Supercritical CO2 impregnation of Radiata pine with organic fungicides: Effect of operating conditions and two-parameters modeling

Supercritical impregnation of Radiata pine with ethyl acetate and decanal using CO₂ as carrier solvent has been studied at pilot plant scale. Radiata pine is one of the most common wood species that is originally from Australia and is widely grown in Spain and Portugal and ethyl acetate and decanal were selected as organic compounds.Some experiences were carried out to obtain the optimal operating conditions for the supercritical impregnation process. Experiments were conducted at pressures of 7–15 MPa, temperatures of 35–50 °C and solvent flow rate between 1.5 and 3.5 kg/h. The results of this study have indicated that the treatment gives much better preservative penetration and retention operating with low pressures (7.5 MPa), low temperatures (close to 35 °C) and moderate CO₂ flow rate (3.5 kg/h) in the selected operating range. Moreover, a simple mathematical model of two adjustable parameters (external mass transfer coefficient and partition coefficient) has demonstrated to fit the experimental impregnation curves with reasonable accuracy (average absolute deviation, 3–10%).     

The Building Blocks of a PaaS

Traditional cloud computing providers enable developers to program and deploy applications in the cloud by using platform as a service (PaaS) providers. Despite the benefits of such an approach, this service model usually comes with a high level of the lock in effect into the service provider. The lock in effect occurs when a software developer needs to change its cloud provider. When this happens, it usually means a major application rewrite, specific for the new PaaS. This paper details the initial results of a project whose goal is to build a PaaS where vendor lock in does not occur. With this PaaS, developers wishing to deploy their applications into the cloud may continue to program using their usual development environments. There are no necessary changes required to make the application PaaS compatible. As a proof of concept, we developed an open source PaaS management application as a regular web application and then deployed it on the cloud.     

Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

Solving the electricity production planning problem by a column generation based heuristic

This paper presents a heuristic method based on column generation for the EDF (Electricité De France) long-term electricity production planning problem proposed as subject of the ROADEF/EURO 2010 Challenge. This is to our knowledge the first-ranked method among those methods based on mathematical programming, and was ranked fourth overall. The problem consists in determining a production plan over the whole time horizon for each thermal power plant of the French electricity company, and for nuclear plants, a schedule of plant outages which are necessary for refueling and maintenance operations. The average cost of the overall outage and production planning, computed over a set of demand scenarios, is to be minimized. The method proceeds in two stages. In the first stage, dates for outages are fixed once for all for each nuclear plant. Data are aggregated with a single average scenario and reduced time steps, and a set-partitioning reformulation of this aggregated problem is solved for fixing outage dates with a heuristic based on column generation. The pricing problem associated with each nuclear plant is a shortest path problem in an appropriately constructed graph. In the second stage, the reload level is determined at each date of an outage, considering now all scenarios. Finally, the production quantities between two outages are optimized for each plant and each scenario by solving independent linear programming problems.     

LEGClust- a clustering algorithm based on layered entropic subgraphs.

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.     

Microstructure and Processing of 3D Printed Tungsten Microlattices and Infiltrated W–Cu Composites

Tungsten is of industrial relevance due its outstanding intrinsic properties (e.g., highest melting‐point of all elements) and therefore difficult to 3D‐print by conventional methods. Here, tungsten micro‐lattices are produced by room‐temperature extrusion‐based 3D‐printing of an ink comprising WO₃–0.5%NiO submicron powders, followed by H₂‐reduction and Ni‐activated sintering. The green bodies underwent isotropic linear shrinkage of ≈50% during the thermal treatment resulting in micro‐lattices, with overall 35–60% open‐porosity, consisting of 95–100% dense W–0.5%Ni struts having ≈80–300 μm diameter. Ball‐milling the powders and inks reduced the sintering temperature needed to achieve full densification from 1400 to 1200 °C and enabled the ink to be extruded through finer nozzles (200 μm). Partial sintering of the struts is achieved when NiO is omitted from the ink, with submicron interconnected‐porosity of ≈34%. Several tungsten micro‐lattices are infiltrated with molten copper at 1300 °C under vacuum, resulting in dense, anisotropic W–Cu composites with 40–65% tungsten volume fraction. Partially sintered struts (containing nickel) with submicron open porosity are also infiltrated with Cu, resulting in co‐continuous W–Cu composites with wide W struts/Cu channels at the lattice scale (hundreds of micrometers), and fine W–Cu interpenetrating network at the strut scale (hundreds of nanometers) allowing for the design of anisotropic mechanical and electrical properties.

     

A class of aggregation functions encompassing two-dimensional OWA operators

In this paper we prove that, under suitable conditions, Atanassov’s K_α operators, which act on intervals, provide the same numerical results as OWA operators of dimension two. On one hand, this allows us to recover OWA operators from K_α operators. On the other hand, by analyzing the properties of Atanassov’s operators, we can generalize them. In this way, we introduce a class of aggregation functions - the generalized Atanassov operators - that, in particular, include two-dimensional OWA operators. We investigate under which conditions these generalized Atanassov operators satisfy some properties usually required for aggregation functions, such as bisymmetry, strictness, monotonicity, etc. We also show that if we apply these aggregation functions to interval-valued fuzzy sets, we obtain an ordered family of fuzzy sets.