The sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research1–3, including the monitoring of temporal variations in aquifers4 and geodesy5. However, it is impractical to use gravity cartography to resolve metre-scale underground features because of the long measurement times needed for the removal of vibrational noise6. Here we overcome this limitation by realizing a practical quantum gravity gradient sensor. Our design suppresses the effects of micro-seismic and laser noise, thermal and magnetic field variations, and instrument tilt. The instrument achieves a statistical uncertainty of 20 E (1 E = 10−9 s−2) and is used to perform a 0.5-metre-spatial-resolution survey across an 8.5-metre-long line, detecting a 2-metre tunnel with a signal-to-noise ratio of 8. Using a Bayesian inference method, we determine the centre to ±0.19 metres horizontally and the centre depth as (1.89 −0.59/+2.3) metres. The removal of vibrational noise enables improvements in instrument performance to directly translate into reduced measurement time in mapping. The sensor parameters are compatible with applications in mapping aquifers and evaluating impacts on the water table7, archaeology8–11, determination of soil properties12 and water content13, and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure14, providing a new window into the underground.
In recent years, global wind power capacity has grown steadily at an annual rate of around 20%. This has led to wind energy becoming the most important renewable energy source on a global scale, with the total installed capacity reaching 430 GW. However, the strong growth of offshore wind power has been somewhat inhibited due to a number of operational challenges that are yet to be addressed in full. The most important of these challenges appears to be the reliability of the wind turbine gearbox (WTG). WTGs are currently unable to survive their anticipated design lifetime of 20-25 years. Most of them hardly reach a useful operational lifetime of more than seven years without serious refurbishment or replacement and, for offshore wind turbines, failures have been reported as early as within one to two years. In this paper, the damage mechanisms influencing WTGs supported by finite element analysis have been considered and presented in the context of condition monitoring diagnosis and prognosis.
Although wind turbine gearboxes are designed to remain in-service for 20-25 years, this is not normally the case due to defects initiating and developing prematurely. A large number of gearboxes fail after 7 to 8-years in service. In offshore wind farms gearbox failures have been reported after only 1-2 years in service leading to noteworthy production losses. Reliability issues associated with wind turbine gearboxes are yet to be resolved. Within this paper the quality of materials used for manufacturing wind turbine gearbox gears and bearings has been evaluated. The damage mechanisms affecting gearbox materials have been investigated based on metallographic analysis carried out on failed samples removed from in-service industrial wind turbines. Finite Element Analysis (FEA) has been carried out in order to simulate damage initiation and propagation under in-service conditions. The results have been compared with the experimental observations made on the failed field samples and have been found to be in good agreement. The applicability of acoustic emission in detecting and identifying defects in different wind turbine gearbox components remotely has been assessed following measurements in the field.
In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.
Borehole gravity sensing can be used in a number of applications to measure features around a well, including rock-type change mapping and determination of reservoir porosity. Quantum technology gravity sensors, based on atom interferometry, have the ability to offer increased survey speeds and reduced need for calibration. While surface sensors have been demonstrated in real world environments, significant improvements in robustness and reductions to radial size, weight, and power consumption are required for such devices to be deployed in boreholes. To realise the first step towards the deployment of cold atom-based sensors down boreholes, we demonstrate a borehole-deployable magneto-optical trap, the core package of many cold atom-based systems. The enclosure containing the magneto-optical trap itself had an outer radius of (60 ± 0.1) mm at its widest point and a length of (890 ± 5) mm. This system was used to generate atom clouds at 1 m intervals in a 14 cm wide, 50 m deep borehole, to simulate how in-borehole gravity surveys are performed. During the survey, the system generated, on average, clouds of (3.0 ± 0.1) × 105 87Rb atoms with the standard deviation in atom number across the survey observed to be as low as 8.9 × 104.
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