Acoustic emission technique for leak detection in an end shield of a pressurised heavy water reactor

This paper discusses the successful application of the Acoustic Emission Technique (AET) for detection and location of leak paths present on the inaccessible side of an end shield of a Pressurised Heavy Water Reactor (PHWR). The methodology was based on the fact that air and water leak AE signals have different characteristic features. Baseline data was generated from a sound end-shield of a PHWR for characterizing the background noise. A mock up end-shield system with saw cut leak paths was used to verify the validity of the methodology. It was found that air leak signals under pressurisation (as low as 3 psi) could be detected by frequency domain analysis. Signals due to air leaks from various locations of a defective end-shield were acquired and analysed. It was possible to detect and locate leak paths. Presence of detected leak paths were further confirmed by alternate test.     

Literature reviews in student project reports

Writing project reports is an important part of the engineering curriculum at Singapore universities. One important section of the formal report is the literature review. Most universities around the world provide guidelines on writing reviews, emphasizing that plagiarism is unethical. However, these guidelines do not offer explicit training on how to avoid plagiarism. In order to write academically acceptable reviews while avoiding copying from source materials, students face a major challenge and resort to employing various strategies to cope with the task. In this study, we examined the literature review sections of final year project reports to find out how engineering undergraduates in a Singapore university cope with writing reviews and to suggest ways in which they can extend their skills to improve their literature reviews.     

Synthesis and characterisation of N-toluyl methacrylatoethyl carbamates,homopolymers and copolymers with methyl methacrylate

The paper describes the synthesis of N-2/4-toluyl methacrylatoethyl carbamates using 2/4-toluyl isocyanate and 2-hydroxyethyl methacrylate. Homopolymerisation and copolymerisation of these novel monomers with methyl methacrylate was carried out using benzoyl peroxide as an initiator and tetrahydrofuran as solvent. Photopolymerisation of N-4-toluyl methacrylatoethyl carbamate could be carried out without the use of photosensitiser. Structural characterisation of copolymers was done using ¹H-NMR. Thermal stability of copolymers was evaluated in a nitrogen atmosphere by dynamic thermogravimetry.     

An improved photographic process for convergent beam electron diffraction applications

The use of a surface developer, pyrocatechol, to process transmission electron microscope negatives has been shown to have significant advantages over the conventional D-19 process. The process described here is tolerant of a large margin of error in the electron exposure and produces a negative that not only retains details both in the highlight as well as the faint regions, but also preserves local contrast. These characteristics are particularly useful in convergent beam electron diffraction applications where one encounters a wide contrast range. Improved acuteness and an enhanced signal to noise ratio due to the prolonged exposures associated with this process have also been observed.     

Analysis of time-dependent flow-sensitive PC-MRI data

Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.     

Biowaste-derived hydroxyapatite reinforced with polyvinyl pyrrolidone/aloevera composite for biomedical applications

The present investigation focuses on the synthesis of crabshell-derived hydroxyapatite (CS-HAP)/ water-soluble synthetic polymer—polyvinylpyrrolidone(PVP)/aloevera(AV)—a natural biopolymer, as a composite for enhanced mechanical, antibacterial and biocompatible properties. The reinforcement of polymer has a significant function in increasing the mechanical property of the composite, whereas the incorporation of AV improves the antibacterial and biocompatibility. Phase composition, morphology, mechanical property, and hydrophilicity of CS-HAP/PVP/AV biocomposite with different concentrations of PVP and AV were examined by Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), scanning electron microscopy with energy dispersive X-ray (SEM-EDX), Vickers microhardness tests, contact angle, respectively. Furthermore, the antibacterial efficiency of the composite is assessed using Escherichia coli (E coli) and Staphylococcus aureus (S aureus). The biocompatibility of HOS MG 63 cells on the CS-HAP/PVP/AV composite is evaluated by MTT assay test. The obtained results evidence that the as-synthesized composite have appropriate mechanical, antibacterial and biocompatible properties. Overall, the combination of mechanical property of PVP, antibacterial and biocompatible property of AV in CS-HAP/PVP/AV, makes the composite a potential therapeutic material for various biomedical applications.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.     

Short-term prediction of traffic flow using a binary neural network

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.