Numerical investigation of the mechanism behind the deflagration to detonation transition in homogeneous and inhomogeneous mixtures of H2-air in an obstructed channel

The accidental release of hydrogen into enclosures can result in a flammable mixture with concentration gradients and possible deflagration-to-detonation transition (DDT). This numerical study aims to investigate the effect of obstacle spacing and mixture concentration on the DDT in a homogeneous and inhomogeneous hydrogen-air mixture. The paper focuses on the mechanisms behind the DDT in two mixtures with an average hydrogen concentration of 15% and 30%. Unlike the near-stoichiometric mixture, in the lean mixture, DDT only occurs in the inhomogeneous mixture. Depending on obstacle spacing, three different regimes of DDT were observed in the near-stoichiometric inhomogeneous mixture: i) Detonation was ignited when a strong Mach stem formed and propagated between the obstacles; ii) two explosion centers appeared when incident shock and Mach stem reflected from upper and lower obstacles, respectively; iii) Mach stem did not form but DDT occurred behind the flame front at the top of the obstacle.     

Thin-Layer Drying of Flax Fiber: I. Analysis of Modeling Using Fick's Second Law of Diffusion

Thin-layer drying of moist flax fiber was performed at four temperatures of 30, 50, 70, and 100°C with a constant absolute humidity of 0.0065 kg water per kg dry air. The coefficients of diffusion of the fiber at different drying conditions were estimated by modeling the drying process using the one- to five-term solutions of the second Fick's law of diffusion. The models underestimated the drying process during the initial stages of drying and overestimated this process during the final stages. The estimated coefficient of diffusions ranged from 5.11 × 10^-9 to 1.92 × 10^-8 m²/s and linearly increased with the drying air temperature.     

On the Role of Dynamic Models in Quantitative Posturography

Conventional approaches to posturography typically make use of force-plate instrumentation to measure the movement of the center of pressure of ground reaction forces during quiet standing. This paper shows that such information can be obtained indirectly by using a four-mass inverted pendulum dynamic model for the body as the basis for processing motion data transmitted directly from television cameras to an on-line computer. Preliminary experimental results suggest that this approach may eventually yield improved clinical procedures.     

Polypyrrole coatings for corrosion protection of Al alloy2024: influence of electrodeposition methods,solvents, and ZnO nanoparticle concentrations

Since ZnO nanoparticles increase the electrical conductivity of the polypyrrole (PPy) coatings, an investigation was carried out to evaluate the effect of ZnO nanoparticles loading on the corrosion protection performance of PPy coatings on AA2024 Al alloy in 3.5% NaCl solution. At first, some measurements were carried out to find the best experimental conditions containing the electrodeposition method, electrosynthesis solvent composition, and ZnO nanoparticles’ concentration for preparing the optimum PPy coating on Al alloy2024. Three different methods of electrodeposition, namely: cyclic voltammetry, galvanostatic, and potentiostatic techniques were analyzed. The anti-corrosion performance of the PPy coatings was evaluated by electrochemical impedance spectroscopy and Tafel polarization methods. The PPy prepared by potentiostatic method exhibited the best performance against corrosion of Al alloy2024 in 3.5% NaCl solution. Then, different mixtures of H₂O/ethanol were tested as electrosynthesis solvents for preparation of PPy coatings on the alloy by optimized electrodeposition mode (i.e., potentiostatic). In evaluation of the prepared coatings, the pure water was introduced as the optimum solvent in electrodeposition of PPy. The investigation of different ZnO nanoparticles’ concentrations proved that the PPy coating containing 0.025% ZnO nanoparticles was the optimum coating against the corrosion of Al alloy in NaCl solution. Finally, the long-term evaluation of the corrosion protection performance of the coatings revealed that the optimum coating provided suitable protection against corrosion up to 14 days after immersion.

     

Water chemistry,solution resistivity,and oxygen reduction reaction in simulated primary coolant of pressurized water reactors

The effect of temperature (25 °C–300 °C) on the ohmic potential drop (ΔE_Ω) and resistivity (R_sol) of a simulated primary coolant of the pressurized water reactors was investigated using electrochemical impedance spectroscopy (EIS) and galvanostatic potentiometry. The variations in the conductivity and pH of the solution showed that by increasing the temperature the magnitude of the R_sol and therefore ΔE_Ω of the system first decreases rapidly (until ~150 °C) and then reaches a relatively stable plateau value. Increasing the pressure of the system decreased the magnitude of the R_sol in the oxygenated coolant. Investigation of the oxygen reduction reaction (ORR) on platinum (Pt), 316L stainless steel (SS), Alloy 690, and carbon steel revealed the great effect of water chemistry, in particular pH on the rate of the ORR on these materials. OH^? adsorption on Pt reduced the rate of the ORR on this metal at temperatures higher than ~100 °C. Finally, it was found that higher H₃O⁺ concentrations reduce the rate of the ORR on iron‐ and nickel‐based alloys, while increasing the OH^? concentration at temperatures higher than ~150 °C accelerates the rate of the ORR on these alloys.     

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm(EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system(ANFIS) and artificial neural networks(ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization(PSO)and ant colony optimization for continuous domains(ACO_R) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACO_R methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity(EC), total dissolved solids(TDS), the sodium adsorption ratio(SAR), carbonate hardness(CH), and total hardness(TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACO_R. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.     

Non-isothermal aging of a high-Zn-containing Al–Zn–Mg–Cu alloy: microstructure and mechanical properties

The non-isothermal aging behaviour of a newly developed Al–Zn–Mg–Cu alloy containing 17?wt-% Zn was investigated. Hardness and shear punch tests demonstrated that during non-isothermal aging, the mechanical properties of the alloy first increased and then decreased. The best properties were obtained in a sample which was non-isothermally aged upto 250°C with heating rate of 20°C?min^?1, due to the presence of η′/η (MgZn₂) phases. This was confirmed by differential scanning calorimetery. After homogenisation, residual eutectic phases remained at triple junctions or in a spherical form. During aging, these phases transformed into rodlike S (Al₂CuMg)-phase at 400°C, with sizes ranging from 50 to 250?nm. The precipitation sequence in this high-Zn alloy was similar to that for conventional Al–Zn–Mg–Cu alloys.     

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide.