A Ride-Sharing System with High Capacity Vehicles and Flexible Meeting Points

In many major cities, fixed route transit systems such as bus and rail serve millions of trips per day. These systems have people collect at common locations (the station or stop), and board at common times (for example according to a predetermined schedule or headway). By using common service locations and times, these modes can consolidate many trips that have similar origins and destinations or overlapping routes. However, the routes are not sensitive to changing travel patterns, and have no way of identifying which trips are going unserved, or are poorly served, by the existing routes. On the opposite end of the spectrum, personal modes of transportation, such as a private vehicle or taxi, offer service to and from the exact origin and destination of a rider, at close to exactly the time they desire to travel. Despite the apparent increased convenience to users, the presence of a large number of small vehicles results in a disorganized, and potentially congested road network during high demand periods. The focus of the research presented in this paper is to develop a system that possesses both the on-demand nature of a personal mode, with the efficiency of shared modes. In this system, users submit their request for travel, but are asked to make small compromises in their origin and destination location by walking to a nearby meeting point, as well as slightly modifying their time of travel, in order to accommodate other passengers. Because the origin and destination location of the request can be adjusted, this is a more general case of the Dial-a-Ride problem with time windows. The solution methodology uses a graph clustering algorithm coupled with a greedy insertion technique. A case study is presented using actual requests for taxi trips in Washington DC, and shows a significant decrease in the number of vehicles required to serve the demand.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Dynamic Modeling and Seasonal Performance Evaluation of Air-to-Water Heat Pump Systems

In this Thesis a series of numerical models for the evaluation of the seasonal performance of reversible air-to-water heat pump systems coupled to residential and non-residential buildings are presented. The exploitation of the energy saving potential linked to the adoption of heat pumps is a hard task for designers due to the influence on their energy performance of several factors, like the external climate variability, the heat pump modulation capacity, the system control strategy and the hydronic loop configuration. The aim of this work is to study in detail all these aspects. In the first part of this Thesis a series of models which use a temperature class approach for the prediction of the seasonal performance of reversible air source heat pumps are shown. An innovative methodology for the calculation of the seasonal performance of an air-to-water heat pump has been proposed as an extension of the procedure reported by the European standard EN 14825. This methodology can be applied not only to air-to-water single-stage heat pumps (On-off HPs) but also to multi-stage (MSHPs) and inverter-driven units (IDHPs). In the second part, dynamic simulation has been used with the aim to optimize the control systems of the heat pump and of the HVAC plant. A series of dynamic models, developed by means of TRNSYS, are presented to study the behavior of On-off HPs, MSHPs and IDHPs. The main goal of these dynamic simulations is to show the influence of the heat pump control strategies and of the lay-out of the hydronic loop used to couple the heat pump to the emitters on the seasonal performance of the system. A particular focus is given to the modeling of the energy losses linked to on-off cycling.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

Risk assessment of liquid hydrogen tanks for heavy duty vehicles

The advent of the hydrogen economy has already been predicted but it does not represent a tangible reality yet. However, decarbonizing the global economy and particularly the energy sector is vital to limit global warming and reduce the incumbent environmental problems. Hydrogen is a promising zero-emission fuel that could replace traditional fossil fuels, playing a key role in the transition towards a more sustainable economy. At present, hydrogen-powered cars are already spread worldwide and the deployment of hydrogen buses seems to be the next goal in the decarbonization process of the transportation sector. In contrast with the undeniable benefits introduced by the use of this alternative fuel, given its hazardous properties, safety is a topic of high concern. The present study concerns the evaluation of the risks linked to the on board storage of hydrogen on hydrogen-powered buses in case of road accident. Currently, hydrogen can be stored on board as a high-pressure gas, as a cryogenic liquid or in cryo-compressed form. Those solutions are compared from a safety point of view. First, the final accidental scenarios that could follow the release of the fuel in case of a road crash are pointed out. Secondly, threshold values for the hazardous effects of each scenario are fixed and the corresponding damage distances are calculated thanks to the use of the software PHAST 8.4. Finally, indicators are quantified to compare the different options. Results are discussed to find out the safer solution and to evaluate whether the replacement of fossil fuels with hydrogen introduces new safety issues.

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

Eficiência do processo de recobrimento de sementes de brócolos recobertas com polímero em leito de jorro

Broccoli seeds were coated in a conical-cylindrical spouted bed with an aqueous suspension of hydroxy ethyl cellulose aiming to improve the seeds coating technique using a fluid-dynamic process. An experimental design was applied to investigate the effects of the operating variables: gas temperature, atomizing air pressure and suspension flow rate on the germination of the seeds and on the process efficiency. Results indicated that the operating variables affect both the coating process efficiency and the germination ability. However, the analysis didn t identify differences between the germination potential of coated and uncoated seeds. Coated seeds absorbed up to 10 percent less moisture than the uncoated ones, when the environment temperature and humidity were controlled over a period of time.

Risco, corpo e socialidade no voo livre

Universidade Estadual de Campinas . Faculdade de Educação Física

Dynamic and steady: shear rheological properties of xanthan and guar gums dispersed in yellow passion fruit pulp (Passiflora edulis f. flavicarpa)

Yellow passion fruit pulp is unstable, presenting phase separation that can be avoided by the addition of hydrocolloids. For this purpose, xanthan and guar gum [0.3, 0.7 and 1.0% (w/w)] were added to yellow passion fruit pulp and the changes in the dynamic and steady - shear rheological behavior evaluated. Xanthan dispersions showed a more pronounced pseudoplasticity and the presence of yield stress, which was not observed in the guar gum dispersions. Cross model fitting to flow curves showed that the xanthan suspensions also had higher zero shear viscosity than the guar suspensions, and, for both gums, an increase in temperature led to lower values for this parameter. The gums showed different behavior as a function of temperature in the range of 5 - 35ºC. The activation energy of the apparent viscosity was dependent on the shear rate and gum concentration for guar, whereas for xanthan these values only varied with the concentration. The mechanical spectra were well described by the generalized Maxwell model and the xanthan dispersions showed a more elastic character than the guar dispersions, with higher values for the relaxation time. Xanthan was characterized as a weak gel, while guar presented a concentrated solution behavior. The simultaneous evaluation of temperature and concentration showed a stronger influence of the polysaccharide concentration on the apparent viscosity and the G' and G" moduli than the variation in temperature.

Quantification and source identification of atmospheric particulate polycyclic aromatic hydrocarbons and their dry deposition fluxes at three sites in Salvador Basin, Brazil, impacted by mobile and stationary sources

The present work has aimed to determine the 16 US EPA priority PAH atmospheric particulate matter levels present in three sites around Salvador, Bahia: (i) Lapa bus station, strongly impacted by heavy-duty diesel vehicles; (ii) Aratu harbor, impacted by an intense movement of goods, and (iii) Bananeira village on Maré Island, a non vehicle-influenced site with activities such as handcraft work and fisheries. Results indicated that BbF (0.130-6.85 ng m-3) is the PAH with highest concentration in samples from Aratu harbor and Bananeira and CRY (0.075-6.85 ng m-3) presented higher concentrations at Lapa station. PAH sources from studied sites were mainly of anthropogenic origin such as gasoline-fueled light-duty vehicles and diesel-fueled heavy-duty vehicles, discharges in the port, diesel burning from ships, dust ressuspension, indoor soot from cooking, and coal and wood combustion for energy production.

Polychaete assemblage of an impacted estuary, Guanabara Bay, Rio de Janeiro, Brazil

Thirty-eight stations were sampled in Guanabara Bay, Rio de Janeiro, Brazil, to assess the spatio-temporal diversity and biomass of sublittoral polychaetes. Samples were collected during the dry (September 2000) and rainy season (May 2001) in shallow sublittoral sediments. The polychaete spatial composition showed a heterogeneous distribution throughout the bay. A negative gradient of diversity and biomass was observed towards the inner parts of the bay and sheltered areas. A wide azoic area was found inside the bay. Some high-biomass and low-diversity spots were found near a sewage-discharge point. In these areas, the polychaete biomass increased after the rainy season. A diversified polychaete community was identified around the bay mouth, with no dramatic changes of this pattern between the two sampling periods. Deposit-feeders were dominant in the entire study area. The relative importance of carnivores and omnivores increased towards the outer sector, at stations with coarse sediment fractions. Guanabara Bay can be divided into three main zones with respect to environmental conditions and polychaete diversity and biomass patterns: A) High polychaete diversity, hydrodynamically exposed areas composed of sandy, oxidized or moderately reduced sediments with normoxic conditions in the water column. B) Low diversity and high biomass of deposit and suspension-feeding polychaete species in the middle part of the bay near continental inflows, comprising stations sharing similar proportions of silt, clay and fine sands. C) Azoic area or an impoverished polychaete community in hydrodynamically low-energy areas of silt and clay with extremely reduced sediments, high total organic matter content and hypoxic conditions in the water column, located essentially from the mid-bay towards the north sector. High total organic matter content and hypoxic conditions combined with slow water renewal in the inner bay seemed to play a key role in the polychaete diversity and biomass. Sedimentation processes and organic load coming from untreated sewage into the bay may have negatively affected the survivorship of the fauna.

FILTERING TECHNIQUES APPLIED ON RAW CARRIER PHASE FOR GPS DETECTING SMALL DYNAMIC DISPLACEMENTS

This work is part of a research under construction since 2000, in which the main objective is to measure small dynamic displacements by using L1 GPS receivers. A very sensible way to detect millimetric periodic displacements is based on the Phase Residual Method (PRM). This method is based on the frequency domain analysis of the phase residuals resulted from the L1 double difference static data processing of two satellites in almost orthogonal elevation angle. In this article, it is proposed to obtain the phase residuals directly from the raw phase observable collected in a short baseline during a limited time span, in lieu of obtaining the residual data file from regular GPS processing programs which not always allow the choice of the aimed satellites. In order to improve the ability to detect millimetric oscillations, two filtering techniques are introduced. One is auto-correlation which reduces the phase noise with random time behavior. The other is the running mean to separate low frequency from the high frequency phase sources. Two trials have been carried out to verify the proposed method and filtering techniques. One simulates a 2.5 millimeter vertical antenna displacement and the second uses the GPS data collected during a bridge load test. The results have shown a good consistency to detect millimetric oscillations.

Traffic-Related Air Pollution and Perinatal Mortality: A Case-Control Study

BACKGROUND: Ambient levels of air pollution may affect the health of children, as indicated by studies of infant and perinatal mortality. Scientific evidence has also correlated low birth weight and preterm birth, which are important determinants of perinatal death, with air pollution. However, most of these studies used ambient concentrations measured at monitoring sites, which may not consider differential exposure to pollutants found at elevated concentrations near heavy-traffic roadways. OBJECTIVES: Our goal was to examine the association between traffic-related pollution and perinatal mortality. METHODS: We used the information collected for a case-control study conducted in 14 districts in the City of Sao Paulo, Brazil, regarding risk factors for perinatal deaths. We geocoded the residential addresses of cases (fetal and early neonatal deaths) and controls (children who survived the 28th day of life) and calculated a distance-weighted traffic density (DWTD) measure considering all roads contained in a buffer surrounding these homes. RESULTS: Logistic regression revealed a gradient of increasing risk of early neonatal death with higher exposure to traffic-related air pollution. Mothers exposed to the highest quartile of the DWTD compared with those less exposed exhibited approximately 50% increased risk (adjusted odds ratio = 1.47; 95% confidence interval, 0.67-3.19). Associations for fetal mortality were less consistent. CONCLUSIONS: These results suggest that motor vehicle exhaust exposures may be a risk factor for perinatal mortality.

Dynamic transitions in a three dimensional associating lattice gas model

The aggregation of interacting Brownian particles in sheared concentrated suspensions is an important issue in colloid and soft matter science per se. Also, it serves as a model to understand biochemical reactions occurring in vivo where both crowding and shear play an important role. We present an effective medium approach within the Smoluchowski equation with shear which allows one to calculate the encounter kinetics through a potential barrier under shear at arbitrary colloid concentrations. Experiments on a model colloidal system in simple shear flow support the validity of the model in the concentration range considered. By generalizing Kramers' rate theory to the presence of shear and collective hydrodynamics, our model explains the significant increase in the shear-induced reaction-limited aggregation kinetics upon increasing the colloid concentration.