Delft University of Technology
  • Delft, Zuid-Holland, Netherlands
Recent publications
To move towards a condition-based maintenance practice for aircraft structures, design of reliable health management methodologies is required. Development of diagnostic methodologies is commonly realised on simplified sample structures with assumptions that methodologies can be adapted for application to realistic aircraft structures under in-service conditions. Yet such actual applications are not conducted. In this work, we study the development of diagnostic methodologies to training structures and their application to dissimilar testing structures. A heterogeneous population is considered, consisting of single-stiffener composite panels for methodology development and training and a multi-stiffener composite panel for application and testing. Characteristics as its composite material, lay-up, and temperature condition are constant while topologies and applied loads differ between the dissimilar structures. Damage in the structural panels is monitored on multiple diagnostic levels using a variety of structural health monitoring (SHM) techniques, including acoustic emission and distributed strain sensing. Specifically, we develop diagnostic methods for localising and monitoring disbond growth after impact using strain data collected during fatigue testing of multiple single-stiffener panels and apply these for disbond monitoring in an upscaled version of a multi-stiffener panel. In this manner, this study aids in the maturement and application of SHM methodologies to realistic aircraft structures.
Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However, discovering or designing a suitable HI for composite structures is a challenging task due to the inherent complexity of the evolution of damage events in such materials. Previous research has shown that data-driven models are efficient for accomplishing this goal. Large labeled datasets, however, are normally required, and the SHM data can only be labeled, respecting prognostic requirements, after a series of nominally identical structures are tested to failure. In this paper, a semi-supervised learning approach based on implicitly imposing prognostic criteria is adopted to design a novel HI suitable. To this end, single-stiffener composite panels were subjected to compression-compression fatigue loading and monitored using acoustic emission (AE). The AE data after signal processing and feature extraction were fused using a multi-layer LSTM neural network with criteria-based hypothetical targets to generate an intelligent HI. The results confirm the performance of the proposed scenario according to the prognostic criteria.
The growing demand towards life cycle sustainability has created a tremendous interest in non-destructive evaluation (NDE) to minimize manufacturing defects and waste, and to improve maintenance and extend service life. Applications of Magnetic Sensors (MSs) in NDE of civil engineering structures have become of great interest in recent years due to their non-contact data collection, and their high sensitivity under the influence of external stimuli such as strain, temperature, and humidity, to detect damage and deficiencies. There have been several advancements in MSs over the years for strain evaluation, corrosion monitoring, etc. based on the magnetic property changes. However, these MSs are at their nascent stages of development, and thus, there are several challenges that exist. This paper summarizes the recent advancements in MSs and their applications in civil engineering. Principle functions of different MSs are discussed, and their comparative characteristics are presented. The research challenges are highlighted and the roadmap towards high technology readiness level is discussed.
This study presents an approach for the detection of evolving degradation in large-scale low-speed roller bearings by clustering of Acoustic Emission (AE) events, and its application to experimental degradation data. To acquire the latter, a purpose-built linear bearing, representative of a segment of a turret bearing, has been instrumented with multiple piezoelectric AE transducers in the frequency range between 40–580 kHz. Clustering based on cross-correlation has identified a number of significant clusters that are linked to the observed damage. The results suggest that condition monitoring based on AE waveform similarity clustering is suitable for detection and identification of degradation in a large-scale roller bearing.
Complex societal challenges cannot be resolved with quick fixes, nor can they be successfully addressed from disciplinary or institutional silos. In this article we propose an innovative approach to tackling contemporary societal challenges based on complexity theory and transdisciplinarity. The lens of complexity reveals that such challenges emerge within complex contexts. Complex challenges cannot simply be resolved, due to their dynamic, non-linear nature. Instead, the complex context itself can be steered in a certain desired direction through iterative action and learning cycles. Transdisciplinary approaches help us understand how different perspectives and ways of knowing held by relevant actors can be combined to serve effective action in complex contexts. We have integrated complexity theory and transdisciplinarity to create a co-evolutionary model of innovation illustrating that who we work with, how we work, and what we learn and create co-evolve over time. We show how an innovation approach based on building a vision and including a reflexive social learning method can provide a guiding structure to this co-evolutionary process. We illustrate this approach with a case study focused on improving the well-being of staff and students at a university. We conclude the paper with implications for design.
Two-dimensional SrTiO 3 -based interfaces stand out among non-centrosymmetric superconductors due to their intricate interplay of gate-tunable Rashba spin-orbit coupling and multi-orbital electronic occupations, whose combination theoretically prefigures various forms of non-standard superconductivity. By employing superconducting transport measurements in nano-devices we present strong experimental indications of unconventional superconductivity in the LaAlO 3 /SrTiO 3 interface. The central observations are the substantial anomalous enhancement of the critical current by small magnetic fields applied perpendicularly to the plane of electron motion, and the asymmetric response with respect to the magnetic field direction. These features cannot be accommodated within a scenario of canonical spin-singlet superconductivity. We demonstrate that the experimental observations can be described by a theoretical model based on the coexistence of Josephson channels with intrinsic phase shifts. Our results exclude a time-reversal symmetry breaking scenario and suggest the presence of anomalous pairing components that are compatible with inversion symmetry breaking and multi-orbital physics.
We consider the problem of deploying a quantum network on an existing fiber infrastructure, where quantum repeaters and end nodes can only be housed at specific locations. We propose a method based on integer linear programming (ILP) to place the minimal number of repeaters on such an existing network topology, such that requirements on end-to-end entanglement-generation rate and fidelity between any pair of end-nodes are satisfied. While ILPs are generally difficult to solve, we show that our method performs well in practice for networks of up to 100 nodes. We illustrate the behavior of our method both on randomly-generated network topologies, as well as on a real-world fiber topology deployed in the Netherlands.
Background Safe and clean drinking water is essential for human life. Persistent, mobile and toxic (PMT) substances and/or very persistent and very mobile (vPvM) substances are an important group of substances for which additional measures to protect water resources may be needed to avoid negative environmental and human health effects. PMT/vPvM substances do not sufficiently biodegrade in the environment, they can travel long distances with water and are toxic (those that are PMT substances) to the environment and/or human health. PMT/vPvM substance research and regulation is arguably in its infancy and in order to get in control of these substances the following (non-exhaustive list of) knowledge gaps should to be addressed: environmental occurrence; the suitability of currently available analytical methods; the effectiveness and availability of treatment technologies; the ability of regional governance and industrial stewardship to contribute to safe drinking water while supporting innovation; the ways in which policies and regulations can be used most effectively to govern these substances; and, the identification of safe and sustainable alternatives. Methods The work is the outcome of the third PMT workshop, held in March 2021, that brought together diverse scientists, regulators, NGOs, and representatives from the water sector and the chemical sector, all concerned with protecting the quality of our water resources. The online workshop was attended by over 700 people. The knowledge gaps above were discussed in the presentations given and the attendees were invited to provide their opinions about knowledge gaps related to PMT/vPvM substance research and regulation. Results Strategies to closing the knowledge, technical and practical gaps to get in control of PMT/vPvM substances can be rooted in the Chemicals Strategy for Sustainability Towards a Toxic Free Environment from the European Commission, as well as recent advances in the research and industrial stewardship. Key to closing these gaps are: (i) advancing remediation and removal strategies for PMT/vPvM substances that are already in the environment, however this is not an effective long-term strategy; (ii) clear and harmonized definitions of PMT/vPvM substances across diverse European and international legislations; (iii) ensuring wider availability of analytical methods and reference standards; (iv) addressing data gaps related to persistence, mobility and toxicity of chemical substances, particularly transformation products and those within complex substance mixtures; and (v) advancing monitoring and risk assessment tools for stewardship and regulatory compliance. The two most effective ways to get in control were identified to be source control through risk governance efforts, and enhancing market incentives for alternatives to PMT/vPvM substances by using safe and sustainable by design strategies.
For Europe's urban agglomerations to be economically competitive, it is vital that international destinations be easily accessible. Although much has been invested in the construction of European rail infrastructure over the past century, passenger transport by rail has not grown as fast as transport by road and air. So why do people not use international trains more, even though they have an extensive international rail network at their disposal? Based on a series of in-depth interviews with relevant public and private stakeholders and two expert meetings, we identify the main bottlenecks and constraints. In order to understand the complexity of international rail transport, we have divided the existing bottlenecks into four groups corresponding to four layers of the rail transport system: mobility services, transport services, traffic services, and the physical and digital infrastructure. We formulate concrete policy recommendations for improvements to be made in the various components of the rail transport system.
Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud encoding solutions handle visually-lossless compression, are discussed.
Developed in this paper is a traffic flow model parametrised to describe abnormal traffic behaviour. In large traffic networks, the immediate detection and categorisation of traffic incidents/accidents is of capital importance to avoid breakdowns, further accidents. First, this claims for traffic flow models capable to capture abnormal traffic condition like accidents. Second, by means of proper real-time estimation technique, observing accident related parameters, one may even categorize the severity of accidents. Hence, in this paper, we suggest to modify the nominal Aw-Rascle (AR) traffic model by a proper incident related parametrisation. The proposed Incident Traffic Flow (ITF) model is defined by introducing the incident parameters modifying the anticipation and the dynamic speed relaxation terms in the speed equation of the AR model. These modifications are proven to have physical meaning. Furthermore, the characteristic properties of the ITF model is discussed in the paper. A multi stage numerical scheme is suggested to discretise in space and time the resulting non-homogeneous system of PDEs. The resulting systems of ODE is then combined with receding horizon estimation methods to reconstruct the incident parameters. Finally, the viability of the suggested incident parametrisation is validated in a simulation environment.
Quantum uncertainty is a well-known property of quantum mechanics that states the impossibility of predicting measurement outcomes of multiple incompatible observables simultaneously. In contrast, the uncertainty in the classical domain comes from the lack of information about the exact state of the system. One may naturally ask, whether the quantum uncertainty is indeed a fully intrinsic property of the quantum theory, or whether similar to the classical domain lack of knowledge about specific parts of the physical system might be the source of this uncertainty. This question has been addressed in the previous literature where the authors argue that in the entropic formulation of the uncertainty principle that can be illustrated using the so-called, guessing games, indeed such lack of information has a significant contribution to the arising quantum uncertainty. Here we investigate this issue experimentally by implementing the corresponding two-dimensional and three-dimensional guessing games. Our results confirm that within the guessing-game framework, the quantum uncertainty to a large extent relies on the fact that quantum information determining the key properties of the game is stored in the degrees of freedom that remain inaccessible to the guessing party. Moreover, we offer an experimentally compact method to construct the high-dimensional Fourier gate which is a major building block for various tasks in quantum computation, quantum communication, and quantum metrology.
Background Foot and ankle joint kinematics are measured during clinical gait analyses with marker-based multi-segment foot models. To improve on existing models, measurement errors due to soft tissue artifacts (STAs) and marker misplacements should be reduced. Therefore, the aim of this study is to define a clinically informed, universally applicable multi-segment foot model, which is developed to minimize these measurement errors. Methods The Amsterdam foot model (AFM) is a follow-up of existing multi-segment foot models. It was developed by consulting a clinical expert panel and optimizing marker locations and segment definitions to minimize measurement errors. Evaluation of the model was performed in three steps. First, kinematic errors due to STAs were evaluated and compared to two frequently used foot models, i.e. the Oxford and Rizzoli foot models (OFM, RFM). Previously collected computed tomography data was used of 15 asymptomatic feet with markers attached, to determine the joint angles with and without STAs taken into account. Second, the sensitivity to marker misplacements was determined for AFM and compared to OFM and RFM using static standing trials of 19 asymptomatic subjects in which each marker was virtually replaced in multiple directions. Third, a preliminary inter- and intra-tester repeatability analysis was performed by acquiring 3D gait analysis data of 15 healthy subjects, who were equipped by two testers for two sessions. Repeatability of all kinematic parameters was assessed through analysis of the standard deviation (σ) and standard error of measurement (SEM). Results The AFM was defined and all calculation methods were provided. Errors in joint angles due to STAs were in general similar or smaller in AFM (≤2.9°) compared to OFM (≤4.0°) and RFM (≤6.7°). AFM was also more robust to marker misplacement than OFM and RFM, as a large sensitivity of kinematic parameters to marker misplacement (i.e. > 1.0°/mm) was found only two times for AFM as opposed to six times for OFM and five times for RFM. The average intra-tester repeatability of AFM angles was σ:2.2[0.9°], SEM:3.3 ± 0.9° and the inter-tester repeatability was σ:3.1[2.1°], SEM:5.2 ± 2.3°. Conclusions Measurement errors of AFM are smaller compared to two widely-used multi-segment foot models. This qualifies AFM as a follow-up to existing foot models, which should be evaluated further in a range of clinical application areas.
In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.
Modal interactions are pervasive effects that commonly emerge in nanomechanical systems. The coupling of vibrating modes can be leveraged in many ways, including to enhance sensing or to disclose complex phenomenologies. In this work we show how machine learning and data-driven approaches could be used to capture intermodal coupling. We employ a quasi-recurrent neural network (QRNN) for identifying mode coupling and verify its applicability on experimental data obtained from tapping mode atomic force microscopy (AFM). Hidden units of the QRNN are monitored to trace fingerprints of modes activation and to quantify their contributions over the global distortion of orbits in the phase space. To demonstrate the broad applicability of the method, the trained model is re-applied over different experiments and on diverse materials. Over a range of tip-sample configurations, dynamic AFM possesses features general enough to be seized by the QRNN and it is not required an ad-hoc re-training for the identification of interacting modes. Our study opens up a route for utilizing established machine learning techniques for rapid recognition of nonlinear complex effect such as internal resonances in nanotechnology. The QRNN analysis is meant to assist AFM sensing operations when exploiting modal interaction to enhance the signal-to-noise ratio of higher harmonics and provide high resolution compositional contrast in multi-frequency AFM applications.
The application of building information modeling (BIM) technology has effectively supported the high-quality development of building sustainability and informatization in China. However, few studies comprehensively analyzed the enacted policies, prevalent applications, and existing barriers of the latest application and development of BIM technology in building industry from building sustainability and informatization perspectives to provide effective consultation and guidelines for its rational scale application in China. This paper firstly made a statistical analysis on the policies and standards of BIM technology issued from 2011 to 2021 in China. Moreover, the latest application, development and existing issues of BIM technology in building sustainability and informatization were also comprehensively discussed and analyzed. The main conclusions indicated that the application status of BIM technology for building sustainability and informatization in China was large in quantity, wide in scope, but low in level. The existing issue and limitation in terms of BIM application in China was mainly due to the lack of standards and domestic-oriented tools. Finally, the future outlook and recommendations of BIM technology for building sustainability and informatization in China were also presented as avenues for upcoming research.
The endeavour towards making power distribution systems (PDSs) smarter has made the interdependence on communication network indispensable. Further, prospective high penetration of intermittent renewable energy sources in the form of distributed energy resources (DERs) has resulted in the necessity for smart controllers on such DERs. Inverters are employed for the purpose of DC to AC power conversion in the distribution network where the present standards require these inverters to be smart. In general, distributed energy resource management systems (DERMS) calculate and send set points/operating points to these smart inverters using protocols such as smart energy profile (SEP) 2.0. Given the nature of sites at which such DERs are installed i.e., home area networks with a pool of IoT(Internet-of-Things) devices, the opportunity for a malicious actor to sabotage the operation is typically higher than that for a transmission system. National Electric Sector Cyber-security Organization Resource (NESCOR) has described several failure scenarios and impact analyses in case of cyber attacks on DERs. One such failure scenario concerns attacks on real/reactive power control commands. In this paper, it is demonstrated that physical invariant based security on the edge devices, i.e. smart controllers deployed in DER inverters, is an effective approach to minimize the impact of cyber attacks targeting reactive power control in DER inverters. The proposed defense is generic and can also be extended to attacks on real-power control. The proposed defense is validated on a co-simulation platform (OpenDSS and MATLAB / SIMULINK). Share link:
There are no accepted procedures that quantify the apparent charge of partial discharge (PD) in gas-insulated substations (GIS). This paper proposes a calibration method for PD charge estimation using unconventional electromagnetic sensors: a magnetic loop antenna (inductive coupler) and an electric antenna (capacitive coupler.) The calibration procedure is intended for the voltage double integral method, which is reviewed for magnetic antennas and extended for electric antennas. By injecting low-frequency sinusoidal signals, the calibration constants are determined for two different test setups: the first one being a testbench where the characteristic impedance is matched and the second one a full-scale 420 kV GIS. The calibration method is validated in three ways: with a calibrated pulse in the testbench, a calibrated pulse in a full-scale GIS, and PD defects in the full-scale GIS. The calibration procedure revealed a frequency limit range dependent on the GIS length and the sensor’s signal-to-noise ratio. The three validation methods showed low charge estimation errors for the magnetic and electric antennas, demonstrating that the PD calibration method applies to any electric/magnetic detector with a low-frequency derivative response. This research paves the way for better GIS insulation monitoring and PD sensor harmonization. © 2017 Elsevier Inc. All rights reserved.
With great concern over the health-promoting environment worldwide, there is a growing body of research into the neighborhood effects on health beyond the sole focus on individual socioeconomic disadvantages and lifestyle risks. Our study contributes to neighborhood health research by investigating the combined effects of multi-dimensional neighborhood environmental characteristics and recreational physical activity under different geographic contexts on residents' self-rated health. Drawing upon a health survey conducted in suburban Shanghai in 2017, we employ a series of multilevel models to examine how the multi-scale environmental and behavioral factors are related to residents' self-rated physical and mental health, respectively. The results show that the greening rate of the community, rather than accessibility to large-scale urban parks, is a significant indicator of self-rated health for suburban residents. Subjective evaluations on neighborhood safety and air pollution exposure are significantly associated with residents' physical and mental health, while neighborhood attachment matters more for mental health. Outdoor recreational exercises, especially in the environment of neighborhood green space, are conducive to better physical health, while indoor physical activity shows weaker and insignificant health benefits. These findings offer a promising way for public health policymakers and urban planners to implement place-based health interventions and develop health-supportive neighborhoods.
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24,242 members
Liza Rassaei
  • Department of Chemical Engineering
Alessandro Bozzon
  • Faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS)
H. Ozkan Sertlek
  • Faculty Of Civil Engineering and Geosciences
Lech Grzelak
  • Department of Applied Mathematics
Mekelweg 2, 2628 CD, Delft, Zuid-Holland, Netherlands
Head of institution Tim van der Hagen
+31 (0)15 27 89111