Automatic mechanism generation for the combustion of advanced biofuels: A case study for diethyl ether

Advanced biofuels have the potential to supplant significant fractions of conventional liquid fossil fuels. However, the range of potential compounds could be wide depending on selected feedstocks and production processes. Not enough is known about the engine relevant behavior of many of these fuels, particularly when used within complex blends. Simulation tools may help to explore the combustion behavior of such blends but rely on robust chemical mechanisms providing accurate predictions of performance targets over large regions of thermochemical space. Tools such as automatic mechanism generation (AMG) may facilitate the generation of suitable mechanisms. Such tools have been commonly applied for the generation of mechanisms describing the oxidation of non-oxygenated, non-aromatic hydrocarbons, but the emergence of biofuels adds new challenges due to the presence of functional groups containing oxygen. This study investigates the capabilities of the AMG tool Reaction Mechanism Generator for such a task, using diethyl ether (DEE) as a case study. A methodology for the generation of advanced biofuel mechanisms is proposed and the resultant mechanism is evaluated against literature sourced experimental measurements for ignition delay times, jet-stirred reactor species concentrations, and flame speeds, over conditions covering φ = 0.5–2.0, P = 1–100 bar, and T = 298–1850 K. The results suggest that AMG tools are capable of rapidly producing accurate models for advanced biofuel components, although considerable upfront input was required. High-quality fuel specific reaction rates and thermochemistry for oxygenated species were required, as well as a seed mechanism, a thermochemistry library, and an expansion of the reaction family database to include training data for oxygenated compounds. The final DEE mechanism contains 146 species and 4392 reactions and in general, provides more accurate or comparable predictions when compared to literature sourced mechanisms across the investigated target data. The generation of combustion mechanisms for other potential advanced biofuel components could easily capitalize on these database updates reducing the need for future user interventions

Predicting the physical properties of three-component lignocellulose derived advanced biofuel blends using a design of experiments approach

Acid-catalysed alcoholysis of lignocellulosic biomass produces a tailorable advanced biofuel blend, with the primary products being an alkyl levulinate, a dialkyl ether, and alcohol. Varying process parameters during production has the potential to produce differing quantities of the three components, affecting both physical and combustion properties. Starting alcohols, ethanol, n-butanol, and n-pentanol were chosen to investigate the effects of carbon chain length on the physical properties of model ethyl, butyl, and pentyl-based blends, produced from alcoholysis. Blends were designed to contain $50 vol% alkyl levulinate, with the remainder composed of the corresponding ether and alcohol. Existing fuel standards set limits for different physical and chemical properties that should be met to enhance commercial viability. In the present work, the flash point, density at 15 °C and kinematic viscosity at 40 °C (KV40) were measured for a range of three-component blends. The study also investigated the impact of diesel (EN 590 compliant) blending on these properties, at 0–95% volume diesel. A design of experiments approach selected optimal blends for testing and was used to develop predictive physical properties models based on polynomial fits. The predictive models for the properties of the three-component blends had average absolute relative deviations <5%, indicating their utility for predicting generalised blend properties. The models facilitated the determination of blend boundaries, within which the formulations would meet existing fuel standards limits. Flash points ranged from 26–57 °C and 54–81 °C for the butyl and pentyl-based blends without diesel, respectively. Densities at 15 °C ranged between 0.879–0.989 g cm−3 , 0.874–0.957 g cm−3 , and 0.878–0.949 g cm−3 for the ethyl, butyl and pentylbased blends without diesel, respectively. The KV40 ranged from 1.186–1.846 mm2 s −1 and 1.578–2.180 mm2 s −1 for butyl and pentyl-based blends without diesel, respectively. Butyl-based blends with diesel were found to be the most practically suitable and met the BS 2869 density limits

Laminar burning velocities and Markstein numbers for pure hydrogen and methane/hydrogen/air mixtures at elevated pressures

Spherically expanding flame propagations have been employed to measure flame speeds for H2/CH4/air mixtures over a wide range of H2 fractions (30 %, 50 %, 70 and 100 % hydrogen by volume), at initial temperatures of 303 K and 360 K, and pressures of 0.1, 0.5 and 1.0 MPa. The equivalence ratio (ϕ) was varied from 0.5 to 2.5 for pure hydrogen and from 0.8 to 1.2 for methane/hydrogen mixtures. Experimental laminar burning velocities and Markstein numbers for methane/hydrogen/air mixtures at high pressures, which are crucial for gas turbine applications, are very rare in the literature. Moreover, simulations using three recent chemical kinetic mechanisms (Konnov-2018 detailed reaction, Aramco-2.0-2016 and San Diego Methane detailed mechanism (version 20161214)) were compared against the experimentally derived laminar burning velocities. The maximum laminar burning velocity for 30 % and 50 % H2 occurs at ϕ = 1.1. However, it shifts to ϕ = 1.2 for 70 % H2 and to ϕ = 1.7 for a pure H2 flame. The laminar burning velocities increased with hydrogen fraction and temperature, and decreased with pressure. Unexpected behaviour was recorded for pure H2 flames at low temperature and ϕ = 1.5, 1.7 wherein ul did not decrease when the pressure increased from 0.1 to 0.5 MPa. Although, the measurement uncertainty is large at these conditions, the flame structure analysis showed a minimum decline in the mass fractions of the active species (H, O, and OH) with the rise in the initial pressure. Markstein length (Lb) and Markstein number (Mab and Masr) varied non-monotonically with hydrogen volume fraction, pressure and temperature. There was generally good agreement between simulations and experimentally derived laminar burning velocities, however, for experiments of rich-pure hydrogen at high initial pressures, the level of agreement decreased but remained within the limits of experimental uncertainty

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict

Output from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects

Mapping the wind resource over UK cities

Decentralised energy sources, such as small-scale-wind energy, have a number of well-known advantages. However, within urban areas, the potential for energy generation from the wind is not currently fully utilised. One of the most significant reasons for this is that the complexity of air flows within the urban boundary layer makes accurate predictions of the wind resource difficult to achieve. Without sufficiently accurate methods of predicting this resource, there is a danger that wind turbines will either be installed at unsuitable locations or that many viable sites will be overlooked. In this paper, we compare the accuracy of three different analytical methodologies for predicting above-roof mean wind speeds across a number of UK cities. The first is based upon a methodology developed by the UK Meteorological Office. We then implement two more complex methods which utilise maps of surface aerodynamic parameters derived from detailed building data. The predictions are compared with measured mean wind speeds from a wide variety of UK urban locations. The results show that the methodologies are generally more accurate when more complexity is used in the approach, particularly for the sites which are well exposed to the wind. The best agreement with measured data is achieved when the influence of wind direction is thoroughly considered and aerodynamic parameters are derived from detailed building data. However, some uncertainties in the building data add to the errors inherent within the methodologies. Consequently, it is suggested that a detailed description of both the shapes and heights of the local building roofs is required to maximise the accuracy of wind speed predictions

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

A detailed investigation of a measure-correlate-predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1-12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods. When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement

Investigation of the Effect of Correlated Uncertain Rate Parameters on a Model of Hydrogen Combustion Using a Generalized HDMR method

The High Dimensional Model Representation (HDMR) method has been applied in several previous studies to obtain global sensitivity indices of uncorrelated model parameters in combustion systems. However, the rate parameters of combustion models are intrinsically correlated and therefore uncertainty analysis methods are needed that can handle such parameters. A generalized HDMR method is presented here, which uses the Rosenblatt transformation on a correlated model parameter sample to obtain a sample of independent parameters. The method provides a full set of both correlated and marginal sensitivity indices. Ignition delay times predicted by an optimizedhydrogen air combustion model in stoichiometric mixtures near the three explosion limits are investigated with this new global sensitivity analysis tool. The sensitivity indices which account for all the correlated effects of the rate parameters are shown to dominate uncertainties in the model output. However these correlated indices mask the individual influence of parameters. The final marginal uncorrelated sensitivity indices for individual parameters better indicate the change of importance of homogeneous gas phase and species wall-loss reactions as the pressure is increased from above the first explosion limit to above the third limit. However, these uncorrelated indices are small and whilst they provide insights into the dominant chemical and physical processes of the model over the range of conditions studied, the correlations between parameters have a very significant effect. The implications of this result on model tuning will be discussed

Sid4p-Cdc11p assembles the septation initiation network and its regulators at the S. pombe SPB

10.1016/j.cub.2004.03.036Current Biology147579-584CUBL