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Graduation of Chris van Hulten

Graduation of Chris van Hulten 30 November 2021 13:00 till 15:00 - Location: CiTG - Lecture Hall F - By: Webredactie Recognizing critically damaged quay wall structures using a three-dimensional numerical model Professor of graduation: Dr.ir. M. Korf Supervisors: Ir. P.A. Korswagen (TU Delft), Ir. M.J. Hemel (TU Delft), Dr. Elisa Ragno (TU Delft), Ir. R. Roggeveld (Sweco NL) Many quay walls in Amsterdam have surpassed their structural lifetime and have started showing signs of damage. The city of Amsterdam is currently tackling the problem and have published a plan of action. This plan includes the renovation of hundreds of kilometres of quay walls. Given this enormous amount, it is necessary to prioritize certain quay walls over others based on the severity of their damage. Some quay walls have reached total collapse, of which the most recent case involves the "Grimburgwal" quay. The municipality has no accurate view of the current condition of quay walls in Amsterdam. On top of that, the vast majority of quay walls have not been assessed on their safety. It is known that the most vulnerable quay walls types consist of masonry walls, supported by wooden foundation structures. Given that the quay wall renovation project requires prioritisation, it is necessary to gain more information on how the most vulnerable walls are recognised. Preferably, a method should be developed in which only visual cues given by the masonry wall are required, as it is quick and relatively cheap. To gain information on what these visual cues might be, a three-dimensional finite element model is made to run simulations on possible behaviours of quay walls. In this thesis, it is attempted to model a quay wall as realistically as possible. Several different deterioration conditions will be applied to see how the masonry responds. The 3D model is built using a parametric model coded in Python. This code can be used to run simulations in the finite element software DIANA FEA. Many behavioural aspects have been incorporated into the model, with the purpose to make the model more realistic. The model consists of a masonry wall, planks on which the wall rests, and supporting piles. The behaviour of each component has been applied in the code and have been obtained through other literature and European norms. The model is loaded by simulating the weight of the soil and its effect on the quay wall structure. The masonry is simulated using a smeared cracking model (macro-model). Long-term deterioration of quay walls is simulated by changing the material properties of each respective component. This thesis focuses on three deterioration conditions: - Non-uniform pile degradation: application of broken piles, simulated by removal of those piles from the model. This is subdivided into two categories: removal of entire rows (a row consisting of a front, middle and end pile) and removal of front piles only. - Non-uniform soil removal: formation of soil pits at the foundation level, which result in decreased bedding around the foundation piles. - Uniform degradation: application of uniform deterioration along a stretch of quay walls. The simulations yield fairly consistent cracking patterns, in which the same crack fields appear in each simulation depending on the chosen case mentioned before. Displacement patterns are also documented and presented in all cases. The quay wall model is able to display in-plane and out-of-plane movement simultaneously. The effect of each parameter on the crack/displacement patterns are analysed as well. This includes masonry and wood quality. The results show that the largest in-plane settlements are reached by damaged piles, while the largest out-of-plane displacements are caused by a loss of soil bedding around the piles. The results can be used to provide better insight on how quay walls with poor quality present themselves in real life and what their cause might be. This research contributes to the possibility of improving recognition of quay walls which find themselves in critical condition, which can then be prioritized for renovations. For future research, it is recommended to see whether time-dependent simulations can be run, to see if it makes a difference in the outcome of displacement/cracking patterns. Another important recommendation is to look into deterioration rates of materials, which could be used as another indicator for critically damaged quay walls.

DDMC 2008

Program for Delft Days on Magnetocaloric (DDMC) October 30 (Thursday) 2008 9:30-9:45 Welcome address by Dr G. Degen, BASF Future Business, Ludwigshafen Workshop Session I 9:45-10:15 Magnetocaloric materials not only for cooling applications, E. Brück , TU Delft 10:15-10:45 Entropy contributions in conventional magnetocalorics and tricritical metamagnets, K. Sandeman , University of Cambridge 11:00-11:30 Improvement of magnetocaloric properties toward high efficiency cooling in La(FexSi1-x)13 by hydrogenation and partial substitution, A. Fujita , Tohoku University 11:30-12:00 Determination of magnetocaloric parameters through magnetic and thermodynamic methods in first-order transitions, R. Burriel , University of Zaragoza Workshop Session II 14:00-14:30 Model of layered AMR, K. Zimm , Astronautics 14:30-15:00 Magnetic refrigerants obtained by novel processing routes, J. Lyubina and O. Gutfleisch, IFW-Dresden 15:15-15:45 Magnetocaloric and Shape-Memory Properties in Ferromagnetic Heusler Alloys, A. Planes , University of Barcelona 15.45-16.15 Magnetocaloric properties of reactively sintered La(Fe,Co,Si)13, M.Katter , Vacuumschmelze 16.15-16.45 Determining the magnetocaloric effect in hysteretic materials, L. Caron , TU Delft 16.45-17.00 Evaluation of Alternative Refrigeration Cycles for Domestic Refrigeration - Performance Metrics and Expectations, E. Oguz, Arcelik A.S. 17.00-17.15 Magnetostriction - a way to detect lattice contributions to the magnetocaloric effect in CoMnSi based materials, A. Barcza October 31 (Friday) Workshop Session III 9:00-9:30 Pressure Dependence of Magnetocaloric Effect of MnAs1-x Sbx and La(Fe1-x Six )13 , H. Wada , Kyushu University 9:30-10:00 Pressure effects on the thermal hysteresis in MnFe(P,Ge) compounds O. Tegus , Inner Mongolia Normal University 10:20-10:50 An Isothermal Calorimeter for direct measurement of magnetocaloric properties, L. Giudici , INRIM Torino 10:50-11:20 Advanced modeling of Active Magnetic Regeneration, K.K. Nielsen , Risø 11:20-11:50 Global warming and refrigeration: how we can make all a difference? A. Pastore , Camfridge Other editions DDMC 2015 DDMC 2013 DDMC 2011 DDMC 2008

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New LDE trainee in D&I office

Keehan Akbari has started since the beginning of September as a new LDE trainee in the Diversity and Inclusion office. What motivated him to work for the D&I office, what does he expect to achieve during this traineeship? Read the short interview below! What motivated you to pursue your LDE traineeship in Diversity and Inclusion office of the TU Delft? I completed both bachelor's and master's degrees in Cultural Anthropology and Development Sociology at Leiden University. Within these studies, my main area of interest was in themes of inclusion and diversity. After being hired as a trainee for the LDE traineeship, and discovering that one of the possible assignments belonged to the Diversity and Inclusion office, my choice was quickly made. I saw this as an excellent opportunity to put the theories I learned during my studies into practice. What specific skills or experiences do you bring to the D&I office that will help promote inclusivity on campus? I am someone who likes to connect rather than polarize, taking into account the importance of different perspectives and stakeholders. I believe that this is how one can achieve the most in fostering diversity and inclusion. You need to get multiple parties on board to get the best results. What are your main goals as you begin your role here, and how do you hope to make an impact? An important goal for me this year is to get students more involved in diversity and inclusion at the university. One way I will try to accomplish this is by contributing to the creation of D&I student teams. By establishing a D&I student team for faculties, it will be possible to deal with diversity- and inclusion-related issues that apply and relate to the specific department. How do you plan to engage with different (student) communities within the university? Since I am new to TU Delft, the first thing I need to do is expand my network here. Therefore, I am currently busy exploring the university and getting to know various stakeholders. Moreover, I intend to be in close contact with various student and study organizations to explore together how to strengthen cooperation on diversity and inclusion. Welcome to the team Keehan and we wish you lots of success with your traineeship!

Researchers from TU Delft and Cambridge University collaborate on innovative methods to combat Climate Change

For over a year and a half, researchers from TU Delft and the Cambridge University Centre for Climate Repair have worked together on groundbreaking techniques to increase the reflectivity of clouds in the fight against global warming. During a two-day meeting, the teams are discussing their progress. Researchers at Cambridge are focusing on the technical development of a system that can spray seawater, releasing tiny salt crystals into the atmosphere to brighten the clouds. The team from TU Delft, led by Prof. Dr. Ir. Herman Russchenberg, scientific director of the TU Delft Climate Action Program and professor of Atmospheric Remote Sensing, is studying the physical effects of this technique. Prof. Russchenberg emphasizes the importance of this research: "We have now taken the first steps towards developing emergency measures against climate change. If it proves necessary, we must be prepared to implement these techniques. Ideally, we wouldn't need to use them, but it's important to investigate how they work now." Prof. Dr. Ir. Stefan Aarninkhof, dean of the Faculty of Civil Engineering and Geosciences, expresses pride in the team as the first results of this unique collaboration are becoming visible. If the researchers in Delft and Cambridge can demonstrate the potential of the concept, the first small-scale experiments will responsibly begin within a year. This research has been made possible thanks to the long-term support from the Refreeze the Arctic Foundation, founded by family of TU Delft alumnus Marc Salzer Levi . Such generous contributions enable innovative and high-impact research that addresses urgent global challenges like climate change. Large donations like these enable the pursuit of innovative, high-impact research that may not otherwise be feasible, demonstrating how our collective effort and investment in science can lead to real, transformative solutions for global challenges like climate change. Climate-Action Programme

How system safety can make Machine Learning systems safer in the public sector

Machine Learning (ML), a form of AI where patterns are discovered in large amounts of data, can be very useful. It is increasingly used, for example, in chatbot Chat GPT, facial recognition, or speech software. However, there are also concerns about the use of ML systems in the public sector. How do you prevent the system from, for example, discriminating or making large-scale mistakes with negative effects on citizens? Scientists at TU Delft, including Jeroen Delfos, investigated how lessons from system safety can contribute to making ML systems safer in the public sector. “Policymakers are busy devising measures to counter the negative effects of ML. Our research shows that they can rely much more on existing concepts and theories that have already proven their value in other sectors,” says Jeroen Delfos. Jeroen Delfos Learning from other sectors In their research, the scientists used concepts from system safety and systems theory to describe the challenges of using ML systems in the public sector. Delfos: “Concepts and tools from the system safety literature are already widely used to support safety in sectors such as aviation, for example by analysing accidents with system safety methods. However, this is not yet common practice in the field of AI and ML. By applying a system-theoretical perspective, we view safety not only as a result of how the technology works, but as the result of a complex set of technical, social, and organisational factors.” The researchers interviewed professionals from the public sector to see which factors are recognized and which are still underexposed. Bias There is room for improvement to make ML systems in the public sector safer. For example, bias in data is still often seen as a technical problem, while the origin of that bias may lie far outside the technical system. Delfos: “Consider, for instance, the registration of crime. In neighbourhoods where the police patrol more frequently, logically, more crime is recorded, which leads to these areas being overrepresented in crime statistics. An ML system trained to discover patterns in these statistics will replicate or even reinforce this bias. However, the problem lies in the method of recording, not in the ML system itself.” Reducing risks According to the researchers, policymakers and civil servants involved in the development of ML systems would do well to incorporate system safety concepts. For example, it is advisable to identify in advance what kinds of accidents one wants to prevent when designing an ML system. Another lesson from system safety, for instance in aviation, is that systems tend to become more risky over time in practice, because safety becomes subordinate to efficiency as long as no accidents occur. “It is therefore important that safety remains a recurring topic in evaluations and that safety requirements are enforced,” says Delfos. Read the research paper .