Ventilation of double facades

This paper deals with the development and thetesting of a simulation algorithm for the temperaturebehaviour and the flow characteristics of doublefaçades. It has been developed in order to obtain atool which enables the energy consultant to makequick design decisions without being required to usefairly complicated CFD tools.In order to determine the degree of accuracy of thealgorithm, a double façade has been monitored undercontrolled conditions and the results have beencompared against the predicted values for severaldesign situations. The resulting inaccuracy in somecases can be traced back to how the flow resistanceof various geometries are modelled. This paper deals with the development and thetesting of a simulation algorithm for the temperaturebehaviour and the flow characteristics of doublefaçades. It has been developed in order to obtain atool which enables the energy consultant to makequick design decisions without being required to usefairly complicated CFD tools.In order to determine the degree of accuracy of thealgorithm, a double façade has been monitored undercontrolled conditions and the results have beencompared against the predicted values for severaldesign situations. The resulting inaccuracy in somecases can be traced back to how the flow resistanceof various geometries are modelled

Pulse-Echo Harmonic Generation Measurements for Non-destructive Evaluation

Ultrasonic harmonic generation measurements have shown great potential for detecting nonlinear changes in various materials. Despite this, the practical implementation of the technique in non-destructive evaluation (NDE) has typically been limited to the through transmission setup case, with which problems arise in certain situations. Recently, works in the fields of nonlinear fluids and biomedical imaging have reported different application of the harmonic generation theory by making use of reflective boundaries and beam focusing. It is thought that such techniques may be similarly applied in the field of NDE to enable single-sided nonlinear inspection of components. In this paper, we initially describe a numerical model which has been used to determine the effects of attenuation and acoustic beam diffraction on measurements of the nonlinear parameter beta. We then extend the model to incorporate first the effects of multiple reflecting boundaries in the propagation medium, then of focused source excitation. Simulations, supported by experimental data, show that nonlinear pulse-echo measurements have the potential to provide a viable (and practical) alternative to the usual through-transmission type as a means of measuring beta in solids. Furthermore, it is shown that such measurements may be optimised, both by adjusting the excitation frequency, and by focusing the acoustic source at a certain point relative to the specimen boundary.</p

Aquatic Animal Health Subprogram: Investigation of Chlamydiales-like organisms in pearl oysters, Pinctada maxima FRDC Project 2008/031

Objectives: • To further develop the current conventional PCRs being used to investigate two CLO’s in pearl oysters and use these PCRs in an attempt to gain further sequence data. An expansion of the current known sequence data will be used to develop a real-time PCR that is specific and sensitive enough to detect and differentiate between the two CLO’s in pearl oysters. The real-time PCR will be validated at two independent laboratories using known OOD-positive and negative control samples. • To test healthy versus OOD-affected pearl oysters to determine if the presence of either or both CLO’s plays a role in the onset of OOD. Pinctada maxima samples from Queensland will be tested as negative control animals to determine the prevalence of the two CLO’s. This study will determine if there is a link between the presence of these CLO’s and the onset of OOD. • To survey non-maxima shellfish associated with pearl farms to determine the prevalence of these organisms in molluscs in Australian waters, and whether there are further molluscan reservoir hosts. Any positive samples obtained will be confirmed by sequencing the PCR product

Potential and limitations of NARX for defect detection in guided wave signals

Previously, a nonlinear autoregressive network with exogenous input (NARX) demonstrated an excellent performance, far outperforming an established method in optimal baseline subtraction, for defect detection in guided wave signals. The principle is to train a NARX network on defect-free guided wave signals to obtain a filter that predicts the next point from the previous points in the signal. The trained network is then applied to new measurement and the output subtracted from the measurement to reveal the presence of defect responses. However, as shown in this paper, the performance of the previous NARX implementation lacks robustness; it is highly dependent on the initialisation of the network and detection performance sometimes improves and then worsens over the course of training. It is shown that this is due to the previous NARX implementation only making predictions one point ahead. Subsequently, it is shown that multi-step prediction using a newly proposed NARX structure creates a more robust training procedure, by enhancing the correlation between the training loss metric and the defect detection performance. The physical significance of the network structure is explored, allowing a simple hyperparameter tuning strategy to be used for determining the optimal structure. The overall detection performance of NARX is also improved by multi-step prediction, and this is demonstrated on defect responses at different times as well as on data from different sensor pairs, revealing the generalisability of this method

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa