Methodology for bus structure torsion stiffness and natural vibration frequency prediction based on a dimensional analysis approach

Engineering bus design requires testing of bus structures prototypes in order to guarantee a certain level of strength and an appropriate static and dynamic behavior of the bus superstructure when exposed to road loads. However, experimental testing of real bus structures is very expensive as it requires expensive resources and space. If testing is done on a scale bus model the previous required expenses are considerably reduced. Therefore, a novel methodology based on dimensional analysis applied to bus structure prediction to evaluate the bus structure static and dynamic performance is proposed. The static performance is evaluated attending to torsion stiffness and the dynamic in terms of the natural vibration frequencies and rollover threshold. A scale bus has been manufactured and dimensionless parameters have been defined in order to project the results obtained in the scale bus model to a larger model. Validation of the proposed methodology has been carried out under experimental and finite element analysis.     

Impact technique for measuring global dynamic stiffness of engine mounts

Engine mounts are used for engine vibration isolation. The dynamic performance of the mount depends on the orientation. Measurements of the dynamic properties of engine mounts are usually performed in the axial direction because of the problem related to actuator loading direction and set up costs. Impact technique is developed here to measure the dynamic driving point stiffness and driving point shear stiffness of engine mount in a single setup. The compressive and shear frequency-dependent stiffnesses are obtained in the vertical orientation. A transformation matrix is used to calculate the frequency-dependent stiffnesses and loss factors in other orientations. Three different designs of engine mounts are used to verify the accuracy of the transformation model. The correlation coefficient between calculation and measurement results show R²≥ 0.995 along the X- and Y-axes. For the Z-axis, mounts B and C showed R²≥ 0.95 and mount A 0.687 ≤ R²≤ 0.791.     

Optimal scheduling of the flexray static segment based on two-dimensional bin-packing algorithm

FlexRay is a reliable and hard real-time in-vehicle communication protocol that is strongly promoted by car manufacturers as the de facto standard in the automotive domain. The protocol offers both a time-triggered and an eventtriggered architecture. This paper focuses on the optimal scheduling of the time-triggered component of FlexRay known as the static (ST) segment using a two-dimensional bin-packing technique. To maximize the bandwidth utilization in the ST segment, a fast heuristic as well as an efficient integer linear programming approach are proposed. Our methods directly schedule signals into slots including frame packing, according to signal-based data scheduling and the slot/ cycle multiplexing mechanisms presented by the latest version of the FlexRay protocol. The benefits of our proposed methods are demonstrated by extensive experiments on synthetic and an automotive X-by-wire system case study. An additional test case is examined to emphasize the superior performance of the proposed approach relative to that of existing optimal scheduling approaches.     

Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.     

柴油机故障诊断专家系统知识库设计

在分析柴油机故障诊断知识特点的基础上,采用模糊化方法对柴油机运行状态参数进行处理,采用综合型知识方式来表达柴油机故障诊断知识,采用多库多层次的方式组织知识库,并对知识库采取一系列方式进行管理与维护。应用表明,柴油机故障诊断专家系统的预报准确率较高,具有较强的实用性。     

Dual extended Kalman filter for vehicle state and parameter estimation

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.     

Conditional modelling of tanker market risk using route specific freight rates

This paper provides statistical evidence in support of the view, widely held in the tanker industry, that there are systematic differences in the degree of risk involved in investing in tankers of different sizes, and in operating tankers in spot and time charter markets. The industry view, broadly supported by the results of this paper, is that larger vessels are 'risker' assets than smaller vessels, and operating vessels in the time-charter market is less risky than employing them on a spot basis. The results are obtained by using a method derived from the financial economics literature, which models both the conditional mean and variance of a variable, known as GARCH modelling. Only one other paper has applied this method to the tanker market, and these results provide confirmatory support of those findings.