Quantification and analysis of heat hydration of blended cement at different temperature

The study of the hydration kinetics appears as a prerequisite for understanding the physical and mechanical phenomena that control the behavior of cementitious materials. This research is based on monitoring the evolution of the degree of hydration for ordinary cement and those containing 10% of limestone powder, 20% of natural pozzolana or 30% of the blast furnace slag under high temperatures. The results provide a better understanding the effect of cure temperature on the hydration kinetics and understand the contribution of mineral additions on improving the cement properties. A new model proposed gives the satisfaction results for predicting in later age the heat of hydration of cements blended kept under constant temperatures. The latter has a wider appreciation of the results, where it gives correlation coefficients very close of unity. This justifies the reliability of this new model proposed.     

Combined effects of blasting and geological structure on rock mass stability—a case study from the Marrakech–Agadir highway,Morocco

Bulletin of Engineering Geology and the Environment - The Marrakech–Agadir highway crosses mountainous areas of the Western High Atlas of Morocco with a high risk of slope instability. The...     

Improving the laser cutting process design by machine learning techniques

In the field of manufacturing engineering, process designers conduct numerical simulation experiments to observe the impact of varying input parameters on certain outputs of the production process. The disadvantage of these simulations is that they are very time consuming and their results do not help to fully understand the underlying process. For instance, a common problem in planning processes is the choice of an appropriate machine parameter set that results in desirable process outputs. One way to overcome this problem is to use data mining techniques that extract previously unknown but valuable knowledge from simulation results. This paper presents a hybrid machine learning approach for applying clustering and classification techniques in a laser cutting planning process. In a first step, a clustering algorithm is used to divide large parts of the simulation data into groups of similar performance values and select those groups that are of major interest (e.g. high cut quality results). Next, classification trees are used to identify regions in the multidimensional parameter space that are related to the found groups. The evaluation shows that the models accurately identify multidimensional relationships between the input parameters and the output values of the process. In addition to that, a combination of appropriate visualization techniques for clustering with interpretable classification trees allows designers to gain valuable insights into the laser cutting process with the aim of optimizing it through visual exploration.     

Model predictive control for continuous lactide ring‐opening polymerization processes

Polylactic acid (PLA) is an attractive environment‐friendly thermoplastic that is bio‐sourced and biodegradable. PLA is industrially produced by the ring‐opening polymerization of lactide. This reaction is sensitive to drifts in the operating conditions and impurities in the raw materials that may affect the reaction rate as well as the polymer properties, which can be very costly in continuous processes. It is therefore crucial to employ a control strategy that allows recovering the nominal conditions and maintaining the desired properties and conversion level in case of drift. Three control strategies are discussed in this paper: proportional‐integral (PI) controller, dynamic optimization, and model predictive control (MPC). The proposed approaches are validated by simulation of a continuous PLA process constituted of three cascade reactors including one loop reactor in the middle. Besides the coupling of inputs and outputs, the process model is highly nonlinear, and the control is done only on the boundaries. The results show that the open‐loop optimization strategy provides better performance compared to the PI controller if the disturbance is assumed to be measured. The MPC also shows superior performances provided that the disturbance is first estimated. A polynomial model is developed to predict the nonmeasured disturbance based on the measured outputs.     

CU placement over a reconfigurable wireless fronthaul in 5G networks with functional splits

Mobile network operators are currently facing a tremendous increase in the level of data traffic. Although cell size reduction is one of the most common ways used to accommodate such traffic demand, densely deployed small cells also dramatically increase the level of intercell interference. By centralizing baseband signal processing at powerful computing infrastructures, called centralized unit (CU) pools, cloud radio access network (C‐RAN) enables advanced coordination algorithms to be employed in dense small cell networks. In C‐RAN, due to stringent bandwidth and latency requirements at the fronthaul links, the optical fiber, thanks to its bandwidth and latency characteristics, continues to be the most prevalent fronthaul medium option. Nevertheless, the optical fiber is one of the fronthaul options, while C‐RAN (physical layer radio frequency [PHY‐RF] split) is one of the functional splits that can be defined each coming with different fronthaul requirements. In this paper, we formulate and solve a dynamic CU placement problem for mobile networks as an integer linear programming (ILP) problem. In the considered network, CU pools are placed at the edges of the network, and a reconfigurable millimeter wave (MMW) wireless fronthaul links are used in order to provide decentralized units (DUs) with connectivity. We study the impact of different functional splits on the placement cost and on the acceptance ratio using different substrate networks. Lastly, we propose and evaluate a CU placement heuristic algorithm using a numerical simulator. The results reveal that the optimal functional split selection can lead to significant resource utilization benefits in the RAN.     

Flicker mitigation in a doubly fed induction generator wind turbine system

This paper describes a doubly fed induction generator (DFIG) control for wind energy generation. The DFIG model is established and the adopted control strategies for machine side and grid side converters are described. Flicker phenomenon is defined and its emission of variable speed wind turbine with DFIG during continuous operation is studied. Calculation of flicker severity is evaluated using flickermeter. Appropriate vector power control of the machine side converter is proposed and applied to achieve flicker mitigation.     

Rheological and crystallization behaviors of low processing temperature poly(aryl ether ketone)

A new low melting temperature poly(aryl ether ketone) (PAEK) thermoplastic polymer (Victrex AE 250) was investigated through thermal and rheological analysis of films and flakes. DSC was assigned to evaluate the influence of cooling rate on crystallinity and thermal transitions. Rheometry was used to assess its flowing behavior through the evaluation of dynamic moduli and complex viscosity in the melted state. The relaxation times were found from the rheological curves: they are between a few ms to 200 ms for AE 250, lower than those found for PEEK 450, meaning a faster mobility of macromolecules. The thermal activation energy, E_a obtained from Time Temperature Superposition is the same for films and flakes in spite of a lower viscosity for flakes. The molecular weight between entanglements is evaluated at 8000 g.mol⁻¹ for FMc and 13,000 for FLc, it is compared to the value of about 2000 g.mol⁻¹ found for PEEK 450 with the same procedure. Also, the viscosity was compared to other commercial PAEK such as PEEK and PEKK based on data from the literature. This polymer appears very efficient to compete with high performance thermoplastics to be processed by compression molding, out of autoclave consolidation, additive manufacturing, and welding.     

Continuous analytic I–V model for GS DG MOSFETs including hot-carrier degradation effects

We have studied the influence of hot-carrier degradation effects on the drain current of a gate-stack double-gate (GS DG) MOSFET device. Our analysis is carried out by using an accurate continuous current-voltage (I-V) model, derived based on both Poisson's and continuity equations without the need of charge-sheet approximation. The developed model offers the possibility to describe the entire range of different regions (subthreshold, linear and saturation) through a unique continuous expression. Therefore, the proposed approach can bring considerable enhancement at the level of multi-gate compact modeling including hot-carrier degradation effects.     

Investigation of Self-Heating Effects in Submicrometer GaN/AlGaN HEMTs Using an Electrothermal Monte Carlo Method

An electrothermal Monte Carlo (MC) method is applied in this paper to investigate electron transport in submicrometer wurtzite GaN/AlGaN high-electron mobility transistors (HEMTs) grown on various substrate materials including SiC, Si, GaN, and sapphire. The simulation method is an iterative technique that alternately runs an MC electronic simulation and solves the heat diffusion equation using an analytical thermal resistance matrix method. Results demonstrate how the extent of the thermal droop in the I_d-V_ds characteristics and the device peak temperature depend upon both the biasing conditions and the substrate material type. Polarization effects are considered in the simulations, as they greatly influence electron transport in GaN/AlGaN HEMTs by creating a highly concentrated two-dimensional electron gas (2DEG) at the GaN/AlGaN interface. It is shown that a higher 2DEG density provides the devices with a better current handling capability but also increases the importance of the thermal effects