A middleware platform for the dynamic evolution of distributed component-based systems

In this paper, we present a middleware platform that supports the dynamic evolution of distributed component-based systems. It leverages the concept of ontologies to model the context of a system and an intrinsic mechanism is integrated to causally connect the dynamic architecture specification to the running system implementation. The ontological modeling covers both the environmental and the architectural knowledge using semantic data modeling. The intrinsic mechanism can automatically derive a run-time polymorphic architecture object to coordinate the involved components. The ontology based contextual representation and the polymorphic architecture-driven dynamic evolution are the two underpinnings of the platform. A scenario application—including the two primitive evolution actions—with the performance analysis is discussed to illustrate the feasibility.     

A comparison between state-of-the-art and neural network modelling of solar collectors

The state-of-the-art modelling of solar collectors as described in the European Standard EN 12975-2 is based on equations describing the thermal behaviour of the collectors by characterising the physical phenomena, e.g. transmission of irradiance through transparent covers, absorption of irradiance by the absorber, temperature dependent heat losses and others. This approach leads to so called collector parameters that describe these phenomena, e.g. the zero-loss collector efficiency η₀ or the heat loss coefficients a₁ and a₂.Although the state-of-the-art approach in collector modelling and testing fits most of the collector types very well there are some collector designs (e.g. “Sydney” tubes using heat pipes and “water-in-glass” collectors) which cannot be modelled with the same accuracy than conventional collectors like flat plate or standard evacuated tubular collectors. The artificial neural network (ANN) approach could be an appropriate alternative to overcome this drawback.To compare the different approaches of modelling investigations for a conventional flat plate collector and an evacuated “Sydney” tubular collector have been carried out based on performance measurements according to the European Standard EN 12975-2. The investigations include the parameter identification (training), the comparisons between measured and modelled collector output and the simulated yearly collector yield for a solar domestic hot water system for both models.The obtained results show better agreement between measured and calculated collector output for the artificial neural network approach compared with the state-of-the-art modelling. The investigations also show that for the ANN approach special test sequences have to be designed and that the determination of the ANN that fits the thermal performance of the collector in the best way depends significantly on the expertise of the user.Nevertheless artificial neural networks have the potential to become an interesting alternative to the state-of-the-art collector models used today.     

DEM‐CFD modeling of particle systems with long‐range electrostatic interactions

To investigate dynamic behaviors of monocharged particle systems, a direct truncation (DT) method and a hybrid particle‐cell (HPC) method are implemented into the discrete element method coupled with computational fluid dynamics (DEM‐CFD) with defined cutoff distances. The DT method only considers electrostatic interactions between particles within the cutoff distance while the HPC method computes electrostatic interactions in the entire computational domain. The deposition process of monocharged particles in a container in air was simulated using the developed DEM‐CFD. It was found that using the DT method, the macrostructure, evolution of granular temperature, and radial distribution function of the particle system were sensitive to the specified cutoff distance. In contrast, using the HPC method, these results were independent of the specified cutoff distance, as expected. This implies that, although electrostatic interactions between particles with large separation distances are weak, they should be considered in DEM‐CFD for accurate modeling of charged particle systems. © 2015 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 61: 1792–1803, 2015     

Avoiding filter cake cracking: Influence of consolidation on desaturation characteristics

In cake filtration processes with an air-blowing step, cracking is an undesirable phenomenon as it leads to deterioration of the filtration process by highly increasing gas throughput. This leads to higher residual moisture if the pressure difference cannot be maintained and an increase in overall cost. Crack formation can be avoided by compacting the filter cake before desaturation. While this action will make the separation process applicable by highly reducing gas consumption, there are also potential negative effects. Compaction increases filter cake resistance and might therefore slow down desaturation kinetics. Therefore, the authors investigated how the filter cake characteristics governing desaturation change from the nonconsolidated to the consolidated state of the filter cake and compared these findings to the actual dewatering kinetics. The results showed that for the case where cracking could be oppressed, dewatering kinetics of the consolidated cake are actually faster than for the nonconsolidated cake, despite higher resistance of the consolidated cake. Thus, compaction is an appropriate action when dealing with filter cake cracking.