Optimising non-destructive examination of newbuilding ship hull structures by developing a data-centric risk and reliability framework based on fracture mechanics

This thesis was previously held under moratorium from 18/11/19 to 18/11/21Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure.Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure

Optimization and Management of Large-scale Scientific Workflows in Heterogeneous Network Environments: From Theory to Practice

Next-generation computation-intensive scientific applications feature large-scale computing workflows of various structures, which can be modeled as simple as linear pipelines or as complex as Directed Acyclic Graphs (DAGs). Supporting such computing workflows and optimizing their end-to-end network performance are crucial to the success of scientific collaborations that require fast system response, smooth data flow, and reliable distributed operation.We construct analytical cost models and formulate a class of workflow mapping problems with different mapping objectives and network constraints. The difficulty of these mapping problems essentially arises from the topological matching nature in the spatial domain, which is further compounded by the resource sharing complicacy in the temporal dimension. We provide detailed computational complexity analysis and design optimal or heuristic algorithms with rigorous correctness proof or performance analysis. We decentralize the proposed mapping algorithms and also investigate these optimization problems in unreliable network environments for fault tolerance.To examine and evaluate the performance of the workflow mapping algorithms before actual deployment and implementation, we implement a simulation program that simulates the execution dynamics of distributed computing workflows. We also develop a scientific workflow automation and management platform based on an existing workflow engine for experimentations in real environments. The performance superiority of the proposed mapping solutions are illustrated by extensive simulation-based comparisons with existing algorithms and further verified by large-scale experiments on real-life scientific workflow applications through effective system implementation and deployment in real networks

Error Signals from the Brain: 7th Mismatch Negativity Conference

The 7th Mismatch Negativity Conference presents the state of the art in methods, theory, and application (basic and clinical research) of the MMN (and related error signals of the brain). Moreover, there will be two pre-conference workshops: one on the design of MMN studies and the analysis and interpretation of MMN data, and one on the visual MMN (with 20 presentations). There will be more than 40 presentations on hot topics of MMN grouped into thirteen symposia, and about 130 poster presentations. Keynote lectures by Kimmo Alho, Angela D. Friederici, and Israel Nelken will round off the program by covering topics related to and beyond MMN

Soil characteristics and soil erosion by water in a semi-arid catchment (Wadi Drâa, South Morocco) under the pressure of global change

Soil resources are crucial for the well-being of man and the environment. The results of the first Global Assessment of Human-induced Soil Degradation (GLASOD) indicate that 13% of the world’s soils are degraded; thereof 55% suffer from soil erosion by water. Drylands are especially vulnerable due to the sparse protecting vegetation cover, soils that feature a low organic matter content and rare but intense rainfall events. Soil erosion in drylands is likely to intensify as a result of climate change and human activities, such as forest clearing or overstocking. This study aims at understanding and describing the spatial distribution of soil characteristics as well as the current extent and distribution of soil erosion by water. Based on these findings, the impact of global change on soil erosion risk is assessed. Soil characteristics in the semi-arid upper and middle Drâa catchment (30 000 km2, South Morocco) are examined by investigating soil profiles that are arranged along toposequences that cover the main geological units. Soil properties are regionalised based on their relationship to environmental factors by using multiple linear regression including dummy variables. The physically-based, distributed soil erosion model, PESERA (Pan European Soil Erosion Risk Assessment), is used to assess the current and future soil erosion risk for five periods between 1980 and 2050. Climate change scenarios that are simulated with the regional climate model REMO are applied together with the scenarios of socio-economic change, which have been defined in the IMPETUS project. Typical semi-arid soil properties are found: high skeleton content, high CaCO3 content, high pH values, low organic matter content and partially strong salinity. The most common soil types are Calcisols, Regosols and Leptosols. Between 22 and 89% of the variance of the soil characteristics can be explained depending on the parameter. The resulting maps reflect the identified relationships to the environmental factors well and provide a reasonable view of the distribution of soil properties in the Drâa catchment. A mean erosion rate of 19.2 t/ha/a is simulated under the current conditions. Erosion hotspots are identified in the high mountain zones, more precisely in the western (Tizi-n-Tichka), central (Skoura Mole) and eastern (M'Goun chain) part of the Central High Atlas. Rainfall reduction and higher temperatures that are expected following the climate change scenarios lead to a decrease in vegetation cover. Together with more intense precipitation events, this will cause an increase in soil erosion by up to 31%. Due to further marginalisation, people are forced to satisfy their energy demand by enhanced extraction of firewood that further degrades vegetation cover. This results in an increase in the erosion rate of 27%. In contrast, rural development brings about a loss of the nomadic lifestyle and, consequently, a reduction in the animal numbers and grazing pressure. Thus, the soil loss is reduced by 54%. Combining the impact of climate and socio-economic changes shows that human activity can aggravate (+64%) or mitigate (-25%) soil erosion risk. The “Mansour Eddahbi” reservoir that is located at the outlet of the upper catchment is endangered by upstream soil loss. Its simulated capacity in 2050 varies between 0 and 46% of the initial storage volume, depending on the scenario. The efficiency of anti-erosive measures is analysed by simulating two intervention scenarios that consider afforestation (6300 ha) and grazing exclusion (75 000 ha). Efficiency depends on the spatial scale that is under consideration. At the local scale (i.e., the intervention zone), soil loss is reduced by 36-99% up to 2050; afforestation is more efficient. At the scale of the upper Drâa catchment, i.e., the relevant scale for reservoir siltation, erosion is reduced by 1 to 13%. Pasture exclusion is more efficient due to the larger intervention zone. This work presents a comprehensive study on the risk of soil erosion by water in the Drâa catchment and can serve as a scientific basis for local decision making processes.Bodeneigenschaften und Bodenerosion durch Wasser in einem semi-ariden Einzugsgebiet (Wadi Draa, Süd-Marokko) unter dem Einfluss des globalen Wandels Die Ressource Boden ist von immenser Bedeutung für Mensch und Umwelt. Die erste globale Abschätzung der durch den Menschen verursachten Bodendegradierung (Global Assessment of Human-induced Soil Degradation GLASOD) ergab dass 13% der weltweiten Böden degradiert sind, davon 55% durch Bodenerosion durch Wasser. Trockengebiete sind aufgrund der geringen Vegetationsbedeckung, des geringen Gehalts an organischer Substanz im Boden und den seltenen aber intensiven Niederschlagsereignissen besonders betroffen. Eine weitere Intensivierung der Bodenerosion in Trockengebieten aufgrund von Klimawandel und menschlicher Beeinflussung, z.B. durch Abholzung oder Überweidung, ist wahrscheinlich. Ziel der vorliegenden Arbeit ist die Analyse der räumlichen Verteilung von Bodeneigenschaften sowie des aktuellen Ausmaßes und der Verbreitung von Bodenerosion. Basierend auf diesen Erkenntnissen wird der Einfluss des globalen Wandels auf die Bodenerosion simuliert. Die Bodeneigenschaften im semi-ariden oberen und mittleren Drâa-Einzugsgebiet (30 000 km2, Süd-Marokko) werden mit Hilfe von Bodenprofilen entlang von Toposequenzen in allen relevanten geologischen Einheiten untersucht. Die Regionalisierung der Eigenschaften erfolgt aufgrund ihrer Abhängigkeit von Umweltfaktoren durch multiple lineare Regression mit Dummy Variablen. Das physikalisch basierte, räumlich explizite Erosionsmodell PESERA (Pan European Soil Erosion Risk Assessment) wird verwendet, um das aktuelle und zukünftige Bodenerosionsrisiko zu simulieren. Dabei werden fünf Perioden zwischen 1980 und 2050 betrachtet. Die in dieser Studie verwendeten Klimaszenarien wurden mit dem regionalen Klimamodell REMO simuliert. In Kombination mit den Klimaszenarien werden sozio-ökonomische Szenarien, die im Rahmen des IMPETUS-Projektes entwickelt wurden, simuliert. Die identifizierten Bodeneigenschaften sind typisch für semi-aride Gebiete: hoher Skelett- und CaCO3-gehalt, hoher pH-Wert, wenig organische Substanz und teilweise hohe Versalzung. Die häufigsten Bodentypen sind Calcisols, Regosols und Leptosols. Zwischen 22 und 89% der Varianz der Bodeneigenschaften wird erklärt. Die identifizierten Beziehungen zwischen Boden und Umweltfaktoren werden in den Karten gut wiedergegeben. Die Verteilung der Bodeneigenschaften im Drâa-Einzugsgebiet ist sinnvoll und nachvollziehbar. Die simulierte mittlere Erosionsrate unter aktuellen Klima- und Landnutzungsbedingungen beträgt 19,2 t/ha/Jahr. Erosionsschwerpunkte wurden vor allem in den Hochgebirgs-regionen identifiziert, genauer im westlichen (Tizi-n-Tichka), zentralen (Skoura Becken) und östlichen (M'Goun Kette) Teil des Zentralen Hohen Atlas. Die in den Klimaszenarien simulierten geringeren Niederschläge und höheren Temperaturen führen zur Reduktion der Vegetationsbedeckung. In Kombination mit intensiveren Niederschlagsereignissen hat dies einen Anstieg der Erosion um bis zu 31% zur Folge. Marginalisierung zwingt die lokale Bevölkerung ihren Energiebedarf durch Feuerholz zu decken, diese zusätzliche Vegetationsdegradierung bewirkt eine Steigerung der Erosionsrate um 27%. Im Gegensatz dazu geht die Entwicklung des ländlichen Raums mit einem Bedeutungsverlust der nomadischen Lebensweise einher, als Folge davon nehmen Tierzahlen und Beweidungsintensität ab. Der Bodenabtrag wird um 54% reduziert. Der kombinierte Einfluss von Klima- und sozio-ökonomischem Wandel kann sowohl eine Verschärfung (+64%) als auch eine Verringerung (-25%) der Bodenerosion bewirken. Der Stausee “Mansour Eddahbi” am Auslass des oberen Einzugsgebiets ist durch Bodenabtrag in seinem Einzugsgebiet bedroht. Seine simulierte Kapazität im Jahr 2050 schwankt zwischen 0 und 46% des anfänglichen Volumens in Abhängigkeit vom betrachteten Szenario. Die Effizienz anti-erosiver Maßnahmen wird in zwei Interventionsszenarien am Beispiel von Aufforstung (6300 ha) sowie Weideausschluss (75 000 ha) analysiert. Der Einfluss der Maßnahme hängt von der betrachteten räumlichen Skala ab. Auf der lokalen Skala, d.h. in dem von der Maßnahme betroffenen Gebiet, wird die Erosion um 36-99% reduziert, wobei Aufforstung die effizientere Maßnahme ist. Auf der Skala des oberen Einzugsgebiets, d.h. der für den Stausee relevanten Skala, wird der Bodenabtrag um 1-13% reduziert. Hier hat der Weideausschluss aufgrund der größeren betroffenen Fläche den stärkeren Einfluss. Diese Arbeit ist eine umfassende Studie zum Bodenerosionsrisiko durch Wasser im Drâa-Einzugsgebiet und kann als wissenschaftliche Grundlage für lokale Entscheidungsprozesse dienen

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

A holistic approach to remote condition monitoring for the accurate evaluation of railway infrastructure and rolling stock

The rail industry needs to address a number of important operational challenges in the foreseeable future. First of all, the safety of rail transport needs to be maintained at an absolute maximum matching the achievements of the European airline industry of zero fatalities. Secondly, promote sustainable growth to support increasing demand for both passenger and freight rail transport. Thirdly, support the implementation of measurable innovations and improvements that help increase capacity of current infrastructure through enhanced availability. Finally, maximise the environmentally benign character of railway transport through exploitation of novel technologies such as hydrogen trains and advanced electrification employing renewable energy sources. This project, primarily focused on the UK Rail infrastructure, investigated the benefits arising from a holistic approach in the application of Remote Condition Monitoring (RCM) as a critical means for the accurate, efficient, reliable and cost-effective evaluation of key railway infrastructure assets and rolling stock. This work involved the use of several techniques and innovative methodologies based primarily on Acoustic Emission (AE) and vibration analysis in order to address the evaluation requirements for different components of interest. The results obtained have been very promising and present rail infrastructure managers and rolling stock operators with new opportunities for improved and more reliable operations. This work has led to the instrumentation of multiple sites across the UK rail network enabling measurements to be carried out on various assets under actual operational conditions. At Cropredy an integrated high-frequency vibro-acoustic RCM system has successfully been installed on the Chiltern railway line on the way from London to Birmingham. This customised system has been fully operational since 2015 measuring more than 200 passenger and freight trains every day moving at speeds up to 100 miles per hour (MPH). Prior to the installation of the system at Cropredy a Certificate (PA05/06524) of Acceptance was issued by Network Rail which after being renewed recently is now valid until September 2021. The system is due for an upgrade in the following stage of development, employing wireless sensors and advanced energy harvesting devices which are being developed under a collaborative Engineering and Physical Sciences Research Council (EPSRC) project between Exeter and Birmingham Universities, Network Rail, Swiss Approval UK and Quatrro. The widespread implementation of the techniques and methodologies researched will give rise to significant potential impact with respect to the effectiveness of maintenance strategies, particularly in terms of cost efficiency, improved availability of railway assets and better planning of available resources. As modern rail transport moves towards 24-hour railway, the inspection, maintenance and track renewal and upgrade regime will need to be re-thought at a fundamental level. Effective RCM will be a key factor in realistically enabling true round the clock operations. The results presented in this thesis have been part of a six-year research effort with a clear focus on addressing the true industrial need. The findings of this work have led to a re-think within Network Rail regarding the new possibilities arising from the effective use of RCM in designing and implementing more efficient and cost-effective railway operations whilst helping reduce the cost. The use of autonomous sensing systems in the future will change the inspection and maintenance strategies currently used shifting towards a truly prognostic operational strategy

A Concept for Deployment and Evaluation of Unsupervised Domain Adaptation in Cognitive Perception Systems

Jüngste Entwicklungen im Bereich des tiefen Lernens ermöglichen Perzeptionssystemen datengetrieben Wissen über einen vordefinierten Betriebsbereich, eine sogenannte Domäne, zu gewinnen. Diese Verfahren des überwachten Lernens werden durch das Aufkommen groß angelegter annotierter Datensätze und immer leistungsfähigerer Prozessoren vorangetrieben und zeigen unübertroffene Performanz bei Perzeptionsaufgaben in einer Vielzahl von Anwendungsbereichen.Jedoch sind überwacht-trainierte neuronale Netze durch die Menge an verfügbaren annotierten Daten limitiert und dies wiederum findet in einem begrenzten Betriebsbereich Ausdruck. Dabei beruht überwachtes Lernen stark auf manuell durchzuführender Datenannotation. Insbesondere durch die ständig steigende Verfügbarkeit von nicht annotierten großen Datenmengen ist der Gebrauch von unüberwachter Domänenanpassung entscheidend. Verfahren zur unüberwachten Domänenanpassung sind meist nicht geeignet, um eine notwendige Inbetriebnahme des neuronalen Netzes in einer zusätzlichen Domäne zu gewährleisten. Darüber hinaus sind vorhandene Metriken häufig unzureichend für eine auf die Anwendung der domänenangepassten neuronalen Netzen ausgerichtete Validierung. Der Hauptbeitrag der vorliegenden Dissertation besteht aus neuen Konzepten zur unüberwachten Domänenanpassung. Basierend auf einer Kategorisierung von Domänenübergängen und a priori verfügbaren Wissensrepräsentationen durch ein überwacht-trainiertes neuronales Netz wird eine unüberwachte Domänenanpassung auf nicht annotierten Daten ermöglicht. Um die kontinuierliche Bereitstellung von neuronalen Netzen für die Anwendung in der Perzeption zu adressieren, wurden neuartige Verfahren speziell für die unüberwachte Erweiterung des Betriebsbereichs eines neuronalen Netzes entwickelt. Beispielhafte Anwendungsfälle des Fahrzeugsehens zeigen, wie die neuartigen Verfahren kombiniert mit neu entwickelten Metriken zur kontinuierlichen Inbetriebnahme von neuronalen Netzen auf nicht annotierten Daten beitragen. Außerdem werden die Implementierungen aller entwickelten Verfahren und Algorithmen dargestellt und öffentlich zugänglich gemacht. Insbesondere wurden die neuartigen Verfahren erfolgreich auf die unüberwachte Domänenanpassung, ausgehend von der Tag- auf die Nachtobjekterkennung im Bereich des Fahrzeugsehens angewendet

MARE-WINT: New Materials and Reliability in Offshore Wind Turbine Technology

renewable; green; energy; environment; law; polic

Energy Use Efficiency

Energy is one of the most important factors of production. Its efficient use is crucial for ensuring production and environmental quality. Unlike normal goods with supply management, energy is demand managed. Efficient energy use—or energy efficiency—aims to reduce the amount of energy required to provide products and services. Energy use efficiency can be achieved in situations such as housing, offices, industrial production, transport and agriculture as well as in public lighting and services. The use of energy can be reduced by using technology that is energy saving. This Special Issue is a collection of research on energy use efficiency

A function allocation framework for the automation of railway maintenance practices

The railway industry has seen significant innovation in intelligent maintenance systems leading to improvements in efficiency and reliability. However, ongoing challenges such as intensity of labour, hazardous environments, and operational inefficiency necessitate advancement in the deployment of Robotic and Autonomous Systems (RAS). Successful implementation of RAS in a railway context requires a comprehensive function allocation process. This thesis presents a novel function allocation framework for systematic task analysis and allocation. The framework includes comprehensive multi-stage evaluation criteria such as technical feasibility, overall system performance, and cost impact. Function allocation for each identified subtask is realised in an iterative manner to reach a final system design, and the structure and elements of the framework are supported by rigorous derivations and practical examples. The proposed framework has been successfully applied and thoroughly demonstrated through case studies based around the maintenance activities of wheelsets. The case studies demonstrate that the proposed framework is capable of providing guidance in system design at the preliminary stages of the introduction of automation into railway maintenance systems; also, can help to re-evaluate an already implemented system and thus propose guidance on whether the current allocation can be optimised