Remote estimation of regional evapotranspiration

Three models are applied to estimating evapotranspiration in central Australia, using limited routine meteorological data and the NOAA-14 AVHRR overpass. By minimizing the difference between model predicted surface temperature and satellite derived temperature to adjust the estimated soil moisture, both an instantaneous physically based model and a one dimensional boundary layer simulation yielded consistent results. This highlights the sensitivity of surface temperature to soil moisture and suggests that radiometric surface temperature can be used to adjust simple water balance estimates of soil moisture providing a simple and effective means of estimating large scale evapotranspiration in remote arid regions.     

Dual Mesh Resampling

The dual of a 2-manifold polygonal mesh without boundary is commonly defined as another mesh with the same topology (genus) but different connectivity (vertex–face incidence), in which faces and vertices occupy complementary locations and the position of each dual vertex is computed as the center of mass (barycenter or centroid) of the vertices that support the corresponding face. This barycenter dual mesh operator is connectivity idempotent but not geometrically idempotent for any choice of vertex positions, other than constants. In this paper we construct a new resampling dual mesh operator that is geometrically idempotent for the largest possible linear subspace of vertex positions. We look at the primal and dual mesh connectivities as irregular sampling spaces and at the rules to determine dual vertex positions as the result of a resampling process that minimizes signal loss. Our formulation, motivated by the duality of Platonic solids, requires the solution of a simple least-squares problem. We introduce a simple and efficient iterative algorithm closely related to Laplacian smoothing and with the same computational cost. We also characterize the configurations of vertex positions where signal loss does and does not occur during dual mesh resampling and the asymptotic behavior of iterative dual mesh resampling in the general case. Finally, we describe the close relation existing with discrete fairing and variational subdivision, and define a new primal–dual interpolatory recursive subdivision scheme.     

Carbide stoichiometry in TiCx and Cu–TiCx produced by self-propagating high-temperature synthesis

TiC_x and Cu–TiC_x have been formed by self-propagating high-temperature synthesis (SHS) from elemental powder mixtures with a range of C/Ti ratios. When no copper was present, the carbide stoichiometry closely followed that of the starting powders. In the presence of copper, formation of copper–titanium intermetallics and solid solutions resulted in a different carbide stoichiometry.     

Employing statistical model emulation as a surrogate for CFD

This work focuses on substituting a computationally expensive simulator by a cheap emulator to enable studying applications where running the simulator is prohibitively expensive. The procedure consists of two steps. In a first step, the emulator is calibrated to closely mimic the simulator response for a number of pre-defined cases. In a second step the calibrated emulator is used as surrogate for the simulator in the otherwise prohibitively expensive application. An appealing feature of the proposed framework contrary to other approaches is that the uncertainty on the emulator prediction can be determined. While the proposed framework is applicable in virtually all areas of natural sciences, we discuss the approach and evaluate its performance based on a typical example in the realm of computational wind engineering, namely the determination of the wind field in an urban area.     

Socio economic influences on the restoration and maintenance of the water environment

Protection of the water environment has become a modern socio economic issue in which the sociological pressures for a healthy water environment must be balanced with affordability. Reconciliation of these aspects requires clear political thinking and rigorous methodologies. It also requires a shift in mind-set which considers members of the public as customers.Water utilities are the major users of the water environment and potentially its greatest threat - so good delivery of water services is very important. The presentation addresses the topic through the experience of Anglian Water, a privatised water utility serving Eastern England.     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.     

Road following by artificial vision using neural network

It has been developed, built and tested an artificial vision based system to follow roads, which provides control signals, in a short time, by means of a joint of artificial neural nets. The image is segmented in “road” or “not road.” The obtained segmentation is the input for two neural nets, a classic architecture net (NN), and a TDNN (Time Delay Neural Network) one. The outputs provided by both nets, with a trajectory estimation, are introduced to a decision-making block, which selects the alternative containing less error. The classifier parameters are updated according to the current segmentation.