Incorporation of Magnetite in Toughened PLA Nanocomposite: Tensile and Thermal Stability

In this study, liquid natural rubber (LNR) toughened polylactic acid (PLA) incorporated with magnetite (Fe3O4) nanocomposites were fabricated via melt-compounding in an internal mixer and followed by hot/cold pressing. The effects of ultrasonic treatment time (1-3 hours) and Fe3O4 (0.5-4.0 wt%) nanoparticles loading on tensile, morphology and thermal stability were investigated. Based on tensile testing results, the ultrasonication time of 1 hour was served as the most suitable treatment period to achieve the optimum distribution of Fe3O4 within PLA/LNR matrix. Among the investigated nanoparticles loading, 1 wt% Fe3O4 nanocomposite presented the highest tensile strength of 23.7 MPa, Young’s modulus of 1293.5 MPa and strain at break of 2.8%. SEM micrographs showed that the over-treated nanocomposites with 2-3 hours and over-high nanoparticles loading had resulted in the formation of clusters in the matrix. With increasing Fe3O4 loading, the decomposition of PLA/LNR nanocomposites was initiated earlier.

Statistics and optimization of random pan stacking

We consider a minimal model of random pan stacking. A single pan consists of a V-shaped object characterized by its internal angle α. The stack is constructed by piling up N pans with different angles in a given, random order. The set of pans is generated by sampling from various kinds of distributions of the pan angles: discrete or continuous, uniform or optimized. For large N the mean height depends principally on the average of the distance between the bases of two consecutive pans, and the effective compaction of the stack, compared with the unstacked pans, is 2 log 2/π. We also obtain the discrete and continuous distributions that maximize the mean stack height. With only two types of pans, the maximum occurs for equal probabilities, while when many types of pans are available, the optimum distribution strongly favours those with the most acute and the most obtuse angles. With a continuous distribution of angles, while one never finds two identical pans, the behaviour is similar to a system with a large number of discrete angles.

Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

Neural network method of the decision Of the nonlinear problem of optimum distribution of the non-uniform resource

Given article is devoted features of the decision of a problem of integer nonlinear programming, by means of developed neural network method and algorithm of nonlinear optimization of means «decision Search» tabular processor Microsoft Excel. In offered neural network method the task in view decision is made by means of a recurrent neural network (RNN) matrix architecture with m neurons in each line and n neurons in each column. All neurons such network are connected with each other by communications, and the signal from an exit neuron can move on its input. Neural network method is characterized by that on inputs mentioned RNN the entrance vector of values of parameters of optimized nonlinear criterion function of a problem of distribution of a non-uniform resource moves, calculation of values of weight factors connected among themselves neurons is carried out and signal RNN is formed. This signal by means of nonlinear function will be transformed to the discrete target signal characterizing values quasi-optimal of the decision of the mentioned problem which size changes from 0 to 1. The estimation of efficiency of the decision of a considered problem was carried out at its various values of an indicator of efficiency on the basis of developed imitating model RNN. As indicators of efficiency of application offered neural network method were used – an average relative error and time of the decision of a problem. The value received by means of algorithm of nonlinear optimization of means was accepted to the exact decision «decision Search» tabular processor Microsoft Excel. The analysis of the received results of the experimental researches, offered neural network method, has allowed to make the conclusion that in comparison with an existing method of nonlinear optimization of tabular processor Microsoft Excel use offered neural network method allows essentially (in 9,4 times) to lower time of the decision of a problem dimension 10 × 8 (m × n) and thus to provide accuracy of its decision not less than 99,8 %.

Comparative Evaluation of Probability Distribution Models of Flood flow in Lower Niger Basin

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

Simulations on vibration mode and distribution of shear stress for icing aluminum plate excited by ultrasonic vibration

Some equipment have low efficiencies and safety when their surfaces are covered with ice, such as wind turbine and airplane, so de-bonding ice on such kind of equipment surface is necessary. In this article, the ultrasonic de-icing method based on icing aluminum plate is researched by finite element method. First, the natural frequency of icing aluminum plate changing with thickness of ice is simulated by modal analysis. Then, the distributions of shear stresses at the interface between aluminum plate and ice layer changing with excitation frequencies are simulated by harmonic analysis. Second, the shear stress and de-icing area influenced by the size of piezoelectric ceramic and excitation voltage are analyzed. The simulation results show that there is lowest natural frequency with optimum thickness of ice layer used to de-bond ice for ultrasonic de-icing system. The optimum distribution law of shear stress at the interface between ice layer and aluminum is decided. In this condition, the shear stress and de-icing area increase along with the excitation voltage. However, the de-icing area increases slowly. All the simulation results lay a theoretical foundation for future experiments and application of ultrasonic de-icing.