Solution of Helmholtz equation by Trefftz method

The application of the Trefftz method for calculating wave forces on offshore structures is presented. Indirect and direct formulations using complete and non-singular systems of Trefftz functions for the Helmholtz equation are posed in this paper. An effective technique using different interpolation functions for the velocity potential and wave force are suggested to improve the computational accuracy of the wave force. The numerical examples show that the present method is highly efficient and accurate.     

Axial-Field Electrical Machines - Design and Applications

Axial-field electrical machines offer an alternative to the conventional machines. In the axial-field machine, the airgap flux is axial in direction and the active current carrying conductors are radially positioned. This paper presents the design characteristics, special features, manufacturing aspects and potential applications for axial-field electrical machines. The experimental results from several prototypes, including d.c. machines, synchronous machines and single-phase machines are given. The special features of the axial-field machine, such as its planar and adjustable airgap, flat shape, ease of diversification, etc., enable axial-field machines to have distinct advantages over conventional machines in certain applications, especially in special-purpose applications.     

An Analysis of Scheduling Policies In Multiechelon Production Systems

Results of a simulation study of the economics of frequency of rescheduling Material Requirements Planning (MRP) systems are presented for a single-product, two-stage system in which demand is uncertain. The results indicate that for systems with moderate demand uncertainty, frequent rescheduling to maintain customer service may be uneconomical when compared with the alternative of more stable schedules in conjunction with safety stock. This result arises primarily because the cost of “emergency” production setups which occur when rescheduling is frequent exceeds the cost of safety stock required to “protect” stable schedules.     

A Recursive Frequency Estimator Using Linear Prediction and a Kalman-Filter-Based Iterative Algorithm

This paper proposes a new Kalman-filter-based recursive frequency estimator for discrete-time multicomponent sinusoidal signals whose frequencies may be time-varying. The frequency estimator is based on the linear prediction approach and it employs the Kalman filter to track the linear prediction coefficients (LPCs) recursively. Frequencies of the sinusoids can then be computed using the estimated LPCs. Due to the coloredness of the linear prediction error, an iterative algorithm is employed to estimate the covariance matrix of the prediction error and the LPCs alternately in the Kalman filter in order to improve the tracking performance. Simulation results show that the proposed Kalman-filter-based iterative frequency estimator can achieve better tracking results than the conventional recursive least-squares-based estimators.     

Modeling tool wear in end-milling using enhanced GMDH learning networks

This paper presents an enhanced approach to predictive modeling for determining tool-wear in end-milling operations based on enhanced-group method of data handling (e-GMDH). Using milling input parameters (speed, feed, and depth-of-cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second-order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used. Moreover, this approach makes it easy to present the realized solution in a form that can be further optimized for the input parameters using some optimization techniques. The results realized using our e-GMDH method are promising, and the comparative study presented shows that the e-GMDH outperforms polynomial neural network (PNN); moreover, it is more flexible than the conventional GMDH, which tends to produce nonlinear solutions even for simple problems. In the investigation, the extended particle swarm optimization (PSO) technique was applied to obtain the optimal parameters. Consequently, the modeling approach is extremely useful in realizing a computer-aided process-planning system in an advanced manufacturing environment.     

Evaluation of Digitally Encoded Layer‐by‐layer Coated Microparticles as Cell Carriers

To obtain more biologically relevant data there is a growing interest in the use of living cells for assaying the biological activity of unknown chemical compounds. Density ‘multiplex’ cell‐based assays, where different cell types are mixed in one well and simultaneously investigated upon exposure to a certain compound are beginning to emerge. To be able to identify the cells they should be attached to microscopic carriers that are encoded. This paper investigates how digitally encoded microparticles can be loaded with cells while keeping the digital code in the microcarriers readable. It turns out that coating the surface of the encoded microcarriers with polyelectrolytes using the layer‐by‐layer (LbL) approach provides the microcarriers with a ‘highly functional’ surface. The polyelectrolyte layer allows the growth of the cells, allows the orientation of the cell loaded microcarriers in a magnetic field, and does not hamper the reading of the code. It has further been shown that the cells growing on the polyelectrolyte layer can become transduced by adenoviral particles hosted by the polyelectrolyte layer. It is concluded that the digitally encoded microparticles are promising materials for use in biomedical and pharmaceutical in‐vitro research where cells are used as tools.     

Noise–Power Optimization of Incremental Data Converters

Incremental data converters (IDCs) are useful in instrumentation and measurement applications, where low-frequency analog signals need to be converted into digital form with high accuracy and low power dissipation. They are particularly well suited for applications where a single analog-digital converter is multiplexed between many channels. This paper proposes an exact design methodology for IDCs, which optimizes the signal-to-noise ratio of the converter under practical design constraints. The process also allows the designer to apportion the noise budget in an arbitrary manner between thermal and quantization noise. The design process is illustrated by an example which describes the optimization of a third-order multiplexed IDC.     

A Novel Ordered Nanopore Array Diode Laser

In this work, we have created a new type of structure, the nanopore active layer, for achieving quantization of carrier states in a semiconductor. The nanopore structure consists of a periodic two-dimensional array of localized energy barriers perturbing an otherwise conventional quantum well. This perturbation leads to the formation of intraband forbidden energy gaps which are observed experimentally.     

Photo-Fenton removal of water-soluble polymers

This paper presents the results of experiments carried out in a laboratory-scale photochemical reactor on the photodegradation of different polymers in aqueous solutions by the photo-Fenton process. Solutions of three polymers, polyethyleneglicol (PEG), polyacrylamide (PAM), and polyvinylpyrrolidone (PVP), were tested under different conditions. The reaction progress was evaluated by sampling and analyzing the total organic carbon concentration in solution (TOC) along the reaction time. The behavior of the different polymers is discussed, based on the evolution of the TOC–time curves. Under specific reaction conditions, the formation and coalescence of solid particles was visually observed. Solids formation occurred simultaneously to a sharp decrease in the TOC of the liquid phase. This may be favorable for the treatment of industrial wastewater containing polymers, since the photodegradation process can be coupled with solid separation systems, which may reduce the treatment cost.     

Implementation of adaptive critic-based neurocontrollers for turbogenerators in a multimachine power system

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontrollers' training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability.