Particulate matter emissions from a heavy duty vehicle fuelled by petroleum diesel and used cooking oil blends

Fuel characteristic and exhaust particulate emissions tests were carried out for a EURO5 compliant Heavy Duty Vehicle operating on both pure petroleum diesel (PD) and used cooking oil (C2G Ultra Biofuel) PD blends under real world driving conditions. Fuel tests showed that fuel temperature, substitution ratio and engine speed play a key role in determining the spray characteristics of the Ultra Biofuel blends. However, under real world operating conditions, the Bioltec fuel blending system was found to overcome these effects by using lower C2G Ultra Biofuel:PD substitution ratios during cold start and low speed conditions. Overall the fuel tests suggested it to be convenient to operate the engine on blends with Ultra Biofuel content up to 80% to avoid higher fuel consumption and higher pollution load on the exhaust after treatment system, particularly at low temperatures and rpm. In the real world tests, average substitution ratios of 85% were achieved, with close to 100% Ultra Biofuel achieved for high speed steady state conditions, with no negative impact on particulate emissions. The vast majority (60-80%) of the particulate mass within the exhaust was found within size fractions below 2.5 μm for both fuels and was thus within the respirablem range. The PD produced around twice the concentration of particulates within these finer fractions compared to the equivalent trips using the blended fuel. Thermo-gravimetric Analysis demonstrated that the PD produced higher concentrations of black carbon (soot) and the Ultra Biofuel blends more organic carbon within the particulates. The tests demonstrate that when using an effective fuel substitution strategy, Ultra Biofuel has the potential to reduce both lifecycle CO2 and respirable particulate emissions leading to potential climate and air quality benefits

Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method

This article describes a method to model roof geometries from widely available low-resolution (2 m horizontal) Light Detection and Ranging (LiDAR) datasets for application on a city wide scale. The model provides roof area, orientation, and slope, appropriate for predictions of solar technology performance, being of value to national and regional policy makers in addition to investors and individuals appraising the viability of specific sites. Where present, similar buildings are grouped together based on proximity and building footprint dimensions. LiDAR data from all the buildings in a group is combined to construct a shared high-resolution LiDAR dataset. The best-fit roof shape is then selected from a catalogue of common roof shapes and assigned to all buildings in that group. Method validation was completed by comparing the model output to a ground-based survey of 169 buildings and aerial photographs of 536 buildings, all located in Leeds, UK. The method correctly identifies roof shape in 87% of cases and the modelled roof slope has a mean absolute error of 3.76°. These performance figures are only possible when segmentation, similar building grouping and ridge repositioning algorithms are used

Urban Wind: Characterization of Useful Gust and Energy Capture

Small-scale wind turbine operations within the urban environment are exposed to high levels of gusts and turbulence compared to flows over less rough surfaces. There is therefore a need for such systems to not only cope with, but to thrive under such fluctuating flow conditions. This paper addresses the potential importance of gust tracking technologies within the urban environment via the analysis of the additional energy present in the gusty wind resource using high resolution measurements at two urban roof-top locations. Results demonstrate significant additional energy present in the gusty wind resource at high temporal resolution. This energy is usually under-represented by the use of mean wind speeds in quantifying the power in the wind over longer averaging times. The results support the promise of capturing a portion of this extra energy through gust tracking solutions. The sensitivity of this “additional” wind energy to averaging time interval is also explored, providing useful information for the design of gust tracking or dynamic control algorithms for small-scale turbines. Relationships between turbulence intensity and excess energy available are drawn. Thus, an analytical model is proposed which may prove useful in predicting the excess energy available across wide areas from, for example, boundary layer turbulence models

Low temperature ignition properties of n-butanol: key uncertainties and constraints

A recent kinetic mechanism (Sarathy et al., 2012) describing the low temperature oxidation of n-butanol was investigated using both local and global sensitivity/uncertainty analysis methods with ignition delays as predictive targets over temperature ranges of 678-898 K and equivalence ratios ranging from 0.5-2.0 at 15 bar. The study incorporates the effects of uncertainties in forward rate constants on the predicted outputs, providing information on the robustness of the mechanism over a range of operating conditions. A global sampling technique was employed for the determination of predictive error bars, and a high dimensional model representation (HDMR) method was further utilised for the calculation of global sensitivity indices following the application of a linear screening method. Predicted ignition delay distributions spanning up to an order of magnitude indicate the need for better quantification of the most dominant reaction rate parameters. The calculated first-order sensitivities from the HDMR study show the main fuel hydrogen abstraction pathways via OH as the major contributors to the predicted uncertainties. Sensitivities indicate that no individual rate constant dominates uncertainties under any of the conditions studied, but that strong constraints on the branching ratio for H abstraction by OH at the α and γ sites are provided by the measurements

A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data

A boundary layer scaling (BLS) method for predicting long-term average near-surface wind speeds and power densities was developed in this work. The method was based on the scaling of reference climatological data either from long-term average wind maps or from hourly wind speeds obtained from high-resolution Numerical Weather Prediction (NWP) models, with case study applications from Great Britain. It incorporated a more detailed parameterisation of surface aerodynamics than previous studies and the predicted wind speeds and power densities were validated against observational wind speeds from 124 sites across Great Britain. The BLS model could offer long-term average wind speed predictions using wind map data derived from long-term observational data, with a mean percentage error of 1.5 % which provided an improvement on the commonly used NOABL (Numerical Objective Analysis of Boundary Layer) wind map. The boundary layer scaling of NWP data was not, however, able to improve upon the use of raw NWP data for near surface wind speed predictions. However, the use of NWP data scaled by the BLS model could offer improved power density predictions compared to the use of the reference data sets. Using a vertical scaling of the shape factor of a Weibull distribution fitted to the BLS NWP data, power density predictions with a 1 % mean percentage error were achieved. This provided a significant improvement on the use of a fixed shape factor which must be utilised when only long-term average wind speeds are available from reference wind maps. The work therefore highlights the advantages that use of a BLS model for wind speed and NWP data for power density predictions can offer for small to medium scale wind energy resource assessments, potentially facilitating more robust annual energy production and financial assessments of prospective small and medium scale wind turbine installations

Real world emissions performance of a HDD truckwith SCR NOx control

Air quality issues and Real Driving Emissions (RDE) in urban areas of cities Factors that influence RDE. Experimental equipment HDD truck RDE test for Euro V with SR

Evaluation of the effect of fuel properties on the fuel spray and jet characteristics in a HGV DI diesel engine operated by used cooking oils

Fuel injection systems in modern diesel engines are designed and built to comply with very stringent environmental standards. They should also meet the highest level of fuel economy. Drivability, rapid response and easy and accurate control are a common demand. Changing the fuel characteristics could affect the performance of the fuel injection system. This study focuses on the evaluation of fuel spray characteristics of straight used cooking oil (SUCO) and its blends with petroleum diesel (PD) as a surrogate for pure PD. Used cooking oil blends have quite different physical properties from those of pure PD. Data for the lower heating value (LHV), density and viscosity were obtained from laboratory analysis. These data were merged with the physical and thermodynamic conditions of the diesel engine of interest to evaluate the dynamic behaviour of the fuel jet in 360° of crank rotation namely, the compression stroke, and the power stroke including the injection process. Engine operational conditions were calculated using a diesel dual thermodynamic cycle taking into account fuel injection adjustment at three different speeds, namely, idle speed, maximum torque speed and rated power speed. The results showed that fuel jet characteristics vary with SUCO content in the fuel blend. Two ranges of SUCO content in the blends were distinguished, 0 – 80% SUCO content and 80 – 100% SUCO content. Both showed a constant rate of change of jet characters per 10% increase in SUCO content in the fuel blend. Lower rates of change of fuel characters were observed at 0-80% SUCO content. The higher the temperature, the lower the rate of change of fuel jet characteristics

Solar Resource Estimation Using a Radiative Transfer with Shading (RTS) Model

A combination of falling technology prices and government financial incentives has led to the rapid expansion of the solar microgeneration industry in the UK and around the world. With conditions for investment becoming more favourable, viability appraisal is now of great interest to potential investors ranging from individuals to large companies and authorities. A methodology to predict solar resource available to solar technologies in urban areas is presented that combines a radiative transfer simulation with shading derived from digital surface model (DSM) data. The radiative transfer simulation includes water vapour, ozone, clouds and surface albedo determined from MODIS satellite products along with aerosol properties that are derived from the GLOMAP model. A DSM is used to calculate the height of near-by obstacles and the horizon from each point of interest to establish when the site is in shade during a year. The site-level radiance field is adjusted by elements in shade and integrated over the view hemisphere of the photovoltaic (PV) panels to take into account panel tilt. Modelled annual global radiation (Wh/m2/a) estimations are validated using power output data from 17 sites across four major UK cities (Bristol, Cambridge, Leeds and Sheffield) and show good agreement with -3.68% and +2.62% mean percentage error under assumed performance ratio conversions of 0.75 and 0.8 respectively. The results are compared to the outputs of both Esri ArcGIS and PVGIS, the first of which predicted annual global radiation with -15.97% and -20.78% mean percentage error with the latter returning +3.34% and +10.23% mean percentage error for the 0.75 and 0.8 performance ratios