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Reliability assessment of flexible dolphins: Reducing uncertainty in the design approach of flexible dolphins
No full textThe abstract outlines a study focusing on improving the design approach for flexible dolphins, vital marine structures used for vessel berthing and mooring. Current design methodologies, particularly those outlined in the CROW C1005 handbook (2018), are questioned due to potential conservatism stemming from insufficiently calibrated partial factors. The study advocates for reliability-based assessments to address these concerns, which consider uncertainties inherent in dolphin design and quantify failure probabilities over their lifespan. The investigation identifies critical failure modes and determines main pile dimensions based on these modes, utilizing the API PY-curves for rapid computation in the design process. Probabilistic assessments, employing Directional Sampling, reveal the significance of berthing load in structural safety and identify soil parameters as dominant variables affecting fixity failure mode. Navigation conditions, ship arrival rates, and variation in ship sizes also influence partial factors, with recommendations provided for adjustments based on different conditions. The study suggests load testing to reduce uncertainties and increase reliability, exemplified by a Bayesian update from Calandkanaal full-scale load tests. Overall, reliability-based assessments yield insights into managing uncertainties in dolphin design, potentially reducing material usage by up to 20% while meeting safety requirements, and offering the potential to decrease failure probabilities tenfold when combined with test loading.Civil Engineerin
Energy-stable discretization of the one-dimensional two-fluid model
No full textIn this paper we present a complete framework for the energy-stable simulation of stratified incompressible flow in channels, using the one-dimensional two-fluid model. Building on earlier energy-conserving work on the basic two-fluid model, our new framework includes diffusion, friction, and surface tension. We show that surface tension can be added in an energy-conserving manner, and that diffusion and friction have a strictly dissipative effect on the energy. We then propose spatial discretizations for these terms such that a semi-discrete model is obtained that has the same conservation properties as the continuous model. Additionally, we propose a new energy-stable advective flux scheme that is energy-conserving in smooth regions of the flow and strictly dissipative where sharp gradients appear. This is obtained by combining, using flux limiters, a previously developed energy-conserving advective flux with a novel first-order upwind scheme that is shown to be strictly dissipative. The complete framework, with diffusion, surface tension, and a bounded energy, is linearly stable to short wavelength perturbations, and exhibits nonlinear damping near shocks. The model yields smoothly converging numerical solutions, even under conditions for which the basic two-fluid model is ill-posed. With our explicit expressions for the dissipation rates, we are able to attribute the nonlinear damping to the different dissipation mechanisms, and compare their effects.Fluid Mechanic
Accumulation of floating particles at hydraulic structures
No full textPlastic pollution is a threat for all ecosystems due to its effects on people, animals, and environment (Mai et al., 2020). Rivers are estimated to transport around 0.5 millions tons of plastic per year (Strokal et al., 2023). When plastic enters a river system, it is transported downstream towards the sea but it is also likely to accumulate at specific cross sections and locations, including hydraulic structures (Al-Zawaidah et al., 2021), eventually increasing the risk of floods.Gates, locks, weirs, and bridges are commonly present in rivers and canals and have several functions, including water level reg-ulation, flood safety, and inland water shipping. These can also be found in water treatment plants, hydropower stations as well as debris/plastic collection systems (Honingh et al., 2020). Riverine plastic accumulation is also known to cause geomorphic changes (Al-Zawaidah et al., 2021).In-depth knowledge on how plastic particles accumulate upstream of hydraulic structures is therefore crucial to understand the processes that affect plastic transport, its influence on the safety and functionality of hydraulic structures and their effects on the hydro- and morphody-namic conditions of the flow (Yan Toe et al., 2022).In this research experiments were performed using simplified plastic particles to analyse the processes that lead to the instability of accumulated particles upstream of a simple gate.Civil Engineering & GeosciencesRivers, Ports, Waterways and Dredging EngineeringHydraulic Structures and Flood RiskEnvironmental Fluid Mechanic
Damage Classification of a Bolted Connection using Guided Waves and Explainable Artificial Intelligence
No full textWith the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due to the black box nature of deep learning, the trained models are usually difficult to interpret, which blocks their practical application. Therefore, it is of great importance to develop explainable artificial intelligence (XAI) methods to understand the internal decision-making mechanisms of damage classification in Deep SHM. In this paper, a novel XAI algorithm named Deep Gradient-weighted Class Activation Mapping (Deep Grad CAM) is proposed by combining the existing method Grad CAM with the convolutional neural network (CNN) deconvolution mechanism. In this paper, Deep Grad CAM is used to interpret a one-dimensional convolutional neural network trained to detect bolt loosening based on guided wave propagation. The interpretation performance of Deep Grad CAM is compared with Grad CAM, and their performances are quantified using Infidelity. The results show that the Infidelity of Deep Grad CAM is much smaller than that of Grad CAM, indicating significant improvements in explanation accuracy and reliability.Structural Integrity & CompositesGroup YueGroup Grove
Beyond control over data: Conceptualizing data sovereignty from a social contract perspective
No full textIn the data economy, data sovereignty is often conceptualized as data providers’ ability to control their shared data. While control is essential, the current literature overlooks how this facet interrelates with other sovereignty facets and contextual conditions. Drawing from social contract theory and insights from 31 expert interviews, we propose a data sovereignty conceptual framework encompassing protection, participation, and provision facets. The protection facets establish data sharing foundations by emphasizing baseline rights, such as data ownership. Building on this foundation, the participation facet, through responsibility divisions, steers the provision facets. Provision comprises facets such as control, security, and compliance mechanisms, thus ensuring that foundational rights are preserved during and after data sharing. Contextual conditions (data type, organizational size, and business data sharing setting) determine the level of difficulty in realizing sovereignty facets. For instance, if personal data is shared, privacy becomes a relevant protection facet, leading to challenges of ownership between data providers and data subjects, compliance demands, and control enforcement. Our novel conceptualization paves the way for coherent and comprehensive theory development concerning data sovereignty as a complex, multi-faceted construct.Information and Communication TechnologyEconomics of Technology and Innovatio
Stationary-phase analysis of time-shift extended imaging in a constant-velocity model
No full textTo estimate the depth errors in a subsurface model obtained from the inversion of seismic data, the stationary-phase approximation in a two-dimensional constant-velocity model with a dipped reflector is applied to migration with a time-shift extension. This produces two asymptotic solutions: one is a straight line, and the other is a curve. If the velocity differs from the true one, a closed-form expression of the depth error follows from the depth and apparent dip of the reflector as well as the position of the amplitude peak at a non-zero time shift, where the two solutions meet and the extended migration image focuses. The results are compared to finite-frequency results from a finite-difference code. A two-dimensional synthetic example with a salt diapir illustrates how depth errors can be estimated in an inhomogeneous model after inverting the seismic data for the velocity model.Applied Geophysics and Petrophysic
Een brug naar circulariteit: Een onderzoek naar de implementatie van kaders voor circulair asset management van verkeersbruggen
No full textManagement of Technology (MoT
Semi-Supervised Integrated Learning of Mutational Signatures for Predicting DNA Repair Pathway Deficiencies
No full textMotivation. DNA molecules mutate thousands of times every day. Some mutations are harmful to human cells, and may lead to the loss of function in important genes involved in DNA damage repair (DDR) mechanisms. Diseases such as tumors can exploit mutations in important, driver DDR genes to rapidly proliferate. Specific patterns of mutations (or signatures) are insightful indicators for the presence of DDR malfunctioning, which can be exploited to provide targeted treatment (e.g., by leveraging synthetic lethalities). Different methods have been developed to successfully extract relevant mutational signatures from the genomes of tumor patients. Most approaches are unsupervised and thus do not optimize toward distinguishing DDR deficiencies (DDRd). Supervised approaches achieve this, but rely on labeled in vitro data from tumor cell line genomes during training, due to the lack of DDRd ground truth for tumor patient genomes. Semi-supervised learning could bridge the gap and jointly exploit labeled cell line and unlabeled patient mutation profiles to generalize to patient tumors and provide more clinically relevant DDRd mutational signatures.Results. We propose Pseudo-labeling Semi-Supervised NMF (PSS-NMF), a novel integrated signature extraction and label prediction method, which extends supervised non-negative matrix factorization (NMF) with the ability to incorporate unlabeled samples into the training via pseudo-labeling. Models learned using PSS-NMF were benchmarked on two different tasks, cancer type and DDRd prediction. PSS-NMF consistently improved prediction for patient tumors over the supervised NMF baseline for both tasks, learning signatures that better transferred to the patient tumor domain: the models achieved Macro F1 scores of 0.3842 and 0.1331 respectively for cancer type prediction, and 0.4928 vs 0.4704 for DDRd prediction. We further validated that PSS-NMF identified DDRd signatures were biologically relevant, by comparing them to known DDRd-related mutational signatures curated in COSMIC and investigating their exposures in patient tumor genomes.Master ThesisComputer Science | Artificial Intelligenc
Transforming Engineering Education in Learning Ecosystems for Resilient Engineers
No full textTU Delft education system is transformed on three levels: 1) new courses and projects in existing B.Sc. and M.Sc. programs for multidisciplinary and reflective learning; 2) new M.Sc. programs focusing on multi and interdisciplinarity, personal development, and professional skills; and 3) central Interdisciplinary Projects for Master Students from different programs. With these steps, the university offers students a learning ecosystem where identity-building can occur, fosters interdisciplinary teamwork, and strong interaction with the professional world and government is necessary to finish projects. In this article, the ecosystem will be explained, and results will be shared of surveys among students who experienced learning in the learning ecosystem. The surveys show that students under stand their future role in the community as engineers, feel that they have acquired new skills, feel better about framing complex problems, and are more competent to work in the industry.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Policy & ImplementationCognitive RoboticsEducation and Student Affair
Formal Control of an Inverted Pendulum on a Cart via Stochastic Abstractions: Using Interval Markov Decision Processes and Linear Temporal Logic on Finite Traces
No full textThe use of machine learning (ML), especially neural networks, in modeling control systems has shown promise, particularly for systems with complex physics. However, applying these models in safety-critical areas requires reliable verification and control synthesis methods due to their inherent complexity. Formal methods, using stochastic finite state models like interval Markov decision processes (IMDPs), provide a way to analyze and verify these systems against detailed safety and performance specifications defined using linear temporal logic over finite traces (LTLf). Abstraction of ML models into such IMDPs, allows the deriving of formal guarantees on the IMDP that carryover to the underlying ML model.This thesis focuses on designing a switched controller for a cart-pendulum system using neural network dynamic models (NNDM) by formal control synthesis, validating it through formal verification methods. The methodology includes modeling the system behavior under different controllers, abstracting these models into IMDPs, applying the respective formal methods, and validating the approach through experiments. The aim is to demonstrate the framework's utility in a practical context, comparing different neural network architectures and researching the applicability of formal guarantees to both the models and the actual system.The main contributions are a practical application of the framework to a specific system, a comparison of neural network architectures for dynamic modeling, and an experiment-based validation of the framework's effectiveness. It confirms that the formal guarantees for abstracted models are relevant to the actual system, providing insights into the framework's potential for real-world applications. The findings suggest areas for further research, particularly in making such frameworks more accessible for practical deployment in safety-critical systems.Mechanical Engineerin
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