Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.Signal Processing System

Railway sleeper vibration measurement by train-borne laser Doppler vibrometer and its speed-dependent characteristics

A train-borne laser Doppler vibrometer (LDV) directly measures the dynamic response of railway track components from a moving train, which has the potential to complement existing train-borne technologies for railway track monitoring. This paper proposes a holistic methodology to characterize train-borne LDV measurements by combining computer-aided approaches and real-life measurements. The focus is on the speed-dependent characteristics because the train speed affects the intensity of railway sleeper vibrations and the intensity of speckle noise, which further affects the quality and usability of the measured signals. First, numerical models are established and validated to simulate sleeper vibrations and speckle noise separately. Then, a vibration–noise separation method is proposed to effectively extract speckle noise and structural vibrations from LDV signals measured at different speeds. The parameters of the separation method are tuned using simulation signals. The method is then validated using laboratory measurements in a vehicle-track test rig and applied to field measurements on a railway track in Rotterdam, the Netherlands. Further, the speed-dependent characteristics of train-borne LDV measurement are determined by analyzing the competition between sleeper vibrations and speckle noise at different speeds. Simulation and measurement results show that an optimal speed range yields the highest signal-to-noise ratio, which varies for different track structures, measurement configurations, and operational conditions. The findings demonstrate the potential of train-borne LDV for large-scale rail infrastructure monitoring.Reservoir EngineeringRailway Engineerin

Optimization of Interplant Water Reuse in Industrial Parks: Considering Water Treatment Systems

Reusing water is a crucial part of the solution for addressing the growing concern regarding the risk of water scarcity in industrialized and urbanized areas. This study introduces a tool for the design of water networks, focusing on water reuse in industrial parks. Utilizing a mixed-integer nonlinear programming (MINLP) model developed earlier, this tool is the first in water network design models that operates with open-source software, while considering water treatment systems and multiple constituents. A literature study is conducted to discover shortcomings in water network design models and to find a foundational model to use to develop the tool. The developed tool creates a water network based on the optimization of the costs of water obtained from water sources, the costs of treatment systems, and optionally the piping costs. The treatment systems are used to regenerate the water for reuse in industrial plants and to meet environmental discharge limits. The tool develops local optimal solutions as an output. Additionally, this study is the first to integrate a water treatment systems database into a water network design model. However, this database needs to be expanded before it is usable. This study demonstrates the tool through three case studies.Civil Engineerin

Drivers for optimum sizing of wind turbines for offshore wind farms

Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.Wind Energ

Flexural Behaviour of Concrete Reinforced With Basalt Fibre Reinforcement Bars: An Experimental and Numerical Research

The emergence of innovative construction materials is dawning a new era of ambition within the civil engineering community. Among these innovative materials, Basalt Fibre Reinforced Polymer (BFRP) has recently surfaced with promising potential as a reinforcing material in concrete. Currently, in the Dutch concrete construction industry, the choice for reinforcement steel has remained unchanged for the past decades. However, the increasing availability of innovative alternatives could help the transition to a more sustainable concrete industry. Although BFRP has promising potential for application in concrete structures, the global application has not been established yet. One of the reasons for this limited research into the structural behaviour of concrete structures reinforced with BFRP-bars. Furthermore, the limited development of codes specifically designed for concrete reinforced with BFRP-bars and the modest availability compared to reinforcement steel also play into the unknowns about the material.BFRP-bars contain certain qualities that reinforcement steel does not. One of the most prominent is resistance against corrosion due to environmental influences on concrete structures. This eliminates the requirement for the concrete cover to protect the reinforcement from corrosion. Hence, the concrete cover only serves its purpose to ensure effective bond action between the reinforcement bars and the concrete. This inherent quality of BFRP-bars eases the crack width control requirements in the codes for the design of structures reinforced with BFRP-bars to a range of 0.5 mm to 0.7 mm. Although this is a significant increase in comparison to the Eurocode for concrete structures (0.2 mm to 0.4 mm), the properties of BFRP-bars cause larger crack width development.The aim of the experiment is to investigate the flexural behaviour of concrete beams reinforced with BFRP-bars as tensile reinforcement. The flexural behaviour of concrete structures reinforced with BFRP-bars is studied both experimentally and numerically. The research program contains 6 beams differing in reinforcement material, concrete covers, reinforcement ratio and bar diameters. To investigate the effects of the concrete cover, 2 beams are designed with concrete covers of 31 mm and 11 mm containing 3 BFRP-bars with a diameter of 8 mm inthe tension zone. To compare the behaviour of these beams, 2 identical beams with reinforcement steel are designed. To determine the effects of the reinforcement bar diameter, 1 beam is designed with 2 bars with a diameter of 10 mm. The reinforcement ratio in beams remains approximately equal, hence the only changing parameter is the bar diameter. The effects reinforcement ratio is investigated by a beam designed with 2 bars with a diameter of 8 mm. By keeping the bar diameter and the concrete cover the same, the reinforcement ratio is the only changing parameter for this beam. By subjecting the beams to a 4-point bending test, a fully developed crack pattern can be established over a certain length. By using digital image correlation (DIC), the flexural behaviour is monitored and analysed. This includes both crack width development and overall pattern forming. The results are verified with linear variable differential transformers (LVDT‘s) and a laser measuring vertical displacements. This procedure is devised to evaluate the stiffness behaviour of the beams as well as the cracking behaviours. In addition, the experimental program includes a series of direct tensile tests with reinforcement bars to determine the stress-strain behaviour of the reinforcement bars themselves...Civil Engineerin

Increasing interpretability in XAI: Addressing the design principles for interactive XUIs to increase interpretability in XAI for end-users

Recent advancements in artificial intelligence (AI), particularly in deep learning, have significantly enhanced AI capabilities but have also led to more complex and less interpretable algorithms. This research addresses the challenge of Explainable AI (XAI) by focusing on enhancing the interpretability of AI decisions through the use of Explainable User Interfaces (XUI). The study identifies two primary knowledge gaps: the predominance of XAI research targeting technically skilled users, neglecting the end-user who often lacks technical expertise, and the insufficient exploration of user-centric design principles in real-world XUI applications.The research adopts the Design Science Research Method (DSRM) to develop an XUI tailored for the FOKUS project, which uses Electrocardiogram (ECG) data to detect myocardial infarctions. The study emphasises the strategic application of interactive design principles such as complementary naturalness, flexibility in explanation methods, and responsiveness through progressive disclosure to improve the system’s interpretability. Notably, sensitivity to context and mind, though not initially implemented, emerged as a critical design principle from the analysis and was subsequently positioned at the pinnacle of a restructured pyramid model of design principles.Key findings highlight the effectiveness of the selected design principles in enhancing interpretability and underscore the importance of involving stakeholders early in the development process to align the XAI and XUI with end-user needs. The research proposes a structured design approach framework for XUI, involving sequential phases from pre-XAI to XUI design, to systematically integrate user feedback and improve the design iteratively. The proposed framework restructured pyramid model of the design principles aim to guide future developments in XAI and XUI, enhancing their practical application and effectiveness in various contexts.Management of Technology (MoT

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) system for research development and demonstration on the TU Delft campus

At present, over half of all primary energy used in Europe is used for heating and cooling. Therefore, decarbonizing the heating supply is essential to achieve climate targets. Underground thermal energy storage is a key enabling technology for the energy transition to buffer the large seasonal mismatch between thermal energy demand and sustainable thermal energy production capabilities. In Delft, a High-Temperature Aquifer Thermal Energy Storage (HT-ATES) system will be installed at the campus of Delft University of Technology (TU Delft). It will be integrated in the wider heating system on and around the TU Delft campus, which itself is undergoing a transformation to optimally supply sustainable thermal energy. The district heating network will be extended and utilize the thermal energy from a geothermal doublet producing heat at around 75-80°C with a flow rate of ~350m3/hr. Excess energy produced by the geothermal well in summer will be stored in the HT-ATES system, and will be utilised when demand exceeds production throughout the winter. The HT-ATES system will comprise of 7 wells (3 hot wells of 80°C and 4 warm wells of 50°C) to a depth of approximately 200m, with storage in an unconsolidated sedimentary aquifer between 160-200m depth. It is designed so that the instantaneous excess power from the geothermal project can be stored and demand from the district heating network be extracted from the system.The HT-ATES system at TU Delft is partially funded by local stakeholders and the European commission within the PUSH-IT project and has two primary goals: (i) to reduce carbon emissions on TU Delft campus , and (ii) to create a unique demonstration, education and research infrastructure. The complexity of a HT-ATES requires innovative solutions during the entire system life cycle. The scientific programme that is initially planned within the project is therefore focusing on various research fields and includes:- Characterisation of the subsurface formations including mechanical, hydraulic, thermal, and chemical properties.- Evaluation and monitoring of the biological conditions and microbial diversity, and potential impact on water quality.- Innovations in drilling and completion, monitoring and performance.- Quantification of the system performance and system impact during multiple storage cycles and the full lifecycle of the HT-ATES. This will include extensively monitoring temperature distribution and water quality in the subsurface to characterise behaviour and improve models.- Demonstrate and develop the implementation of HT-ATES in an urban setting, including control of the system in the built-environment and transforming the conventional heat network to a future-proof heat network.- To allow access to other universities or institutions with active programmes in the field of Geothermal Science and Engineering to jointly carry out research and perform experiments.-Societal engagement and legal evaluation for improving the just energy transition.Geo-engineeringWater Resource

How Far Ahead Should Autonomous Vehicles Start Resolving Predicted Conflicts? Exploring Uncertainty-Based Safety-Efficiency Trade-Off

Resolving predicted conflicts is vital for safe and efficient autonomous vehicles (AV). In practice, vehicular motion prediction faces inherent uncertainty due to heterogeneous driving behaviours and environments. This spatial uncertainty increases non-linearly with prediction time horizons, leading AVs to perceive more road space occupied by conflicting vehicles. Reacting early to resolve predicted conflicts can ensure safety but may adversely affect traffic efficiency. Therefore, determining how far ahead AVs should start resolving predicted conflicts based on safety and traffic efficiency constraints is crucial. To answer this question, this study proposes a novel approach to explore the trade-off between safety and traffic efficiency considering prediction uncertainty. Firstly, a continuous-time motion prediction framework is proposed for estimating the spatial probability distribution of a vehicle’s future position at any moment within the maximum time horizon. Subsequently, average driver space and the corresponding traffic flow are derived from the safety settings of AV and prediction uncertainty. As such, the safety-efficiency trade-off can be quantified. Experiments show that mandatory decision points, high speeds, and traffic state transitions usually cause fast-increasing prediction uncertainty. A case study of Intelligent Driver Models (IDM) shows that traffic efficiency drops rapidly when AVs resolve predicted conflicts longer than 1.5 seconds ahead. AVs can act earlier on motorways for efficiency concerns but must be myopic at urban intersections. Prediction uncertainty fundamentally constrains the safety-efficiency performance of AVs. These findings are instructive for designing traffic-compatible AVs.Transport and Plannin

Unravelling uncertainty in trajectory prediction using a non-parametric approach

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver's near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent's future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.Transport and Plannin