Frequency-Domain Laser Ultrasound (FDLU) Non-destructive Evaluation of Stress–Strain Behavior in an Aluminum Alloy

The evaluation of the stress–strain state of metallic materials is an important problem in the field of non-destructive testing (NDT). Prolonged cyclic loading or overloading will lead to permanent changes of material strength in an inconspicuous manner that poses threat to the safety of structures, components and products. This research focuses on gauging the mechanical strength of metallic alloys through the application of frequency-domain laser ultrasound (FDLU) based on a continuous-wave diode laser source. The goal is to develop industrial NDT procedures for fatigue monitoring in metallic substrates and coatings so that the technique can be used for mechanical strength assessment. A small-scale, non-commercial rig was fabricated to hold the sample and conduct tensile FDLU testing in parallel with an adhesive strain gauge affixed on the tested sample for independent measurement of the applied stress. Harmonic modulation and lock-in detection were used to investigate the LU signal sensitivity to the stress–strain state of ordinary aluminum alloy samples. A 1 MHz focused piezoelectric transducer was used to detect the LU signal. During the tensile procedure, both amplitude and phase signals exhibited good repeatability and sensitivity to the increasing stress–strain within the elastic regime. Signals beyond the elastic limit also revealed significant change patterns.     

New formulations for the setup assembly line balancing and scheduling problem

We present three new formulations for the setup assembly line balancing and scheduling problem (SUALBSP). Unlike the simple assembly line balancing problem, sequence-dependent setup times are considered between the tasks in the SUALBSP. These setup times may significantly influence the station times. Thus, there is a need for scheduling the list of tasks within each station so as to optimize the overall performance of the assembly line. In this study, we first scrutinize the previous formulation of the problem, which is a station-based model. Then, three new formulations are developed by the use of new sets of decision variables. In one of these formulations, the schedule-based formulation, SUALBSP is completely formulated as a scheduling problem. That is, no decision variable in the model directly denotes a station. All the proposed formulations will be improved by the use of several enhancement techniques such as preprocessing and valid inequalities. These improved formulations can be applied to establishing lower bounds on the problem. To assess the performance of new formulations, results of an extensive computational study on the benchmark data sets are also reported.     

Structural insights of mechanically induced aluminum-doped hydroxyapatite nanoparticles by Rietveld refinement

Aluminum doped hydroxyapatite(HA:Al~(3+)) nanopowders were successfully prepared via a simple and efficient one-pot mechanochemical route. The effects of dopant loading on phase compositions and structural features were assessed by Rietveld analysis. The XRD-Rietveld refinement revealed the stabilization of HA in hexagonal structure for all the samples. The sharpness and intensity of the apatite-derived XRD peaks decreased as the dopant content increased to 10% due to the increase in lattice imperfections and mechanically induced amorphization. The incorporation of Al3+into the HA lattice decreased the unit cell parameters. From the FTIR measurements, the representing bands of apatite were identified in all cases. The mechanosynthesized nanopowders consisted of nanospheroids with an average size of 44 ± 20 nm and therefore are promising for bone tissue regeneration.     

A Novel Predictive Technique for Estimating the Hydrate Inhibition Effects of Single and Mixed Thermodynamic Inhibitors

A new correlation has been developed for predicting hydrate inhibition characteristics, as well as water freezing point depression of aqueous solutions containing salts and/or organic inhibitors. The correlation has been developed using hydrate inhibition/freezing point depression data at different concentrations of salt and/or organic inhibitor generated using a previously reported thermodynamic model. Independent data have been used in examining the reliability of this method. The predictions of this correlation are in acceptable agreement with the independent experimental data, demonstrating the reliability of this predictive technique for estimating the hydrate inhibition effects of salts and/or organic inhibitors.     

An Adaptable Job Submission System Based on Moderate Price-Adjusting Policy in Market-Based Grids

A market-based computational grid is made up of large sets of heterogeneous and geographically distributed resources that are gathered into virtual organizations for executing consumer’s applications. One of the most important challenges in market-based grid systems is the management of grid users, which is called resource providers and consumers. The existing methods provide some alternative mechanisms for this problem, but they are not fully adequate. To address this problem, we propose an enhanced approach for adjusting price of grid resource using new effective parameters of microeconomic issue and also for prioritizing current jobs in the queue. This proposed approach is integrated with a cooperative method among local schedulers to accept jobs based on auction model. The results conclude that the inclusion of new parameters in price-adjusting affects the payment budget and job submission behavior of the schedulers. The evaluations of experimental results prove a remarkable performance of the proposed approach in diverse conditions and job workloads.     

A practical algorithm for distribution state estimation including renewable energy sources

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder–Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems.     

Construction of nonpathological Lyapunov functions for discontinuous systems with Caratheodory solutions

This paper presents a method based on the Nonpathological Lyapunov theorem for constructing Lyapunov function (LF) for discontinuous time invariant dynamical systems with Caratheodory solutions. The origin is stable, if the method constructs a Nonpathological Lyapunov Function (NPLF) for the system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

A multi-layer feed-forward artificial neural network has been presented for accurate prediction of the vapor liquid equilibrium (VLE) of CO₂+alkanol mixtures. Different types of alkanols namely, 1-propaol, 2-propanol, 1-butanol, 1-pentanol, 2-pentanol, 1-hexanol and 1-heptanol, are used in this study. The proposed network is trained using the Levenberg-Marquardt back propagation algorithm, and the tan-sigmoid activation function is applied to calculate the output values of the neurons of the hidden layers. According to the network’s training, validation and testing results, a six layer neural network is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperatures. Comparison of the suggested neural network model with the most important thermodynamic correlations shows that the proposed neuromorphic model outperforms the other available alternatives. The predicted equilibrium pressure and vapor phase CO₂ mole fraction are in good agreement with experimental data suggesting the accuracy of the proposed neural network model for process design.     

Stress test optimization using an integrated production test and field reliability model

Numerous papers and texts have been written in the reliability literature regarding the determination of the optimum test duration for a production stress or a burn‐in test. The techniques presented have largely been based on the identification of the change point at which infant mortality has largely been removed from the units. The time‐on‐test is typically the only factor that influences this decision. Few of these models have attempted to integrate the field performance or the influence of warranty costs into this decision. This paper proposes and validates a methodology that integrates the influence of the production test failures and the field performance including their respective costs into a single unified model. The objective is to identify a production test duration that minimizes the overall cost. A Weibull model is initially developed for the production test that incorporates the failure observations in different time segments of the test based on the ability to detect latent defects in the product. A separate Weibull model is then developed for the product's performance in the field that includes the lifetime of the unit. This paper identifies how both these Weibull models can be combined into a single model including both test and field costs with the objective of minimizing the overall cost. The advantage of the proposed technique is that it does not require one to track individual units from production through to the field in order to develop an integrated test and field cost model. Copyright © 2009 John Wiley & Sons, Ltd.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.