Application of design parameters space search for belt conveyor design

Belt conveyor design is examined as an application of a proposed Design Parameters Space Search technique. First, the main characteristics of the belt-conveyor design process are presented as they appear in the current literature. Furthermore, a proposed general knowledge-representation platform is described, and its ability to house the relevant conveyor design knowledge is also shown.

Next, the extended search technique of the design space is discussed, and an integrated example of a belt-conveyor design is presented, based on the proposed representation platform and the extended search technique. Finally, it is shown that the design of belt conveyors according to the proposed approach presents the following significant advantages:

• • Due to the knowledge-representation scheme adopted, both qualitative and quantitative knowledge can be used within the same platform.

• • Multiple solutions can be easily produced through user-defined design criteria. These solutions can be further modified and/or evaluated to produce more-specific designs.

• • The required for user-input data is kept to a minimum. Due to the applied extended search method, semiautomatic design can be achieved. As a consequence, the design process is completed in less time than that required by the conventional methods.

     

Integration of fuzzy logic, genetic algorithms and neural networks in collaborative parametric design

The solution to a design problem is extracted through the exploitation of the design knowledge in the context of a space of solution alternatives. The design process incorporates a series of decision making and knowledge management issues, which should be often addressed through collaboration among diverse stakeholders. The alternative solutions must usually be shaped under different formalisms and evaluated against commonly accepted objective criteria.The current paper presents an approach that integrates soft-computing techniques in order to facilitate the computer-aided collaboration among designers. CopDeSC (Collaborative parametric Design with Soft-Computing) is the name of the system developed in order to implement this approach whose key features are: (a) the collaborative structuring of design parameter hierarchies, (b) the modeling of the design objectives through fuzzy preferences stated by the designers on certain design parameters, (c) the deployment of genetic algorithms for locating the optimum solution and (d) the utilization of records of elite solutions that are submitted in a neuro-fuzzy approximation in order to produce a simplified problem formulation, suitable for addressing redesign tasks in significantly less computational time.The efficiency of CopDeSC is evaluated in an example case of the parametric design of oscillating conveyor that has been conducted by a group of designers.     

Resonance controlled fatigue crack propagation

Fatigue crack growth in resonating structural members is studied. The crack propagation rate is related to the stress intensity factor range by way of the well known power law. The depth of the crack determines the local flexibility due to crack which in turn influences the dynamic response of the system under an external force with constant amplitude and frequency. The propagating crack introduces additional flexibility to the system which results in gradual shift away from the resonance with smaller loading of the cracked section. This slows down the crack growth rate.It was shown that this mechanism can guide the system to a value of the crack growth rate below a conventional threshold rate which can be interpreted as dynamic crack arrest. It was found that material damping is the decisive factor determining the crack growth rate in a resonant system where the material damping is the dominant damping mechanism of the system.     

Understanding and improving the reduced integration of Mindlin shell elements

The reason for looking in some depth at locking of Mindlin shell elements is introduced. A simple example of straight beam locking is examined in detail. The observations about reduced integration that emerge are subsequently unified with some other facts the writer has come across. A simple curved beam element is then formulated and examined under inextensional bending. The performance of this element under reduced integration is predicted for both the ‘undistorted’ and ‘distorted’ configurations (see text) and for a selective-very reduced integration scheme. The previous predictions are then verified through the performance of an 8 node Mindlin shell element in a suitable cylindrical shell test. The paper finally concludes with a set of important observations that so easily could be drawn from such a simple analysis.     

Peptidomimetic–functionalized carbon nanotubes with antitrypsin activity

Water-soluble functionalized carbon nanotubes (CNTs) have been prepared and further conjugated through a stable covalent bond with two peptidomimetics. The structural design of the covalently grafted peptidomimetics to the CNTs is based on their structural similarity with the metabolites of antagonist G of substance P. A variety of analytical spectroscopic methods, in combination with electron microscopy and thermal analysis, aided the structural and morphological characterization of the four newly synthesized peptidomimetic–CNT conjugates. It is demonstrated that the trypsin inhibitory effect of the peptidomimetic–CNT is enhanced as a result of the high-loading of peptidomimetics onto the skeleton of the modified water-soluble CNTs. Additionally, the peptidomimetic–functionalized CNT conjugates can be recovered and re-employed up to six biological evaluation cycles showing the same trypsin inhibitory activity. Such a nanosized system is extremely advantageous for the inhibition of inflammation or malignancy and could find potential future biological applications in the area of drug delivery systems.     

Key factors to improve the efficiency of roll-to-roll printed organic photovoltaics

In order to achieve the cost-efficient scalability of flexible organic photovoltaics (OPVs), the optimization of key factors related to the materials and roll-to-roll (R2R) processes is necessary. The limited drying during the R2R printing process induces a vertical phase separation leading to the formation of a P3HT-rich top region on the photoactive layer which acts as an electron barrier in normal geometry. We show that the increase of R2R drying time and/or post-annealing can enhance the OPV efficiency by the diffusion of PCBM towards the photoactive layer surface forming an electron transport network. It is estimated that the volume fraction of PCBM at the top region of the films triples from about 9% to 30%. In addition, the direct exposure of PEDOT:PSS to air after printing leads to morphological changes that negatively affect the efficiency. Therefore, the protection of PEDOT:PSS from air in combination to the increase of the R2R drying time enables the significant increase of the R2R printed OPVs efficiency to 1%.     

Merging high doxorubicin loading with pronounced magnetic response and bio-repellent properties in hybrid drug nanocarriers

Hybrid magnetic drug nanocarriers are prepared via a self-assembly process of poly(methacrylic acid)-graft-poly(ethyleneglycol methacrylate) (p(MAA-g-EGMA)) on growing iron oxide nanocrystallites. The nanocarriers successfully merge together bio-repellent properties, pronounced magnetic response, and high loading capacity for the potent anticancer drug doxorubicin (adriamicin), in a manner not observed before in such hybrid colloids. High magnetic responses are accomplished by engineering the size of the magnetic nanocrystallites (～13.5 nm) following an aqueous single-ferrous precursor route, and through adjustment of the number of cores in each colloidal assembly. Complementing conventional magnetometry, the magnetic response of the nanocarriers is evaluated by magnetophoretic experiments providing insight into their internal organization and on their response to magnetic manipulation. The structural organization of the graft-copolymer, locked on the surface of the nanocrystallites, is further probed by small-angle neutron scattering on single-core colloids. Analysis showed that the MAA segments selectively populate the area around the magnetic nanocrystallites, while the poly(ethylene glycol)-grafted chains are arranged as protrusions, pointing towards the aqueous environment. These nanocarriers are screened at various pHs and in highly salted media by light scattering and electrokinetic measurements. According to the results, their stability is dramatically enhanced, as compared to uncoated nanocrystallites, owing to the presence of the external protective PEG canopy. The nanocarriers are also endowed with bio-repellent properties, as evidenced by stability assays using human blood plasma as the medium.     

Artificial neural networks used for the performance prediction of a thermosiphon solar water heater

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R²-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R²-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2°C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method can successfully be used for the estimation of the performance of the particular thermosiphon system at any of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network.     

Ariane 5 attitude control system passivation: theoretical and experimental determination of the explosion threshold pressure

The Attitude Control System (ACS) of the future European heavy launcher Ariane 5 is a Hydrazine system including two bladder tanks pressurised by Nitrogen ; depending on the mission, a residual pressure of some 10 to 20 bars may still remain at the end of the mission. In order to avoid any debris generation, the explosion threshold pressure has been determined theoretically, then demonstrated experimentally. This pressure is defined by the behaviour of the tank under the impact of a micrometeoroid or a space debris : for pressures above it, the tank explodes ; otherwise, it is simply punctured. A theoretical analysis based on fracture mechanics was led first, showing that the crack propagation would become divergent for pressures in the range of 20 to 25 bars. A series of 2D simulations was then performed in two steps : first the local effect of the impacting particle on the tank wall, which enables the computation of the stress fields in the material ; second, the global propagation of such initial conditions in the complete tank. Several cases were computed, with different particles, impacting at different velocities on tanks with different pressures. The worst case was determined and a test prevision was led. The test was performed with a tank pressurized at 18 bar; the impacting particle was 1 mm diameter Aluminium sphere, launched at a velocity of 9 km/s. The tank did not explode, and the resulting hole was close to the one predicted (3.2 mm in diameter, vs. 3 to 3.5 predicted). The resulting maximal residual pressure specified to the ACS is 15 bar, a 20% margin being selected. With this new requirement, we are confident that no explosion can occur in orbit and that the general debris mitigation rules are fully applied.     

Modelling the dynamic characteristics of a robot arm joint

The dynamic behavior of a robot arm has been modelled, taking into account the elasticity and damping of the joints and the backlash introduced by the gear pairs of the transmission mechanism. The links of the robot arm are considered to be rigid and its joints rotational. A technique for automatic formulation of the differential equations of the lumped mass dynamic model of the gear train, incorporating the backlash nonlinearity, has been developed. Each link and the accompanying joint comprise a module. The dynamic equations of the manipulator are formulated through an iterative technique. The algorithm will provide automatic formulation of the dynamic equations of the model of the arm including the referenced nonlinearities of the joint drives. Finally, an application of the algorithm to a planar manipulator arm with two links and two rotational joints is presented.