Study of Ultrasonic Logs and Seepage Potential on Sandwich Sections Retrieved from a North Sea Production Well

Summary Important functions of well cement are to provide zonal isolation behind casing strings and to mechanically support and protect the casing. Experience suggests that many wells develop integrity problems related to fluid migration or loss of zonal isolation, which often manifest themselves in sustained casing pressure (SCP) or surface casing vent flows. Because the characteristic sizes of realistic migration paths are typically only on the order of tens of micrometers, detecting, diagnosing, and eventually treating migration paths remain challenging problems for the industry. As part of the recent abandonment operation of an offshore production well, sandwich joints comprising production casing, annulus cement, and intermediate casing were cut and retrieved to surface. Two of these joints were subjected to an extensive test campaign, including surface relogging, chemical analyses, and seepage testing, to better understand the ultrasonic-log response and its potential connection to rates of fluid migration. One of the joints contained an apparently well-defined top of cement (TOC) with settled barite on top. Although the settled material initially provided a complete seal against gas flow, the sealing capability was irreversibly lost as part of subsequent testing. The two joints have effective microannuli sizes in the range of tens of micrometers, in agreement with previous reports on SCP buildup in wells. On a local scale, however, we observed significant variations in cement quality from both the log results and the seepage testing. Further, we found qualitatively very good correlations between seepage-test results and the log results for the bond between cement and casings. The best bonded cement was found directly above a production casing collar, where a short segment of well-bonded cement prevented measurable steady-state seepage of nitrogen. Additional tests involving internal pressurization of the production casing suggested that certain annular-seepage characteristics are well-described by an effective microannulus at the cement/casing interfaces. We consider the two sandwich joints to be highly representative and relevant for similar mature wells that are to be abandoned.

Fractional Super-Resolution of Voxelized Point Clouds

<div>We present a method to super-resolve voxelized point clouds down-sampled by a fractional factor, using look-up-tables (LUT) constructed from self-similarities from its own down-sampled neighborhoods. Given a down-sampled point cloud geometry Vd, and its corresponding fractional down-sampling factor s, the proposed method determines the set of positions that may have generated Vd, and estimates which of these positions were indeed occupied (super-resolution). Assuming that the geometry of a point cloud is approximately self-similar at different scales, LUTs relating down-sampled neighborhood configurations with children occupancy configurations can be estimated by further down-sampling the input point cloud to Vd2 , and by taking into account the irregular children distribution derived from fractional down-sampling. For completeness, we also interpolate texture by averaging colors from adjacent neighbors. We present extensive test results over different point clouds, showing the effectiveness of the proposed method against baseline methods.</div>

Fractional Super-Resolution of Voxelized Point Clouds

<div>We present a method to super-resolve voxelized point clouds down-sampled by a fractional factor, using look-up-tables (LUT) constructed from self-similarities from its own down-sampled neighborhoods. Given a down-sampled point cloud geometry Vd, and its corresponding fractional down-sampling factor s, the proposed method determines the set of positions that may have generated Vd, and estimates which of these positions were indeed occupied (super-resolution). Assuming that the geometry of a point cloud is approximately self-similar at different scales, LUTs relating down-sampled neighborhood configurations with children occupancy configurations can be estimated by further down-sampling the input point cloud to Vd2 , and by taking into account the irregular children distribution derived from fractional down-sampling. For completeness, we also interpolate texture by averaging colors from adjacent neighbors. We present extensive test results over different point clouds, showing the effectiveness of the proposed method against baseline methods.</div>

Performance of the 5th Generation Indoor Wireless Technologies-Empirical Study

The evolution of 5th generation (5G) cellular technology has introduced several enhancements and provides better performance compared to previous generations. To understand the real capabilities, the importance of the empirical studies is significant to also understand the possible limitations. This is very important especially from the service and use case point of view. Several test sites exist around the globe for introducing, testing, and evaluating new features, use cases, and performance in restricted and secure environments alongside the commercial operators. Test sites equipped with the standard technology are the perfect places for performing deep analysis of the latest wireless and cellular technologies in real operating environments. The testing sites provide valuable information with sophisticated quality of service (QoS) indicators when the 5G vertical use cases are evaluated using the actual devices in the carrier grade network. In addition, the Wi-Fi standards are constantly evolving toward higher bit rates and reduced latency, and their usage in 5G dedicated verticals can even improve performance, especially when lower coverage is sufficient. This work presents the detailed comparative measurements between Wi-Fi 6 and 5G New Radio (NR) performance in indoor facilities and extensive results carried out in 5G and beyond test site located in Finland. The results gathered from the extensive test sets indicate that the Wi-Fi 6 can outperform the 5G in the indoor environment in terms of throughput and latency when distance and coverage do not increase enormously. In addition, the usage of wireless technologies allows improved uplink performance, which is usually more limited in cellular networks. The gained results of our measurements provide valuable information for designing, developing, and implementing the requirements for the next-generation wireless applications.

Experimental and numerical studies on small contra-rotating electrical ducted fan engines

Electrical propulsion has been identified as one of the key fields of future research within the aerospace sector. The Institute of Aeronautical Engineering at the Universität der Bundeswehr München aims to contribute to the ongoing development of small-sized electrical ducted fan engines with a thrust in the range of 100 N. A special emphasis is placed on electrically powered contra-rotating fan stages. When compared to a conventional rotor–stator stage, contra-rotating fan stages allow for a more compact design, considering a given pressure ratio, or an increased pressure ratio at a constant fan diameter. Since numerous new aircraft concepts are presently being developed, a high demand for compact and powerful electrically driven engines arises. Electrically driven contra-rotating fan engines provide a high potential in terms of compactness, emissions and efficiency. Using electric motors offers the ability to overcome common issues, such as design and integration of a contra-rotating stage into a gas turbine. An innovative new engine design featuring such a contra-rotating stage is developed and tested at one of the Institute’s test benches for electrical propulsion. Key components are two brushless motors powering the fan stage, one for each rotor. Various operation points are investigated experimentally during an extensive test campaign. Experimental results are compared to results of numerical simulations computed by ANSYS CFX. Results indicate a good agreement between experiment and simulation. The engine is running very smooth throughout all tested operation points. Yet, intensive heating up of the electric motors and high-temperature zone are found to be an issue at higher rotation speeds.

A fuzzy-inference-based reinforcement learning method of overtaking decision making for automated vehicles

Intelligent decision control is one key issue of automated vehicles. Complex dynamic traffic flow and multi-requirement of passengers including vehicle safety, comfort, vehicle efficiency bring about tremendous challenges to vehicle decision making. Overtaking maneuver is a demanding task due to its large potential of traffic collision. Therefore, this paper proposes a fuzzy-inference-based reinforcement learning (FIRL) approach of autonomous overtaking decision making. Firstly, the problem of overtaking is formulated as a multi-objective Markov decision process (MDP) considering vehicle safety, driving comfort, and vehicle efficiency. Secondly, a temporal difference learning based on dynamic fuzzy (DF-TDL) is presented to learn optimized policies for autonomous overtaking decision making. Fuzzy inference is introduced to deal with continuous states and boost learning process. The RL algorithm decides whether to overtake or not based on the learned policies. Then, the automated vehicle executes local path planning and tracking. Furthermore, a simulation platform based on simulation of urban mobility (SUMO) is established to generate the random training data, that is, various traffic flows for algorithm iterative learning and validate the proposed method, extensive test results demonstrate the effectiveness of the overtaking decision-making method.

Social Compliance During High Stringency Periods Efficiently Reduces COVID-19 Incidence: Evidence from Google Mobility Reports

It is crucial to understand and learn as much as possible from the current global Sars-CoV-2 pandemic for the sake of future precautions. Apart from strong government restrictions such as complete lockdowns, curfews, and mask mandates, other factors influence viral transmission. Since June 2020, Denmark has had an extensive test and surveillance program and made data publicly available at the municipality level. Here we use these data and integrate publicly available data on government restrictions, weather data, and mobility data to model COVID-19 incidence in 98 Danish municipalities from September 2020 to February 2021. The inclusion of municipality heterogeneity, weather and mobility data increases the amount of variance explained by ~29% compared to a simpler model taking only incidence and restrictions into account. We found a strong and significant effect from temperature which interacts with government restrictions. Our results indicate that higher temperatures limit viral transmission when government restrictions are low, but that the temperature effect diminishes under stronger restrictions. This is most likely due to a change in human behavior rather than a biological effect. Likewise, we found that changes in residential mobility were significant factors that also interacted with restrictions. When restrictions were strong, we found that increased residential mobility resulted in decreased COVID-19 incidence, suggesting residential mobility as a proxy for compliance. Our results show the increased explanatory power of integrating different variables when modeling COVID-19 incidence. The weather seems to predict human behavior in a quite predictable way and mobility data could be used to measure current compliance with government restrictions.

Integration and evaluation of a meander-shaped fibre-optical sensor in a GFRP leaf spring

Fibre-reinforced plastics experience an increasing attention also in the non-aerospace sector. However, the detection of possible internal damage of safety-relevant components remains a challenge. In previous work, a meander-shaped sensor layout with a single fibre-optical sensor was proposed as a particularly efficient and reliable layout and its suitability was demonstrated in coupon tests. In the present work, the approach was transferred to a leaf spring made of glass fibre reinforced plastics and investigated in an extensive test campaign with a realistic test setup. The investigations show the particular suitability of the proposed meander-shaped sensor layout for simultaneous loads monitoring and structural health monitoring.

Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

Benchmark assessment of molecular geometries and energies from small molecule force fields

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.