Hypersonic flight is important for both military and civilian purposes. A major challenge at these high speeds is the extreme heat threatening the safety and performance of the vehicle. Recent advances in high temperature super conducting magnets (HTS) and high performance computing have renewed interest in this subject. We simulate an upcoming experiment to place an HTS magnet in the Gottingen shock tunnel. We show that the 2 T HTS magnet can increase the shock standoff distance by 1.5 cm (a 60% increase) and reduce the heat flux on the front surface by up to 12% .

Corrosion under insulation (CUI) inspection is crucial for asset owners using insulated steel pipes susceptible to CUI, such as steam transport pipes. Current systems are effective for areas of corrosion above 0.01 m2 but are liable to miss small, deep localised corrosion below this size, which is the focus of the work presented. Defect sizing equations for a new encircling coil eddy current system have been developed from 2D simulation results. Tests of these equations on randomly sized defects in 2D simulations have shown an average error of 10%. In addition, 3D simulations confirmed that 2D results effectively translate to 3D, assuming a minimum circumferential extent. Initial lab tests of regular defects showed results comparable to simulations, but minor sensor placement errors can cause significant variations. This system's potential to detect corrosion as short as 5 mm in the axial direction has been shown with a wall thickness error of 10%. However, this effectiveness is limited to specific corrosion and pipe morphologies; further study is required to expand these limits and to produce a system capable of detecting pitting corrosion in a wide range of applications.

A spacecraft re-entering the Earth's atmosphere must endure extremely high heat loads. These heat loads are created by the rapid deceleration of the spacecraft causing shock waves which in turn create a high-temperature plasma. Passive thermal protection based on ablative materials is the current solution for spacecraft heat shielding, but it is limited by material durability. As an alternative, magnetic heat shielding has shown great potential to deflect and redirect the plasma. However, fully understanding of the concept requires further experimental validation. Paihau-Robinson Research Institute has designed and built a high-temperature superconductor (HTS) system, which will be used to test the magnetic heat shielding concept at the German Aerospace Centre's (DLR) shock tunnel, where realistic flow conditions for hypersonic flight configurations are created. This paper reports the design of the HTS system with some preliminary experimental results on its performance. The shock wave stand-off distance is calculated based on the designed field (2T), which will be compared with the experimental data using a high-speed camera at DLR's shock tunnel in 2025.

A large portion of the pipe infrastructure used in the chemical processing industry is susceptible to corrosion under insulation (CUI). Eddy current-based magnetic sensing is one of the methods that can be used as an early detector of this corrosion. However, the large sensor-to-pipe distances used in this method, due to the presence of insulation, limits the sensitivity to corrosion. This paper will describe the development of instrumentation and methods based on eddy current sensing with thin-film magnetic sensors. In particular, it focuses on the influence of the sensor angle relative to the radial magnetic field. The influence of this parameter on the amplitude of the measured signal was investigated by both finite element simulations and experimental observations. The measured magnetic field was found to be highly sensitive to small changes in sensor angle, with the estimated depth of a defect changing at a rate of 11.2 mm/degree of sensor rotation for small angles. It is also shown that a sensor aligned with the radial direction should be avoided, with an optimal sensor angle between 0.5 and 4 degrees. With the sensor in this angle range, the simulations have shown it should be possible to resolve the depth of corrosion to a resolution of 0.1 mm.

Brain imaging MRI comprises a significant proportion of MRI scans, but the requirement for including the shoulders in the magnet bore means there is not a significant size reduction in the magnet compared to whole-body magnets. Here we present a new design approach for brain imaging MRI magnets targeting +/- 20 kHz B-0 variation over the imaging volume rather than the more usual +/- 200 Hz making use of novel high-bandwidth MRI pulse sequences and distortion correction. Using this design approach, we designed and manufactured a 1.5 T class ReBCO cryogen-free magnet. The magnet is dome-like in form, completely excludes the shoulders and is <400 mm long. The magnet was wound using no-insulation style coils with a conductive epoxy encapsulant where the contact resistance of the coils was controlled so the emergency shut-down time of the magnet was less than 30 s. Despite acceptable coil testing results ahead of manufacture, during testing of the magnet, several of the epoxy coils showed signs of damage limiting stable performance to <55 A compared to the designed 160 A. These coils were replaced with insulated paraffin encapsulated coils. Subsequently the magnet was re-ramped and was stable at 81 A, generating 0.71 T as several other coils had sustained damage not visible in the first magnet iteration. The magnet has been passive shimmed to +/- 20 kHz B0 variation over the imaging volume and integrated into an MRI scanner. The stability of the magnet has been evaluated and found to be acceptable for MRI.

Tracing groundwater flow is of vital importance for managing water resources and understanding the natural water cycle. However, it is a challenge to make accurate measurements of groundwater flow due to its extremely low velocity. A new electromagnetic flowmeter has been developed to conduct non-invasive groundwater flow velocity measurements. In the setup, a customized trapezoidal current source was connected to a 1 m $\times $ 1 m square coil to generate a strong vertical magnetic field. Two electrodes were used to sense the potential difference caused by water flowing through the magnetic field. A simulation has been developed based on Faraday’s law to evaluate the flow signal under an idealized condition. Two different lab environments have been built for testing the device: an artificial mini-aquifer and a rolling gantry. The measurements show that the flow signal, in practice, generated by the slow-moving water was orders of magnitude smaller than the inductive and capacitive interferences in the system. A linear signal processing model has been developed, allowing the flow signal to be extracted from the measured results. The results show good agreement with simulation and a strong correlation between the flow speed and the processed flow signal, having an $R^{2}$ coefficient of 0.94.

Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and damage the system itself. High current Low Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are equally hazardous, they are much more challenging to detect due to the fault current being much lower than load currents and their time-varying and nonlinear behaviour. Moreover, New Zealand distribution networks are extensive and largely unmonitored beyond the substation, and suitable HIF detection schemes are still an ongoing research challenge. To date, we have built a physical test facility for power system fault analysis and developing and evaluating our sensing and fault detection system. We have simulated LIF and HIF with different fault surface materials and load-switching events. From the data collected, we have characterized the unique fault behaviour for both LIF and HIF in 400V networks and trained a Deep Learning classifier to recognize the type of fault present from its unique signature. We have developed an outdoor pole mountable sensing system and have installed this in Wellington Electricity's network for ongoing data collection and evaluation. This paper will describe the test facility and our experience developing and implementing the sensing system. The widest range of HIF phenomena observed was in the fault experiments involving the tree branch. For brevity, therefore, this paper reports on the results of just these tree-branch experiments. HIF faults on other surface materials will be reported elsewhere. Finally, we will detail the pole-mountable sensing system installed in Wellington Electricity's network and the outcomes thus far.

Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused at higher voltage levels and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

High Impedance Fault (HIF) detection is a demanding task for distribution power system operators and an ongoing research challenge. Recent developments in pattern recognition and data analytics has motivated researchers to develop detection algorithms based on Deep Learning (DL). Inherently, an HIF generates an arbitrary and non-linear signal. The varying nature of the fault is an ideal match for a DL sequential algorithm as the basis for a pattern classification technique. Here we present two hybrid DL models to classify the progression of an HIF observed in a 400V network. The hybrid models were developed with a Convolutional Neural Network (CNN) and two variants of the Recurrent Neural Network (RNN) algorithm. The models have been trained on real-world Giant Magneto-Resistive (GMR) Sensor data collected via a purpose-built 400-Volt test facility. The preliminary testing of models demonstrated 99.48% accuracy in classifying different states of the HIF. The robustness of the model on a large and more varied data-set is the subject of ongoing work.

High impedance fault detection is an active research area, and is important to distribution power system operators. Research to date has focused on new sensor technologies and signal processing schemes to detect these faults. However, development of these schemes requires real fault incident data. Experimental HIF data at low voltage (0.4 kV) are currently unavailable, therefore we have designed a low voltage test facility design to physically simulate HIFs. The facility includes the geometrical representation of actual low voltage distribution lines, as an objective of the facility is for its use in the evaluation of (Giant Magneto-resistance) GMR sensors as a basis for a fault detection system. The initial HIF experimental data collected in this facility demonstrates the capability of the test facility to simulate such faults. The test facility will therefore be beneficial for generating actual data for studying HIF phenomena and for the development of a GMR sensor based detection scheme. This will be the subject of ongoing work.

We describe the design, construction, and testing of a proof-of-principle 45 kVA, 450/450 V, single phase superconducting transformer with fault tolerant capability. This small-scale transformer demonstrates extended short circuit withstand time as a result of the high thermal mass of the windings as well as improved recovery under load due to the high heat transfer achieved in subcooled liquid nitrogen with wire coated to optimise boiling heat transfer. Primary and secondary windings consist of 38-turn single layer solenoids wound in helical trenches milled in glass-reinforced epoxy formers. The thermal mass of the 0.4 mm thick brass-laminated conductor provides for adiabatic fault withstand times over 1 s, allowing greater flexibility in the design of protection systems required to isolate faults. Enhanced heat transfer from the windings to the liquid nitrogen coolant allows the transformer to recover from the short circuit event, cooling from around 300 K to return to the superconducting state even while current close to the rated current continues to flow. Two factors contribute to the high heat transfer. Firstly the HTS transformer operates in subcooled liquid nitrogen, at 65 K–66 K at atmospheric pressure, significantly increasing the heat transfer compared to operation at saturated vapour pressure. Secondly, the HTS conductor is coated with solid polymer insulation with optimised thickness, which allows efficient cooling by nucleate boiling to take place over an extended range of conductor temperature.

This work investigates an eddy current-based non-destructive testing (NDT) method to characterize corrosion of pipes under thermal insulation, one of the leading failure mechanisms for insulated pipe infrastructure. Artificial defects were machined into the pipe surface to simulate the effect of corrosion wall loss. We show that by using a giant magnetoresistance (GMR) sensor array and a high current (300 A), single sinusoidal low frequency (5–200 Hz) pipe-encircling excitation scheme it is possible to quantify wall loss defects without removing the insulation or weather shield. An analysis of the magnetic field distribution and induced currents was undertaken using the finite element method (FEM) and analytical calculations. Simple algorithms to remove spurious measured field variations not associated with defects were developed and applied. The influence of an aluminium weather shield with discontinuities and dents was ascertained and found to be small for excitation frequency values below 40 Hz. The signal dependence on the defect dimensions was analysed in detail. The excitation frequency at which the maximum field amplitude change occurred increased linearly with the depth of the defect by about 3 Hz/mm defect depth. The change in magnetic field amplitude due to defects for sensors aligned in the azimuthal and radial directions were measured and found to be linearly dependent on the defect volume between 4400–30,800 mm3 with 1.2 × 10−3−1.6 × 10−3 µT/mm3. The results show that our approach is well suited for measuring wall loss defects similar to the defects from corrosion under insulation.

Corrosion is the leading failure mechanism for metallic structures. One of the standard non-destructive techniques to assess the status and predict remaining lifetime and possible failure is based on the excitation with a varying magnetic field and measuring the change of the magnetic field due to eddy currents in the device under test. Since the magnetic field is decaying quickly a large lift-off between the excitation source, magnetic sensors and the test object will reduce the signals considerably. In order to obtain a deep penetration into the test object excitation at low frequency is desirable. In this study an investigation of a high power excitation system in combination with giant magneto resistance (GMR) based sensors was done. GMR sensors have a good sensitivity and are suitable for low frequency eddy current testing due to their low 1/f noise. Finite element analysis was used to evaluate the excitation setup, sensor alignment and positions and study the influence of different parameters of the excitation and sensor setup as well as the device under test. Based on these results a laboratory setup was build and used to study the influence of main measurement parameters.

The thesis investigates the use of giant magneto resistance sensors for eddy current testing in order to identify defects in steel pipes. An automated test rig which included the device under test, sensor array, excitation unit, electronic measurement equipment, mechanical setup and LabVIEW automation was designed and built. This was used to investigate the effect of excitation parameters such as current, frequency and distance to the pipe. Some preliminary algorithms to improve the signal were developed and tested. The effect of the shape and size of the defect and aluminum shield on the magnetic field was investigated. A qualitative model to describe the magnetic field, including measured defect signals, was developed. Minimum defect parameters and maximum distance values were evaluated in the context of signal to noise.