1,262 research outputs found

    ADVANCES IN SYSTEM RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM) FOR SYSTEM RESILIENCE ANALYSIS AND DESIGN

    Get PDF
    Failures of engineered systems can lead to significant economic and societal losses. Despite tremendous efforts (e.g., $200 billion annually) denoted to reliability and maintenance, unexpected catastrophic failures still occurs. To minimize the losses, reliability of engineered systems must be ensured throughout their life-cycle amidst uncertain operational condition and manufacturing variability. In most engineered systems, the required system reliability level under adverse events is achieved by adding system redundancies and/or conducting system reliability-based design optimization (RBDO). However, a high level of system redundancy increases a system's life-cycle cost (LCC) and system RBDO cannot ensure the system reliability when unexpected loading/environmental conditions are applied and unexpected system failures are developed. In contrast, a new design paradigm, referred to as resilience-driven system design, can ensure highly reliable system designs under any loading/environmental conditions and system failures while considerably reducing systems' LCC. In order to facilitate the development of formal methodologies for this design paradigm, this research aims at advancing two essential and co-related research areas: Research Thrust 1 - system RBDO and Research Thrust 2 - system prognostics and health management (PHM). In Research Thrust 1, reliability analyses under uncertainty will be carried out in both component and system levels against critical failure mechanisms. In Research Thrust 2, highly accurate and robust PHM systems will be designed for engineered systems with a single or multiple time-scale(s). To demonstrate the effectiveness of the proposed system RBDO and PHM techniques, multiple engineering case studies will be presented and discussed. Following the development of Research Thrusts 1 and 2, Research Thrust 3 - resilience-driven system design will establish a theoretical basis and design framework of engineering resilience in a mathematical and statistical context, where engineering resilience will be formulated in terms of system reliability and restoration and the proposed design framework will be demonstrated with a simplified aircraft control actuator design problem

    Determination of Dark Matter Properties at High-Energy Colliders

    Full text link
    If the cosmic dark matter consists of weakly-interacting massive particles, these particles should be produced in reactions at the next generation of high-energy accelerators. Measurements at these accelerators can then be used to determine the microscopic properties of the dark matter. From this, we can predict the cosmic density, the annihilation cross sections, and the cross sections relevant to direct detection. In this paper, we present studies in supersymmetry models with neutralino dark matter that give quantitative estimates of the accuracy that can be expected. We show that these are well matched to the requirements of anticipated astrophysical observations of dark matter. The capabilities of the proposed International Linear Collider (ILC) are expected to play a particularly important role in this study.Comment: 124 pages, 62 figures; corrections and new material in Section 2.6 (direct detection); misc. additional correction

    Determining Optimal Reliability Targets Through Analysis of Product Validation Cost and Field Warranty Data

    Get PDF
    This work develops a new methodology to minimize the life cycle cost of a product using the decision variables controlled by a reliability/quality professional during a product development process. This methodology incorporates all product dependability-related activities into a comprehensive probabilistic cost model that enables minimization of the product's life cycle cost using the product dependability control variables. The primary model inputs include the cost of ownership of test equipment, forecasted cost of warranty returns, and environmental test parameters of a product validation program. Among these parameters, an emphasis is placed upon test duration and test sample size for durability related environmental tests. The warranty forecasting model is based on data mining of past warranty claims, parametric probabilistic analysis of the existing field data, and a piecewise application of several statistical distributions. The modeling process is complicated by insufficient knowledge about the relationship between product quality and product reliability. This can be attributed to the lack of studies establishing the effect of product validation activities on future field failures, overall lack of comprehensive field failure studies, and the market's dictation of warranty terms as opposed to warranties based on engineering rationale. As a result of these complicating factors an innovative approach to estimating the quality-reliability relationship using probabilistic methods and stochastic simulation has been developed. The overall cost model and its minimization are generated using a Monte Carlo method that accounts for the propagation of uncertainties from the model inputs and their parameters to the life cycle cost solution. This research provides reliability and quality professionals with a methodology to evaluate the efficiency of a product validation program from a life cycle cost point of view and identifies ways to improve the validation test flow by adjusting test durations, sample sizes, and equipment utilization. Solutions balance a rigorous theoretical treatment and practical applications and are specifically applied to the electronics industry

    The EnMAP Managed Vegetation Scientific Processor

    Get PDF
    Nach jahrelanger wissenschaftlicher und technischer Vorbereitungszeit wird voraussichtlich Ende des Jahres 2020 der Start der orbitalen Phase einer unbemannten deutschen Weltraum-Mission initiiert. Das Environmental Mapping and Analysis Program (EnMAP) wird an Bord des gleichnamigen Satelliten einen hyperspektralen Sensor zur Erfassung terrestrischer Oberflächen tragen. In den Umweltdisziplinen zur Erforschung von Ökosystemen, landwirtschaftlicher, forstwirtschaftlicher und urbaner Flächen, im Bereich der Küsten- und Inlandsgewässer sowie der Geologie und Bodenkunde bereitete man sich im Vorfeld des Starts auf die kommenden Daten vor. Zwar existiert bereits eine Vielzahl an Algorithmen zur wissenschaftlichen Analyse von spektralen Daten, allerdings ergeben sich auch neue Herausforderungen, da die EnMAP-Mission bislang im weltweiten Kontext der Fernerkundung einzigartig ist. Die Abdeckung des vollen optischen Spektrums (420 nm – 2450 nm) in Verbindung mit einer moderaten räumlichen Auflösung von 30 m und einem hohen Signal-Rausch-Verhältnis von mindestens 180 im kurzwelligen Infrarot und über 400 im sichtbaren Spektrum, ermöglichen eine Aufnahmequalität, die bislang nur von flugzeuggestützten Systemen erreicht werden konnte. Die Bemühungen in dieser Dissertation umfassen Aktivitäten in der wissenschaftlichen Vorbereitungsphase zu agrargeographischen Fragestellungen. Algorithmen und Tools zur Analyse der hyperspektralen Daten werden kostenlos im QGIS-Plugin EnMAP-Box 3 zur Verfügung gestellt. Die drängenden Fragen im Agrarsektor drehen sich hierbei um die Ableitung biochemischer und biophysikalischer Parameter aus Fernerkundungsdaten, weshalb die übergeordnete Problemstellung des Promotionsvorhabens die Entwicklung eines wissenschaftsbasierten EnMAP-Tools für bewirtschaftete Vegetationsflächen (EnMAP Managed Vegetation Scientific Processor) darstellt. Zu Beginn wurde eine umfassende Feldkampagne geplant, welche ab April 2014 umgesetzt wurde. Neben der spektralen Erfassung von Blatt-, Bestands- und Bodensignaturen in einem Winterweizen- und einem Maisfeld erfolgte auch die Messung wesentlicher Pflanzenparameter an den exakt gleichen Positionen. Hierzu zählt die non-destruktive Ableitung des Blattflächenindex (LAI), des Blattchlorophyllgehalts (Ccab), des Blattwassergehalts (EWT oder Cw), des relativen Blatttrockengewichts (LMA oder Cm), des mittleren Blattneigungswinkels im Bestand (ALIA) sowie weiterer sekundärer Parameter wie Wuchshöhe, das phänologisches Stadium und der Sonnenvektor. Um die Fähigkeit des späteren EnMAP-Satelliten sich um bis zu 30° orthogonal zur Flugrichtung zu kippen nachzustellen, wurden die spektralen Aufnahmen aus verschiedenen Betrachtungswinkeln erstellt, die dieser Aufnahme-Geometrien nachempfunden sind. Ein gängiges Verfahren zur Ableitung der relevanten Pflanzenparameter ist die Verwendung des Strahlungstransfermodells PROSAIL, welches das spektrale Signal einer Vegetationsfläche auf Basis der zugrundeliegenden biophysikalischen und biochemischen Parameter simuliert. Bei der Umkehr dieses Prozesses können ebendiese Variablen von gemessenen spektralen Daten abgeleitet werden. Hierzu wurde eine Datenbank (Look-Up-Table, LUT) aus PROSAIL-Modellläufen aufgebaut und die in den Feldkampagnen gemessenen Spektren mit dieser abgeglichen. Mit dieser Methode der LUT-Invertierung aus unterschiedlichen Aufnahmewinkeln konnten Genauigkeiten bei der LAI-Schätzung von 18 % und bei Blattchlorophyll von 20 % erzielt werden. Eine starke Anisotropie, also eine Reflexionsabhängigkeit von der Beleuchtungs- und Aufnahmerichtung, wurde bei Winterweizen vor allem für frühe Entwicklungsstadien festgestellt. Bei einer anschließenden Studie zur Unsicherheitsanalyse des Spektralmodells wurden PROSAIL-Ergebnisse, bei denen real gemessene Pflanzenparameter als Input dienten, den zugehörigen Reflektanzspektren gegenübergestellt. Es zeigten sich hierbei mitunter starke Abweichungen zwischen gemessenen und modellierten Spektren, die im Falle des Winterweizens einen saisonalen Verlauf zeichneten. Vor allem während frühen Wachstumsstadien tendierte das Modell dazu die Reflektanz im nahen Infrarot zu überschätzen, während es gegen Ende der Wachstumsperiode eher eine Unterschätzung aufwies. Als Unsicherheitsfaktor wurde die Parametrisierung des Modells ausgemacht, wenn der ALIA-Parameter als echter physikalische Blattwinkel interpretiert wird. Es wurde geschlussfolgert, dass eine Separierung von LAI und ALIA bei der Invertierung von PROSAIL eine korrekte Abschätzung der weniger sensitiven Parameter behindert. Die Erstellung des Vegetations-Prozessors erforderte die Verwendung von Regressions-Algorithmen des maschinellen Lernens (MLRA), da eine Verteilung von großen LUTs an die User nicht praktikabel wäre. Die MLRAs wurden an synthetischen Datensätzen trainiert, wobei zunächst die Optimierung der Hyperparameter im Vordergrund stand, bevor die Anwendung an echten Spektraldaten unternommen wurde. Es konnten dabei erst aussagekräftige Ergebnisse produziert werden, als die Trainingsdaten mit einem künstlichen Rauschen belegt wurden, da die Algorithmen unter einer Überanpassung an die Modellumgebung litten. Mithilfe des Prozessors konnten schließlich LAI, ALIA, Ccab und Cw aus hyperspektralen Daten abgeleitet werden. Künstliche neuronale Netze dienen dabei als Blackbox-Modelle, die in kurzer Zeit große Datenmengen verarbeiten können und somit einen entscheidenden Beitrag zur modernen angewandten Fernerkundung für eine breite User-Community leisten.After years of scientific and technical preparation, the launch of an unmanned German space-mission is planned to be initiated in 2020. The Environmental Mapping and Analysis Program (EnMAP) is going to provide an equally named hyperspectral imager to map land surfaces. Scientists of environmental disciplines of monitoring of ecosystems, agricultural, forestry and urban areas as well as coastal and inland waters, geology and soils prepared themselves for the upcoming data prior to the actual launch. Although there already exists a variety of useful algorithms for a profound analysis of spectral data, new challenges will arise given the uniqueness of the EnMAP-mission in the global context of remote sensing; i.e. coverage of the full range of the optical spectrum (420 nm – 2450 nm) in combination with a moderate spatial resolution of 30 m and a high signal-to-noise ratio of at least 180 in the shortwave infrared and above 400 in the visible spectrum. This enables an imaging quality which to this date has only been reached by airborne systems. The efforts of this dissertation comprise activities in the scientific preparation phase for agro-geographical tasks. Algorithms and tools for an analysis of hyperspectral data are being provided for free in the QGIS-plugin EnMAP-Box 3. Urgent questions in the agricultural sector revolve around the derivation of biochemical and biophysical parameters from remote sensing data. For this reason, the overarching objective of this promotion is the development of a scientific EnMAP-tool for managed areas of vegetation (EnMAP Managed Vegetation Scientific Processor). At first, an extensive field campaign was planned and then started in April, 2014. Apart from spectral observations of leaves, canopies and soils in a winter wheat and a maize field, also relevant plant parameters were acquired at the exact same spots. Namely, they are the Leaf Area Index (LAI), leaf chlorophyll content (Ccab), leaf water content (EWT or Cw), relative dry leaf weight (LMA or Cm), Average Leaf Inclination Angle (ALIA) as well as other secondary parameters like canopy height, phenological stage and the solar vector. Spectral measurements were captured from different observation angles to match ground data with the sensing geometry of the future EnMAP-satellite, which can be tilted up to 30° orthogonal to its direction of flight. A common procedure to derive relevant crop parameters is to make use of the radiative transfer model PROSAIL, which simulates the spectral signal of a vegetated surface based on biophysical and biochemical input parameters. If this process is reverted, said parameters can be derived from measured spectral data. To do so, a Look-Up-Table (LUT) is built containing model runs of PROSAIL and then subsequently compared against spectra from the field campaigns. With this approach of LUT-inversions from different observation angles, an accuracy of 18 % could be achieved for LAI and 20 % for Ccab. Strong anisotropic effects, i.e. dependence on illumination geometry and sensor orientation, were identified for winter wheat mainly in the early stages of plant development. In a consecutive study about uncertainties of the spectral model, PROSAIL results fed with in situ measured crop parameters as input, were opposed to their associated reflectance signatures. A strong deviation between measured and modelled spectra was observed, which – in the case of winter wheat – showed a seasonal behavior. The model tended to overestimate reflectances in the near infrared for early phenological stages and to underestimate them at end of the growing period. The parametrization of the model was identified as an uncertainty factor if the ALIA parameter is interpreted as true physical leaf inclinations. It was concluded that a separation of LAI and ALIA at inversion of PROSAIL prevents an adequate estimation of the less sensitive parameters. The development of the vegetation processor required the use of Machine Learning Regression Algorithms (MLRA), since distribution of large LUTs to the user would be impracticable. The MLRAs were trained with synthetic datasets with primary importance to optimize their hyperparameters, before attempting to apply the algorithms to real spectral data. Significant results could not be obtained until training data were altered with artificial noise, because algorithms suffered from overfitting to the model environment. Executing the processor allowed to derive LAI, ALIA, Ccab and Cw from hyperspectral data. Artificial neural networks served as black box models, which digest great amount of data in a short period of time and thus make a decisive contribution to modern applied remote sensing with relevance for a broad user-community

    The EnMAP Managed Vegetation Scientific Processor

    Get PDF
    Nach jahrelanger wissenschaftlicher und technischer Vorbereitungszeit wird voraussichtlich Ende des Jahres 2020 der Start der orbitalen Phase einer unbemannten deutschen Weltraum-Mission initiiert. Das Environmental Mapping and Analysis Program (EnMAP) wird an Bord des gleichnamigen Satelliten einen hyperspektralen Sensor zur Erfassung terrestrischer Oberflächen tragen. In den Umweltdisziplinen zur Erforschung von Ökosystemen, landwirtschaftlicher, forstwirtschaftlicher und urbaner Flächen, im Bereich der Küsten- und Inlandsgewässer sowie der Geologie und Bodenkunde bereitete man sich im Vorfeld des Starts auf die kommenden Daten vor. Zwar existiert bereits eine Vielzahl an Algorithmen zur wissenschaftlichen Analyse von spektralen Daten, allerdings ergeben sich auch neue Herausforderungen, da die EnMAP-Mission bislang im weltweiten Kontext der Fernerkundung einzigartig ist. Die Abdeckung des vollen optischen Spektrums (420 nm – 2450 nm) in Verbindung mit einer moderaten räumlichen Auflösung von 30 m und einem hohen Signal-Rausch-Verhältnis von mindestens 180 im kurzwelligen Infrarot und über 400 im sichtbaren Spektrum, ermöglichen eine Aufnahmequalität, die bislang nur von flugzeuggestützten Systemen erreicht werden konnte. Die Bemühungen in dieser Dissertation umfassen Aktivitäten in der wissenschaftlichen Vorbereitungsphase zu agrargeographischen Fragestellungen. Algorithmen und Tools zur Analyse der hyperspektralen Daten werden kostenlos im QGIS-Plugin EnMAP-Box 3 zur Verfügung gestellt. Die drängenden Fragen im Agrarsektor drehen sich hierbei um die Ableitung biochemischer und biophysikalischer Parameter aus Fernerkundungsdaten, weshalb die übergeordnete Problemstellung des Promotionsvorhabens die Entwicklung eines wissenschaftsbasierten EnMAP-Tools für bewirtschaftete Vegetationsflächen (EnMAP Managed Vegetation Scientific Processor) darstellt. Zu Beginn wurde eine umfassende Feldkampagne geplant, welche ab April 2014 umgesetzt wurde. Neben der spektralen Erfassung von Blatt-, Bestands- und Bodensignaturen in einem Winterweizen- und einem Maisfeld erfolgte auch die Messung wesentlicher Pflanzenparameter an den exakt gleichen Positionen. Hierzu zählt die non-destruktive Ableitung des Blattflächenindex (LAI), des Blattchlorophyllgehalts (Ccab), des Blattwassergehalts (EWT oder Cw), des relativen Blatttrockengewichts (LMA oder Cm), des mittleren Blattneigungswinkels im Bestand (ALIA) sowie weiterer sekundärer Parameter wie Wuchshöhe, das phänologisches Stadium und der Sonnenvektor. Um die Fähigkeit des späteren EnMAP-Satelliten sich um bis zu 30° orthogonal zur Flugrichtung zu kippen nachzustellen, wurden die spektralen Aufnahmen aus verschiedenen Betrachtungswinkeln erstellt, die dieser Aufnahme-Geometrien nachempfunden sind. Ein gängiges Verfahren zur Ableitung der relevanten Pflanzenparameter ist die Verwendung des Strahlungstransfermodells PROSAIL, welches das spektrale Signal einer Vegetationsfläche auf Basis der zugrundeliegenden biophysikalischen und biochemischen Parameter simuliert. Bei der Umkehr dieses Prozesses können ebendiese Variablen von gemessenen spektralen Daten abgeleitet werden. Hierzu wurde eine Datenbank (Look-Up-Table, LUT) aus PROSAIL-Modellläufen aufgebaut und die in den Feldkampagnen gemessenen Spektren mit dieser abgeglichen. Mit dieser Methode der LUT-Invertierung aus unterschiedlichen Aufnahmewinkeln konnten Genauigkeiten bei der LAI-Schätzung von 18 % und bei Blattchlorophyll von 20 % erzielt werden. Eine starke Anisotropie, also eine Reflexionsabhängigkeit von der Beleuchtungs- und Aufnahmerichtung, wurde bei Winterweizen vor allem für frühe Entwicklungsstadien festgestellt. Bei einer anschließenden Studie zur Unsicherheitsanalyse des Spektralmodells wurden PROSAIL-Ergebnisse, bei denen real gemessene Pflanzenparameter als Input dienten, den zugehörigen Reflektanzspektren gegenübergestellt. Es zeigten sich hierbei mitunter starke Abweichungen zwischen gemessenen und modellierten Spektren, die im Falle des Winterweizens einen saisonalen Verlauf zeichneten. Vor allem während frühen Wachstumsstadien tendierte das Modell dazu die Reflektanz im nahen Infrarot zu überschätzen, während es gegen Ende der Wachstumsperiode eher eine Unterschätzung aufwies. Als Unsicherheitsfaktor wurde die Parametrisierung des Modells ausgemacht, wenn der ALIA-Parameter als echter physikalische Blattwinkel interpretiert wird. Es wurde geschlussfolgert, dass eine Separierung von LAI und ALIA bei der Invertierung von PROSAIL eine korrekte Abschätzung der weniger sensitiven Parameter behindert. Die Erstellung des Vegetations-Prozessors erforderte die Verwendung von Regressions-Algorithmen des maschinellen Lernens (MLRA), da eine Verteilung von großen LUTs an die User nicht praktikabel wäre. Die MLRAs wurden an synthetischen Datensätzen trainiert, wobei zunächst die Optimierung der Hyperparameter im Vordergrund stand, bevor die Anwendung an echten Spektraldaten unternommen wurde. Es konnten dabei erst aussagekräftige Ergebnisse produziert werden, als die Trainingsdaten mit einem künstlichen Rauschen belegt wurden, da die Algorithmen unter einer Überanpassung an die Modellumgebung litten. Mithilfe des Prozessors konnten schließlich LAI, ALIA, Ccab und Cw aus hyperspektralen Daten abgeleitet werden. Künstliche neuronale Netze dienen dabei als Blackbox-Modelle, die in kurzer Zeit große Datenmengen verarbeiten können und somit einen entscheidenden Beitrag zur modernen angewandten Fernerkundung für eine breite User-Community leisten.After years of scientific and technical preparation, the launch of an unmanned German space-mission is planned to be initiated in 2020. The Environmental Mapping and Analysis Program (EnMAP) is going to provide an equally named hyperspectral imager to map land surfaces. Scientists of environmental disciplines of monitoring of ecosystems, agricultural, forestry and urban areas as well as coastal and inland waters, geology and soils prepared themselves for the upcoming data prior to the actual launch. Although there already exists a variety of useful algorithms for a profound analysis of spectral data, new challenges will arise given the uniqueness of the EnMAP-mission in the global context of remote sensing; i.e. coverage of the full range of the optical spectrum (420 nm – 2450 nm) in combination with a moderate spatial resolution of 30 m and a high signal-to-noise ratio of at least 180 in the shortwave infrared and above 400 in the visible spectrum. This enables an imaging quality which to this date has only been reached by airborne systems. The efforts of this dissertation comprise activities in the scientific preparation phase for agro-geographical tasks. Algorithms and tools for an analysis of hyperspectral data are being provided for free in the QGIS-plugin EnMAP-Box 3. Urgent questions in the agricultural sector revolve around the derivation of biochemical and biophysical parameters from remote sensing data. For this reason, the overarching objective of this promotion is the development of a scientific EnMAP-tool for managed areas of vegetation (EnMAP Managed Vegetation Scientific Processor). At first, an extensive field campaign was planned and then started in April, 2014. Apart from spectral observations of leaves, canopies and soils in a winter wheat and a maize field, also relevant plant parameters were acquired at the exact same spots. Namely, they are the Leaf Area Index (LAI), leaf chlorophyll content (Ccab), leaf water content (EWT or Cw), relative dry leaf weight (LMA or Cm), Average Leaf Inclination Angle (ALIA) as well as other secondary parameters like canopy height, phenological stage and the solar vector. Spectral measurements were captured from different observation angles to match ground data with the sensing geometry of the future EnMAP-satellite, which can be tilted up to 30° orthogonal to its direction of flight. A common procedure to derive relevant crop parameters is to make use of the radiative transfer model PROSAIL, which simulates the spectral signal of a vegetated surface based on biophysical and biochemical input parameters. If this process is reverted, said parameters can be derived from measured spectral data. To do so, a Look-Up-Table (LUT) is built containing model runs of PROSAIL and then subsequently compared against spectra from the field campaigns. With this approach of LUT-inversions from different observation angles, an accuracy of 18 % could be achieved for LAI and 20 % for Ccab. Strong anisotropic effects, i.e. dependence on illumination geometry and sensor orientation, were identified for winter wheat mainly in the early stages of plant development. In a consecutive study about uncertainties of the spectral model, PROSAIL results fed with in situ measured crop parameters as input, were opposed to their associated reflectance signatures. A strong deviation between measured and modelled spectra was observed, which – in the case of winter wheat – showed a seasonal behavior. The model tended to overestimate reflectances in the near infrared for early phenological stages and to underestimate them at end of the growing period. The parametrization of the model was identified as an uncertainty factor if the ALIA parameter is interpreted as true physical leaf inclinations. It was concluded that a separation of LAI and ALIA at inversion of PROSAIL prevents an adequate estimation of the less sensitive parameters. The development of the vegetation processor required the use of Machine Learning Regression Algorithms (MLRA), since distribution of large LUTs to the user would be impracticable. The MLRAs were trained with synthetic datasets with primary importance to optimize their hyperparameters, before attempting to apply the algorithms to real spectral data. Significant results could not be obtained until training data were altered with artificial noise, because algorithms suffered from overfitting to the model environment. Executing the processor allowed to derive LAI, ALIA, Ccab and Cw from hyperspectral data. Artificial neural networks served as black box models, which digest great amount of data in a short period of time and thus make a decisive contribution to modern applied remote sensing with relevance for a broad user-community

    Development of Remote Sensing Assisted Water Quality Nowcasting and Forecasting Models for Coastal Beaches

    Get PDF
    A remote sensing assisted water quality modeling framework is developed in this dissertation for nowcasting and forecasting recreational water quality of Holly Beach in Louisiana, USA. The modeling framework is composed of four models/systems: (1) an Artificial Neural Network (ANN) model (Model 1) and an US EPA Virtual Beach (VB) Program-based model for predicting early morning enterococci (ENT) levels in beach waters; (2) an ANN model (Model 2) and an VB model for predicting early morning Fecal Coliform (FC) levels in beach waters; (3) a remote sensing assisted modeling system (Model 3) for predicting near real time ENT levels during daytime; and (4) a hybrid probabilistic/deterministic modeling approach (Model 4) for predicting the probability of beach water quality violation. New findings from Model 1 include (1) the identification of 7 explanatory variables and various combinations of the 7 variables responsible for the ENT level in coastal beach waters; and (2) Model 1 with Linear Correlation Coefficient (LCC) of 0.857 performs consistently better than the VB model with LCC of 0.320. A major finding from Model 2 is that a total of 6 independent environmental variables along with 8 different combinations are capable of explaining about 76% of variation in FC levels for model training data and 44% for independent data. Major new contributions made in Model 3 include (1) development of remote sensing algorithms for turbidity using Terra and Aqua satellite data; (2) development of an enhanced ANN model for predicting ENT levels at sunrise time by taking into account the cumulative effect of solar radiation on ENT inactivation; (3) development of a real-time model for predicting ENT level during the daytime by considering the turbidity effect on ENT inactivation. A novel feature of Model 4 (hybrid model) is the combination of advantages of a deterministic ANN model and a probabilistic Bayesian model. The hybrid model is capable of reproducing 86.25% of historical beach water quality advisories with 6.39% of false positive predictions and 7.36% of false negative predictions over the past 7-years. Applications of the models will improve the management of recreational beaches and the protection of public health

    Optimal capacity decisions of airlines under supply-demand equilibrium

    Get PDF
    In the last three decades, airlines across the globe have experienced significant incidents and milestones such economic recessions, de-regulations, and jet fuel fluctuations, leading to many consolidations and even bankruptcies. Airlines seem to have a few options to respond to these disruptions and fluctuations. Capacity planning is one of the key tools that airlines apply to manage air traffic demand and their operating costs. As such, the carriers may alter the number of flights, use different types of airplanes, upgrade the seats in the aircraft, and even increase the load factor to maintain their market share and profitability, which can occasionally lead to passenger dissatisfaction. 'Capacity Planning' is defined in this research as a combination of the number of flights and aircraft size that airlines choose to manage traffic demand on a given origin-destination route. It affects the airlines' service quality and operating costs, in turn, influencing their market share and profitability. Capacity planning has become more important for airlines due to the diminishing relative significance of traditional tools such as airfare management or hedging contracts. However, capacity planning seems to be a difficult decision-making task for airlines as they need to consider many factors on both sides of the supply-demand equilibrium of the flight market and different limitations such as access to specific aircrafts, airports, or even flight regulations. Any changes in the capacity would trigger a sophisticated set of interrelated changes in passenger demand, flight frequency, aircraft size, airfare, and flight delay, finally leading to an equilibrium shift. This statement considers economies of density that means, given no congestion, more density in terms of higher passenger demand leads to more plane-miles by either more flights or larger aircrafts. In fact, with no capacity constraints, there is an ongoing loop causing higher density from the demand side and more plane-miles from the supply side of the flight equilibrium. However, this picture is no longer valid once the capacity constraint is added to the equilibrium. Capacity constraint introduces a new player, flight delay, to the equilibrium. In other words, higher density leads to more flight delays because of capacity constraints. Flight delays bring extra costs to airlines, diminishing economies of density. Therefore, airlines need to consider all these interrelated interactions to make efficient capacity plans on their operating networks. This thesis develops an optimisation model to assist airlines to make the optimum capacity decisions for individual routes of a given market such as a specific airport or network to maximise the potential passenger demand under the flight supply-demand equilibrium. To address this research, three key questions are identified as follows: What are the key determinants of airlines' capacity decisions under the supply-demand equilibrium of flight market? How does an airline's capacity decision influence flight delays? How can airline capacity decisions be optimised for the individual routes of a given market to maximise the total potential flight demand with respect to the market's capacity constraints? Furthermore, this research answers some significant questions related to the interactions among the key players of the supply-demand equilibrium of the flight market. To answer these questions, this research is implemented in three steps. In the first step, the key drivers of capacity planning and demand modelling are statistically identified on both sides of the supply-demand equilibrium by applying the two-stage least square technique on the time-series cross-sectional data of 21 major routes of the Australian domestic market. In the second step, the impact of changes in the elements of capacity decisions in flight delay are investigated by using the Hausman-Taylor regression technique on the Australian domestic data. By connecting the findings of step 1 and 2, a research framework is created to be used as the basis of the optimisation algorithm in the final step. The model is developed by the inclusion of a series of exogenous and endogenous factors under the supply-demand equilibrium. To address the simultaneity among the variables, a system of four non-linear equations, flight demand, flight frequency, aircraft size, and flight delay, is developed and estimated individually by two statistical simultaneous techniques - three-stage least square technique (3SLS) and maximum likelihood estimator (MLE). The data of seven Australian domestic routes, linking Melbourne to other major cities in Australia, was applied, as the case study, to estimate the model's coefficients. Finally, the non-linear optimisation technique was applied to the estimates of 3SLS and MLE separately to find the optimum capacity plan of the given routes. All proposed models were verified and tested in different steps. As the key contribution, this thesis proposes an optimisation model based on a system of non-linear equations of the flight supply-demand equilibrium to maximise the potential flight demand of a given market with respect to the market's capacity constraints. This model is based on the theory of economies of density and applied the time-series cross-sectional data of flight market to empirically estimate the coefficients of passenger demand equation as the objective function. Compared to other models of capacity planning that generally contain a relatively a short list of micro-level factors in modelling, the proposed model contains all required macro- and micro-level factors. As the key contribution, this thesis highlights the key drivers of capacity planning and demand modeling of supply-demand equilibrium and their relationships in the Australian flight domestic market. As a part of results, there is a bilateral relation among the elements of capacity decisions and passenger demand. The results statistically differentiate the airlines' policies of capacity planning across the different markets. The results suggest that a higher demand for flights primarily results in increased flight frequency rather than increased aircraft size or load factor. The load factor is identified to be an insignificant variable in capacity planning of the airlines. Competition between airlines, participation of low-cost carriers, and jet fuel expenses are thought to influence airlines' capacity decisions, albeit differently across the given markets. Interestingly, jet fuel cost inflations stimulate the flight demand in the short-haul market as well as the routes linking the major cities to the industrial ones. The socio-economic parameters of population and employment rates affect the flight demand in the different markets in different ways. The findings indicate the airlines' capacity decisions influence flight delays. The results indicate that more frequent flights and larger aircrafts together are associated with more flight delays. Route congestion is caused by more flights, albeit to a higher degree for low-cost carriers. Jet fuel cost inflation is expected to cause flight delays, but more for the legacy airlines than low-cost carriers. From the results of the optimisation model, for a given period, December 2015, the optimum solutions of 3SLS and MLE indicate, respectively, a 1.72% and 0.66% improvement on the flight demand compared to the reported actual plan for the airlines. The estimated MSE of the MLE model is smaller than that of 3SLS; however, estimated coefficients of 3SLS are statistically more significant than those of MLE, resulting in more practical results in the optimisation section. The proposed model and findings of this thesis can potentially be applied by airlines as well as policy makers to fleet planning and airport infrastructure development projects in different airports and hub-and-spoke networks across the globe. The proposed optimisation model may be enhanced by using the theory of full equilibrium to develop the optimisation model through adding the factors of the other transportation modes. Due to the data limitation, airfare was only applied as an exogenous parameter in the passenger demand equation of the optimisation model. Airfare can potentially be upgraded to become a key variable of airline capacity planning under the supply-demand equilibrium. In future research, the data of individual airlines can be applied separately at the route level. With the airline dimension in modelling, further explorations can be done on the airline's policies and performance of capacity planning in different markets. The proposed model can potentially be applied to other airports and hub-and-spoke networks across the globe which it surely leads to further explorations about the airlines' policies and capacity planning as well as the demand modelling under the supply-demand equilibrium

    MECHANISTIC MODELLING FRAMEWORK AND LIFE CYCLE ASSESSMENT APPROACH FOR PAVEMENT REHABILITATION USING ASPHALT CONCRETE OVERLAYS

    Get PDF
    Efficient and effective rehabilitation of existing roadways continues to be a top priority for local, state, and federal agencies to provide safe travel of people and goods. Asphalt concrete (AC) overlays on deteriorated Portland Cement Concrete (PCC) are a popular rehabilitation option to extend the service life of a roadway. However, the combination of load and environmentally induced movements at underlying joint locations can cause high amounts of stresses and strains, leading to the formation of cracks in the AC overlay. Ensuring that a suitable asphalt mixture (cracking resistance) and sufficient overlay structure (thickness) are selected is critical to avoid pre-mature failure of overlays and excess funding requirements on pavement maintenance and rehabilitation (M&R). This dissertation research aimed to improve the decision process of rehabilitation PCC pavements with AC overlays through the development of a Microsoft Excel®-based decision tree tool for screening of asphalt mixtures and overlay designs. A combination of laboratory testing, field performance data from full-scale pavement test sections and predicted modeling results were utilized to assess varying overlay options. The two main outputs from the decision tree tool are (1) a life cycle cost estimate and (2) predicted reflective cracking performance curves with both time and truck traffic. Furthermore, this dissertation work sought to improve pavement life cycle assessment (LCA) and life cycle cost analysis (LCCA) practices by considering both realistic traffic conditions and future climate projections in the analysis framework. Traditional pavement LCAs are performed using historical climate data to evaluate pavement performance and provide recommendations for budgeting and planning of M&R strategies in the future. However, due to climate change, this assumption may not be appropriate as pavements’ performance is influenced by climate stressors. Research conducted as part of this dissertation showed that incorporating future project climate data and realistic traffic data can lead to a substantial increase in agency LCA impacts (up to 20% for the presented case-study), where the increase is a function of pavement structure and M&R scenario over the analysis period
    • …
    corecore