Modelling airport surface safety: a framework for a holistic airport safety management

Airports are complex systems involving the continuous interaction of human operators with the physical infrastructure, technology and procedures to ensure the safe and efficient conduct of flights. From an operational perspective, airport surface operations (i.e. runway and taxiway operations) require the interaction of five main stakeholders (i.e. crew or pilots, air traffic control, airport operator, ground handling and regulator) both to facilitate the ground movement of aircraft and vehicles, and to maintain the surface in a working condition. The complexity of these operations makes the runway and taxiway system vulnerable and presents a risk of failure with the consequent potential for the occurrence of accidents. Therefore, the development and implementation of an effective Safety Management System (SMS) are required to ensure the highest level of safety for surface operations. A SMS is a systematic approach to managing safety based on the four cornerstones of safety policy and objectives, risk management, assurance, and safety promotion. Although the International Civil Aviation Organisation (ICAO) provides the global legislative framework for SMS, the relevant regulations are still to be established at the national level with the consequence that practical guidance on the development and implementation of SMS is rare, and reliable tools to support SMS are lacking. The consequence of this is that the current approach to surface safety management is piecemeal and not integrated. Typically, a single accident and incident type is investigated from the perspective of an individual stakeholder with the consequence that resulting proposals for safety mitigation measures are biased and limited in terms of their impact. In addition, the industry is characterised by non-standardised data collection and investigation practices, insufficient or missing definitions, differing reporting levels, and a lack of a coherent and standardised structure for efficient coding and analysis of safety data. Since these shortcomings are a major barrier to the required holistic and integrated approach to safety management, this thesis addresses the four cornerstones of SMS and recommends major enhancements. In particular, a framework for a holistic airport surface safety management is proposed. The framework comprises the static airport architecture, a process model of surface operations, the determination of causal factors underlying failure modes of these operations, a macroscopic scenario tool and a functional relationship model. Safety data and other data sources feed the framework and a dedicated data pre-processing strategy ensures its validity. Unlike current airport surface safety management practices, the proposed framework assesses the safety of the operations of all relevant actors. Firstly, the airport architecture is modelled and the physical and functional variability of airports defined. Secondly, a process model of surface operations is developed, which captures the tasks of the stakeholders and their interactions with physical airport surface infrastructure. This model serves as a baseline model and guides the further development of the airport SMS. To manage the safety of surface operations, the causes of accidents and incidents must be identified and their impacts understood. To do so, a reference data set combining twelve databases from airlines, airport operators, Air Navigation Service Providers (ANSPs), ground handling companies and regulators is collected. Prior to its analysis, the data is assessed for its quality, and in particular, for its internal validity (i.e. precision), external validity (i.e. accuracy) and in terms of reporting levels. A novel external data validation framework is developed and each database is rated with a data quality index (DQI). In addition, recommendations for reporting systems and safety policies are given. Subsequently, the data is analysed for causal factors across stakeholders and the contribution of the individual actors are highlighted. For example, the analysis shows that the various stakeholders capture different occurrence types and underlying causal factors, often including information that is of potential use for another party. The analysis is complemented by interviews, observations and statistical analysis, and the results are summarised in a new taxonomy. This taxonomy is applicable to all relevant stakeholders and is recommended for operational safety risk management. After the airport surface operations have been modelled and the drivers to safety identified, the results are combined, resulting in a macroscopic scenario tool which supports the management of change (i.e. safety assurance), training and education, and safety communication (i.e. safety promotion) functions of the SMS. Finally, a structured framework to assess the functional relationship between airport surface accidents / incidents and their underlying causal factors is proposed and the system is quantified in terms of safety. Compared to the state-of-the-art safety assessments that are biased and limited in terms of their impact, the holistic approach to surface safety allows modelling the safety impact of each system component, their interactions and the entire airport surface system architecture. The framework for a holistic airport surface safety management developed in this thesis delivers a SMS standard for airports. The standard exceeds international requirements by standardizing the two SMS core functions (safety risk management and safety assurance) and integrating safety-relevant information across all relevant stakeholders. This allows a more effective use of safety information and provides an improved overview on, and prediction of, safety risks and ultimately improves the safety level of airports and their stakeholders. Furthermore, the methodology employed in this thesis is flexible and could be applied to all aspects of aviation SMS and system analysis.Open Acces

Validation of an assessment tool for mental fatigue applied to rotational shift work

Mental fatigue has been proven to be highly prominent during shift work, due to long, irregular working hours and disruption of the circadian rhythm. Measuring mental fatigue has been a challenge for many years, where commonly cognitive test tasks are used to assess mental fatigue. Moreover, these test tasks do not isolate where fatigue is occurring during human information processing. The human information processing system consists of four core stages, each of which requires numerous cognitive functions in order to process information. The Human Kinetics and Ergonomics Department at Rhodes University has developed six cognitive test tasks where each isolates a cognitive function: an accommodation test task, a visual detection test task, a reading test task, a memory test task, a tapping test task and a neural control test task. The cognitive functions include: eye accommodation, visual discrimination, visual pattern recognition, memory duration, motor programming and peripheral neural control. General task-related effect can also be examined for each of these cognitive test tasks which include choice reaction time, visual detection, reading performance, short-term memory, motor control and tracking performance. Additionally, a simple reaction time test task has been developed to analyse simple reaction time. This test task does not isolate a cognitive function. One or more parameters can be examined for each cognitive function and task-related effect. The first aim of this study was to validate numerous cognitive test tasks for mental fatigue in a simulated shift work laboratory setting. The second aim was to assess the validated cognitive test tasks in Phase 1 in a field-based rotational shift work setting. Parameters revealing sensitivity to mental fatigue would be validated for mental fatigue applied to rotational shift work and would be inserted into an assessment tool. In the laboratory setting, the seven cognitive test tasks were examined on four different types of shift work regimes. The first regime was a standard eight-hour shift work system, and the other three were non-conventional shift work regimes. Participants (n = 12 per regime) were required to complete one day shift followed by four night shifts, where testing occurred before and after each shift and four times within each shift. The cognitive test tasks revealing sensitivity to fatigue included: visual detection test task, reading test task, memory test task, tapping test task, neural control test task and simple reaction time test task. The testing of Phase 2 was conducted in three different companies, where each performed a different type of rotational shift work. The six cognitive test tasks validated for mental fatigue in Phase 1 were tested before and after work for each shift type within the rotational shift work system adopted by each company. Company A (n = 18) and Company B (n = 24) performed two-shift rotational shift work systems, where the shift length of Company A was 12-hours and the shift length of Company B was irregular hours. Company C (n = 21) performed an eight-hour three-shift rotational shift work system. Nine parameters revealed fatiguing effects and were inserted into the assessment tool, five of which provided information on a specific cognitive function: error rate for visual discrimination, processing time for visual pattern recognition, error rate for visual pattern recognition, impact of rehearsal time on memory recall rate for memory duration and the high-precision condition for motor programming time. The remaining four parameters provided information on general task-related effects: reading speed for reading performance, recall rate for short-term memory, reaction time for motor control and simple reaction time. Therefore, an assessment tool comprising nine parameters was validated for mental fatigue applied to rotational shift work, where five of the parameters were able to isolate exactly where fatigue was occurring during human information processing and the other four parameters were able to assess fatigue occurring throughout the human information processing chain

The characteristics and perception of small wind system noise

The UK has committed to sourcing 15% of its energy from renewable sources by 2020 and wind turbines have the potential to contribute towards this target. Due to the Feed-In-Tariffs introduced by the UK Government in 2010, the potential uptake of micro-generation methods such as small wind is likely to increase. However, many barriers exist which prevent widespread implementation, such as noise concerns. There is little work available in the open literature quantifying the problem because much of the existing research focuses on large scale turbines. The need for an increase in interdisciplinary research in this area has also been called for. This research fills the gap in the literature by seeking to better understand the noise levels generated by small wind systems, the characteristics of the noise and people’s reactions to this noise. The research is interdisciplinary, incorporating engineering, to measure, characterise and model the noise from small wind systems and psychology, to identify the type of people who are most likely to perceive the noise. Environmental noise measurements have been taken at small wind system installations to quantify and characterise the noise levels. This work included an assessment of the attenuation of the noise. Studies have been carried out on individuals living close to small wind system installations, as well as individuals being played recordings of wind turbine noise to investigate the level and type of noise they perceive and to link this to an individual’s attitude towards wind turbines, personality traits and symptom reporting. CFD has been used to model the flow fields around 2D blade sections to identify the likely noise mechanisms associated with small wind systems by observing the turbulent regions near the aerofoil wall. Finally, a comparison of the three methods has been carried out to identify that the overall level of small wind system noise is low but it is the nature of the sounds that increase the likely perception of the noise

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

The characteristics and perception of small wind system noise

The UK has committed to sourcing 15% of its energy from renewable sources by 2020 and wind turbines have the potential to contribute towards this target. Due to the Feed-In-Tariffs introduced by the UK Government in 2010, the potential uptake of micro-generation methods such as small wind is likely to increase. However, many barriers exist which prevent widespread implementation, such as noise concerns. There is little work available in the open literature quantifying the problem because much of the existing research focuses on large scale turbines. The need for an increase in interdisciplinary research in this area has also been called for. This research fills the gap in the literature by seeking to better understand the noise levels generated by small wind systems, the characteristics of the noise and people’s reactions to this noise. The research is interdisciplinary, incorporating engineering, to measure, characterise and model the noise from small wind systems and psychology, to identify the type of people who are most likely to perceive the noise. Environmental noise measurements have been taken at small wind system installations to quantify and characterise the noise levels. This work included an assessment of the attenuation of the noise. Studies have been carried out on individuals living close to small wind system installations, as well as individuals being played recordings of wind turbine noise to investigate the level and type of noise they perceive and to link this to an individual’s attitude towards wind turbines, personality traits and symptom reporting. CFD has been used to model the flow fields around 2D blade sections to identify the likely noise mechanisms associated with small wind systems by observing the turbulent regions near the aerofoil wall. Finally, a comparison of the three methods has been carried out to identify that the overall level of small wind system noise is low but it is the nature of the sounds that increase the likely perception of the noise

Development of a synthetic solar irradiance generator that produces time series with high temporal and spatial resolutions using readily available mean hourly observations

Photovoltaics (PV) have seen rapid global penetration into the low voltage (LV) electricity distribution grid year-on-year. The result of high PV penetration levels is grid impacts of voltage fluctuations, harmonic distortions and reverse flow among others. Research that attempts to quantify the maximum allowable PV penetration into the LV grid before experiencing detrimental impacts is an important. The most commonly reported barrier to enabling grid impact analysis is the lacking availability of high-resolution and geographically flexible solar irradiance data. As an alternative, synthetically generated solar irradiance data can be used. There is a distinct lack of synthetic solar irradiance generators that can derive high resolution and statistically accurate solar irradiance data using only readily available inputs. This thesis presents the development of two synthetic generators: the Solar Irradiance Generator (SIG), and the Spatially Decorrelating Solar Irradiance Generator (SDSIG). The SIG proves the concept that synthetic minutely irradiance time series can be generated using readily available mean hourly observations of total cloud amount, atmospheric pressure, wind speed and cloud base height. The SDSIG presents the first ever methodology to synthetically generate unique and spatially decorrelating minutely irradiance time series for any number of uniquely orientated and tilted houses inside a spatial domain using the same inputs as the SIG. The SDSIG employs (1) Markov chains, to derive stochastic weather variable time series, (2) synthetic representations of clouds in the sky, using a novel method called cloud fields, (3) globally flexible irradiance estimation models, and (4) distributions of clear-sky irradiance by total cloud amount, to create the irradiance time series. The SDSIG outputs are temporally validated using metrics of ramp rates, variability indices and irradiance magnitude frequencies against real world observations at two UK sites and two USA sites, representing three distinct climates. Daily 2-sample Kolmogorov-Smirnov tests of each metric passed a minimum of 95.34% of the time with a 99% confidence limit. The lowest CDF correlation coefficient between modelled and observed data for all metrics and sites was R=0.908; the mean was R=0.987. The SDSIG outputs are spatially validated at Oahu, HI USA, showing R=0.955, RMSE=0.01 and MAPE=0.865% when comparing modelled and observed spatial correlation versus site separation. The SDSIG outputs are applied to a grid impacts power flow model of an LV grid with increasing PV penetration to test the over voltage metric of daily on-load tap changer (OLTC) operations. Using correlating irradiance time series at each house in the LV grid overestimates OLTC operations in every instance of PV penetration when compared to using spatially decorrelating irradiance time series from the SDSIG

City of Meridian v. Petra Inc. Clerk\u27s Record v. 7 Dckt. 39006

https://digitalcommons.law.uidaho.edu/idaho_supreme_court_record_briefs/4737/thumbnail.jp