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

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

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

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

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

In-street wind direction variability in the vicinity of a busy intersection in central London

We present results from fast-response wind measurements within and above a busy intersection between two street canyons (Marylebone Road and Gloucester Place) in Westminster, London taken as part of the DAPPLE (Dispersion of Air Pollution and Penetration into the Local Environment; www.dapple.org.uk) 2007 field campaign. The data reported here were collected using ultrasonic anemometers on the roof-top of a building adjacent to the intersection and at two heights on a pair of lamp-posts on opposite sides of the intersection. Site characteristics, data analysis and the variation of intersection flow with the above-roof wind direction (θref) are discussed. Evidence of both flow channelling and recirculation was identified within the canyon, only a few metres from the intersection for along-street and across-street roof-top winds respectively. Results also indicate that for oblique rooftop flows, the intersection flow is a complex combination of bifurcated channelled flows, recirculation and corner vortices. Asymmetries in local building geometry around the intersection and small changes in the background wind direction (changes in 15-min mean θref of 5–10 degrees) were also observed to have profound influences on the behaviour of intersection flow patterns. Consequently, short time-scale variability in the background flow direction can lead to highly scattered in-street mean flow angles masking the true multi-modal features of the flow and thus further complicating modelling challenges

Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus

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