Filling the gap: A 60 ky record of mixed carbonate-siliciclastic turbidite deposition from the Great Barrier Reef

Late Pleistocene to Holocene margin sedimentation on the Great Barrier Reef, a mixed carbonatesiliciclastic margin, has been explained by a transgressive shedding model. This model has challenged widely accepted sequence stratigraphic models in terms of the timing and type of sediment (i.e. carbonate vs. siliciclastic) deposited during sea-level oscillations. However, this model documents only hemipelagic sedimentation and the contribution of coarse-grained turbidite deposition, and the role of submarine canyons in this process, remain elusive on this archetypal margin. Here we present a new model of turbidite deposition for the last 60 ky in the north-eastern Australia margin. Using highresolution bathymetry, 58 new and existing radiometric ages, and the composition of 81 turbidites from 15 piston cores, we found that the spatial and temporal variation of turbidites is controlled by the relationship between sea-level change and the variable physiography along the margin. Siliciclastic and mixed carbonate-siliciclastic turbidites were linked to canyons indenting the shelf-break and the welldeveloped shelf-edge reef barriers that stored sediment behind them. Turbidite deposition was sustained while the sea-level position allowed the connection and sediment bypassing through the interreef passages and canyons. Carbonate turbidites dominated in regions with more open conditions at the outer-shelf and where slope-confined canyons dominated or where canyons are generally less abundant. The turn-on and maintenance of carbonate production during sea-level fluctuations also influenced the timing of carbonate turbidite deposition. We show that a fundamental understanding of the variable physiography inherent to mixed carbonate-siliciclastic margins is essential to accurately interpret deep-water, coarse-grained deposition within a sequence stratigraphic context. 

Processing, microstructure and mechanical properties of air plasma-sprayed ceria-yttria co-stabilized zirconia coatings

Thermal barrier coatings (TBCs) are widely adopted to protect mechanical components in gas turbine engines operating at high temperature. Basically, the surface temperature of these components must be low enough to retain material properties within acceptable bounds and to extend component life. From this standpoint, air plasma-sprayed (APS) ceria and yttria co-stabilized zirconia (CYSZ) is particularly promising because it provides enhanced thermal insulation capabilities and resistance to hot corrosion. However, essential mechanical properties, such as hardness and Young's modulus, have been less thoroughly investigated. Knowledge of Young's modulus is of concern because it has a significant effect on strain tolerance and stress level and, hence, on durability. The focus of the present study was to determine the mechanical properties of APS CYSZ coatings. In particular, X-ray diffraction (XRD) is adopted for phase analysis of powders and as-sprayed coatings. In addition, scanning electron microscopy (SEM) and image analysis (IA) are employed to explore coating microstructure and porosity. Finally, the Young's modulus of the coating is determined using nanoindentation and a resonant method. The results obtained are then discussed and a cross-check on their consistency is carried out by resorting to a micromechanical model. © 2010 Blackwell Publishing Ltd.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Investigation into the potential of using micro gas turbines in plug-in hybrid electric vehicles

The transport sector is considered to be one of the most dependent sectors on fossil fuels. Meeting ecological, social and economic demands throughout the sector has got increasingly important in recent times. A passenger vehicle with a more environmentally friendly propulsion system is the hybrid electric vehicle. Combining an internal combustion engine and an electric motor offers the potential to reduce carbon dioxide emissions. The overall objective of this research is to provide an appraisal of the use of a micro gas turbine as the range extender in a plug-in hybrid electric vehicle. In this application, the gas turbine can always operate at its most efficient operating point as its only requirement is to recharge the battery. For this reason, it is highly suitable for this purpose. Gas turbines offer many benefits over traditional internal combustion engines which are traditionally used in this application. They offer a high power-to-weight ratio, multi-fuel capability and relatively low emission levels due to continuous combustion.

Modelling of Organic Rankine Cycle for Waste Heat Recovery Process in Supercritical Condition

Organic Rankine Cycle (ORC) is the most commonly used method for recovering energy from small sources of heat. The investigation of the ORC in supercritical condition is a new research area as it has a potential to generate high power and thermal efficiency in a waste heat recovery system. This paper presents a steady state ORC model in supercritical condition and its simulations with a real engine’s exhaust data. The key component of ORC, evaporator, is modelled using finite volume method, modelling of all other components of the waste heat recovery system such as pump, expander and condenser are also presented. The aim of this paper is to investigate the effects of mass flow rate and evaporator outlet temperature on the efficiency of the waste heat recovery process. Additionally, the necessity of maintaining an optimum evaporator outlet temperature is also investigated. Simulation results show that modification of mass flow rate is the key to changing the operating temperature at the evaporator outlet.

A Numerical Study of Inlet Geometry for a Low Inertia Mixed Flow Turbocharger Turbine

Mixed flow turbines can offer improvements over typical radial turbines used in automotive turbochargers, with regards to transient performance and low velocity ratio efficiency. Turbine rotor mass dominates the rotating inertia of the turbocharger, and any reductions of mass in the outer radii of the wheel, including the rotor back-disk, can significantly reduce this inertia and improve the acceleration of the assembly. Off-design, low velocity ratio conditions are typified by highly tangential flow at the rotor inlet and a non-zero inlet blade angle is preferred for such operating conditions. This is achievable in a Mixed Flow Turbine without increasing bending stresses within the rotor blade, which is beneficial in high speed and high inlet temperature turbine design. A range of mixed flow turbine rotors was designed with varying cone angle and inlet blade angle and each was assessed at a number of operating points. These rotors were based on an existing radial flow turbine, and both the hub and shroud contours and exducer geometry were maintained. The inertia of each rotor was also considered. The results indicated that there was a trade-off between efficiency and inertia for the rotors and certain designs may be beneficial for the transient performance of downsized, turbocharged engines.

Investigations Into the Performance of a Turbogenerated Biogas Engine During Speed Transients

Turbogenerating is a form of turbocompounding whereby a Turbogenerator is placed in the exhaust stream of an internal combustion engine. The Turbogenerator converts a portion of the expelled energy in the exhaust gas into electricity which can then be used to supplement the crankshaft power. Previous investigations have shown how the addition of a Turbogenerator can increase the system efficiency by up to 9%. However, these investigations pertain to the engine system operating at one fixed engine speed. The purpose of this paper is to investigate how the system and in particular the Turbogenerator operate during engine speed transients. On turbocharged engines, turbocharger lag is an issue. With the addition of a Turbogenerator, these issues can be somewhat alleviated. This is done by altering the speed at which the Turbogenerator operates during the engine’s speed transient. During the transients, the Turbogenerator can be thought to act in a similar manner to a variable geometry turbine where its speed can cause a change in the turbocharger turbine’s pressure ratio. This paper shows that by adding a Turbogenerator to a turbocharged engine the transient performance can be enhanced. This enhancement is shown by comparing the turbogenerated engine to a similar turbocharged engine. When comparing the two engines, it can be seen that the addition of a Turbogenerator can reduce the time taken to reach full power by up to 7% whilst at the same time, improve overall efficiency by 7.1% during the engine speed transient.

Biomass-derived oxymethylene ethers as diesel additives: A techno-economic assessment

Conversion of agricultural biomass such as wood chips, wheat straw and forest residue for the production of fuels can help in reducing GHG emissions since they are considered as nearly carbon neutral. Around the world there is a significant amount of forest and agricultural-biomass available which could be used for the production of liquid fuels that can be blended with the petroleum-based diesel. Oxymethylene ethers (OMEs) can be derived from biomass via gasification, water-gas shift reaction and methanol production. The addition of OMEs to conventional diesel fuel has great potential to reduce soot formation during the combustion in diesel engines. Unlike methanol and dimethyl ether (DMM) which can also reduce soot formation, the physical properties of OMEs allow the use in modern diesel engines without significant change of the engines infrastructure. In this study, a detailed and data intensive process simulation model was developed to simulate all the unit operations involved in the production of OMEs from biomass. The unit operation considered include biomass drying, gasification, gas cleaning, water gas shift reaction, methanol production and OMEs synthesis. The simulation results were then utilized to conduct a detailed techno-economic assessment study of the whole biomass conversion chain to determine the most attractive pathways for OMEs production. Our recent study shows that the key parameters affecting the OMEs production are equivalence ratio, H2/CO ratio and optimal air flow. Overall, the cost of production ($/liter) of OMEs from different biomass feedstock in Alberta will be determined

Query Suggestions for Textual Problem Solution Repositories

Textual problem-solution repositories are available today in
various forms, most commonly as problem-solution pairs from community
question answering systems. Modern search engines that operate on
the web can suggest possible completions in real-time for users as they
type in queries. We study the problem of generating intelligent query
suggestions for users of customized search systems that enable querying
over problem-solution repositories. Due to the small scale and specialized
nature of such systems, we often do not have the luxury of depending on
query logs for finding query suggestions. We propose a retrieval model
for generating query suggestions for search on a set of problem solution
pairs. We harness the problem solution partition inherent in such
repositories to improve upon traditional query suggestion mechanisms
designed for systems that search over general textual corpora. We evaluate
our technique over real problem-solution datasets and illustrate that
our technique provides large and statistically significant