Aeronautical engineering: A continuing bibliography, supplement 122

This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

Turbocharger Structural Integrity

Since the introduction of Euro VI in January 2014, all new diesel powered commercial vehicles have been equipped with turbocharged engines. It is virtually impossible to meet these emission regulations without using a turbocharger. Similarly, in the passenger car sector both on diesel and petrol (gasoline) powered vehicles, legislative pressure to reduce emissions of carbon dioxide are seeing the introduction of turbochargers across almost all new power units. Future legislation will continue this trend with engine manufacturers becoming increasingly reliant on turbocharging. As well as increasing the requirement for turbochargers, these external factors are also demanding that turbochargers become more responsive with reduced rotor inertia and lower thermal inertias. This in turn makes the task of ensuring that turbocharger components remain fit for purpose for the life of the turbocharger that much more difficult. In this paper some of the recent developments in turbocharger technology will be identified and the demands that these place on the structural components will be explored. The limitations of current methods of structural integrity assessment for some of these components will be discussed. Future developments of these methods will then be proposed

Aeronautical Engineering: A continuing bibliography with indexes, supplement 99

This bibliography lists 292 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Development of an electronic control unit for the T63 gas turbine

Includes bibliographical references.Fundamental research has been undertaken at the SASOL Advanced Fuels Laboratory to investigate the effects of the chemistry and physical properties of both conventional and synthetic jet fuels on threshold combustion. This research was undertaken using a purpose built low pressure continuous combustion test facility. Researchers at the laboratory now wish to examine these effects on an aviation gas turbine in service for which “off-map” scheduling of fuel to the engine would be required. A two phase project was thus proposed to develop this capability; the work of this thesis embodies Phase I of that project

Modelling the causation of accidents: human performance separated system and human performance included system

Jedes Jahr ereignen sich weltweit Millionen von Arbeitsunfällen, die zahlreiche Opfer fordern und enorme wirtschaftliche Verluste zur Folge haben. Vorangegangene Studien aus dem Feld der Risikoeinschätzung zeigten, dass es wichtig ist die Wahrscheinlichkeit von Faktoren, welche zum Auftreten von Unfällen beitragen, zu quantifizieren. Mehrere Methoden, wie z. B. die Technik zur Vorhersage der menschlichen Fehlerrate (Technique for Human Error Rate Prediction, THERP), wurden dafür vorgeschlagen, potenzielle Risikofaktoren zu bewerten und die Systemsicherheit zu verbessern. Diese Methoden haben jedoch einige Einschränkungen, wie z.B. ihre geringe Generalisierbarkeit, die Behandlung von Unfallursachen und menschlichem Einfluss als zwei voneinander getrennte Forschungsthemen, die Notwendigkeit ausgiebiger Datensätze, oder die ausschließliche Abhängigkeit von Expertenwissen. Um diese Einschränkungen zu überwinden, 1) klassifiziert diese Dissertation die Systeme in zwei Kategorien. Zum einen in von menschlichem Einfluss separierte Systeme (Human Performance Separated System, HPSS) und zum anderen in Systeme mit menschlichem Einfluss (Human Performance Included System, HPIS); 2) entwickelt ein auf Bayes‘schen Netzwerken (BN) basierendes Unfallkausalitätsmodell, das auf beide Arten von Systemen angewendet werden kann, um den Einfluss menschlicher Wahrnehmung in HPSS und den Einfluss menschlichen Versagens in HPIS zu untersuchen; 3) untersucht zwei Methoden zur Analyse menschlichen Versagens. Die erste Methode geht von einer kognitiven Wahrnehmung aus und die zweite behandelt das menschliche Versagen als essenziellen Teil des Systems. 4) schlägt eine innovative Taxonomie namens Contributors Taxonomy for construction Occupational Accidents (CTCOA) für HPIS vor, die nicht nur auf die Unfallkausalität abzielt, sondern auch zur Rückverfolgung menschlichen Versagens im Bauwesen verwendet werden kann. 5) erstellt BN-Beispielmodelle aus unterschiedlichen Industriesektoren. Dazu zählen Gasturbinenausfälle als typisches Beispiel für HPSS-Maschinenversagen, das Multi-Attribute Technological Accidents Dataset (MATA-D) für einfaches HPIS-Systemversagen und das Contributors to Construction Occupational Accidents Dataset (CCOAD) für komplexes HPIS-Systemversagen. Diese drei BN-Modelle zeigen, wie die von uns vorgeschlagene Methode in Bezug auf spezifische Probleme aus verschiedenen Industriesektoren angepasst und angewendet werden kann. Unsere Analyse zeigt die Effizienz der Kombination von Expertenwissen und mathematischer Unabhängigkeitsanalyse bei der Identifizierung der wichtigsten Abhängigkeitsbeziehungen innerhalb der BN-Struktur. Vor der Parameteridentifizierung auf Basis von Expertenwissen sollten die Auswirkungen der menschlichen Wahrnehmung auf die Modellparameter gemessen werden. Die vorgeschlagene Methodik basierend auf der Kombination der menschlichen Zuverlässigkeitsanalyse mit statistischen Analysen kann zur Untersuchung menschlichen Versagens eingesetzt werden.Millions of work-related accidents occur each year around the world, leading to a large number of deaths, injuries, and a huge economic cost. Previous studies on risk assessment have revealed that it is important to calculate the probabilities of factors that can contribute to the occurrence of accidents. Several methods, such as the Technique for Human Error Rate Prediction (THERP), have been proposed to evaluate potential risk factors and to improve system safety. However, these methods have some limitations, such as their low generalizability, treating accident causation and human factor as two separate research topics, requiring intensive data, or relying solely on expert judgement. To address these limitations, this dissertation 1) classifies systems into two types, Human Performance Separated System (HPSS) and Human Performance Included System (HPIS), depending on whether the system involves human performance; 2) develops accident causal models based on Bayesian Network (BN) that can be applied to both types of systems while examining the influence of human perception in HPSS and human errors in HPIS; 3) examines two methods for the analysis of human errors with the first method based on the cognitive view and the other method treating human errors as an essential part of the system; 4) proposes an innovative taxonomy as an example for HPIS, known as the Contributors Taxonomy for Construction Occupational Accidents (CTCOA), which not only targeting accident causation, but can also be used for tracking human error in construction; 5) builds example BN models in the different industrial sectors, including gas turbine failures as a typical example of HPSS machine failures, Multi-Attribute Technological Accidents Dataset (MATA-D) as simple HPIS failures, and Contributors to Construction Occupational Accidents Dataset (CCOAD) as complex HPIS failures. These three types of BN models demonstrate how our proposed methodology can be adapted to specific questions and how it can be applied in various industrial sectors. Our analysis demonstrates that it is efficient to combine expert judgement with mathematical independence analysis to identify the main dependency links for the BN structure in all models. The influence of human perception on model parameters should be measured before these parameters being identified based on expert judgement. Our proposed methodology can be used to study human errors by combining traditional human reliability analysis with statistical analysis