Public responses to policies designed to internalise environmental and social externalities from road transport in New Zealand

Based on a public survey of registered voters, we explore four options for internalising the social and environmental costs of road transport. The options were presented together with generalised factual information about their benefits and costs. Respondents are highly supportive of fuel use efficiency standards and exhaust gas quality standards, with lesser support for proposed initiatives of road user charges and speed reduction. Demographic analysis of responses enables identification of those who might favour or oppose particular options. In this case women are identified as being strongly supportive of the speed reduction option.     

Panama Canal expansion: alleviating global climate change

This paper quantifies likely changes in greenhouse gas emissions which contribute to anthropogenic climate change, resulting from the expansion of the Panama Canal and the consequent increase in ship-borne commercial transportation from east Asia to the US east coast. Ocean transportation offers higher fuel economy and lower pollutant emissions compared with land transport. Additionally, truck and train transportation of cargo along the US land bridge threatens to overwhelm existing highway and rail systems and limit economic growth. The alternate transportation route for easterly transit of the Panama Canal will reduce overland traffic congestion and enhance economic development by maintaining freight flow efficiency.     

Aging characterization of metals for exhaust systems

The mechanical characteristics of four materials used in automotive exhaust systems have been compared after an aging treatment to evaluate the combined effects of thermo-mechanical fatigue and corrosion. For this purpose, an experimental aging procedure has been developed. This procedure is composed of chemical, thermal and mechanical cycles, which are combined and repeated to simulate the actual operating conditions of automotive exhaust systems. Three austenitic steels (AISI 309, AISI 316Ti, and AISI 321) and a nickel-based alloy (Inconel 625) are tested. The results show that Inconel 625 and AISI 309 are less affected by the aging process than the other materials.     

Effects of ground vehicle inclination on underhood compartment cooling

This work reports on experiments carried out on a real passenger vehicle in a large wind tunnel to investigate the effects of car inclination on underhood cooling. The vehicle??s underhood is instrumented by thermocouples that measure the temperatures of many underhood components during different car functioning modes. Measurements are carried out for three thermal functioning points at car speeds 90, 110 and 130 km/h. In these experiments the engine was in operation and the front wheels positioned on the test facility with power-absorption-controlled rollers. Three car inclinations are investigated: flat, uphill and downhill. The results show that car inclination, even if very small, can have significant effects on underhood cooling and must be taken into consideration in numerical simulations and controlled in experimental tests. It was shown that down-hill and up-hill inclinations increase temperatures of components, air zones and engine parameters in the underhood, with higher effect for the down-hill inclination. Temperature increases in constant-speed driving phase are higher than in thermal soak phase.     

Direct method for optimal power management in hybrid electric vehicles

Hybrid electric vehicles are powered by an electric system and an internal combustion engine. The components of a hybrid electric vehicle need to be coordinated in an optimal manner to deliver the desired performance. This paper presents an approach based on direct method for optimal power management in hybrid electric vehicles with inequality constraints. The approach consists of reducing the optimal control problem to a set of algebraic equations by approximating the state variable which is the energy of electric storage, and the control variable which is the power of fuel consumption. This approximation uses orthogonal functions with unknown coefficients. In addition, the inequality constraints are converted to equal constraints. The advantage of the developed method is that its computational complexity is less than that of dynamic and non-linear programming approaches. Also, to use dynamic or non-linear programming, the problem should be discretized resulting in the loss of optimization accuracy. The propsed method, on the other hand, does not require the discretization of the problem producing more accurate results. An example is solved to demonstrate the accuracy of the proposed approach. The results of Haar wavelets, and Chebyshev and Legendre polynomials are presented and discussed.     

Combined effect of boost pressure and injection timing on the performance and combustion of CNG in a DI spark ignition engine

There is an increasing interest in supercharging spark ignition engines operating on CNG (compressed natural gas) mainly due to its superior knock resisting properties. However, there is a penalty in volumetric efficiency when directly injecting the gaseous fuel at early and partial injection timings. The present work reports the combined effects of a small boost pressure and injection timing on performance and combustion of CNG fueled DI (direct injection) engine. The experimental tests were carried out on a 4-stroke DI spark ignition engine with a compression ratio of 14. Early injection timing, when inlet valves are still open (at 300°BTDC), and partial injection timing, in which part of the injection occurs after the inlet valves are closed (at 180°BTDC), were varied at each operating speed with variation of the boost pressure from 2.5 to 10 kPa. A narrow angle injector (NAI) was used to increase the mixing rate at engine speeds between 2000 and 5000 rpm. Similar experiments were conducted on a naturally aspirated engine and the results were then compared with that of the boosting system to examine the combined effects of boost pressure and injection timing. It was observed that boost pressure above 7.5 kPa resulted in an improvement of performance and combustion of CNG DI engine at all operating speeds. This was manifested in the faster heat release rates and mass fraction burned that in turn improved combustion efficiency of the boosting system. An increased in cylinder pressure and temperature was also observed with boost pressure compared to naturally aspirated engine. Moreover, the combustion duration was reduced due to concentration of the heat release near to the top dead center as the result of the boost pressure. Supercharging was also found to reduce the penalty of volumetric efficiency at both the simulated port and partial injection timings.     

NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

Summary Two NARX-type neural networks are developed for modelling nonlinear dynamic characteristics of passive twin-tube hydraulic dampers used in vehicle suspension systems. Quasi-isothermal and variable temperature NARX models are rigorously tested and compared with a state-of-the-art physical model proposed by Duym and Reybrouck (1998) and Duym (2000). Measured damper data, generated under isothermal and temperature varying conditions, is used for NARX training, physical model calibration, and predictive comparisons. Test kinematics include high amplitude sinusoidal displacements up to 14 Hz, and realistic random road profiles. The NARX models are trained via 'teacher forcing' and the feedforward backpropagation algorithm using both 'Early Stopping' and Bayesian Regularisation. Stable network design is also examined using the minimum posterior prediction error as the criterion for selecting a good network from a small number of tests. Calibration of the physical model proves highly complicated owing to considerable nonlinearity-in-the-parameters, requiring use of Sequential Quadratic Programming with an implicitly nonlinear constraint. The paper shows that NARX neural network modelling is vastly superior in terms of calibration efficiency, and prediction times, whilst offering roughly similar, if not better, model accuracy.