Evaluation of low-temperature diesel combustion regimes with n-Heptane fuel in a constant-volume chamber

The concept of Low Temperature Combustion (LTC) has been advancing rapidly because it may reduce emissions of NOx and soot simultaneously. Various LTC regimes that yield specific emissions have been investigated by a great number of experiments. To accelerate the evaluation of the spray combustion characteristics of LTC, to identify the soot formation threshold in LTC, and to implement the LTC concept in real diesel engines, LTC is modeled and simulated. However, since the physics of LTC is rather complex, it has been a challenge to precisely compute LTC regimes by applying the available diesel combustion models and considering all spatial and temporal characteristics as well as local properties of LTC. In this paper, LTC regimes in a constant-volume chamber with n-Heptane fuel were simulated using the ECFM3Z model implemented in a commercial STAR-CD code. The simulations were performed for different ambient gas O₂ concentrations, ambient gas temperatures and injection pressures. The simulation results showed very good agreement with available experimental data, including similar trends in autoignition and flame evolution. In the selected range of ambient temperatures and O₂ concentrations, soot and NOx emissions were simultaneously reduced.     

Bonnet weight reduction and VRU protection: Design proposals implementing non-conventional materials

The use of non-conventional and innovative materials is increasingly important in automotive design. They allow reductions in the weights of structures with consequent reductions in fuel consumption and carbon dioxide emissions. At the same time, they ensure excellent safety levels. Once excellent results in terms of active and passive safety have been obtained, the attention of car manufacturers, and of the public opinion, is focused on the safety of vulnerable road users (VRU), pedestrians and cyclists. Some examples concerning the redesign of a bonnet with the double targets of weight reduction and pedestrian safety are illustrated. Hybrid metal/plastic and more conventional metal sheet solutions are compared. The static performance (stiffness and denting resistance), as well as the impact against a pedestrian head, are evaluated by means of FEM models along with actual regulations.     

Preview-Based Control of Suspension Systems for Commercial Vehicles

An active suspension with preview is developed for the rear axle of a commercial vehicle. The obtained improvements are promising and justify further investigation of the more feasible semi-active suspensions with preview. The inherent non-linearity of semi-active suspensions with switching shock absorbers and the need for controllers that can handle a broad class of control objectives has led to the development of several numerical control methods for both multi-level and continuously variable shock absorbers. The most promising control method and shock absorber type are selected and mounted in a test vehicle. Some first experimental results are discussed.     

Formulating a methodology for modelling revealed preference discrete choice data-the selectively replicated logit estimation

There are a number of studies on modelling with Revealed Preference (RP) data. It is a traditional technique and it is based on actual market data. The method has been extensively used in transportation as a tool for predicting travel demand. Although the method constitutes a relevant analysis on the process of modelling, it suffers from limitations, mainly associated with the lack of control over the experiment, that sometimes overwhelm the model results. This work proposes and tests a methodology for estimating a more efficient binary RP sample set. The objective is to develop and test a methodology that identifies and eliminates potentially irrational choices made. Responses are evaluated according to the set of trade-offs in values of time. Having identified these individuals they are eliminated from the original sample and a new sample is created, the selectively replicated (SR) sample. Original and SR samples are then re-estimated in a tree nested logit structure.     

Active Control of Non-stationary Response of Vehicles with Nonlinear Suspensions

Active control of non-stationary response of a single degree of freedom vehicle model with nonlinear passive suspension elements is considered in this paper. The method of equivalent linearization is used to derive the equivalent linear model and the optimal control laws are obtained by using stochastic optimal control theory based on full state information. Velocity squared quadratic damping and hysteresis type of stiffness nonlinearities are considered. The effect of the nonlinearities on the active system performance is studied. The performance of active suspensions with nonlinear passive elements is found to be superior to the corresponding passive suspension systems.     

Automated detection of lane-blocking freeway incidents using artificial neural networks

A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use.     

Optimization for Vehicle Suspension II: Frequency Domain

The objective of this study is optimizing the components design of a vehicle suspension system under excitation due to road roughness. The vehicle is modelled as a dynamic system made of masses interconnected by, linear, springs and dampers. The optimizing code provides values corresponding to the caracteristics of masses, dampers and springs which, within a range, minimize the objective function for a defined excitation. This objective function auantifies the vehicle comfort level.

The optimization method used is the sequential linear programming by iteratively applying the Simplex algorithm. The model response is obtained in frequency domain and the vehicle excitation can be either random or deterministic.

The exact nature of the optimization problem, objective function and restrictions, depend on the type of excitation considered.

In succeeding paragraphs, the problem formulation together with a comparison with other authors is presented.     

On altruists and egoists in activity participation and travel: who are they and do they live together?

Formulation and specification of activity analysis models require better understanding of time allocation behavior that goes beyond the more recent within household analyses to understand selfish and altruistic behavior and how this relates to travel behavior. Using data from 1,471 persons in a recent 2-day time use/activity diary and latent class cluster analysis we identify 11 distinct daily behaviors that span from the intensely self-serving to intensely altruistic. Predicted cluster membership is then used to study within household interactions. The analysis shows strong correlation exists between social role and patterns of altruistic behavior. However, a substantial amount of heterogeneity is also found within social roles. In addition, travel behavior is also very different among altruistic and self-serving time allocation groups. At the household level, a substantial number of households contain persons with similar behavior. Another group of households contains a mix of self-serving and altruistic persons that follow specialized household roles within their households. The majority of households, however, are populated by altruistic persons. Single person households are more likely to be in the self-serving groups but not in their entirety. Altruism at home is directed most often toward the immediate family members. This is less pronounced when we examine altruistic acts outside the home. Konstadinos G. Goulias is a professor of Geography at the University of California Santa Barbara, has been a professor of Civil Engineering at the Pennsylvania State University from 1991 to 2004, and he is the founder and chair of the TRB task force on moving activity-based approaches to practice. Kriste M. Henson is a technical staff member at Los Alamos National Laboratory in the Decision Applications Division and is currently pursing a Ph.D. in Geography at the University of California—Santa Barbara.