Rapid Processing of Net-Shape Thermoplastic Planar-Random Composite Preforms

A novel thermoplastic composite preforming and moulding process is investigated to target cost issues in textile composite processing associated with trim waste, and the limited mechanical properties of current bulk flow-moulding composites. The thermoplastic programmable powdered preforming process (TP-P4) uses commingled glass and polypropylene yarns, which are cut to length before air assisted deposition onto a vacuum screen, enabling local preform areal weight tailoring. The as-placed fibres are heat-set for improved handling before an optional preconsolidation stage. The preforms are then preheated and press formed to obtain the final part. The process stages are examined to optimize part quality and throughput versus processing parameters. A viable processing route is proposed with typical cycle times below 40s (for a plate 0.5 × 0.5m2, weighing 2kg), enabling high production capacity from one line. The mechanical performance is shown to surpass that of 40wt.% GMT and has properties equivalent to those of 40wt.% GMTex at both 20°C and 80°

VOID EVOLUTION DURING STAMP-FORMING OF THERMOPLASTIC COMPOSITES

SUMMARY: A thermoplastic stamp-forming process has been investigated using glass fibre (GF), carbon fibre (CF), and hybrid carbon-glass fibre fabric materials. For monolithic GF/PA6 and CF/PA66 materials, stamping pressure was the dominating variable to achieve high mechanical properties, low void contents, and minimal void content distributions across the stamped part. Use of a hybrid flow core material composed of CF/PA66 textile skins and a GF/PA66 random fibre core reduced this tendency such that tool temperature dominated the process. The increased local flow of the core layer accommodated the varying local superficial fabric density. Use of the flow core did not significantly affect flexural properties, but with a 29% and 17% drop in tensile modulus and strength. A substantial cost saving resulted from the use of a hybrid glass and carbon structure. In mould cycle times of 30s resulted for 3mm thick parts

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

Flow Properties of Tailored Net-Shape Thermoplastic Composite Preforms

A novel thermoplastic programmable preforming process, TP-P4, has been used to manufacture preforms for non-isothermal compression molding. Commingled glass and polypropylene yarns are deposited by robot onto a vacuum screen, followed by a heat-setting operation to stabilize the as-placed yarns for subsequent handling. After an optional additional preconsolidation stage, the preforms are molded by preheating and subsequent press forming in a shear edge tool. The in- and out-of-plane flow capabilities of the material were investigated, and compared to those of 40 wt% Glass Mat Thermoplastics (GMTs). Although the TP-P4 material has a fiber fraction of 60 wt%, the material could be processed to fill 77 mm deep ribs with a thickness of 3 mm, indicative of complex part production. The pressure requirements for out-of-plane flow were shown to depend on the fiber length and fiber alignment. Segregation phenomena were found to be less severe with TP-P4 than with GMT material

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Closed-Loop Recycling of Copper from Waste Printed Circuit Boards Using Bioleaching and Electrowinning Processes

International audienceIn the present study, a model of closed-loop recycling of copper from PCBs is demonstrated, which involves the sequential application of bioleaching and electrowinning to selectively extract copper. This approach is proposed as part of the solution to resolve the challenging ever-increasing accumulation of electronic waste, e-waste, in the environment. This work is targeting copper, the most abundant metal in e-waste that represents up to 20% by weight of printed circuit boards (PCBs). In the first stage, bioleaching was tested for different pulp densities (0.25–1.00% w/v) and successfully used to extract multiple metals from PCBs using the acidophilic bacterium, Acidithiobacillus ferrooxidans. In the second stage, the method focused on the recovery of copper from the bioleachate by electrowinning. Metallic copper foils were formed, and the results demonstrated that 75.8% of copper available in PCBs had been recovered as a high quality copper foil, with 99 + % purity, as determined by energy dispersive X-ray analysis and Inductively-Coupled Plasma Optical Emission Spectrometry. This model of copper extraction, combining bioleaching and electrowinning, demonstrates a closed-loop method of recycling that illustrates the application of bioleaching in the circular economy. The copper foils have the potential to be reused, to form new, high value copper clad laminate for the production of complex printed circuit boards for the electronics manufacturing industry. Graphic Abstract: [Figure not available: see fulltext.] © 2020, The Author(s)

Post-Construction Support and Sustainability in Community-Managed Rural Water Supply

Executive Summary This volume reports the main findings from a multi-country research project that was designed to develop a better understanding of how rural water supply systems are performing in developing countries. We began the research in 2004 to investigate how the provision of support to communities after the construction of a rural water supply project affected project performance in the medium term. We collected information from households, village water committees, focus groups of village residents, system operators, and key informants in 400 rural communities in Bolivia, Ghana, and Peru; in total, we discussed community water supply issues with approximately 10,000 individuals in these communities. To our surprise, we found the great majority of the village water systems were performing well. Our findings on the factors influencing their sustainability will, we hope, be of use to policy makers, investors, and managers in rural water supply

Constraints and entropy in a model of network evolution

Barab´asi-Albert’s ‘Scale Free’ model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the ‘Scale Free’ model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the ‘Scale Free’ and ‘constraints’ model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics

Maximum expected accuracy structural neighbors of an RNA secondary structure

International audienceBACKGROUND: Since RNA molecules regulate genes and control alternative splicing by allostery, it is important to develop algorithms to predict RNA conformational switches. Some tools, such as paRNAss, RNAshapes and RNAbor, can be used to predict potential conformational switches; nevertheless, no existent tool can detect general (i.e., not family specific) entire riboswitches (both aptamer and expression platform) with accuracy. Thus, the development of additional algorithms to detect conformational switches seems important, especially since the difference in free energy between the two metastable secondary structures may be as large as 15-20 kcal/mol. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accuracy (MEA) structure, rather than the minimum free energy (MFE) structure. RESULTS: Given an arbitrary RNA secondary structure S₀ for an RNA nucleotide sequence a = a₁,..., a(n), we say that another secondary structure S of a is a k-neighbor of S₀, if the base pair distance between S₀ and S is k. In this paper, we prove that the Boltzmann probability of all k-neighbors of the minimum free energy structure S₀ can be approximated with accuracy ε and confidence 1 - p, simultaneously for all 0 ≤ k N(ε,p,K)=Φ⁻¹(p/2K)²/4ε², where Φ(z) is the cumulative distribution function (CDF) for the standard normal distribution. We go on to describe the algorithm RNAborMEA, which for an arbitrary initial structure S₀ and for all values 0 ≤ k < K, computes the secondary structure MEA(k), having maximum expected accuracy over all k-neighbors of S₀. Computation time is O(n³ * K²), and memory requirements are O(n² * K). We analyze a sample TPP riboswitch, and apply our algorithm to the class of purine riboswitches. CONCLUSIONS: The approximation of RNAbor by sampling, with rigorous bound on accuracy, together with the computation of maximum expected accuracy k-neighbors by RNAborMEA, provide additional tools toward conformational switch detection. Results from RNAborMEA are quite distinct from other tools, such as RNAbor, RNAshapes and paRNAss, hence may provide orthogonal information when looking for suboptimal structures or conformational switches. Source code for RNAborMEA can be downloaded from http://sourceforge.net/projects/rnabormea/ or http://bioinformatics.bc.edu/clotelab/RNAborMEA/