyzed and optimized in details.

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.

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.

The electronic control of direct injection fuel system, which could improve engine fuel efficiency, dynamics and engine emission performance through good atomization, precise control of fuel injection time and improvement of fuel-gas mixture, is the key technology to achieve the stratified combustion and lean combustion. In this paper, a direct injection injector that based on voice coil motor was designed aiming at the technical characteristics of one 800cc two-stroke cam-less engine. Prior to a one - dimensional simulation model of injector was established by AMEsim and the maximal fuel injection demand was met via the optimization of the main parameters of the injector, the structure of the voice coil motor was optimized by magnetic equivalent circuit method. After that, the maximal flow rate of the injector was verified by the injector bench test while the atomization characteristic of the injector was verified by using a high-speed camera.

The combustion characteristics of hydrogen-air mixtures have significance significant impact on the performance and control of hydrogen-fueled internal combustion engines and the combustion velocity is an important parameter in characterizing the combustion characteristics of the mixture. A four-cylinder hydrogen internal combustion engine was used to study hydrogen combustion; the combustion characteristics of a hydrogen mixture were experimentally studied in a constant-volume incendiary bomb, and the turbulent premixed combustion characteristics of hydrogen were calculated and analyzed. Turbulent hydrogen combustion comes under the folded laminar flame model. The turbulent combustion velocity in lean hydrogen combustion is related not only to the turbulent velocity and the laminar burning velocity, but also to the additional turbulence term caused by the instability of the flame.

With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. To seek for an optimized volumetric ratio for ABE-diesel blends, the previous work in our team has experimentally investigated and analyzed the combustion features of ABE-diesel blends with different volumetric ratio (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %) in a constant volume chamber. It was found that an increased amount of acetone would lead to a significant advancement of combustion phasing whereas butanol would compensate the advancing effect. Both spray dynamic and chemistry reaction dynamic are of great importance in explaining the unique combustion characteristic of ABE-diesel blend. In this study, a semi-detailed chemical mechanism is constructed and used to model ABE-diesel spray combustion in a constant volume chamber.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

This study proposes a method for presenting maneuver request information of accelerator pedal to a driver via the accelerator pedal itself. By applying periodic force like vibration on an accelerator pedal, information is transferred to the driver without displacing the accelerator pedal. In this study, the authors focus on a saw-tooth wave as the periodic force. When the saw-tooth-waved force is applied on the accelerator pedal, a human driver feels as if the accelerator pedal is knocked by someone periodically. In addition, information about the quantity of requested maneuver can be transferred by the amplitude of the saw-tooth wave. Based on these facts, the saw-tooth wave is modified and optimized empirically with ten human drivers so that the information of direction is transferred most reliably. In addition, the relationship between the amplitude of the saw-tooth wave and requested quantity of the pedal maneuver that the drivers feel is formulated.

Finite Element (FE) models are widely used in automotive for vehicle design. Even with increasing speed of computers, the simulation of high fidelity FE models is still too time-consuming to perform direct design optimization. As a result, response surface models (RSMs) are commonly used as surrogates of the FE models to reduce the turn-around time. However, RSM may introduce additional sources of uncertainty, such as model bias, and so on. The uncertainty and model bias will affect the trustworthiness of design decisions in design processes. This calls for the development of stochastic model interpolation and extrapolation methods that can address the discrepancy between the RSM and the FE results, and provide prediction intervals of model responses under uncertainty.

ACOUSTOMIZE™ is a new method of acoustic evaluation used for the purpose of understanding and optimizing NVH performance of vehicles. The following paper documents a case study of the ACOUSTOMIZE™ test methodology on a passenger car BIW. This study includes an analysis of noise flow through BIW locations, a comparison of noise sound levels through BIW cavities with and without a sound treatment package and a comparison of the original cavity sealing design package consisting of baffles, tapes and baggies to low density polyurethane NVH Foam. The results of the study show detection of complex BIW pass throughs that the body leakage test (BLT) was not able to find. In addition, the data shows improved noise reduction with the low density polyurethane foam versus the original cavity sealing design package.

Hydrodynamic torque converter parameter integrated optimization design system is established based on tri-dimensional flow field theory. Design segments such as optimization initial values searching by meanline theory, cascade solid modeling, structure mesh of flow passage, CFD(computational fluid dynamics), DOE(design of experiment), RSM(response surface model)and optimization algorithm are integrated in this system and therefore a three dimensional optimization design method for hydrodynamic torque converter is presented and realized. An optimization design instance is accomplished by workstation computer cluster, and its result shows that speed and accuracy of design are improved and design system based on 3D flow field theory is accurate and effective.

Performances of a centrifugal turbocharger compressor are investigated and validated in this paper. Based on the validation results, numerical optimizations are performed using ANN and CFD methods. Different impeller geometry with free parameters controlling stacking laws, end-wall, blade sectional camber curves and corresponding performances are used as input layer of ANN in the optimization, while adiabatic total-to-total efficiency and total pressure ratio are used as output layer of the optimization cycle. With this method, the performances of the compressor investigated in this paper are improved notably.

In this paper, the finite element model for static analysis of an electric tractor's frame is presented firstly, and the rigidity and strength of one electric tractor's frame is calculated. Based upon the analysis results, the topology and shape of this electric tractor's frame is optimized. As to the topology optimization, the optimization goal under multiple load cases is defined and the frame is optimized by two steps-one is to determine the position of the transverse rails using solid elements which can simulate the material-filling space, another is to obtain the shape of the frame in which shell elements are applied as to increase the calculation efficiency. After the topology optimization the frame's stiffness is improved significantly but there still is local stress concentration. So the shape of the stress concentration area is optimized using control points method, and the greatest stress is reduced below the strength limits.

When the EFI system is used in a specific engine, lots of experiments are needed to optimize the control data (MAP). This work is time and financial consuming. This paper aims to develop a computer-based simulation and test system, which can produce the initial control MAP with good accuracy, and calibrate the ECU on-line. So the experiments are reduced and calibration is accelerated. In order to improve the accuracy of the initial control data, the mathematical models are built not only based on theoretical equations, but also on the control data of typical operation points, which is obtained by the on- line calibration of specific engines. This system can also perform some special calibrations, like "constant pulse width" and "square wave modulation."