132571 research outputs found
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Naturally sourced epoxies for High-performance composite applications: A feasibility study of renewable algae-derived epoxy resin system
No full textComposite materials have revolutionized the world as we know it, but along the way, we have transformed that world, which is changing and suffering from global warming. This thesis explores the potential of bio-based resins for high-performance composite applications, focusing on a bio-based epoxy derived from brown algae, PHTE. The research investigates the feasibility of PHTE to replace BADGE epoxy, a bisphenol-A-based toxic and synthetic fossil-fuel-derived epoxy that comprises a major portion of aerospace composites. The study involved comprehensive material analysis and testing, including an examination of thermal, physical, and mechanical performance and a comparison of both systems. PHTE demonstrated excellent mechanical and thermal properties with high bio-based content, although its high viscosity posed challenges for traditional manual manufacturing techniques, especially composite manufacturing. The promising properties motivates to study and develop the recipe further for other high-performance applications as well apart from aerospace.Aerospace Engineerin
Letter to the Editor: Failure of Screw/Shell Interface in Trident II Acetabular System in Total Hip Arthroplasty
No full textSafety and Security Scienc
Developing a Decision Support System for Operating Room Schedule Management
No full textEffective management of operating room (OR) schedules is vital for the efficient operation of healthcare facilities, especially considering the impact of decisions on staff, patients and costs. While extensive research has focused on optimizing initial schedules, the dynamic nature of surgery days introduces uncertainties that impact scheduling decisions. The study proposes equipping OR coordinators with a decision support system that leverages real-time insights and readily available hospital data to aid in decision making on the day of surgery. This thesis underscores the usefulness of OR policy documents in guiding decision support system design. Additionally, emphasis is placed on the need for standardized data and frequent real-time updates to improve the decision support systems accuracy and efficiency. To improve the performance of the decision support system efforts should be focussed on refining input data generated by hospitals.Biomedical Engineerin
Enhancing resilience: Understanding the impact of flood hazard and vulnerability on business interruption and losses
No full textWithout taking additional measures, flooding is becoming more likely and intense in a changing climate, which causes large economic damage. Households and firms are directly impacted by physical flood damage, but further ripple effects on society occur through business disruptions. By using post-disaster survey data from the 2021 flood event in the Netherlands, this study adds to the literature on business interruption duration and losses after flooding. The current empirical literature on flood impacts on firms is often unable to distinguish separate effects for flooded and non-flooded firms and does not incorporate flood severity and the influence of risk reduction measures. Here, we use multivariate regression models to determine depth-duration functions that describe the relationship between flood hazard characteristics and business interruption duration. This relationship can be used to calibrate flood damage models that capture indirect firm impacts. The prediction of business interruption after flooding allows for differentiation in business interruption between firms within a flooded area, reducing the reliance of these macroeconomic models on restrictive assumptions. Our results indicate that a day of business interruption duration costs a firm on average 0.5 % of their annual revenue; an effect that is stronger for firms with a weaker connection to their region. Flood damage mitigation (FDM) measures taken at the building level do not significantly affect business interruption duration, although further research on this is required. Finally, quick damage compensation is found to reduce business interruption duration and thus revenue losses, calling for higher insurance uptake and rapid and streamlined post-disaster insurance and government compensation.Hydraulic Structures and Flood Ris
Comparativa analysis of dynamic stall models for wind turbines
No full textWind energy is a crucial component of the ongoing energy transition. Over the past decade, technologies in this field have advanced significantly. Currently, the industry is following a trend of increasing the size of wind turbines, both onshore and offshore units. However, this trend brings about complexities in design, maintenance, and operation. Wind turbines are highly dynamic structures, and accurate prediction of dynamic loads is crucial for future design and, consequently, installation costs and lifetime expectancy. Dynamic stall is a highly dependent process influenced by various parameters such as Reynolds number and airfoil geometry. It involves significant changes in pressure in the flow field around the airfoil due to the development and shedding of vortex structures from the leading or trailing edge. Another characteristic is flow reversal from the trailing edge, leading to a delay in boundary layer attachment and the formation of significant hysteresis in lift force, pitching moment, and drag force values. Due to the constant rotation of the rotor, this process is cyclic, necessitating accurate modeling for proper assessment of fatigue damage, instability levels, and turbine noise. This study aims to compare the results of dynamic stall models for the FFA-W3-211 airfoil, with a maximum thickness of 21.1% of the chord length. Previously unavailable dynamic data for the considered airfoil is obtained through experiments in a Low-Speed Low-Turbulence Wind Tunnel located at TU Delft. Static and dynamic polars are obtained for various Reynolds numbers, amplitudes, mean angles, and oscillation frequencies. Measurements are conducted using pressure taps located on the airfoil model, and static data is validated against existing data and RFOIL results. The study considers two semi-empirical models. The first is the Beddoes-Leishman model, one of the most popular at the time of writing. Its optimization and comparison with the default version revealed that optimized shape-based coefficients are crucial for obtaining correct results. It is found that the optimized model significantly outperforms the default version in almost all cases. Additionally, it is established that the model lacks the leading edge vortex effect in modeling negative stall. The second model chosen is the first-order Snel model. Optimization of this model also led to significant improvements in the results obtained. A significant improvement in modeling both hysteresis size and reattachment area with increasing Reynolds number is identified. However, a significant drawback of the model in modeling negative stall is revealed. During uppstroke motion, the model predicts trailing edge vortex effects instead of leading edge, which negatively affects the overall model performance. Comparison of the two models identified their strengths and weaknesses. The Beddoes-Leishman model appears more stable for all cases considered, while the Snel model is sensitive to changes in Reynolds number. It is also noted that both models have difficulties in correctly determining dynamic stall onset angle of attack and the slope of the normal force coefficient. Additionally, both models tend to overpredict values in a greater number of cases. When comparing negative stall, the optimized Beddoes-Leishman model performs significantly better than the Snel model. It accurately predicts the overall form and severity of hysteresis as well as the reattachment area. The Snel model requires additional modifications for correct modeling of negative stall.Electrical Engineering | Sustainable Energy Technolog
On a Coupled Aerodynamic and Aeroacoustic Shape Optimization Framework for a 2D Airfoil
No full textIn recent years, aircraft noise has emerged as a pressing concern within aeronautics, due to its adverse health impacts and the increasing annoyance experienced by affected populations. Factors such as rapid urbanization, urban encroachment resulting in closer proximity of residential areas to airports, and the continuous growth of air traffic have exacerbated this issue. Current approaches to mitigate aircraft noise primarily focus on integrating additional components rather than optimizing the airfoil profile a priori.This thesis aims to actively address this challenge by developing a coupled aerodynamic and aeroacoustic shape optimization framework tailored for a 2D airfoil. The primary objective is to minimize trailing edge noise, identified as one of the dominant noise generating mechanisms during approach and landing, while simultaneously maximizing aerodynamic performance. The solving strategy combines an aerodynamic solver with a state-of-the-art wall pressure spectrum model and Amiet’s trailing edge noise (TEN) model, whose inputs are boundary layer parameters extracted from the aerodynamic evaluation.The efficacy of both lower fidelity (XFOIL) and higher fidelity (Reynolds-Averaged Navier Stokes (RANS)) aerodynamic solvers is evaluated. The airfoil is optimized at different points of the flight envelope: for maximum lift-to-drag ratio during cruise and for minimum trailing edge noise during landing.Results demonstrate that the genetic algorithm (NSGA-II) optimization framework yields promising airfoil shapes and reliable outcomes at a computationally feasible cost. Many optimizations with varying generations and population sizes are ran: remarkably, the results consistently showcase well-defined Pareto fronts, with superior definition observed particularly at a population size of 200.The lower fidelity solver proves particularly effective in the landing scenario, while it shows limitations for higher Mach numbers. In this view, the RANS code is necessary for capturing flow phenomena at cruise speeds, and notably, it also provides convincing results in terms of aeroacoustic prediction during landing phase.In general, this research highlights the potential for significant advancements in designing optimal airfoils for aerodynamic performance and TEN.Aerospace Engineerin
Improving detection of river surface flow using p yOpenRiverCam and AI augmentation
No full textThis thesis investigates the efficacy of artificial intelligence (AI) models, particularly convolutional neural networks (CNNs) and U Net architectures, in reconstructing datasets with missing velocity data in river flow analysis. Optical flow and Particle Image Velocimetry (PIV) techniques have emerged as valuable tools for analyzing river flow patterns.Through a comprehensive literature review, CNNs, and UNet models are identified as promising tools for this task due to their ability to capture intricate patterns in datasets. The study compares the performance of the U-Net model against a statistics-based hydrological benchmark model, revealing the superior performance of the U-Net model.Furthermore, the analysis explores how the performance of AI models varies with differing quantities of missing data, by masking available data and comparing reconstructed values against the ground truth, highlighting the importance of data availability.Additionally, the study investigates the influence of spatial patterns in training data on model performance, including patchy versus random missing data in the field of view, simulating more datasets more likely available in reality. This clarifies the challenges encountered in predicting grid points under different training dataset conditions.Finally, the study identifies areas within the dataset that are particularly challenging to predict, shedding light on factors contributing to prediction errors. These findings underscore the potential of AI models in hydrological applications and provide valuable insights for future research in the field.Our findings show that U net is capable of reconstructing velocity fields from a river flow better than an average benchmark that uses the average values, with varying accuracy depending on input data.The average benchmark model had a relative error close to 0.2 in every instance, whereas the U-Net model showed relative errors ranging from 0.085 to 0.006. Errors from a patchy mask are ranging from 0.09 8 to 0.031.Civil Engineering | Environmental Engineerin
Disjunctive Multi-Level Digital Forgetting Scheme
No full textThe virtue of data forgetting has become a substantial demand in the digital era. Once online content has served its purpose, the concept of forgetting arises to ensure that data remains private between data owners and service providers. Despite significant advancements in supporting data forgetting through approaches like access heuristics, elastic expiration times, and manual revocation, the existing research falls short in addressing the demand for a multi-level forgetting structure that can cater to diverse audience-based expiration requirements while considering additional criteria. To the best of our knowledge, no prior works have investigated this gap, emphasizing the need for a comprehensive solution that can effectively accommodate the varying expiration needs of different audience groups. In this paper, we introduce a novel disjunctive multi-level forgetting scheme designed to meet the aforementioned demand for data forgetting. Our scheme introduces unique expiration periods for the encrypted data the service provider stores, called levels. Users are grouped into different levels based on priorities assigned by the data owners. Each level corresponds to a specific expiration threshold, enabling designated user groups to access the content within its validity period before it is forgotten. This approach enables selective data forgetting for one group while enabling concurrent access and retention for other user groups until the stipulated expiration period elapses. To achieve this, we have devised a cutting-edge system that integrates a hierarchical and dynamic scheme utilizing a key decay for managing expiration periods. Moreover, we introduce an innovative approach that harnesses smart contracts on a local Ethereum blockchain to enforce regulations and streamline the secure and efficient expiration and deletion of data. Finally, we thoroughly evaluate our proposed scheme, focusing on decay sensitivity, computational complexity, and rigorous security analysis.Cyber Securit
Bayesian network-based fault detection and diagnosis of heating components in heat recovery ventilation
No full textThis study investigates the diagnostic capabilities of a Diagnostic Bayesian Network (DBN) for air handling unit (AHU) components, particularly focusing on the heat recovery wheel (HRW) and heating coil valve (HCV). Unlike data-driven methods relying heavily on high-quality labeled data, this knowledge-based DBN is more suitable for real-world applications, where labeled faulty and normal data are hard to obtain. Notably, existing studies predominantly concentrate on developing DBN for AHU with recirculated air, neglecting thorough investigations into AHU with HRW, a prevalent system in North Europe and increasingly recommended post-COVID-19 for mitigating viral propagation. This paper presents a DBN setup with expert knowledge for an AHU with HRW, which is evaluated using experimental data from an office building in the Netherlands. The results show that the proposed DBN can successfully diagnose typical faults in HRW and HCV.Environmental & Climate Desig
The effect of the laser beam intensity profile in laser-based directed energy deposition: A high-fidelity thermal-fluid modeling approach
No full textModeling the thermal and fluid flow fields in laser-based directed energy deposition (DED-LB) is crucial for understanding process behavior and ensuring part quality. However, existing models often fail to accurately predict these fields due to simplifying assumptions, particularly regarding powder particle-induced attenuation in laser power and energy density distribution, and the variable material properties and process parameters. The present work introduces a high-fidelity multi-phase thermal-fluid model driven by a combination of the discrete element method (DEM) and the finite volume method (FVM). Incorporating an enhanced attenuation model for laser energy enables a more precise approximation of powder particle-induced attenuation effects in the laser power and energy density distribution. The study focuses on the influence of laser beam intensity profiles during DED-LB of austenitic stainless steel (AISI 316 L), with model validation conducted through experimental measurements of deposited track dimensions for different beam shapes. The results of numerical simulations demonstrate the critical impact of powder-induced attenuation on the laser power and intensity profiles. Neglecting laser energy attenuation, a common assumption in numerical simulations of DED-LB, leads to overestimations of the absorbed energy of the laser beam, affecting thermal and fluid flow fields, and melt pool dimensions. The present study unravels the complex relationship between the attenuation coefficient (due to the powder stream) and powder stream characteristics, describing the variations of the attenuation coefficient with changes in the powder mass flow rate and powder stream incidence angle. The findings show the critical effects of laser beam shaping on melt pool behavior in DED-LB, with square beams inducing larger melt pool volumes and circular beams creating smaller but deeper melt pools. The proposed enhanced thermal-fluid modeling framework offers a robust approach for optimizing laser-based additive manufacturing across diverse materials and laser systems.Team Marcel Herman
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