Slip tendency analysis of major faults in Germany

Natural seismicity and tectonic activity are important processes for the site-selection and for the long-term safety assessment of a nuclear waste repository, as they can influence the integrity of underground structures significantly. Therefore, it is crucial to gain insight into the reactivation potential of faults. The two key factors that control the reactivation potential are (a) the geometry and properties of the fault such as strike direction and friction angle, and (b) the orientations and magnitudes of the recent stress field and future changes to it due to exogenous processes such as glacial loading as well as anthropogenic activities in the subsurface. One measure of the reactivation potential of faults is the ratio of resolved shear stress to normal stresses at the fault surface, which is called slip tendency. However, the available information on fault properties and the stress field in Germany is sparse. Geomechanical numerical modelling can provide a prediction of the required 3D stress tensor in places without stress data. Here, we present slip tendency calculations on major faults based on a 3D geomechanical numerical model of Germany and adjacent regions of the SpannEnD project (Ahlers et al., 2021). Criteria for the selection of faults relevant to the scope of the SpannEnD project were identified and 55 faults within the model area were selected. For the selected faults, simplified geometries were created. For a subset of the selected faults, vertical profiles and seismic sections could be used to generate semi-realistic 3D fault geometries. Slip tendency calculations using the stress tensor from the SpannEnD model were performed for both 3D fault sets. The slip tendencies were calculated without factoring in pore pressure and cohesion, and were normalized to a coefficient of friction of 0.6. The resulting values range mainly between 0 and 1, with 6 % of values larger than 0.4. In general, the observed slip tendency is slightly higher for faults striking in the NW and NNE directions than for faults of other strikes. Normal faults show higher slip tendencies than reverse and strike slip faults for the majority of faults. Seismic events are generally in good agreement with the regions of elevated slip tendencies; however, not all seismicity can be explained through the slip tendency analysis.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Tensile Damage Study of Wind Turbine Tower Material Q345 Based on an Acoustic Emission Method

As essential load-bearing equipment to support the nacelle and blades, the tower is subjected to the whole wind turbine loading. This study proposes a new method of combining acoustic emission and normalized accumulation parameters to characterize wind turbine towers Q345 steel damage. First of all, tendency analysis of the acoustic emission signal parameter was conducted to determine damage degree during the damage stage. Secondly, we normalized the accumulation of amplitude and other parameters to compare the proportion of each accumulation parameter at different stages, while studying the spectra of common acoustic emission signals. Finally, comparing the differences and similarities of the normalized accumulation parameters between three different rates, we analyze the effect of rate on the normalized accumulation parameters. These results indicate that the normalized cumulative duration parameter is suitable for characterizing the yield damage occurrence, the normalized cumulative energy parameter is very sensitive to the fracture stage, the normalized cumulative energy parameter is least influenced by the loading rate, and the energy parameter is a sensitivity factor for normalized expression, which to realizes the stage of damage judgment.

Evaluation of a Limited-area Energy Budget Cycle of an Extratropical Storm Under Lagrangian and Eulerian Frameworks

To conceptualize the uncertainties regarding the mechanisms of extratropical cyclones (EC), a study of their energy cycle can provide key information of their fundamental structure. This study applies a set of equations built from earlier works for a limited-area energy decomposed into temporal mean and deviations. It compares the results obtained with a reference frame that tracks an EC through its eddy kinetic energy with those obtained with a larger but fixed frame. A specific storm that occurred throughout the period of December 10-18th 2004 and simulated by the Canadian Regional Climate Model (CRCM – version 5) was studied. Results support the notion that the moving reference results in larger amplitudes for all temporal deviation components of the cycle than for the fixed reference. A time tendency analysis of the energetic reservoirs reveals noteworthy phases in the storm’s energy, with an increase and decrease occurring during the periods of 10-14 December and 14-18 December, respectively. The energy budget is overall fairly well balanced, with the exception of a lateral boundary term, hkTV , with considerable negative values; this term exhibits a spatially larger scale than the other contributions in the EC. An evaluation of the sensibility of the tracking scheme related to its size and positioning was also performed to determine its influence on the boundary term hkTV.