Turbulent velocity and suspended sediment concentration measurements in an urban environment of the Brisbane River Flood Plain at Gardens Point on 12-13 January 2011

The flood flow in urbanised areas constitutes a major hazard to the population and infrastructure as seen during the summer 2010-2011 floods in Queensland (Australia). Flood flows in urban environments have been studied relatively recently, although no study considered the impact of turbulence in the flow. During the 12-13 January 2011 flood of the Brisbane River, some turbulence measurements were conducted in an inundated urban environment in Gardens Point Road next to Brisbane's central business district (CBD) at relatively high frequency (50 Hz). The properties of the sediment flood deposits were characterised and the acoustic Doppler velocimeter unit was calibrated to obtain both instantaneous velocity components and suspended sediment concentration in the same sampling volume with the same temporal resolution. While the flow motion in Gardens Point Road was subcritical, the water elevations and velocities fluctuated with a distinctive period between 50 and 80 s. The low frequency fluctuations were linked with some local topographic effects: i.e, some local choke induced by an upstream constriction between stairwells caused some slow oscillations with a period close to the natural sloshing period of the car park. The instantaneous velocity data were analysed using a triple decomposition, and the same triple decomposition was applied to the water depth, velocity flux, suspended sediment concentration and suspended sediment flux data. The velocity fluctuation data showed a large energy component in the slow fluctuation range. For the first two tests at z = 0.35 m, the turbulence data suggested some isotropy. At z = 0.083 m, on the other hand, the findings indicated some flow anisotropy. The suspended sediment concentration (SSC) data presented a general trend with increasing SSC for decreasing water depth. During a test (T4), some long -period oscillations were observed with a period about 18 minutes. The cause of these oscillations remains unknown to the authors. The last test (T5) took place in very shallow waters and high suspended sediment concentrations. It is suggested that the flow in the car park was disconnected from the main channel. Overall the flow conditions at the sampling sites corresponded to a specific momentum between 0.2 to 0.4 m2 which would be near the upper end of the scale for safe evacuation of individuals in flooded areas. But the authors do not believe the evacuation of individuals in Gardens Point Road would have been safe because of the intense water surges and flow turbulence. More generally any criterion for safe evacuation solely based upon the flow velocity, water depth or specific momentum cannot account for the hazards caused by the flow turbulence, water depth fluctuations and water surges.

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

Graphene-like nano-sheets for surface acoustic wave gas sensor applications

The gas sensing properties of graphene-like nano-sheets deposited on 36° YX lithium tantalate (LiTaO3) surface acoustic wave (SAW) transducers are reported. The thin graphene-like nano-sheets were produced via the reduction of graphite oxide which was deposited on SAW interdigitated transducers (IDTs). Their sensing performance was assessed towards hydrogen (H2) and carbon monoxide (CO) in a synthetic air carrier gas at room temperature (25 °C) and 40 °C. Raman and X-ray photoelectron spectroscopy (XPS) revealed that the deposited graphite oxide (GO) was not completely reduced creating small, graphitic nanocrystals ∼2.7 nm in size. © 2008 Elsevier B.V.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Experimentally induced diesel engine injector faults and some preliminary acoustic emission signal observations

Diesel engine fuel injector faults can lead to reduced power, increased fuel consumption and greater exhaust emission levels and if left unchecked, can eventually lead to premature engine failure. This paper provides an overview of the Diesel, or compression ignition combustion process, and of the two basic fuel injector nozzle designs used in Diesel engines, namely, the pintle-type and hole-type nozzles. Also described are some common faults associated with these two types of fuel injector nozzles and the techniques previously used to experimentally simulate these faults. This paper also presents a recent experimental campaign undertaken using two different diesel engines whereby various fuel injector nozzle faults were induced into the engines. The first series of tests was undertaken using a turbo-charged 5.9 litre; Cummins Diesel engine whist the second series of tests was undertaken using a naturally aspirated 4 cylinder, 2.216 litre, Perkins Diesel engine. Data corresponding to different injector fault conditions was captured using in-cylinder pressure, and acoustic emission transducers along with both crank-angle encoder and top-dead centre reference signals. Using averaged in-cylinder pressure signals, it was possible to qualify the severity of the faults whilst averaged acoustic emission signals were in turn, used as the basis for wavelets decomposition. Initial observations from this signal decomposition are also presented and discussed.

A study on acoustic emission signal propagation of a small size multi-cylinder diesel engine

Acoustic emission has been found effective in offering earlier fault detection and improving identification capabilities of faults. However, the sensors are inherently uncalibrated. This paper presents a source to sensor paths calibration technique which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time domain, time-frequency domain, and the root mean square (RMS) energy. The results reveal how the RMS energy of a source propagates to the adjacent sensors. The findings lead to allocate the source and estimate its inferences to the adjacent sensor, and finally help to diagnose the small size diesel engines by minimising the crosstalk from multiple cylinders.

Turbulence characteristics of a small subtropical estuary during and after some moderate rainfall

In natural estuaries, scalar diffusion and dispersion are driven by turbulence. In the present study, detailed turbulence measurements were conducted in a small subtropical estuary with semi-diurnal tides under neap tide conditions. Three acoustic Doppler velocimeters were installed mid-estuary at fixed locations close together. The units were sampled simultaneously and continuously at relatively high frequency for 50 h. The results illustrated the influence of tidal forcing in the small estuary, although low frequency longitudinal velocity oscillations were observed and believed to be induced by external resonance. The boundary shear stress data implied that the turbulent shear in the lower flow region was one order of magnitude larger than the boundary shear itself. The observation differed from turbulence data in a laboratory channel, but a key feature of natural estuary flow was the significant three dimensional effects associated with strong secondary currents including transverse shear events. The velocity covariances and triple correlations, as well as the backscatter intensity and covariances, were calculated for the entire field study. The covariances of the longitudinal velocity component showed some tidal trend, while the covariances of the transverse horizontal velocity component exhibited trends that reflected changes in secondary current patterns between ebb and flood tides. The triple correlation data tended to show some differences between ebb and flood tides. The acoustic backscatter intensity data were characterised by large fluctuations during the entire study, with dimensionless fluctuation intensity I0b =Ib between 0.46 and 0.54. An unusual feature of the field study was some moderate rainfall prior to and during the first part of the sampling period. Visual observations showed some surface scars and marked channels, while some mini transient fronts were observed.

Acoustic component detection for automatic species recognition in environmental monitoring

Automatic species recognition plays an important role in assisting ecologists to monitor the environment. One critical issue in this research area is that software developers need prior knowledge of specific targets people are interested in to build templates for these targets. This paper proposes a novel approach for automatic species recognition based on generic knowledge about acoustic events to detect species. Acoustic component detection is the most critical and fundamental part of this proposed approach. This paper gives clear definitions of acoustic components and presents three clustering algorithms for detecting four acoustic components in sound recordings; whistles, clicks, slurs, and blocks. The experiment result demonstrates that these acoustic component recognisers have achieved high precision and recall rate.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

Acoustic hazard detection for pedestrians with obscured hearing

Pedestrians’ use of mp3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel, simple, non resource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three component heuristic. The resulting Acoustic Hazard Detection (AHD) system has a very low false positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.

Magnetic resonance spectroscopy and neurocognitive dysfunction in obstructive sleep apnea before and after CPAP treatment

STUDY OBJECTIVES: To determine whether cerebral metabolite changes may underlie abnormalities of neurocognitive function and respiratory control in OSA. DESIGN: Observational, before and after CPAP treatment. SETTING: Two tertiary hospital research institutes. PARTICIPANTS: 30 untreated severe OSA patients, and 25 age-matched healthy controls, all males free of comorbidities, and all having had detailed structural brain analysis using voxel-based morphometry (VBM). MEASUREMENTS AND RESULTS: Single voxel bilateral hippocampal and brainstem, and multivoxel frontal metabolite concentrations were measured using magnetic resonance spectroscopy (MRS) in a high resolution (3T) scanner. Subjects also completed a battery of neurocognitive tests. Patients had repeat testing after 6 months of CPAP. There were significant differences at baseline in frontal N-acetylaspartate/choline (NAA/Cho) ratios (patients [mean (SD)] 4.56 [0.41], controls 4.92 [0.44], P = 0.001), and in hippocampal choline/creatine (Cho/Cr) ratios (0.38 [0.04] vs 0.41 [0.04], P = 0.006), (both ANCOVA, with age and premorbid IQ as covariates). No longitudinal changes were seen with treatment (n = 27, paired t tests), however the hippocampal differences were no longer significant at 6 months, and frontal NAA/Cr ratios were now also significantly different (patients 1.55 [0.13] vs control 1.65 [0.18] P = 0.01). No significant correlations were found between spectroscopy results and neurocognitive test results, but significant negative correlations were seen between arousal index and frontal NAA/Cho (r = -0.39, corrected P = 0.033) and between % total sleep time at SpO(2) < 90% and hippocampal Cho/Cr (r = -0.40, corrected P = 0.01). CONCLUSIONS: OSA patients have brain metabolite changes detected by MRS, suggestive of decreased frontal lobe neuronal viability and integrity, and decreased hippocampal membrane turnover. These regions have previously been shown to have no gross structural lesions using VBM. Little change was seen with treatment with CPAP for 6 months. No correlation of metabolite concentrations was seen with results on neurocognitive tests, but there were significant negative correlations with OSA severity as measured by severity of nocturnal hypoxemia. CITATION: O'Donoghue FJ; Wellard RM; Rochford PD; Dawson A; Barnes M; Ruehland WR; Jackson ML; Howard ME; Pierce RJ; Jackson GD. Magnetic resonance spectroscopy and neurocognitive dysfunction in obstructive sleep apnea before and after CPAP treatment.

Separation of acoustic emission signals from small size multi-cylinder diesel engine

This paper presents techniques which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outline, including time-frequency analysis and selection of optimum frequency band.The results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals and the effects of changing parameter values are also outlined. The results on separation of RMS signals show thsi technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events within the combustion process of multi-cylinder diesel engines.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

A study on the use of acoustic emission technique as a structural health monitoring tool

As civil infrastructures such as bridges age, there is a concern for safety and a need for cost-effective and reliable monitoring tool. Different diagnostic techniques are available nowadays for structural health monitoring (SHM) of bridges. Acoustic emission is one such technique with potential of predicting failure. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). AEtechnique involves recording the stress waves bymeans of sensors and subsequent analysis of the recorded signals,which then convey information about the nature of the source. AE can be used as a local SHM technique to monitor specific regions with visible presence of cracks or crack prone areas such as welded regions and joints with bolted connection or as a global technique to monitor the whole structure. Strength of AE technique lies in its ability to detect active crack activity, thus helping in prioritising maintenance work by helping focus on active cracks rather than dormant cracks. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. One is the generation of large amount of data during the testing; hence an effective data analysis and management is necessary, especially for long term monitoring uses. Complications also arise as a number of spurious sources can giveAEsignals, therefore, different source discrimination strategies are necessary to identify genuine signals from spurious ones. Another major challenge is the quantification of damage level by appropriate analysis of data. Intensity analysis using severity and historic indices as well as b-value analysis are some important methods and will be discussed and applied for analysis of laboratory experimental data in this paper.