115 research outputs found
Semi-empirical framework for predicting the noise from wind-turbine blades with serrated trailing edges
No full textThis work proposes a semi-empirical framework to predict the noise of wind turbines with serrated trailing edge blades. The framework is employed for studying the reduction of the noise of the SWT 2.3-93 benchmark wind turbine. The framework is verified against field acoustic measurements of the real wind-turbine model and of noise reduction measured for airfoil geometries with serrated trailing edges. Two different serration design strategies are proposed, respectively one with the same serration geometry along the blade and one with serrations scaled with the local boundary-layer properties along the radius. Results show the predicted noise reduction obtained with each of the add-ons and explore the benefits of tailoring the design of the serrations according to the varying flow conditions along the blade span
The missing link in adaptive delta management: Insights on the potential of pumps in reducing flood risk under sea level rise and adaptive social learning to improve decision-making in the Rhine-Meuse estuary
No full textDecision-makers in low-lying coastal zones are confronted with uncertain developments around flood risk.These drivers are surrounded by large uncertainties, which requires delta management to be adaptive. This research offers insights about adaptive delta management for the Rhine-Meuse estuary from three perspectives: a physical, a socio-political and a integrated perspective. In the physical perspective, the effect of pump capacity on the water system of the Rhine-Meuse estuary is quantified; one of the adaptation options against sea level rise. To assess the adaptation potential of pumps, the case study Delta21 is used; a plan to construct an artificial lake with an area of 35 km2 next to the Maasvlakte 2 in combination with a pump capacity of 10 000 m3/s. The Rhine-Meuse estuary is divided into four sub-areas depending on thedominant hydrodynamic process: storm surge dominant area, flood storage dominant area, discharge dominant area and transition area. For each sub-area, the effects of Delta21 are computed on 1) water flows, 2) hydraulic loads and 3) failure probabilities. Delta21 succeeds in lowering the hydraulic loads and corresponding failure probabilities. At the same time, reductions are disproportionately over the Rhine-Meuse estuary leading to low reductions in some sub-areas. In the socio-political perspective, the focus is on decision-making and learning processes in management in delta management. The Rhine-Meuse estuary is characterized by a network structure, which means that knowledge and decisions do not belong to one single actor, but those decisions come about during interactions between various groups of actors. Not only the decisions need to be adaptive to cope with uncertain circumstances, but the learning process itself must also become adaptive. To achieve this aim, a conceptual mode is developed. Two case studies are used; the Delta Program and Knowledge Program Sea Level Rise. A longitudinal analysis of the Delta Program resulted in different narratives over time and various learning types. Subsequently, the Knowledge Program Sea Level Rise is analyzed with semi-structured interviews, process-tracing and the conceptual model. Observations and challenges are translated into a roadmap of learning activities. Moreover, the DEALTa learning handbook is developed, to support actors in designing learning activities. The physical and socio-political perspective are united in the integrated perspective. On the level of technical studies, insights are shared about the relation between both aspects and how the integration can be improved in the future.Civil Engineering | Hydraulic Engineering | Hydraulic Structures and Flood Ris
Anomaly Detection in Sleep Staging in Critically Ill Children
No full textStudy objectives: Conventional sleep scoring is based on the scoring criteria of the American Association of Sleep Medicine (AASM) but may not be suited to describe sleep in critically ill children admitted to the Pediatric Intensive Care Unit (PICU). In this study, an anomaly detection model using Gaussian Models trained on sleep stages in data from non-critically ill children is developed to assess if polysomnography(PSG)-derived electroencephalography (EEG) data from critically ill children can be categorized into sleep stages based on these AASM scoring criteria.Methods: A retrospective study at Erasmus MC Sophia Children’s Hospital, using PSG recordings obtained in non-critically ill children between 2017 and 2021 and in critically ill children between 2020 and 2022.Gaussian Models were individually trained for each sleep stage using data from non-critically ill children. Anomaly detection was carried out by computing the Mahalanobis Distances and assigning datapoints to specific sleep stages or categorizing them as anomalous. Errors were quantified by calculating the ratio of anomalous epochs to the total number of epochs. The trained Gaussian Models were applied to distinct sleep stages in the data from non-critically ill children. Subsequently, the models were applied to data from critically ill children to determine the categorization of their epochs. This was also analyzed over time and involved comparisons related to medication, mechanical ventilation, and the severity of illness assessed by the PELOD-2 score.Results: In non-critically ill children the models obtained validation errors aligning with the margin error of the training set. The models could not fully differentiate the distinct sleep stages. In critically ill children, the majority of epochs were classified into multiple sleep stages. High error rates were evident for sleep stages N1, R, and N. Some patients exhibited elevated error rates specifically for sleep stage N1. REM sleep was reduced, consistent with findings from previous studies. In contrast, N3 sleep did not show a reduction. When compared to the sleep stage labels assigned by neurophysiologists, the model classified epochs into multiple sleep stages, while neurophysiologists frequently used the label N. A higher PELOD-2 score did not consistently correlate with an increased occurrence of anomalous classifications in the epochs of these patients to those with lower PELOD-2 scores. Discussion: Overlap of sleep stages was observed in non-critically ill children. Epochs from critically ill children were classified into multiple sleep stages without clear associations in time or severity of illness. Building upon the established anomaly detection framework is recommended by employing more advanced anomaly detection methods using an informative feature selection. This study marks an initial step, indicating that applying the AASM.TM30004; 35 ECTSTechnical Medicine | Sensing and Stimulatio
Diffractive optical elements are all you need: Designing an optical system using physics-informed and data-driven methods
No full textIn this work, we consider how to optimize an optical system, specifically one with diffractive optical elements (DOE). We start by describing optical theory called Fourier optics also known as wave optics. This type of optics is found by making assumptions from the Maxwell equations for magnetic and electrical fields. This leads us to the Rayleigh-Sommerfeld diffraction integral, which we need to propagate light. To optimize an optical system, we introduce the standard optimization methods used when gradients are available and also dive into data-driven methods. Two wellknown algorithms in each category: the Adam optimization method, which is an extension of normal gradient descent methods, and the UNet convolutional neural network. To make the optimization methods work with our physics simulation, we use an automatically differentiable implementation which gives the gradients for the optimization. Combining the two optimization methods with our optics engine, we optimize optical designs such that the resulting intensity on the sample plane resembles some target intensity. We are able to optimize systems with single and multiple DOE and for high and low resolution DOE designs. We find that more lenses makes the optimization better and increases the variability in the created projection. We also find that increasing the resolution severely slows down the optimization with the Adam method. Although, the optimization method Adam is well suited for this optimization task. It becomes computationally very expensive on high resolution due to the physics simulation at every optimization step. Some physical simulations require high resolution to make sure the simulation does not contain to much artefacts. We show that the data-driven approach has potential to solve this issue. We train a network that takes as input a target intensity and outputs the lens that produces that intensity. Combining these results, we conclude that modern optimization methods are well suited for optical system optimization and we find that there is a large untapped potential for data-driven methods in optics.Applied Mathematic
Literatuur in een nieuw tijdperk: de functies van Pottermore
No full textThe main topic of this thesis is the website Pottermore created by Warner Bros. and J.K. Rowling. The interactive website is a new phenomenon in the literary world. The purpose of this thesis is to gain insight in the different functions that Pottermore can fulfill for both the author/maker of the site and the reader/visitor of it, in relation to the Harry Potter book series by J.K. Rowling.
The website operates in a literary field that is not used to having additional information about a story on an interactive website. Pottermore has a navigating, interpreting and immersive function for the audience. For the author it is a way to protect her intellectual property and for the publishing house and the marketing team Pottermore has a huge commercial function
Produktie van penicilline in een continu proces
No full textDocument uit de collectie Chemische ProcestechnologieDelftChemTechApplied Science
Aircraft component health analysis for predictive maintenance: using a dilated convolutional autoencoder and KL divergence
No full textThe detection of anomalous behaviour is fundamental to component health analysis techniques. However, detecting anomalies is a difficult and time consuming task if their form, location, and frequency are unknown. This research introduces an innovative unsupervised predictive maintenance pipeline that requires minimal domain knowledge and time to create competitive and insightful health monitoring models. First, a Dilated Convolutional Autoencoder learns to recreate healthy sensor data. Then, a Kullback-Leibler (KL) divergence based health analysis transforms discrepancies between the reconstruction and the sensor data into a single performance metric per sensor per flight. A novel evaluation method based on the KL divergence metric allows for quantitative evaluation and hyperparameter tuning of the autoencoder. Results provide new insights and show competitive performance on analysing the fuel level measuring system. Additionally, in a generalisability study on the braking system of a different aircraft type the proposed method outperforms the currently employed health monitoring model in precision and F1 score. The main advantages of the proposed method are; the ability to rapidly create unbiased health indicators on a sensor level, the capability to generalise to other components, and a framework to quantitatively evaluate the model’s performance when no truth labels are available.Mechanical Engineering | Vehicle Engineering | Cognitive Robotic
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