A genetic algorithm based back propagation network for simulation of stress–strain response of ceramic-matrix-composites

Ceramic-matrix-composites (CMCs) are fast replacing other materials in many applications where the higher production costs can be offset by significant improvement in performance. In applications such as cutting and forming tools, wear parts in machinery, nozzles, valve seals and bearings, improvement in toughness and hardness translate into longer life. However, the recent resurgence in the field of development of CMCs has been due to their potential use for the Space Transport systems, Combustion engines and other energy conversion systems. The CMCs are ideal structural material for these applications. However, due to their lack of toughness, they are prone to brittle fractures. Therefore, the main consideration in the development of CMCs has been to toughen them. To achieve this, the bi-material interface should be weak and must allow debonding, resulting in crack deflection. In the present work, the stress–strain response of Al₂O₃ (matrix)/SiC (whisker) ceramic composite has been simulated using a back propagation neural network (BPN), which incorporates the effect of interface shear strength (IFS) in the analysis. For efficient and quick training, the weights for the BPN have been obtained by using a genetic algorithm (GA). The GA has been modelled with 150 genes and a chromosome string length of 750. The network simulation is based on the stress–strain response obtained from the finite element analysis. A three noded isoparametric interface element has been employed to model the whisker/matrix interface in finite element analysis. The finite element analysis has been carried out only for a limited number of specimens. However, the simulation model is capable of predicting the stress–strain relationship for a new interface shear strength even with this limited information. Thus, the robustness and the generalisation capability of the neural network model is demonstrated. The development stages of the GA/BPN model such as the preparation of training set, selection of a network configuration, training of the net and a testing scheme, etc., have been addressed at length in this paper.     

Effect of temperature and salt on the maturation of white-salted cheese

White-salted cheeses were prepared from ultrafiltered (UF) cows' milk and salted to give final salt-in-moisture (SM) levels of 2.5, 3.2 and 4.0%. The cheeses were stored at 5°C and 10°C for up to 15 weeks. The microflora was dominated by lactic acid bacteria (LAB) but some mould growth was evident within 15 weeks at all SM levels and both temperatures. Levels of water-soluble nitrogen (WSN), attributed to chymosin activity, increased significantly with time, the rate being inversely proportional to the SM level and increasing with storage temperature. Similar effects were noted for trichloroacetic acid-soluble nitrogen (TCA-SN) and free amino acid (FAA) levels, both of which would also be affected by bacterial protease activity. The proteolytic activity was reflected by changes in the hardness and fracturability of the cheeses.     

Wafer bonding using Cu–Sn intermetallic bonding layers

Wafer-level Cu–Sn intermetallic bonding is an interesting process for advanced applications in the area of MEMS and 3D interconnects. The existence of two intermetallic phases for Cu–Sn system makes the wafer bonding process challenging. The impact of process parameters on final bonding layer quality have been investigated for transient liquid phase wafer-level bonding based on the Cu–Sn system. Subjects of this investigation were bonding temperature profile, bonding time and contact pressure as well as the choice of metal deposition method and the ratio of deposited metal layer thicknesses. Typical failure modes in intermetallic compound growth for the mentioned process and design parameters have been identified and were subjected to qualitative and quantitative analysis. The possibilities to avoid abovementioned failures are indicated based on experimental results.     

Simulation model for natural gas transmission pipeline network system

This paper focuses on developing a simulation model for the analysis of transmission pipeline network system (TPNS) with detailed characteristics of compressor stations. Compressor station is the key element in the TPNS since it provides energy to keep the gas moving. The simulation model is used to create a system that simulates TPNS with different configurations to get pressure and flow parameters. The mathematical formulations for the TPNS simulation were derived from the principles of flow of fluid through pipe, mass balance and compressor characteristics. In order to determine the unknown pressure and flow parameters, a visual C++ code was developed based on Newton–Raphson solution technique. Using the parameters obtained, the model evaluates the energy consumption for various configurations in order to guide for the selection of optimal TPNS. Results from the evaluations of the model with the existing TPNS and comparison with the existing approaches showed that the developed simulation model enabled to determine the operational parameters with less than 10 iterations. Hence, the simulation model could assist in decisions regarding the design and operations of the TPNS.     

Adaptive PSODV algorithm for OPF with non-smooth cost functions and statistical analysis

This paper presents a particle swarm optimization with differentially perturbed velocity hybrid algorithm with adaptive acceleration coefficient (APSO-DV) for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The APSO-DV employs differentially perturbed velocity with adaptive acceleration coefficient for updating the positions of particles for the particle swarm optimization. The feasibility of the proposed approach was tested on IEEE 30-bus and IEEE 118-bus systems with three different objective functions. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. The effectiveness of the proposed approach was tested including contingency also. Simulation results demonstrate that the APSO-DV provides superior results compared to classical DE, PSO, PSO-DV and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

Power quality diagnosis using time frequency analysis and rule based techniques

Diagnosing a power quality disturbance means identifying the type and cause of the disturbance. Fast diagnosis of power quality disturbances is important so as to assist network operators in performing counter measures and implementing suitable power quality mitigation actions. In this study a novel method for performing power quality diagnosis is presented by using the S-transform and rule based classification techniques. The proposed power quality diagnosis method was evaluated for its functionality in detecting the type of short duration voltage disturbances and identifying the cause of the disturbances which may be due to permanent or non permanent faults. Based on the results, this new method has the potential to be used in the existing real time power quality monitoring system in Malaysia to expedite the diagnosis on the recorded voltage disturbances.     

Application of ant colony, genetic algorithm and data mining-based techniques for scheduling

In this paper, we have proposed a novel use of data mining algorithms for the extraction of knowledge from a large set of flow shop schedules. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an ant colony algorithm performing a scheduling operation and to develop a rule set scheduler which approximates the ant colony algorithm's scheduler. Ant colony optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The natural metaphor on which ant algorithms are based is that of ant colonies. Fascinated by the ability of the almost blind ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called ‘pheromone’ and use its trails as a medium for communicating information among each other. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions. Further, we employed the genetic algorithm operators such as crossover and mutation to generate the new regions of solution. The data mining tool we have used is Decision Tree, which is produced by the See5 software after the instances are classified. The data mining is for mining the knowledge of job scheduling about the objective of minimization of makespan in a flow shop environment. Data mining systems typically uses conditional relationships represented by IF-THEN rules and allowing the production managers to easily take the decisions regarding the flow shop scheduling based on various objective functions and the constraints.     

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.