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It is challenging to perform probabilistic analysis and design of large-scale structures because probabilistic analysis requires repeated finite element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability based design optimization of large scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for many probability distributions of the design variables by performing a single Monte Carlo simulation.

In this study a dynamic simulation was conducted on a forged steel crankshaft from a single cylinder four stroke engine. Finite element analysis was performed to obtain the variation of the stress magnitude at critical locations. The dynamic analysis resulted in the development of the load spectrum applied to the crankpin bearing. This load was then applied to the FE model and boundary conditions were applied according to the engine mounting conditions. Results obtained from the aforementioned analysis were then used in optimization of the forged steel crankshaft. Geometry, material, and manufacturing processes were optimized using different geometric constraints, manufacturing feasibility, and cost. The first step in the optimization process was weight reduction of the component considering dynamic loading. This required the stress range under dynamic loading not to exceed the magnitude of the stress range in the original crankshaft.

During metal forming processes such as rolling and forging, deformable manganese sulfide (MnS) inclusions become elongated. Such elongated MnS inclusions can have considerable adverse effects on mechanical properties, if the inclusions are not aligned with the loading direction. The objectives of this study were to evaluate and compare fatigue, monotonic tensile and CVN impact behavior of SAE 4140 steel with high (0.077% S), low (0.012% S) and ultra low (0.004% S) sulfur contents at two hardness levels (40 HRC and 50 HRC). The longitudinally oriented samples at 40 HRC, where MnS inclusions were oriented along the loading direction, did not exhibit any significant sensitivity of tensile or fatigue properties to the sulfur content. For the transversely oriented MnS inclusions, however, the monotonic tensile test results indicate very low ductility of the high sulfur material at both hardness levels, where specimens failed shortly after yielding.

In this study a dynamic simulation was conducted on a crankshaft from a single cylinder four stroke engine. Finite element analysis was performed to obtain the variation of stress magnitude at critical locations. The pressure-volume diagram was used to calculate the load boundary condition in dynamic simulation model, and other simulation inputs were taken from the engine specification chart. The dynamic analysis was done analytically and was verified by simulation in ADAMS which resulted in the load spectrum applied to crank pin bearing. This load was applied to the FE model in ABAQUS, and boundary conditions were applied according to the engine mounting conditions. The analysis was done for different engine speeds and as a result critical engine speed and critical region on the crankshaft were obtained. Stress variation over the engine cycle and the effect of torsional load in the analysis were investigated.

In reliability design, often, there is scarce data for constructing probabilistic models. Probabilistic models whose parameters vary in known intervals could be more suitable than Bayesian models because the former models do not require making assumptions that are not supported by the available evidence. If we use models whose parameters vary in intervals we need to calculate upper and lower bounds of the failure probability (or reliability) of a system in order to make design decisions. Monte Carlo simulation can be used for this purpose, but it is too expensive for all but very simple systems. This paper proposes an efficient Monte-Carlo simulation approach for estimation of upper and lower probabilities. This approach is based on two ideas: a) use an efficient approach for reliability reanalysis of a system, which is introduced in this paper, and b) approximate the probability distribution of the minimum and maximum failure probabilities using extreme value statistics.

Probabilistic studies can be prohibitively expensive because they require repeated finite element analyses of large models. Re-analysis methods have been proposed with the premise to estimate accurately the dynamic response of a structure after a baseline design has been modified, without recalculating the new response. Although these methods increase computational efficiency, they are still not efficient enough for probabilistic analysis of large-scale dynamic systems with low failure probabilities (less or equal to 10-3). This paper presents a methodology that uses deterministic and probabilistic re-analysis methods to generate sample points of the response. Subsequently, tail modeling is used to estimate the right tail of the response PDF and the probability of failure a highly reliable system. The methodology is demonstrated on probabilistic vibration analysis of a realistic vehicle FE model.

In reliability design, often, there is scarce data for constructing probabilistic models. It is particularly challenging to model uncertainty in variables when the type of their probability distribution is unknown. Moreover, it is expensive to estimate the upper and lower bounds of the reliability of a system involving such variables. A method for modeling uncertainty by using Polynomial Chaos Expansion is presented. The method requires specifying bounds for statistical summaries such as the first four moments and credible intervals. A constrained optimization problem, in which decision variables are the coefficients of the Polynomial Chaos Expansion approximation, is formulated and solved in order to estimate the minimum and maximum values of a system’s reliability. This problem is solved efficiently by employing a probabilistic re-analysis approach to approximate the system reliability as a function of the moments of the random variables.

The crash performance of an automobile largely depends on the ability to identity impact damage, maintain the passenger safety through deployment of various safety restraint systems, and steer away the vehicle from impact. So, this work is focused on the impact response of an automobile structure so as to find the location, magnitude of impact and asses the severity of damage. The results of the developed generalized forward plate model compared within 2% for FEM and previous other theoretical approaches. The inverse model compared within 7% for location and reconstructed force. Damage severity assessment is also investigated.

A vehicle steering knuckle undergoes time-varying loadings during its service life. Fatigue behavior is, therefore, a key consideration in its design and performance evaluation. This research program aimed to assess fatigue life and compare fatigue performance of steering knuckles made from three materials of different manufacturing processes. These include forged steel, cast aluminum, and cast iron knuckles. In light of the high volume of forged steel vehicle components, the forging process was considered as base for investigation. Monotonic and strain-controlled fatigue tests of specimens machined from the three knuckles were conducted. Static as well as baseline cyclic deformation and fatigue properties were obtained and compared. In addition, a number of load-controlled fatigue component tests were conducted for the forged steel and cast aluminum knuckles. Finite element models of the steering knuckles were also analyzed to obtain stress distributions in each component.

This paper presents an air-fuel ratio control of a spark ignition (SI) engine. The control strategy depends on the evaluation of the model parameters such as fuel puddle parameters, time constant and delay time values using fuzzy logic. These values in turn are used in the air-fuel ratio control that implements self-tuning regulator (STR) system. The effectiveness of the proposed design is demonstrated through simulation for various throttle transients. The results obtained show that the air-fuel ratio is maintained within 0.5% of the commanded stoichiometric value.

Fatigue is the primary cause of failure of crankshafts in internal combustion engines. The cyclic loading conditions and the stress concentrations in the crank pin fillets are unavoidable, and can result in fatigue failure. The objectives of this study were to compare the fatigue behavior of forged steel and ductile iron crankshafts from a one-cylinder engine as well as to determine if the fatigue life of a crankshaft can be accurately estimated using fatigue life predictions. Monotonic tensile tests as well as strain-controlled fatigue tests were conducted using specimens machined from the crankshafts to obtain the monotonic and cyclic deformation behavior and fatigue properties of the two materials. The forged steel had higher tensile strength and better fatigue performance than the ductile cast iron. Charpy v-notch impact tests were also conducted using specimens machined from the crankshafts to obtain and compare the impact toughness of the materials.