Natural convection boundary-layer adjacent to an inclined flat plate subject to sudden and ramp heating

The natural convection thermal boundary layer adjacent to an inclined flat plate subject to sudden heating and a temperature boundary condition which follows a ramp function up until a specified time and then remains constant is investigated. The development of the flow from start-up to a steady-state has been described based on scaling analyses and verified by numerical simulations. Different flow regimes based on the Rayleigh number are discussed with numerical results for both boundary conditions. For ramp heating, the boundary layer flow depends on the comparison of the time at which the ramp heating is completed and the time at which the boundary layer completes its growth. If the ramp time is long compared with the steady state time, the layer reaches a quasi steady mode in which the growth of the layer is governed solely by the thermal balance between convection and conduction. On the other hand, if the ramp is completed before the layer becomes steady; the subsequent growth is governed by the balance between buoyancy and inertia, as for the case of instantaneous heating.

Scaling of natural convection of an inclined flat plate : sudden cooling condition

The natural convection boundary layer adjacent to an inclined plate subject to sudden cooling boundary condition has been studied. It is found that the cold boundary layer adjacent to the plate is potentially unstable to Rayleigh-Bénard instability if the Rayleigh number exceeds a certain critical value. A scaling relation for the onset of instability of the boundary layer is achieved. The scaling relations have been developed by equating important terms of the governing equations based on the development of the boundary layer with time. The flow adjacent to the plate can be classified broadly into a conductive, a stable convective or an unstable convective regime determined by the Rayleigh number. Proper scales have been established to quantify the flow properties in each of these flow regimes. An appropriate identification of the time when the instability may set in is discussed. A numerical verification of the time for the onset of instability is also presented in this study. Different flow regimes based on the stability of the boundary layer have been discussed with numerical results.

Natural convection in attic-shaped spaces subject to sudden and ramp heating boundary conditions

In this study, a discussion of the fluid dynamics in the attic space is reported, focusing on its transient response to sudden and linear changes of temperature along the two inclined walls. The transient behaviour of an attic space is relevant to our daily life. The instantaneous and non-instantaneous (ramp) heating boundary condition is applied on the sloping walls of the attic space. A theoretical understanding of the transient behaviour of the flow in the enclosure is performed through scaling analysis. 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 heating-up 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.

ESD starts where STEM stops : integrating the social sciences into STEM

The Earth and its peoples are facing great challenges. As a species, humans are over-consuming the Earth’s resources and compromising the capacity of both natural and social systems to function in healthy and sustainable ways. Education at all levels and in all contexts, has a key role in helping societies move to more sustainable ways of living. Two areas in need of catch-up in relation to Education for Sustainable Development (ESD) are early childhood education and teacher education. Another area of challenge for ESD is the way it is currently oriented. To date, a great deal of emphasis has been placed on scientific and technological solutions to sustainability issues. This has led to an emphasis on STEM education as education’s main way of addressing sustainability. However, in this paper it is argued that sustainably is primarily a social issue that requires interdisciplinary education approaches. STEM approaches to ESD - emphasising knowledge construction and problem-solving - cannot, on their own, deal effectively with attitudes, values and actions towards more sustainable ways of living. In China and Australia, there are already policies, frameworks, guidelines and initiatives, such as Green Schools and Sustainable Schools that support such forms of ESD. STEM educators need to reach out to social scientists and social educators in order to more fully engage with activist and collaborative educational responses that equip learners with the knowledge, dispositions and capacities to ‘make a difference’.

Pedestrian-cyclist interactions at bus stops along segregated bike paths : a case study of Montreal

Safety is one of the major world health issues, and is even more acute for “vulnerable” road users, pedestrians and cyclists. At the same time, public authorities are promoting the active modes of transportation that involve these very users for their health benefits. It is therefore important to understand the factors and designs that provide the best safety for vulnerable road users and encourage more people to use these modes. Qualitative and quantitative shortcomings of collisions make it necessary to use surrogate measures of safety in studying these modes. Some interactions without a collision such as conflicts can be good surrogates of collisions as they are more frequent and less costly. To overcome subjectivity and reliability challenges, automatic conflict analysis using video cameras and deriving users’ trajectories is a solution to overcome shortcomings of manual conflict analysis. The goal of this paper is to identify and characterize various interactions between cyclists and pedestrians at bus stops along bike paths using a fully automated process. Three conflict severity indicators are calculated and adapted to the situation of interest to capture those interactions. A microscopic analysis of users’ behavior is proposed to explain interactions more precisely. Eventually, the study aims to show the capability of automatically collecting and analyzing data for pedestrian-cyclist interactions at bus stops along segregated bike paths in order to better understand the actual and perceived risks of these facilities.

Factors potentiating the risk of sudden infant death syndrome associated with the prone position

Background. In several studies the sudden infant death syndrome (SIDS) has been significantly associated with sleeping in the prone position. It is not known how the prone position increases the risk of SIDS. Methods. We analyzed data from a case-control study (58 infants with SIDS and 120 control infants) and a prospective cohort study (22 infants with SIDS and 213 control infants) in Tasmania. Interactions were examined in matched analyses with a multiplicative model of interaction. Results. In the case-control study, SIDS was significantly associated with sleeping in the prone position, as compared with other positions (unadjusted odds ratio, 4.5; 95 percent confidence interval, 2.1 to 9.6). The strength of this association was increased among infants who slept on natural-fiber mattresses (P = 0.05), infants who were swaddled (P = 0.09), infants who slept in heated rooms (P = 0.006), and infants who had had a recent illness (P = 0.02). These variables had no significant effect on infants who did not sleep in the prone position. A history of recent illness was significantly associated with SIDS among infants who slept prone (odds ratio, 5.7; 95 percent confidence interval, 1.8 to 19) but not among infants who slept in other positions (odds ratio, 0.83). In the cohort study, the risk of SIDS was greater among infants who slept prone on natural-fiber mattresses (odds ratio, 6.6; 95 percent confidence interval, 1.3 to 33) than among infants who slept prone on other types of mattresses (odds ratio, 1.8). Conclusions. When infants sleep prone, the elevated risk of SIDS is increased by each of four factors: the use of natural-fiber mattresses, swaddling, recent illness, and the use of heating in bedrooms.

Home monitoring for infants at risk of the sudden infant death syndrome

This study evaluates the effectiveness and social implications of home monitoring of 31 infants at risk of sudden infant death syndrome (SIDS). Thirteen siblings of children dying of SIDS, nine near miss SIDS infants and nine preterm infants with apnoea persisting beyond 40 weeks post conceptual age were monitored from a mean age of 15 days to a mean of 10 months. Chest movement detection monitors were used in 27 and thoracic impedance monitors in four. Genuine apnoeic episodes were reported by 21 families, and 13 infants required resuscitation. Apnoeic episodes occurred in all nine preterm infants but in only five (38%) of the siblings of SIDS (P<0.05). Troublesome false alarms were a major problem occurring with 61% of the infants and were more common with the preterm infants than the siblings of SIDS. All but two couples stated that the monitor decreased anxiety and improved their quality of life. Most parents accepted that the social restrictions imposed by the monitor were part of the caring process but four couples were highly resentful of the changes imposed on their lifestyle. The monitors used were far from ideal with malfunction occurring in 17, necessitating replacement in six, repair in six and cessation of monitoring in three. The parents became ingenious in modifying the monitors to their own individual requirements Although none of these 31 ‘at risk’ infants died the study sample was far too small to conclude whether home monitoring prevented any cases of SIDS.

Effects of fare collection policy on operating characteristics of a Brisbane busway station

Transit agencies across the world are increasingly shifting their fare collection mechanisms towards fully automated systems like the smart card. One of the objectives in implementing such a system is to reduce the boarding time per passenger and hence reduce the overall dwell time for the buses at the bus stops/bus rapid transit (BRT) stations. TransLink, the transit authority responsible for public transport management in South East Queensland, has introduced ‘GoCard’ technology using the Cubic platform for fare collection on its public transport system. In addition to this, three inner city BRT stations on South East Busway spine are operating as pre-paid platforms during evening peak time. This paper evaluates the effects of these multiple policy measures on operation of study busway station. The comparison between pre and post policy scenarios suggests that though boarding time per passenger has decreased, while the alighting time per passenger has increased slightly. However, there is a substantial reduction in operating efficiency was observed at the station.

Biomechanical force displacement analysis of strength of fixation of tracker holding devices to bone in computer aided joint replacement

Computer aided joint replacement surgery has become very popular during recent years and is being done in increasing numbers all over the world. The accuracy of the system depends to a major extent, on accurate registration and immobility of the tracker attachment devices to the bone. This study was designed to asses the forces needed to displace the tracker attachment devices in the bone simulators. Bone simulators were used to maintain the uniformity of the bone structure during the study. The fixation devices tested were 3mm diameter self drilling, self tapping threaded pin, 4mm diameter self tapping cortical threaded pin, 5mm diameter self tapping cancellous threaded pin and a triplanar fixation device ‘ortholock’ used with three 3mm pins. All the devices were tested for pull out, translational and rotational forces in unicortical and bicortical fixation modes. Also tested was the normal bang strength and forces generated by leaning on the devices. The forces required to produce translation increased with the increasing diameter of the pins. These were 105N, 185N, and 225N for the unicortical fixations and 130N, 200N, 225N for the bicortical fixations for 3mm, 4mm and 5mm diameter pins respectively. The forces required to pull out the pins were 1475N, 1650N, 2050N for the unicortical, 1020N, 3044N and 3042N for the bicortical fixated 3mm, 4mm and 5mm diameter pins. The ortholock translational and pull out strength was tested to 900N and 920N respectively and still it did not fail. Rotatory forces required to displace the tracker on pins was to the magnitude of 30N before failure. The ortholock device had rotational forces applied up to 135N and still did not fail. The manual leaning forces and the sudden bang forces generated were of the magnitude of 210N and 150N respectively. The strength of the fixation pins increases with increasing diameter from three to five mm for the translational forces. There is no significant difference in pull out forces of four mm and five mm diameter pins though it is more that the three mm diameter pins. This is because of the failure of material at that stage rather than the fixation device. The rotatory forces required to displace the tracker are very small and much less that that can be produced by the surgeon or assistants in single pins. Although the ortholock device was tested to 135N in rotation without failing, one has to be very careful not to put any forces during the operation on the tracker devices to ensure the accuracy of the procedure.

Utilizing robustness of positive buck-boost converter against input voltage and load current disturbances

A Positive Buck- Boost (PBB) converter is a known DC-DC converter that can operate in step up and step down modes. Unlike Buck, Boost, and Inverting Buck Boost converters, the inductor current of a PBB can be controlled independently of its voltage conversion ratio. In other words, the inductor of PBB can be utilised as an energy storage unit in addition to its main function of energy transfer. In this paper, the capability of PBB to store energy has been utilised to achieve robustness against input voltage fluctuations and output current changes. The control strategy has been developed to keep accuracy, affordability, and simplicity acceptable. To improve the efficiency of the system a Smart Load Controller (SLC) has been suggested. Applying SLC extra current storage occurs when there is sudden loads change otherwise little extra current is stored.

Head and eye movements and lane keeping in drivers with hemianopia and quadrantanopia compared to controls

Purpose: To compare the eye and head movements and lane-keeping of drivers with hemianopia and quadrantanopia with that of age-matched controls when driving under real world conditions. Methods: Participants included 22 hemianopes and 8 quadrantanopes (M age 53 yrs) and 30 persons with normal visual fields (M age 52 yrs) who were ≥ 6 months from the brain injury date and either a current driver or aiming to resume driving. All participants drove an instrumented dual-brake vehicle along a 14-mile route in traffic that included non-interstate city driving and interstate driving. Driving performance was scored using a standardised assessment system by two “backseat” raters and the Vigil Vanguard system which provides objective measures of speed, braking and acceleration, cornering, and video-based footage from which eye and head movements and lane-keeping can be derived. Results: As compared to drivers with normal visual fields, drivers with hemianopia or quadrantanopia on average were significantly more likely to drive slower, to exhibit less excessive cornering forces or acceleration, and to execute more shoulder movements off the seat. Those hemianopic and quadrantanopic drivers rated as safe to drive by the backseat evaluator made significantly more excursive eye movements, exhibited more stable lane positioning, less sudden braking events and drove at higher speeds than those rated as unsafe, while there was no difference between safe and unsafe drivers in head movements. Conclusions: Persons with hemianopic and quadrantanopic field defects rated as safe to drive have different driving characteristics compared to those rated as unsafe when assessed using objective measures of driving performance.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

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.