Review of Well-to-Wheel lifecycle emissions of liquefied natural gas heavy goods vehicles

It has been suggested that using liquefied natural gas as a fuel source for heavy goods vehicles could provide a reduction in greenhouse gas emissions. Various studies have investigated different aspects of the lifecycle emissions of natural gas heavy goods vehicles throughout the past decade, however, there has been little comparative analysis across these studies. This review provides a comprehensive examination of the well-to-wheel lifecycle emissions of liquefied natural gas for heavy goods vehicles in comparison to diesel, the current standard. A systematic selection criteria based on relevance to the defined well-to-wheel system boundary of liquefied natural gas as a fuel source for heavy goods vehicles, including greenhouse gas emissions, were augmented by the authors knowledge of the field. The various data are categorised by engine technology and model year (pre- and post-2015), average speed of the duty cycle, and then statistically analysed to identify clear trends and correlations in the emissions produced. The two primary factors affecting the well-to-wheel greenhouse gas emissions of natural gas heavy hoods vehicles are: (i) natural gas engine fuel efficiency relative to diesel, and (ii) methane leakage across the supply chain. Methane leakage rates are a significant uncertainty and range from 0.3 to 20 % of throughput. With long-term perspective of efficiency penalty (10 %) in natural gas engines, the well-to-wheel greenhouse gas emissions reduction of natural gas fuelled trucks against diesel is up to 10 %, which appears insufficient toward net zero emissions by 2050. The use of biomethane further reduces the greenhouse gas emissions by 34–66 % depending on the engine technology. Controlling fugitive methane emissions in the fuel production and supply chain remains critical

Natural gas fuel and greenhouse gas emissions in trucks and ships

Natural gas is a transport fuel which may help reduce greenhouse gas emissions in shipping and trucks. However, there is some disagreement regarding the potential for natural gas to provide significant improvements relative to current ships and trucks. In 2015, road freight represented ~7% of global energy related CO2 emissions, with international shipping representing ~2.6% of global emissions. These emissions are also expected to grow, with some estimates suggesting road freight emission growing by a third, and shipping emissions growing by between 50% and 250% from 2012 to 2050, making absolute emissions reductions challenging. In addition, reducing emissions in ships and trucks has proved technically difficult given the relatively long distances that ships and trucks travel. This paper documents a systematic review of literature detailing well-to-wheel/wake greenhouse gas emissions and economic costs in moving from diesel and heavy fuel oil to natural gas as a fuel for trucks and ships. The review found a number of important issues for greenhouse gas reduction. First, moderate greenhouse gas reductions of 10% were found when switching to natural gas from heavy fuel oil in shipping when comparing the lowest estimates. Comparing lowest well-to-wheel greenhouse gas emissions estimates for trucks, the benefit of switching to natural gas fuel is approximately a 16% reduction in greenhouse gas emissions. However, these emissions are highly variable, driven particularly by methane emissions in exhaust gas. Given this, in the worst cases natural gas ships and trucks emit more greenhouse gasses than the diesel trucks and heavy fuel oil ships that they would replace. It appears relatively cost effective to switch to natural gas as a transport fuel in ships and trucks. However, the limited emissions reduction potential raises questions for the ongoing role of natural gas to reduce greenhouse gas emissions in line with the challenging greenhouse gas reduction targets emerging in the transport sector

Net-zero solutions and research priorities in the 2020s

Key messages • Technological, societal and nature-based solutions should work together to enable systemic change towards a regenerative society, and to deliver net-zero greenhouse gas (GHG) emissions. • Prioritise research into efficient, low-carbon and carbon-negative solutions for sectors that are difficult to decarbonise; i.e. energy storage, road transport, shipping, aviation and grid infrastructure. • Each solution should be assessed with respect to GHG emissions reductions, energy efficiency and societal implications to provide a basis for developing long-term policies, maximising positive impact of investment and research effort, and guiding industry investors in safe and responsible planning

Estimating the Dynamic Characteristics of Road Vehicles Using Vibration Response Data

A developed and well-maintained road network is imperative for the distribution of freight in the modern world. During transportation, both passengers and products are subjected to dynamic motion due to the irregular nature of pavement surfaces. This dynamic interaction is difficult to accurately predict due to the random and nonstationary nature of pavements and the complicated (and often nonlinear) dynamic characteristics of vehicles. Accurately characterising the dynamic motion generated by vehicles during transport would provide significant benefits to numerous fields. One field of interest is in the development of protective packaging systems to prevent, or minimise, product damage occurring during the distribution phase. Often, the level of packaging used is far greater than required, resulting in excessive waste which is of significant environmental concern. Another is in evaluating the performance of heavy vehicles to prevent and minimise pavement damage. As a heavy vehicle passes over a pavement, dynamic forces are exerted onto the pavement and induce damage, resulting in rougher roads. The maximum allowable loads of heavy vehicles is constantly increasing, further emphasising the importance of designing suspension systems which are considered road-friendly. For both fields it is important to establish accurate estimates of the dynamic characteristics, namely the Frequency Response Function (FRF), of vehicles

Practical Considerations for Estimating Road Vehicle Frequency Response Functions from Response Data

Velocities of charged cloud and precipitation particles in thunderclouds change because of the electrical forces acting on them. It is shown in this paper that this change in their velocities will cause a change in their concentrations in the regions of the cloud having intense electrification. These changes in the concentrations of cloud and precipitation particles have been calculated here in a simplified model. In accordance with the recent observations, small regions of the cloud having strong electric fields imbedded in large scale but comparatively weaker electric fields have been assumed to exist over short periods of time inside thunderclouds. No particular charge generating mechanism has been considered. However, cloud particles have been assumed to carry positive charge and the precipitation particles negative charge. A uniform and constant updraft has been assumed to exist for short periods of time in these small regions of the cloud. The results depict accumulation of the particles of certain sizes and charge densities in the regions of cloud having intense electrification. Dependence of this accumulation of particles on the electric field, charge and size of the particles, updraft velocity, and the size of the region of intense electrification has been studied. Variations of these accumulations with time have also been examined. Some possible consequences and implications of these calculations have been broadly discussed

A multi-resolution time domain technique for monitoring fatigue progression in elements subjected to random loads

Materials and structures subjected to random loading can deteriorate in a complex fashion. A technique for monitoring the manner in which this decay occurs can be useful, not in the least, for comparative analysis. One method for monitoring structural deterioration is to continually track variations in the system's modal parameters. Modal parameters are often extracted using the system's frequency response function, obtained using the Fourier transform. However, for continual parameter extraction, the Fourier transform requires that a compromise be made between the spectral accuracy of the estimates and how frequently they can be obtained. This compromise significantly limits the potential of Fourier transform based techniques as continuous structural integrity assessment tools. The technique presented herein applies the Hilbert transform to the system's instantaneous impulse response function, captured using the coefficients of an adaptive finite-impulse-response filter, in order to continually monitor shifts in the system's natural frequency. This approach allows for the properties of systems to be evaluated at regular intervals without compromising spectral uncertainty. Numerous damage scenarios were performed (using both physical and numerical systems) in order to test the sensitivity of the technique as well as its ability to converge with changes in system characteristics