Natural convection in attics subject to instantaneous and ramp cooling boundary conditions

A fundamental study of the fluid dynamics inside an attic shaped triangular enclosure with cold upper walls and adiabatic horizontal bottom wall is reported in this study. The transient behaviour of the attic fluid which is relevant to our daily life is examined based on a scaling analysis. The transient phenomenon begins with the instantaneous cooling and the cooling with linear decreases of temperature up to some specific time (ramp time) and then maintain constant of the upper sloped walls. It is shown that both inclined walls develop a thermal boundary layer whose thicknesses increase towards steady-state or quasi-steady values. A proper identification of the timescales, the velocity and the thickness relevant to the flow that develops inside the cavity makes it possible to predict theoretically the basic flow features that will survive once the thermal flow in the enclosure reaches a steady state. A time scale for the cooling-down of the whole cavity together with the heat transfer scales through the inclined walls has also been obtained through scaling analysis. All scales are verified by the numerical simulations.

Scaling analysis of natural convection boundary layers : Application to attic space

The natural convection thermal boundary layer adjacent to an inclined flat plate and inclined walls of an attic space subject to instantaneous and ramp heating and cooling is investigated. A scaling analysis has been performed to describe the flow behaviour and heat transfer. Major scales quantifying the flow velocity, flow development time, heat transfer and the thermal and viscous boundary layer thicknesses at different stages of the flow development are established. Scaling relations of heating-up and cooling-down times and heat transfer rates have also been reported for the case of attic space. The scaling relations have been verified by numerical simulations over a wide range of parameters. Further, a periodic temperature boundary condition is also considered to show the flow features in the attic space over diurnal cycles.

Rotational Molding Cycle Time Reduction Using a Combination of Physical Techniques

Rotational molding is a process used to manufacture hollow plastic products, and has been heralded as a molding method with great potential. Reduction of cycle times is an important issue for the rotational molding industry, addressing a significant disadvantage of the process. Previous attempts to reduce cycle times have addressed surface enhanced molds, internal pressure, internal cooling, water spray cooling, and higher oven air flow rates within the existing process. This article explores the potential benefits of these cycle time reduction techniques, and combinations of them. Recommendations on a best practice combination are made, based on experimental observations and resulting product quality. Applying the proposed molding conditions (i.e., a combination of surface-enhanced molds, higher oven flow rates, internal mold pressure, and water spray cooling), cycle time reductions of up to 70% were achieved. Such savings are very significant, inviting the rotomolding community to incorporate these techniques efficiently in an industrial setting. POLYM. ENG. SCI., 49:1846-1854, 2009. (C) 2009 Society of Plastics Engineers

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Optical fiber cavity ring down measurement of refractive index with a microchannel drilled by femtosecond laser

A microchannel was inscribed in the fiber of a ring cavity which was constructed using two 0.1%:99.9% couplers and a 10-m fiber loop. Cavity ring down spectroscopy was used to measure the refractive index (RI) of gels infused into the microchannel. The ring down time discloses a nonlinear increase with respect to RI of the gel and sensitivity up to 300 µs/RI unit and an index resolution of 1.4 × 10 was obtained. © 2009 IEEE.

Optical fiber cavity ring down measurement of refractive index with a microchannel drilled by femtosecond laser

A microchannel was inscribed in the fiber of a ring cavity which was constructed using two 0.1%:99.9% couplers and a 10-m fiber loop. Cavity ring down spectroscopy was used to measure the refractive index (RI) of gels infused into the microchannel. The ring down time discloses a nonlinear increase with respect to RI of the gel and sensitivity up to 300 µs/RI unit and an index resolution of 1.4 × 10 was obtained. © 2009 IEEE.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Detecting distributed Denial-of-Service attacks and Flash Events

This thesis investigates and develops techniques for accurately detecting Internet-based Distributed Denial-of-Service (DDoS) Attacks where an adversary harnesses the power of thousands of compromised machines to disrupt the normal operations of a Web-service provider, resulting in significant down-time and financial losses. This thesis also develops methods to differentiate these attacks from similar-looking benign surges in web-traffic known as Flash Events (FEs). This thesis also addresses an intrinsic challenge in research associated with DDoS attacks, namely, the extreme scarcity of public domain datasets (due to legal and privacy issues) by developing techniques to realistically emulate DDoS attack and FE traffic.

Post-fire mechanical properties of cold-formed steels

Cold-formed steel members have been widely used in residential, industrial and commercial buildings as primary load-bearing and non-load bearing structural elements. These buildings must be properly evaluated after a fire event to assess the nature and extent of structural damage. If the general appearance of the structure is satisfactory after a fire event then the question that has to be answered is how the structural capacity of cold-formed steel members in these buildings has been affected. Elevated temperatures during a fire event affect the structural performance of cold-formed steel members even after cooling down to ambient temperature due to the possible detrimental changes in their mechanical properties. However, the post-fire behaviour of cold-formed steel members has not been investigated in the past and hence there is a need to investigate the post-fire mechanical properties of cold-formed steels. Therefore an experimental study was undertaken at the Queensland University of Technology to understand the residual mechanical properties of cold-formed steels after fire events. Tensile coupon tests were conducted on three different steel grades and thicknesses to obtain their stress-strain curves and relevant mechanical properties after cooling them down from different elevated temperatures. It was found that the post-fire mechanical properties of cold-formed steels are different to the original ambient temperature mechanical properties. Hence a new set of equations is proposed to predict the reduced mechanical properties of cold-formed steels after a fire event.

Distortional buckling behaviour of fire exposed cold-formed steel columns

Cold-formed steel sections are commonly used in low-rise commercial and residential buildings. During fire events, cold-formed steel structural elements in these buildings can be exposed to elevated temperatures. Hence after such events there is a need to evaluate their residual strengths. However, only limited information is available in relation to the residual strength of fire exposed cold-formed steel sections. This research is aimed at investigating the distortional buckling capacities of fire exposed cold-formed lipped channel sections. A series of compression tests of fire exposed, short lipped channel columns made of varying steel grades and thicknesses was undertaken in this research. Test columns were first exposed to different elevated temperatures up to 800 oC, and then tested to failure after cooling down. Suitable finite element models were developed with post-fire mechanical properties to simulate the behaviour of tested columns and were validated using test results. The residual compression capacities of short columns were also predicted using the current cold-formed steel standards and compared with test and finite element analysis results. This comparison showed that ambient temperature design rules for columns can be used to predict the residual compression capacities of fire exposed short or laterally restrained cold-formed steel columns provided the maximum temperature experienced by the column can be estimated after a fire event. Such residual capacity assessments will allow engineers to evaluate the safety of fire exposed buildings. This paper presents the details of this experimental study, finite element analyses and the results.

Synthesis of BiFeO by carbonate precipitation

Magnetoelectric multiferroic BiFeO3 (BFO) was synthesized by a simple carbonate precipitation technique of metal nitrate solutions. X-ray powder diffraction and thermo-gravimetric analysis (TGA) revealed that the precipitate consists of an intimate mixture of crystalline bismuth carbonate and an amorphous hydroxide of iron. The precipitate yielded BiFeO3 at an optimal calcination temperature of similar to 560A degrees C. Energy dispersive X-ray (EDX) analysis showed 1:1 ratio between Bi and Fe in the oxide. X-ray photoelectron spectroscopy (XPS) studies confirmed that Fe to be in +3 oxidation states both in the precipitated powder and BiFeO3. The synthesized BFO exhibits a very weak ferromagnetic correlation at room temperature and the degree of which increases slightly on cooling down to 10 K suggesting alteration in the long range spatial modulation of the spins arrangement as compared to the bulk BiFeO3.

Acoustic emission techniques for NDE & structural health monitoring of advanced composite materials

Structural Health Monitoring (SHM) is an effective extension of NDE to reduce down time and cost of Inspection of structural components. On – line monitoring is an essential part of SHM. Acoustic Emission Techniques have most of the desirable requirements of an effective SHM tool. With the kind of advancement seen in the last couple of decades in the field of electronics, computers and signal processing technologies it can only be more helpful in obtaining better and meaningful quantitative results which can further enhance the potential of AET for the purpose. Advanced Composite materials owing to their specific high performance characteristics are finding a wide range of engineering applications. Testing and Evaluation of this category of materials and SHM of composite structures have been very challenging problems due to the very nature of these materials. Mechanical behaviour of fiber composite materials under different loading conditions is complex and involves different types of failure mechanisms. This is where the potential of AET can be exploited effectively. This paper presents an over view of some relevant studies where AET has been utilised to test, evaluate and monitor health of composite structures.

Curvature Management in Buffer Layer for Device Quality GaN Growth on Si (111)

An efficient buffer layer scheme has been designed to address the issue of curvature management during metalorganic chemical vapour deposition growth of GaN on Si (111) substrate. This is necessary to prevent cracking of the grown layer during post-growth cooling down from growth temperature to room temperature and to achieve an allowable bow (<40 m) in the wafer for carrying out lithographic processes. To meet both these ends simultaneously, the stress evolution in the buffer layers was observed carefully. The reduction in precursor flow during the buffer layer growth provided better control over curvature evolution in the growing buffer layers. This has enabled the growth of a suitable high electron mobility transistor (HEMT) stack on 2'' Si (111) substrate of 300 m thickness with a bow as low as 11.4 m, having a two-dimensional electron gas (2DEG) of mobility, carrier concentration, and sheet resistance values 1510 cm(2)/V-s, 0.96 x 10(13)/cm(2), and 444 /, respectively. Another variation of similar technique resulted in a bow of 23.4 m with 2DEG mobility, carrier concentration, and sheet resistance values 1960 cm(2)/V-s, 0.98 x 10(13)/cm(2), and 325 /, respectively.