Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

Development and assessment of traffic-related emission abatement measures for the Madrid city (Spain) through the WRF-SMOKE-CMAQ modelling system

The achievement of the limit values established in the European legislation pose an important handicap for large urban areas with intense road traffic, such as Madrid (Spain). Despite permanent measures included in air quality plans it is important to assess additional measures that may be temporally applied under unfavourable conditions. This paper reports on the simulation of different traffic restriction strategies in Madrid for high-pollution episodes.

Incorporating variable peak -to -daily ratios in the equilibrium traffic assignment procedure of the Florida Standard Urban Transportation Modeling Structure (FSUTMS)

The standard highway assignment model in the Florida Standard Urban Transportation Modeling Structure (FSUTMS) is based on the equilibrium traffic assignment method. This method involves running several iterations of all-or-nothing capacity-restraint assignment with an adjustment of travel time to reflect delays encountered in the associated iteration. The iterative link time adjustment process is accomplished through the Bureau of Public Roads (BPR) volume-delay equation. Since FSUTMS' traffic assignment procedure outputs daily volumes, and the input capacities are given in hourly volumes, it is necessary to convert the hourly capacities to their daily equivalents when computing the volume-to-capacity ratios used in the BPR function. The conversion is accomplished by dividing the hourly capacity by a factor called the peak-to-daily ratio, or referred to as CONFAC in FSUTMS. The ratio is computed as the highest hourly volume of a day divided by the corresponding total daily volume. ^ While several studies have indicated that CONFAC is a decreasing function of the level of congestion, a constant value is used for each facility type in the current version of FSUTMS. This ignores the different congestion level associated with each roadway and is believed to be one of the culprits of traffic assignment errors. Traffic counts data from across the state of Florida were used to calibrate CONFACs as a function of a congestion measure using the weighted least squares method. The calibrated functions were then implemented in FSUTMS through a procedure that takes advantage of the iterative nature of FSUTMS' equilibrium assignment method. ^ The assignment results based on constant and variable CONFACs were then compared against the ground counts for three selected networks. It was found that the accuracy from the two assignments was not significantly different, that the hypothesized improvement in assignment results from the variable CONFAC model was not empirically evident. It was recognized that many other factors beyond the scope and control of this study could contribute to this finding. It was recommended that further studies focus on the use of the variable CONFAC model with recalibrated parameters for the BPR function and/or with other forms of volume-delay functions. ^

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

Modelling grain boundary migration during geometric dynamic recrystallization

High-angle grain boundary migration is predicted during geometric dynamic recrystallization (GDRX) by two types of mathematical models. Both models consider the driving pressure due to curvature and a sinusoidal driving pressure owing to subgrain walls connected to the grain boundary. One model is based on the finite difference solution of a kinetic equation, and the other, on a numerical technique in which the boundary is subdivided into linear segments. The models show that an initially flat boundary becomes serrated, with the peak and valley migrating into both adjacent grains, as observed during GDRX. When the sinusoidal driving pressure amplitude is smaller than 2 pi, the boundary stops migrating, reaching an equilibrium shape. Otherwise, when the amplitude is larger than 2 pi, equilibrium is never reached and the boundary migrates indefinitely, which would cause the protrusions of two serrated parallel boundaries to impinge on each other, creating smaller equiaxed grains.

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

Experiments and modelling of phenanthrene biodegradation in the aqueous phase by a mixed culture

Pollution by polycyclic aromatic hydrocarbons(PAHs) is widespread due to unsuitable disposal of industrial waste. They are mostly defined as priority pollutants by environmental protection authorities worldwide. Phenanthrene, a typical PAH, was selected as the target in this paper. The PAH-degrading mixed culture, named ZM, was collected from a petroleum contaminated river bed. This culture was injected into phenanthrene solutions at different concentrations to quantify the biodegradation process. Results show near-complete removal of phenanthrene in three days of biodegradation if the initial phenanthrene concentration is low. When the initial concentration is high, the removal rate is increased but 20%-40% of the phenanthrene remains at the end of the experiment. The biomass shows a peak on the third day due to the combined effects of microbial growth and decay. Another peak is evident for cases with a high initial concentration, possibly due to production of an intermediate metabolite. The pH generally decreased during biodegradation because of the production of organic acid. Two phenomenological models were designed to simulate the phenanthrene biodegradation and biomass growth. A relatively simple model that does not consider the intermediate metabolite and its inhibition of phenanthrene biodegradation cannot fit the observed data. A modified Monod model that considered an intermediate metabolite (organic acid) and its inhibiting reversal effect reasonably depicts the experimental results.

Shear stress and sediment transport calculations for swash zone modelling

It is shown that the observed difference in sediment transporting efficiency by the swash uprush, compared with the downrush, could be mainly due to greater bed shear stress for a given velocity in the more abruptly accelerated uprush. The bed shear stress generated by an arbitrary free stream velocity time series is modelled in terms of usual wave boundary layer models plus a phase lead (phi(tau) of the bed shear stress compared with the free stream velocity at the peak frequency. With this approach, the total transport amounts in uprush and downrush can be modelled satisfactorily with the same sediment transport formula, without the need for different uprush and downrush coefficients. While the adaptation of sediment transport formulae from steady flow can thus lead to the right total amounts of sediment moved by this method, the timing of the instantaneous sediment transport rates are probably not accurately modelled due to the highly unsteady nature of the swash and the presence of pre-suspended sediment in the uprush. Nevertheless, the proposed method is a useful intermediate step before we have a complete understanding of sediment transport under very rapid accelerations and of the relative contribution of pre-suspended sediment to the onshore sediment transport in swash zones. (C) 2002 Published by Elsevier Science B.V.

The t-SNARE syntaxin 4 is regulated during macrophage activation to function in membrane traffic and cytokine secretion

Activation of macrophages with lipopolysaccharide (LPS) induces the rapid synthesis and secretion of proinflammatory cytokines, such as tumor necrosis factor (TNFalpha), for priming the immune response [1, 2]. TNFalpha plays a key role in inflammatory disease [3]; yet, little is known of the intracellular trafficking events leading to its secretion. In order to identify molecules involved in this secretory pathway, we asked whether any of the known trafficking proteins are regulated by LPS. We found that the levels of SNARE proteins were rapidly and significantly up- or downregulated during macrophage activation. A subset of t-SNAREs (Syntaxin 4/SNAP23/Munc18c) known to control regulated exocytosis in other cell types [4, 5] was substantially increased by LPS in a temporal pattern coinciding with peak TNFalpha secretion. Syntaxin 4 formed a complex with Munc18c at the cell surface of macrophages. Functional studies involving the introduction of Syntaxin 4 cDNA or peptides into macrophages implicate this t-SNARE in a rate-limiting step of TNFalpha secretion and in membrane ruffling during macrophage activation. We conclude that in macrophages, SNAREs are regulated in order to accommodate the rapid onset of cytokine secretion and for membrane traffic associated with the phenotypic changes of immune activation. This represents a novel regulatory role for SNAREs in regulated secretion and in macrophage-mediated host defense.

Surge damping analysis in pipe systems: modelling and experiments

The current study focuses on the analysis of pressure surge damping in single pipeline systems generated by a fast change of flow, conditions. A dimensionless form of pressurised transient flow equations was developed. presenting the main advantage of being independent of the system characteristics. In lack of flow velocity profiles. the unsteady friction in turbulent regimes is analysed based on two new empirical corrective-coefficients associated with local and convective acceleration terms. A new, surge damping approach is also presented taking into account the pressure peak time variation. The observed attenuation effect in the pressure wave for high deformable pipe materials can be described by a combination of the non-elastic behaviour of the pipe-wall with steady and unsteady friction effects. Several simulations and experimental tests have been carried out. in order to analyse the dynamic response of single pipelines with different characteristics, such as pipe materials. diameters. thickness. lengths and transient conditions.

Modelos de simulação em MATSim aplicados à análise de sistemas de transporte

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes