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The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.
SHEMAT-Suite: An open-source code for simulating flow, heat and species transport in porous media
(2020)
SHEMAT-Suite is a finite-difference open-source code for simulating coupled flow, heat and species transport in porous media. The code, written in Fortran-95, originates from geoscientific research in the fields of geothermics and hydrogeology. It comprises: (1) a versatile handling of input and output, (2) a modular framework for subsurface parameter modeling, (3) a multi-level OpenMP parallelization, (4) parameter estimation and data assimilation by stochastic approaches (Monte Carlo, Ensemble Kalman filter) and by deterministic Bayesian approaches based on automatic differentiation for calculating exact (truncation error-free) derivatives of the forward code.
The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.
In this article, we introduce how eye-tracking technology might become a promising tool to teach programming skills, such as debugging with ‘Eye Movement Modeling Examples’ (EMME). EMME are tutorial videos that visualize an expert's (e.g., a programming teacher's) eye movements during task performance to guide students’ attention, e.g., as a moving dot or circle. We first introduce the general idea behind the EMME method and present studies that showed first promising results regarding the benefits of EMME to support programming education. However, we argue that the instructional design of EMME varies notably across them, as evidence-based guidelines on how to create effective EMME are often lacking. As an example, we present our ongoing research on the effects of different ways to instruct the EMME model prior to video creation. Finally, we highlight open questions for future investigations that could help improving the design of EMME for (programming) education.
BACKGROUND: Muscle stretch reflexes are widely used to examine neural muscle function. The knowledge of reflex response in muscles crossing the shoulder is limited. OBJECTIVE: To quantify reflex modulation according to various subject postures and different procedures of muscle pre-activation steering. METHODS: Thirteen healthy male participants performed two sets of external shoulder rotation stretches in various positions and with different procedures of muscle pre-activation steering on an isokinetic dynamometer over a range of two different pre-activation levels. All stretches were applied with a dynamometer acceleration of 104∘/s2 and a velocity of 150∘/s. Electromyographical response was measured via sEMG. RESULTS: Consistent reflexive response was observed in all tested muscles in all experimental conditions. The reflex elicitation rate revealed a significant muscle main effect (F (5,288) = 2.358, ρ= 0.040; η2= 0.039; f= 0.637) and a significant test condition main effect (F (1,288) = 5.884, ρ= 0.016; η2= 0.020; f= 0.143). Reflex latency revealed a significant muscle pre-activation level main effect (F (1,274) = 5.008, ρ= 0.026; η2= 0.018; f= 0.469). CONCLUSION: Muscular reflexive response was more consistent in the primary internal rotators of the shoulder. Supine posture in combination with visual feedback of muscle pre-activation level enhanced the reflex elicitation rate.
It is investigated whether a nonrotating lifting fan remaining uncovered during cruise flight, as opposed to being covered by a shutter system, can be realized with limited additional drag and loss of lift during cruise flight. A wind-tunnel study of a wing-embedded lifting fan has been conducted at the Side Wind Test Facility Göttingen of DLR, German Aerospace Center in Göttingen using force, pressure, and stereoscopic particle image velocimetry techniques. The study showed that a step on the lower side of the wing in front of the lifting fan duct increases the lift-to-drag ratio of the whole model by up to 25% for all positive angles of attack. Different sizes and inclinations of the step had limited influence on the surface pressure distribution. The data indicate that these parameters can be optimized to maximize the lift-to-drag ratio. A doubling of the curvature radius of the lifting fan duct inlet lip on the upper side of the wing affected the lift-to-drag ratio by less than 1%. The lifting fan duct inlet curvature can therefore be optimized to maximize the vertical fan thrust of the rotating lifting fan during hovering without affecting the cruise flight performance with a nonrotating fan.
Design, evaluation and comparison of endorectal coils for hybrid MR-PET imaging of the prostate
(2020)
Prostate cancer is one of the most common cancers among men and its early detection is critical for its successful treatment. The use of multimodal imaging, such as MR-PET, is most advantageous as it is able to provide detailed information about the prostate. However, as the human prostate is flexible and can move into different positions under external conditions, it is important to localise the focused region-of-interest using both MRI and PET under identical circumstances. In this work, we designed five commonly used linear and quadrature radiofrequency surface coils suitable for hybrid MR-PET use in endorectal applications. Due to the endorectal design and the shielded PET insert, the outer face of the coils investigated was curved and the region to be imaged was outside the volume of the coil. The tilting angles of the coils were varied with respect to the main magnetic field direction. This was done to approximate the various positions from which the prostate could be imaged. The transmit efficiencies and safety excitation efficiencies from simulations, together with the signal-to-noise ratios from the MR images were calculated and analysed. Overall, it was found that the overlapped loops driven in quadrature were superior to the other types of coils we tested. In order to determine the effect of the different coil designs on PET, transmission scans were carried out, and it was observed that the differences between attenuation maps with and without the coils were negligible. The findings of this work can provide useful guidance for the integration of such coil designs into MR-PET hybrid systems in the future.
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Energy saving ordinances requires that buildings must be designed in such a way that the heat transfer surface including the joints is permanently air impermeable. The prefabricated roof and wall panels in lightweight steel constructions are airtight in the area of the steel covering layers. The sealing of the panel joints contributes to fulfil the comprehensive requirements for an airtight building envelope. To improve the airtightness of steel sandwich panels, additional sealing tapes can be installed in the panel joint. The influence of these sealing tapes was evaluated by measurements carried out by the RWTH Aachen University - Sustainable Metal Building Envelopes. Different installation situations were evaluated by carrying out airtightness tests for different joint distances. In addition, the influence on the heat transfer coefficient was also evaluated using the Finite Element Method (FEM). The combination of obtained air volume flow and transmission losses enables to create an "effective heat transfer coefficient" due to transmission and infiltration. This summarizes both effects in one value and is particularly helpful for approximate calculations on energy efficiency.
We generalize our work on Carlitz prime power torsion extension to torsion extensions of Drinfeld modules of arbitrary rank. As in the Carlitz case, we give a description of these extensions in terms of evaluations of Anderson generating functions and their hyperderivatives at roots of unity. We also give a direct proof that the image of the Galois representation attached to the p-adic Tate module lies in the p-adic points of the motivic Galois group. This is a generalization of the corresponding result of Chang and Papanikolas for the t-adic case.
In this article, we describe the structure, the functioning, and the tests of parabolic trough solar thermal cooker (PSTC). This oven is designed to meet the needs of rural residents, including Urban, which requires stable cooking temperatures above 200 °C. The cooking by this cooker is based on the concentration of the sun's rays on a glass vacuum tube and heating of the oil circulate in a big tube, located inside the glass tube. Through two small tubes, associated with large tube, the heated oil, rise and heats the pot of cooking pot containing the food to be cooked (capacity of 5 kg). This cooker is designed in Germany and extensively tested in Morocco for use by the inhabitants who use wood from forests.
During a sunny day, having a maximum solar radiation around 720 W/m2 and temperature ambient around 26 °C, maximum temperatures recorded of the small tube, the large tube and the center of the pot are respectively: 370 °C, 270 °C and 260 °C. The cooking process with food at high (fries, ..), we show that the cooking oil temperature rises to 200 °C, after 1 h of heating, the cooking is done at a temperature of 120 °C for 20 min. These temperatures are practically stable following variations and decreases in the intensity of irradiance during the day. The comparison of these results with those of the literature shows an improvement of 30–50 % on the maximum value of the temperature with a heat storage that could reach 60 min of autonomy. All the results obtained show the good functioning of the PSTC and the feasibility of cooking food at high temperature (>200 °C).
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
The Kremer–Grest (KG) polymer model is a standard model for studying generic polymer properties in molecular dynamics simulations. It owes its popularity to its simplicity and computational efficiency, rather than its ability to represent specific polymers species and conditions. Here we show that by tuning the chain stiffness it is possible to adapt the KG model to model melts of real polymers. In particular, we provide mapping relations from KG to SI units for a wide range of commodity polymers. The connection between the experimental and the KG melts is made at the Kuhn scale, i.e., at the crossover from the chemistry-specific small scale to the universal large scale behavior. We expect Kuhn scale-mapped KG models to faithfully represent universal properties dominated by the large scale conformational statistics and dynamics of flexible polymers. In particular, we observe very good agreement between entanglement moduli of our KG models and the experimental moduli of the target polymers.
The application of mathematical optimization methods for water supply system design and operation provides the capacity to increase the energy efficiency and to lower the investment costs considerably. We present a system approach for the optimal design and operation of pumping systems in real-world high-rise buildings that is based on the usage of mixed-integer nonlinear and mixed-integer linear modeling approaches. In addition, we consider different booster station topologies, i.e. parallel and series-parallel central booster stations as well as decentral booster stations. To confirm the validity of the underlying optimization models with real-world system behavior, we additionally present validation results based on experiments conducted on a modularly constructed pumping test rig. Within the models we consider layout and control decisions for different load scenarios, leading to a Deterministic Equivalent of a two-stage stochastic optimization program. We use a piecewise linearization as well as a piecewise relaxation of the pumps’ characteristics to derive mixed-integer linear models. Besides the solution with off-the-shelf solvers, we present a problem specific exact solving algorithm to improve the computation time. Focusing on the efficient exploration of the solution space, we divide the problem into smaller subproblems, which partly can be cut off in the solution process. Furthermore, we discuss the performance and applicability of the solution approaches for real buildings and analyze the technical aspects of the solutions from an engineer’s point of view, keeping in mind the economically important trade-off between investment and operation costs.
Nacre-mimetic nanocomposites based on high fractions of synthetic high-aspect-ratio nanoclays in combination with polymers are continuously pushing boundaries for advanced material properties, such as high barrier against oxygen, extraordinary mechanical behavior, fire shielding, and glass-like transparency. Additionally, they provide interesting model systems to study polymers under nanoconfinement due to the well-defined layered nanocomposite arrangement. Although the general behavior in terms of forming such layered nanocomposite materials using evaporative self-assembly and controlling the nanoclay gallery spacing by the nanoclay/polymer ratio is understood, some combinations of polymer matrices and nanoclay reinforcement do not comply with the established models. Here, we demonstrate a thorough characterization and analysis of such an unusual polymer/nanoclay pair that falls outside of the general behavior. Poly(ethylene oxide) (PEO) and sodium fluorohectorite form nacre-mimetic, lamellar nanocomposites that are completely transparent and show high mechanical stiffness and high gas barrier, but there is only limited expansion of the nanoclay gallery spacing when adding increasing amounts of polymer. This behavior is maintained for molecular weights of PEO varied over four orders of magnitude and can be traced back to depletion forces. By careful investigation via X-ray diffraction and proton low-resolution solid-state NMR, we are able to quantify the amount of mobile and immobilized polymer species in between the nanoclay galleries and around proposed tactoid stacks embedded in a PEO matrix. We further elucidate the unusual confined polymer dynamics, indicating a relevant role of specific surface interactions.
Exercise training effectively mitigates aging-induced health and fitness impairments. Traditional training recommendations for the elderly focus separately on relevant physiological fitness domains, such as balance, flexibility, strength and endurance. Thus, a more holistic and functional training framework is needed. The proposed agility training concept integratively tackles spatial orientation, stop and go, balance and strength. The presented protocol aims at introducing a two-armed, one-year randomized controlled trial, evaluating the effects of this concept on neuromuscular, cardiovascular, cognitive and psychosocial health outcomes in healthy older adults. Eighty-five participants were enrolled in this ongoing trial. Seventy-nine participants completed baseline testing and were block-randomized to the agility training group or the inactive control group. All participants undergo pre- and post-testing with interim assessment after six months. The intervention group currently receives supervised, group-based agility training twice a week over one year, with progressively demanding perceptual, cognitive and physical exercises. Knee extension strength, reactive balance, dual task gait speed and the Agility Challenge for the Elderly (ACE) serve as primary endpoints and neuromuscular, cognitive, cardiovascular, and psychosocial meassures serve as surrogate secondary outcomes. Our protocol promotes a comprehensive exercise training concept for older adults, that might facilitate stakeholders in health and exercise to stimulate relevant health outcomes without relying on excessively time-consuming physical activity recommendations.
To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.
Retinal endothelial function in cardiovascular risk patients: A randomized controlled exercise trial
(2020)
The aim of this study was to investigate, for the first time, the effects of high-intensity interval training (HIIT) on retinal microvascular endothelial function in cardiovascular (CV) risk patients. In the randomized controlled trial, middle-aged and previously sedentary patients with increased CV risk (aged 58 ± 6 years) with ≥ two CV risk factors were randomized into a 12-week HIIT (n = 33) or control group (CG, n = 36) with standard physical activity recommendations. A blinded examiner measured retinal endothelial function by flicker light-induced maximal arteriolar (ADmax) and venular (VDmax) dilatation as well as the area under the arteriolar (AFarea) and venular (VFarea) flicker curve using a retinal vessel analyzer. Standardized assessments of CV risk factors, cardiorespiratory fitness, and retinal endothelial function were performed before and after HIIT. HIIT reduced body mass index, fat mass, and low-density lipoprotein and increased muscle mass and peak oxygen uptake (VO2peak). Both ADmax (pre: 2.7 ± 2.1%, post: 3.0 ± 2.2%, P = .018) and AFarea (pre: 32.6 ± 28.4%*s, post: 37.7 ± 30.6%*s, P = .016) increased after HIIT compared with CG (ADmax, pre: 3.2 ± 1.8%, post: 2.9 ± 1.8%, P = .254; AFarea, pre: 41.6 ± 28.5%*s, post: 37.8 ± 27.0%*s, P = .186). Venular function remained unchanged after HIIT. There was a significant association between ∆-change VO2peak and ∆-changes ADmax and AFarea (P = .026, R² = 0.073; P = .019, R² = 0.081, respectively). 12-weeks of HIIT improved retinal endothelial function in middle-aged patients with increased CV risk independent of the reduction in classical CV risk factors. Exercise has the potential to reverse or at least postpone progression of small vessel disease in older adults with increased CV risk under standard medication. Dynamic retinal vessel analysis seems to be a sensitive tool to detect treatment effects of exercise interventions on retinal microvascular endothelial function in middle-aged individuals with increased CV risk.