5,678 research outputs found

    Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

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    In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

    Radiotherapy dosimetry with ultrasound contrast agents

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    Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability

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    This Special Issue, "Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability", covers various subjects related to advanced temperature reduction technologies in bituminous materials. It can help civil engineers and material scientists better identify underlying views for sustainable pavement constructions

    A Practical Introduction to Regression Discontinuity Designs: Extensions

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    This monograph, together with its accompanying first part Cattaneo, Idrobo and Titiunik (2020), collects and expands the instructional materials we prepared for more than 4040 short courses and workshops on Regression Discontinuity (RD) methodology that we taught between 2014 and 2022. In this second monograph, we discuss several topics in RD methodology that build on and extend the analysis of RD designs introduced in Cattaneo, Idrobo and Titiunik (2020). Our first goal is to present an alternative RD conceptual framework based on local randomization ideas. This methodological approach can be useful in RD designs with discretely-valued scores, and can also be used more broadly as a complement to the continuity-based approach in other settings. Then, employing both continuity-based and local randomization approaches, we extend the canonical Sharp RD design in multiple directions: fuzzy RD designs, RD designs with discrete scores, and multi-dimensional RD designs. The goal of our two-part monograph is purposely practical and hence we focus on the empirical analysis of RD designs

    Spatial Distribution of Meso- and Microplastics in Floodplain Soilscapes: Novel Insights from Rural to Urban Floodplains in Central Germany

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    Plastics and especially microplastics have become an emerging threat to global ecosystems. Despite the manifold benefits and applications of the human-made material plastic, the uncontrolled release of plastics into the environment has led to a “global plastic crisis”. During the last decades it becomes apparent that this crisis leads to the presence of plastics within different environments including marine, aquatic and terrestrial systems under worldwide evidence. Furthermore, environmental plastic research was able to reveal that although plastic often ends up in oceans, the majority of plastics in the environment are transported as part of a “global plastic cycle” from the land to sea via river systems. Those river systems are not isolated in the landscape, but rather a part of an “aquatic-terrestrial interface” which also encompasses floodplains and their soilscapes. The present thesis focuses on the spatial distribution and spatio-temporal accumulation of meso- and microplastics in floodplain soilscapes following the overall objective to unravel the role of floodplain soilscapes as depositional areas of plastics within the global plastic cycle. In this context, a number of individual contributions have been published, reaching from conceptual spatial research approaches, over case studies conducted within two different floodplain soilscapes, to further opinions on the scientific benefit of plastic residues in floodplain soils. The individual contributions are linked by the major hypothesis that floodplain soilscapes act as temporal accumulation sites for plastics, driven by flood-related processes and land use over the last 70 years. To proof this major hypothesis and to overcome the lack of spatial reference in microplastics research, a geospatial sampling approach was conducted. Initial spatial data on meso- and microplastics in floodplain soils were obtained by a holistic analysis approach including the analysis of basic soil feature and metal analysis, the quantification of meso- and microplastics as well as sediment dating. Within both studied river floodplains geospatial sampling enables a detection of meso- and microplastics over the entire floodplain area and within the entire soil column reaching depths of two meters. Additionally, a frequent accumulation of plastics was found within the upper 50 cm of floodplain soils. In combination with dating of near-channel floodplain sites, it could be demonstrated that those plastic accumulations are related to recent sedimentary deposits since the 1960s. However, evidence of plastic from deeper soil layers suggests that vertical displacements in floodplain soils occur and that plastics become mobilized. Furthermore, the presence of plastics in upstream areas suggests that plastics are released to river systems and deposited via flood dynamics already in rural areas. Additionally it appears that anthropogenic impacts, such as tillage or floodplain restoration influence plastic distributions. The findings of this thesis clarify that floodplain soilscapes are part of the global plastic cycle as temporally depositional areas of plastics, but raising further questions on the mobility of plastics in soils and about the exact contribution of different environmental drivers towards plastic deposition. Finally, the present thesis indicates that the spatial reference of environmental plastic research should be rethought, in order to understand the spatial dynamics of plastics within the aquatic-terrestrial interface

    On the path integration system of insects: there and back again

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    Navigation is an essential capability of animate organisms and robots. Among animate organisms of particular interest are insects because they are capable of a variety of navigation competencies solving challenging problems with limited resources, thereby providing inspiration for robot navigation. Ants, bees and other insects are able to return to their nest using a navigation strategy known as path integration. During path integration, the animal maintains a running estimate of the distance and direction to its nest as it travels. This estimate, known as the `home vector', enables the animal to return to its nest. Path integration was the technique used by sea navigators to cross the open seas in the past. To perform path integration, both sailors and insects need access to two pieces of information, their direction and their speed of motion over time. Neurons encoding the heading and speed have been found to converge on a highly conserved region of the insect brain, the central complex. It is, therefore, believed that the central complex is key to the computations pertaining to path integration. However, several questions remain about the exact structure of the neuronal circuit that tracks the animal's heading, how it differs between insect species, and how the speed and direction are integrated into a home vector and maintained in memory. In this thesis, I have combined behavioural, anatomical, and physiological data with computational modelling and agent simulations to tackle these questions. Analysis of the internal compass circuit of two insect species with highly divergent ecologies, the fruit fly Drosophila melanogaster and the desert locust Schistocerca gregaria, revealed that despite 400 million years of evolutionary divergence, both species share a fundamentally common internal compass circuit that keeps track of the animal's heading. However, subtle differences in the neuronal morphologies result in distinct circuit dynamics adapted to the ecology of each species, thereby providing insights into how neural circuits evolved to accommodate species-specific behaviours. The fast-moving insects need to update their home vector memory continuously as they move, yet they can remember it for several hours. This conjunction of fast updating and long persistence of the home vector does not directly map to current short, mid, and long-term memory accounts. An extensive literature review revealed a lack of available memory models that could support the home vector memory requirements. A comparison of existing behavioural data with the homing behaviour of simulated robot agents illustrated that the prevalent hypothesis, which posits that the neural substrate of the path integration memory is a bump attractor network, is contradicted by behavioural evidence. An investigation of the type of memory utilised during path integration revealed that cold-induced anaesthesia disrupts the ability of ants to return to their nest, but it does not eliminate their ability to move in the correct homing direction. Using computational modelling and simulated agents, I argue that the best explanation for this phenomenon is not two separate memories differently affected by temperature but a shared memory that encodes both the direction and distance. The results presented in this thesis shed some more light on the labyrinth that researchers of animal navigation have been exploring in their attempts to unravel a few more rounds of Ariadne's thread back to its origin. The findings provide valuable insights into the path integration system of insects and inspiration for future memory research, advancing path integration techniques in robotics, and developing novel neuromorphic solutions to computational problems

    Contactless excitation for electric machines: high temperature superconducting flux pumps

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    With the intensification of global warming and climate change, the pace of transformation to a neutral-emission society is accelerating. In various sectors, electrification has become the absolute tendency to promote such a movement, where electric machines play an important role in the current power generation system. It is widely convinced that electric machines with very high power density are essential for future applications, which, however, can be hardly achieved by conventional technologies. Owing to the maturation of the second generation (2G) high temperature superconducting (HTS) technologies, it has been recognized that superconducting machine could be a competitive candidate to realize the vision. One significant obstacle that hinders the implementation of superconducting machines is how to provide the required magnetic fields, or in other words, how to energise them appropriately. Conventional direct injection is not suitable for HTS machines, because the current leads would bridge ambident temperature to the cryogenic environment, which can impose considerable heat load on the system and increase the operational cost. Thus, an efficient energisation method is demanded by HTS machines. As an emerging technology that can accumulate substantial flux in a closed loop without any physical contact, HTS flux pumps have been proposed as a promising solution. Among the existing developed HTS flux pumps, rotary HTS flux pumps, or so-called HTS dynamo, can output non-zero time-averaged DC voltage and charge the rest of the circuit if a closed loop has been formed. This type of flux pump is often employed together with HTS coils, where the HTS coils can potentially work in the persistent current mode, and act like electromagnets with a considerable magnetic field, having a wide range of applications in industry. The output characteristics of rotary HTS flux pumps have been extensively explored through experiments and finite element method (FEM) simulations, yet the work on constructing statistical models as an alternative approach to capture key characteristics has not been studied. In this thesis, a 2D FEM program has been developed to model the operation of rotary HTS flux pumps and evaluate the effects of different factors on the output voltage through parameter sweeping and analysis of variance. Typical design considerations, including the operating frequency, air gap, HTS tape width, and remanent flux density have been investigated, in particular, the bilateral effect of HTS tape width has been discovered and explained by looking at the averaged integration of the electric field over the HTS tape. Based on the data obtained from various simulations, regression analysis has been conducted through a collection of machine learning methods. It has been demonstrated that the output voltage of a rotary HTS flux pump can be obtained promptly with satisfactory accuracy via Gaussian process regression, aiming to provide a novel approach for future research and a powerful design tool for industrial applications using rotary HTS flux pumps. To enhance the applicability of the proposed statistical models, an updated FEM program has been built to take more parameters into account. The newly added parameters, namely the rotor radius and the width of permanent magnet, together with formerly included ones, should have covered all the key design parameters for a rotary HTS flux pump. Based on data collected from the FEM model, a well-trained semi-deep neural network (DNN) model with a back-propagation algorithm has been put forward and validated. The proposed DNN model is capable of quantifying the output voltage of a rotary HTS flux pump instantly with an overall accuracy of 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behaviour of rotary HTS flux pumps by integrating all design parameters, and the output characteristics of rotary HTS flux pumps have been successfully demonstrated and visualized using this model. Compared to conventional time-consuming FEM-based numerical models, the proposed DNN model has the advantages of fast learning, accurate computation, as well as strong programmability. Therefore, the DNN model can greatly facilitate the design and optimization process for rotary HTS flux pumps. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of rotary HTS flux pumps. A new variant inspired by the working principles of rotary HTS flux pumps has been proposed and termed as stationary wave HTS flux pumps. The superiority of this type is that it has a simple structure without any moving components, and it utilises a controllable current-driven electromagnet to provide the required magnetic field. It has been demonstrated that the origin of the output voltage is determined by the asymmetric distribution of the dynamic resistance in the HTS tape, for which the electromagnet must be placed at such a position that its central line is not aligned with that of the HTS tape. A numerical model has been built to simulate the operation of a stationary wave HTS flux pump, based on which the output characteristics and dynamic resistance against various parameters have been investigated. Besides, accurate and reliable statistical models have been proposed to predict the open circuit voltage and effective dynamic resistance by adapting the previously developed machine learning techniques. The work presented in this PhD thesis can bring more insight into HTS flux pumps as an emerging promising contactless energisation technology, and the proposed statistical models can be particularly useful for the design and optimization of such devices

    Integrated Optical Fiber Sensor for Simultaneous Monitoring of Temperature, Vibration, and Strain in High Temperature Environment

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    Important high-temperature parts of an aero-engine, especially the power-related fuel system and rotor system, are directly related to the reliability and service life of the engine. The working environment of these parts is extremely harsh, usually overloaded with high temperature, vibration and strain which are the main factors leading to their failure. Therefore, the simultaneous measurement of high temperature, vibration, and strain is essential to monitor and ensure the safe operation of an aero-engine. In my thesis work, I have focused on the research and development of two new sensors for fuel and rotor systems of an aero-engine that need to withstand the same high temperature condition, typically at 900 °C or above, but with different requirements for vibration and strain measurement. Firstly, to meet the demand for high temperature operation, high vibration sensitivity, and high strain resolution in fuel systems, an integrated sensor based on two fiber Bragg gratings in series (Bi-FBG sensor) to simultaneously measure temperature, strain, and vibration is proposed and demonstrated. In this sensor, an L-shaped cantilever is introduced to improve the vibration sensitivity. By converting its free end displacement into a stress effect on the FBG, the sensitivity of the L-shaped cantilever is improved by about 400% compared with that of straight cantilevers. To compensate for the strain sensitivity of FBGs, a spring-beam strain sensitization structure is designed and the sensitivity is increased to 5.44 pm/ΌΔ by concentrating strain deformation. A novel decoupling method ‘Steps Decoupling and Temperature Compensation (SDTC)’ is proposed to address the interference between temperature, vibration, and strain. A model of sensing characteristics and interference of different parameters is established to achieve accurate signal decoupling. Experimental tests have been performed and demonstrated the good performance of the sensor. Secondly, a sensor based on cascaded three fiber Fabry-PĂ©rot interferometers in series (Tri-FFPI sensor) for multiparameter measurement is designed and demonstrated for engine rotor systems that require higher vibration frequencies and greater strain measurement requirements. In this sensor, the cascaded-FFPI structure is introduced to ensure high temperature and large strain simultaneous measurement. An FFPI with a cantilever for high vibration frequency measurement is designed with a miniaturized size and its geometric parameters optimization model is established to investigate the influencing factors of sensing characteristics. A cascaded-FFPI preparation method with chemical etching and offset fusion is proposed to maintain the flatness and high reflectivity of FFPIs’ surface, which contributes to the improvement of measurement accuracy. A new high-precision cavity length demodulation method is developed based on vector matching and clustering-competition particle swarm optimization (CCPSO) to improve the demodulation accuracy of cascaded-FFPI cavity lengths. By investigating the correlation relationship between the cascaded-FFPI spectral and multidimensional space, the cavity length demodulation is transformed into a search for the highest correlation value in space, solving the problem that the cavity length demodulation accuracy is limited by the resolution of spectral wavelengths. Different clustering and competition characteristics are designed in CCPSO to reduce the demodulation error by 87.2% compared with the commonly used particle swarm optimization method. Good performance and multiparameter decoupling have been successfully demonstrated in experimental tests

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Current issues of the management of socio-economic systems in terms of globalization challenges

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    The authors of the scientific monograph have come to the conclusion that the management of socio-economic systems in the terms of global challenges requires the use of mechanisms to ensure security, optimise the use of resource potential, increase competitiveness, and provide state support to economic entities. Basic research focuses on assessment of economic entities in the terms of global challenges, analysis of the financial system, migration flows, logistics and product exports, territorial development. The research results have been implemented in the different decision-making models in the context of global challenges, strategic planning, financial and food security, education management, information technology and innovation. The results of the study can be used in the developing of directions, programmes and strategies for sustainable development of economic entities and regions, increasing the competitiveness of products and services, decision-making at the level of ministries and agencies that regulate the processes of managing socio-economic systems. The results can also be used by students and young scientists in the educational process and conducting scientific research on the management of socio-economic systems in the terms of global challenges
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