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This research focuses on the commercial 6111 aluminum alloy as the subject of investigation. By designing tensile specimens with the same characteristic dimensions but varying fillet radii, the effects of fillet radius on the tensile properties and stress concentration effects of the aluminum alloy were studied through tensile testing and digital image correlation techniques. The results demonstrate that with an increase in fillet radius, the failure strength and stress distribution of the aluminum alloy specimens have both undergone alterations. This phenomenon can be attributed to the reduction of stress concentration at the fillet due to the larger fillet radius. Further verification through digital image correlation reaffirms that samples with a fillet radius of 10mm exhibit notable stress concentration effects at the fillet, while specimens with a fillet radius increased to 40mm display uniform plastic deformation across the parallel section.

In-cylinder pressure measurement is an important tool in internal combustion engine research and development for combustion, cycle performance, and knock analysis in spark-ignition engines. In a typical laboratory setup, a sub crank angle resolved (typically between 0.1o and 0.5o) optical encoder is installed on the engine crankshaft, and a piezoelectric pressure transducer is installed in the engine cylinder. The charge signal produced by the transducer due to changes in cylinder pressure during the engine cycle is converted to voltage by a charge amplifier, and this analog voltage is read by a high-speed data acquisition (DAQ) system at each encoder trigger pulse. The high speed of engine operation and the need to collect hundreds of engine cycles for appropriate cycle-averaging requires significant processor speed and memory, making typical data acquisition systems very expensive.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

Adhesively bonded joints have been applied in the automotive industry for the past few decades due to their advantages such as higher fatigue resistance, light weight, capability of joining dissimilar materials, good energy absorption, and high torsional stiffness for overall body structure. They also provide an effective seal against noise and vibration at a low cost. There exists the challenge of defining the fatigue characteristics of adhesive joints under cyclic loading conditions, and conventional methods have limitations in detecting the crack initiation of a bonded joint. This study introduces a method of detecting crack initiation by using the frequency method. It is found that stiffness change in the system is highly correlated to change in natural frequencies. By monitoring the change in natural frequencies, the crack initiation can be detected.

In the automotive industry, performing steady-state tests on an internal combustion engine can be a time consuming and costly process, but it is necessary to ensure the engine meets performance and emissions criteria set by the manufacturer and regulatory agencies. Any measures that can reduce the amount of time required to complete these testing campaigns provides significant benefits to manufacturers. The purpose of this work is then to develop a systematic approach to minimize the time required to conduct a steady-state engine test campaign using a Savitsky-Golay filter to calculate measured signal gradients for continuous steady-state detection. Experiments were conducted on an Armfield CM11-MKII Gasoline Engine test bench equipped with a 1.2L 3-cylinder Volkswagen EA111 R3 engine. The test bench utilizes throttle position control and an eddy current dynamometer braking system with automatic PID control of engine speed.

Vacuum suction cups are used as transforming handles in stamping lines, which are essential in developing automation and mechanization. However, the vacuum suction cup will crack due to fatigue or long-term operation or installation angle, which directly affects production productivity and safety. The better design will help increase the cups' service life. If the location of stress concentration can be predicted, this can prevent the occurrence of cracks in advance and effectively increase the service life. However, the traditional strain measurement technology cannot meet the requirements of tracking large-field stains and precise point tracking simultaneously in the same area, especially for stacking or narrow parts of the suction cups. The application must allow multiple measurements of hidden component strain information in different fields of view, which would add cost.

This paper addresses the uncertainty quantification of time-dependent problems excited by random processes represented by Karhunen Loeve (KL) expansion. The latter expresses a random process as a series of terms involving the dominant eigenvalues and eigenfunctions of the process covariance matrix weighted by samples of uncorrelated standard normal random variables. For many engineering appli bn vb nmcations, such as random vibrations, durability or fatigue, a long-time horizon is required for meaningful results. In this case however, a large number of KL terms is needed resulting in a very high computational effort for uncertainty propagation. This paper presents a new approach to generate time trajectories (sample functions) of a random process using KL expansion, if the time horizon (duration) is much larger than the process correlation length.

Fasteners, commonly used in automotive industry, play an important role in the safety and reliability of the vehicle structural system. In practical application, bolted joints would never undergo fully reversed loading; there always will be positive mean stress on bolt. The mean stress has little influence on the fatigue life if the maximum stress is lower than a threshold which is near the yield stress of the bolt. However, when the sum of the mean stress and the stress amplitude exceeds the threshold, the endurance limit stress amplitude decreases fast as the mean stress increases. The purpose of this paper is to research the fatigue endurance limit of a fastener and establish the threshold for safe design in automotive application. In order to obtain the fatigue endurance limit at different mean stress levels, various mechanical tests were performed on M12x1.75 and M16x1.5 Class 10.9 fasteners using MTS test systems.

Fatigue testing is a specialized form of mechanical testing that is performed by applying cyclic loading to a coupon or structure. Two common forms of fatigue testing are load controlled high cycle and strain controlled low cycle fatigue. Some strain measurement device, such as extensometers, strain gage, that are often used as a feedback sensor on strain controlled fatigue test. However, in applications where strain controlled fatigue testing could face some extreme conditions as well as high temperature and unusual sizing which requires the strain measurement to be nondestructive and full field. While digital image correlation (DIC), an advanced optical measurement technique, has a decent solution on challenges of fatigue testing measurement. The problem is how to turn DIC from a measurement system to a feedback controller unit. Due to the developments in camera and computation techniques, the sequential process can now be performed as a parallel process.

In this research, ball-on-plate reciprocating sliding wear tests were utilized on austempered and quench-tempered gray cast iron samples with and without shot-peening treatment. The wear volume loss of the gray cast iron samples with different heat treatment designs was compared under equivalent hardness. The phase transformation in the matrix was studied using metallurgical evaluation and hardness measurement. It was found that thin needle-like ferrite became coarse gradually with increasing austempering temperature and was converted into feather-like shape when using the austempering temperatures of 399°C (750°F). The residual stress on the surface and sub-surface before and after shot-peening treatment was analyzed using x-ray diffraction. Compressive residual stress was produced after shot-peening treatment and showed an increasing trend with austempering temperature.

Design of sheet metal drawing processes requires accurate information about the distribution of restraining forces, which is usually accomplished by a set of drawbeads positioned along the perimeter of the die cavity. This study is targeting bringing together the results of finite element analysis and experimental data in order to understand the most critical factors influencing the restraining force. The experimental study of the restraining force was performed using drawbead simulator tool installed into a tensile testing machine. Based upon the experimental results, it was observed that the restraining force of the given drawbead configuration is dependent upon the depth of bead penetration, friction between the drawbead surfaces as well as the clearance between the flanges of the drawbead simulator. This clearance is often adjusted during stamping operations to increase or decrease material inflow into the die cavity without any modification in the die.

This paper describes a laboratory-based test system and procedure for determining the durability of RTV sealant with fretting movement. A test machine is described in which shear and tensile stress-generating displacements at room temperature and temperature of 100°C are produced to load an RTV seal. The test system utilizes an air pressurized hollow cylinder with a cap sealed by RTV sealant on a reciprocating test rig. An external air leakage monitoring system detects the health of the tested RTV seal. When air leakage occurs, the seal is determined to have failed. RTV sealant used in the test was fully cured at room temperature and then aged with engine oil. In the experiments, a total of 6 displacements were used to generate cycle/amplitude graphs for both shear and tensile modes. Failures were determined to be caused by the loss of adhesion in tensile mode, and by crack nucleation due to the special step design in shear mode.

Creating a fuel map for simulation of an engine with Variable Valve Actuation (VVA) can be computationally demanding. Design of Experiments (DOE) and metamodeling is one way to address this issue. In this paper, we introduce a sequential process to generate an engine fuel map using Kriging metamodels which account for different engine characteristics such as load and fuel consumption at different operating conditions. The generated map predicts engine output parameters such as fuel rate and load. We first create metamodels to accurately predict the Brake Mean Effective Pressure (BMEP), fuel rate, Residual Gas Fraction (RGF) and CA50 (Crank Angle for 50% Heat Release after top dead center). The last two quantities are used to ensure acceptable combustion. The metamodels are created sequentially to ensure acceptable accuracy is achieved with a small number of simulations.

While experimental data measured directly on the engine are very valuable, there is a limitation of what measurements can be made without modifying the engine or the process that is being investigated, such as cylinder temperature. In order to supplement the experimental results, a Three Pressure Analysis (TPA) GT-Power model of the Cooperative Fuel Research (CFR) engine was previously developed and validated for estimating cylinder temperature and residual fraction. However, this model had only been validated for normal and knocking spark ignition (SI) combustion with RON-like intake conditions (naturally aspirated, <52 °C). This work presents improvements made to the GT-Power model and the expansion of its use for HCCI combustion. The burn rate estimation sub-model was modified to allow for low temperature heat release estimation and compression ignition operation.

Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.

Given increasingly stringent emission targets, engine efficiency has become of foremost importance. While increasing engine compression ratio can lead to efficiency gains, it also leads to higher in-cylinder pressure and temperatures, thus increasing the risk of knock. One potential solution is the use of a Variable Compression Ratio system, which is capable of exploiting the advantages coming from high compression ratio while limiting its drawbacks by operating at low engine loads with a high compression ratio, and at high loads with a low compression ratio, where knock could pose a significant threat. This paper describes the design of a model for the evaluation of fuel consumption for an engine equipped with a VCR system over representative drive cycles. The model takes as inputs; a switching time for the VCR system, the vehicle characteristics, engine performance maps corresponding to two different compression ratios, and a drive cycle.

Variable Compression Ratio systems are an increasingly attractive solution for car manufacturers in order to reduce vehicle fuel consumption. By having the capability to operate with a range of compression ratios, engine efficiency can be significantly increased by operating with a high compression ratio at low loads, where the engine is normally not knock-limited, and with a low compression ratio at high load, where the engine is more prone to knock. In this way, engine efficiency can be maximized without sacrificing performance. This study aims to analyze how the effectiveness of a VCR system is affected by various powertrain and vehicle parameters. By using a Matlab model of a VCR system developed in Part 1 of this work, the influence of the vehicle characteristics, the drive cycle, and of the number of stages used in the VCR system was studied.

In order to constrain the restraining force and control the speed of metal flow, drawbeads are widely used in industry. They prevent wrinkling or necking in formed panels, reduce the binder force, and minimize the usage of sheet metal to make a part. Different drawbead configurations can satisfy various stamping production. Besides local design of drawbeads, other factors like pulling directions, binder angles and single or multiple beads play an important role too. Moreover, it was found that the same beads configuration can own a different rate of change of pulling force on different gaps by experience. In this paper, to study the effect of each factor, the Aluminum and Steel sheet metals were tested to obtain the pulling force as they passed through a draw bead. Three gap cases between a male and a female beads are set to figure out the trend of pulling force.

Flame speed data reported in most literature are acquired in conventional apparatus such as the spherical combustion bomb and counterflow burner, and are limited to atmospheric pressure and ambient or slightly elevated unburnt temperatures. As such, these data bear little relevance to internal combustion engines and gas turbines, which operate under typical pressures of 10-50 bar and unburnt temperature up to 900K or higher. These elevated temperatures and pressures not only modify dominant flame chemistry, but more importantly, they inevitably facilitate pre-ignition reactions and hence can change the upstream thermodynamic and chemical conditions of a regular hot flame leading to modified flame properties. This study focuses on how auto-ignition chemistry affects flame propagation, especially in the negative-temperature coefficient (NTC) regime, where dimethyl ether (DME), n-heptane and iso-octane are chosen for study as typical fuels exhibiting low temperature chemistry (LTC).

We present an Accelerated Life Testing (ALT) methodology along with a design for fatigue approach, using Gaussian or non-Gaussian excitations. The accuracy of fatigue life prediction at nominal loading conditions is affected by model and material uncertainty. This uncertainty is reduced by performing tests at a higher loading level, resulting in a reduction in test duration. Based on the data obtained from experiments, we formulate an optimization problem to calculate the Maximum Likelihood Estimator (MLE) values of the uncertain model parameters. In our proposed ALT method, we lift all the assumptions on the type of life distribution or the stress-life relationship and we use Saddlepoint Approximation (SPA) method to calculate the fatigue life Probability Density Functions (PDFs).