Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Modeling of a Heat-Induced Buckling of Plates Using the Mesh-free Method

In the process of engineering design of structural shapes, the flat plate analysis results can be generalized to predict behaviors of complete structural shapes. In this case, the purpose of this project is to analyze a thin flat plate under conductive heat transfer and to simulate the temperature distribution, thermal stresses, total displacements, and buckling deformations. The current approach in these cases has been using the Finite Element Method (FEM), whose basis is the construction of a conforming mesh. In contrast, this project uses the mesh-free Scan Solve Method. This method eliminates the meshing limitation using a non-conforming mesh. I implemented this modeling process developing numerical algorithms and software tools to model thermally induced buckling. In addition, convergence analysis was achieved, and the results were compared with FEM. In conclusion, the results demonstrate that the method gives similar solutions to FEM in quality, but it is computationally less time consuming.

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

Modeling of shape memory alloys with plasticity

Shape memory alloys are a special class of metals that can undergo large deformation yet still be able to recover their original shape through the mechanism of phase transformations. However, when they experience plastic slip, their ability to recover their original shape is reduced. This is due to the presence of dislocations generated by plastic flow that interfere with shape recovery through the shape memory effect and the superelastic effect. A one-dimensional model that captures the coupling between shape memory effect, the superelastic effect and plastic deformation is introduced. The shape memory alloy is assumed to have only 3 phases: austenite, positive variant martensite and negative variant martensite. If the SMA flows plastically, each phase will exhibit a dislocation field that permanently prevents a portion of it from being transformed back to other phases. Hence, less of the phase is available for subsequent phase transformations. A constitutive model was developed to depict this phenomena and simulate the effect of plasticity on both the shape memory effect and the superelastic effect in shape memory alloys. In addition, experimental tests were conducted to characterize the phenomenon in shape memory wire and superelastic wire. ^ The constitutive model was then implemented in within a finite element context as UMAT (User MATerial Subroutine) for the commercial finite element package ABAQUS. The model is phenomenological in nature and is based on the construction of stress-temperature phase diagram. ^ The model has been shown to be capable of capturing the qualitative and quantitative aspects of the coupling between plasticity and the shape memory effect and plasticity and the super elastic effect within acceptable limits. As a verification case a simple truss structure was built and tested and then simulated using the FEA constitutive model. The results where found to be close the experimental data. ^

Computational modeling of intermediate temperature proton exchange membrane (PEM) fuel cells

A two-phase three-dimensional computational model of an intermediate temperature (120--190°C) proton exchange membrane (PEM) fuel cell is presented. This represents the first attempt to model PEM fuel cells employing intermediate temperature membranes, in this case, phosphoric acid doped polybenzimidazole (PBI). To date, mathematical modeling of PEM fuel cells has been restricted to low temperature operation, especially to those employing Nafion ® membranes; while research on PBI as an intermediate temperature membrane has been solely at the experimental level. This work is an advancement in the state of the art of both these fields of research. With a growing trend toward higher temperature operation of PEM fuel cells, mathematical modeling of such systems is necessary to help hasten the development of the technology and highlight areas where research should be focused.^ This mathematical model accounted for all the major transport and polarization processes occurring inside the fuel cell, including the two phase phenomenon of gas dissolution in the polymer electrolyte. Results were presented for polarization performance, flux distributions, concentration variations in both the gaseous and aqueous phases, and temperature variations for various heat management strategies. The model predictions matched well with published experimental data, and were self-consistent.^ The major finding of this research was that, due to the transport limitations imposed by the use of phosphoric acid as a doping agent, namely low solubility and diffusivity of dissolved gases and anion adsorption onto catalyst sites, the catalyst utilization is very low (∼1--2%). Significant cost savings were predicted with the use of advanced catalyst deposition techniques that would greatly reduce the eventual thickness of the catalyst layer, and subsequently improve catalyst utilization. The model also predicted that an increase in power output in the order of 50% is expected if alternative doping agents to phosphoric acid can be found, which afford better transport properties of dissolved gases, reduced anion adsorption onto catalyst sites, and which maintain stability and conductive properties at elevated temperatures.^

Computational modeling of biodegradable starch based polymer composites

Purpose. The goal of this study is to improve the favorable molecular interactions between starch and PPC by addition of grafting monomers MA and ROM as compatibilizers, which would advance the mechanical properties of starch/PPC composites. ^ Methodology. DFT and semi-empirical methods based calculations were performed on three systems: (a) starch/PPC, (b) starch/PPC-MA, and (c) starch-ROM/PPC. Theoretical computations involved the determination of optimal geometries, binding-energies and vibrational frequencies of the blended polymers. ^ Findings. Calculations performed on five starch/PPC composites revealed hydrogen bond formation as the driving force behind stable composite formation, also confirmed by the negative relative energies of the composites indicating the existence of binding forces between the constituent co-polymers. The interaction between starch and PPC is also confirmed by the computed decrease in stretching CO and OH group frequencies participating in hydrogen bond formation, which agree qualitatively with the experimental values. ^ A three-step mechanism of grafting MA on PPC was proposed to improve the compatibility of PPC with starch. Nine types of 'blends' produced by covalent bond formation between starch and MA-grafted PPC were found to be energetically stable, with blends involving MA grafted at the 'B' and 'C' positions of PPC indicating a binding-energy increase of 6.8 and 6.2 kcal/mol, respectively, as compared to the non-grafted starch/PPC composites. A similar increase in binding-energies was also observed for three types of 'composites' formed by hydrogen bond formation between starch and MA-grafted PPC. ^ Next, grafting of ROM on starch and subsequent blend formation with PPC was studied. All four types of blends formed by the reaction of ROM-grafted starch with PPC were found to be more energetically stable as compared to the starch/PPC composite and starch/PPC-MA composites and blends. A blend of PPC and ROM grafted at the ' a&d12; ' position on amylose exhibited a maximal increase of 17.1 kcal/mol as compared with the starch/PPC-MA blend. ^ Conclusions. ROM was found to be a more effective compatibilizer in improving the favorable interactions between starch and PPC as compared to MA. The ' a&d12; ' position was found to be the most favorable attachment point of ROM to amylose for stable blend formation with PPC.^

Quantifying the effects of temperature and concentration on variable-density flow in numerical modeling of groundwater systems: Implications for predictive uncertainty and data collection

Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection may be justified if it will cost-effectively aid in reducing the uncertainty of a model's prediction. The collection of salinity, as well as, temperature data could aid in reducing predictive uncertainty in a variable-density model. However, before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents the benchmark testing of a new modeling code, SEAWAT Version 4. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. The verified code, SEAWAT, was then applied to two different hydrological analyses to explore the capacity of a variable-density model to guide data collection. ^ The first analysis tested a linear method to guide data collection by quantifying the contribution of different data types and locations toward reducing predictive uncertainty in a nonlinear variable-density flow and transport model. The relative contributions of temperature and concentration measurements, at different locations within a simulated carbonate platform, for predicting movement of the saltwater interface were assessed. Results from the method showed that concentration data had greater worth than temperature data in reducing predictive uncertainty in this case. Results also indicated that a linear method could be used to quantify data worth in a nonlinear model. ^ The second hydrological analysis utilized a model to identify the transient response of the salinity, temperature, age, and amount of submarine groundwater discharge to changes in tidal ocean stage, seasonal temperature variations, and different types of geology. The model was compared to multiple kinds of data to (1) calibrate and verify the model, and (2) explore the potential for the model to be used to guide the collection of data using techniques such as electromagnetic resistivity, thermal imagery, and seepage meters. Results indicated that the model can be used to give insight to submarine groundwater discharge and be used to guide data collection. ^

Lattice Boltzmann modeling of fluid flow and solute transport in karst aquifers

A novel modeling approach is applied to karst hydrology. Long-standing problems in karst hydrology and solute transport are addressed using Lattice Boltzmann methods (LBMs). These methods contrast with other modeling approaches that have been applied to karst hydrology. The motivation of this dissertation is to develop new computational models for solving ground water hydraulics and transport problems in karst aquifers, which are widespread around the globe. This research tests the viability of the LBM as a robust alternative numerical technique for solving large-scale hydrological problems. The LB models applied in this research are briefly reviewed and there is a discussion of implementation issues. The dissertation focuses on testing the LB models. The LBM is tested for two different types of inlet boundary conditions for solute transport in finite and effectively semi-infinite domains. The LBM solutions are verified against analytical solutions. Zero-diffusion transport and Taylor dispersion in slits are also simulated and compared against analytical solutions. These results demonstrate the LBM’s flexibility as a solute transport solver. The LBM is applied to simulate solute transport and fluid flow in porous media traversed by larger conduits. A LBM-based macroscopic flow solver (Darcy’s law-based) is linked with an anisotropic dispersion solver. Spatial breakthrough curves in one and two dimensions are fitted against the available analytical solutions. This provides a steady flow model with capabilities routinely found in ground water flow and transport models (e.g., the combination of MODFLOW and MT3D). However the new LBM-based model retains the ability to solve inertial flows that are characteristic of karst aquifer conduits. Transient flows in a confined aquifer are solved using two different LBM approaches. The analogy between Fick’s second law (diffusion equation) and the transient ground water flow equation is used to solve the transient head distribution. An altered-velocity flow solver with source/sink term is applied to simulate a drawdown curve. Hydraulic parameters like transmissivity and storage coefficient are linked with LB parameters. These capabilities complete the LBM’s effective treatment of the types of processes that are simulated by standard ground water models. The LB model is verified against field data for drawdown in a confined aquifer.

Structural Characterization of Metal Hydrides for Energy Applications

Hydrogen can be an unlimited source of clean energy for future because of its very high energy density compared to the conventional fuels like gasoline. An efficient and safer way of storing hydrogen is in metals and alloys as hydrides. Light metal hydrides, alanates and borohydrides have very good hydrogen storage capacity, but high operation temperatures hinder their application. Improvement of thermodynamic properties of these hydrides is important for their commercial use as a source of energy. Application of pressure on materials can have influence on their properties favoring hydrogen storage. Hydrogen desorption in many complex hydrides occurs above the transition temperature. Therefore, it is important to study the physical properties of the hydride compounds at ambient and high pressure and/or high temperature conditions, which can assist in the design of suitable storage materials with desired thermodynamic properties. ^ The high pressure-temperature phase diagram, thermal expansion and compressibility have only been evaluated for a limited number of hydrides so far. This situation serves as a main motivation for studying such properties of a number of technologically important hydrides. Focus of this dissertation was on X-ray diffraction and Raman spectroscopy studies of Mg2FeH6, Ca(BH4) 2, Mg(BH4)2, NaBH4, NaAlH4, LiAlH4, LiNH2BH3 and mixture of MgH 2 with AlH3 or Si, at different conditions of pressure and temperature, to obtain their bulk modulus and thermal expansion coefficient. These data are potential source of information regarding inter-atomic forces and also serve as a basis for developing theoretical models. Some high pressure phases were identified for the complex hydrides in this study which may have better hydrogen storage properties than the ambient phase. The results showed that the highly compressible B-H or Al-H bonds and the associated bond disordering under pressure is responsible for phase transitions observed in brorohydrides or alanates. Complex hydrides exhibited very high compressibility suggesting possibility to destabilize them with pressure. With high capacity and favorable thermodynamics, complex hydrides are suitable for reversible storage. Further studies are required to overcome the kinetic barriers in complex hydrides by catalytic addition. A comparative study of the hydride properties with that of the constituting metal, and their inter relationships were carried out with many interesting features.^

Evaluation of the internal structural validity of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) anxiety disorders in children and adolescents

The purpose of the present dissertation was to evaluate the internal validity of symptoms of four common anxiety disorders included in the Diagnostic and Statistical Manual of Mental Disorders fourth edition (text revision) (DSM-IV-TR; American Psychiatric Association, 2000), namely, separation anxiety disorder (SAD), social phobia (SOP), specific phobia (SP), and generalized anxiety disorder (GAD), in a sample of 625 youth (ages 6 to 17 years) referred to an anxiety disorders clinic and 479 parents. Confirmatory factor analyses (CFAs) were conducted on the dichotomous items of the SAD, SOP, SP, and GAD sections of the youth and parent versions of the Anxiety Disorders Interview Schedule for DSM-IV (ADIS-IV: C/P; Silverman & Albano, 1996) to test and compare a number of factor models including a factor model based on the DSM. Contrary to predictions, findings from CFAs showed that a correlated model with five factors of SAD, SOP, SP, GAD worry, and GAD somatic distress, provided the best fit of the youth data as well as the parent data. Multiple group CFAs supported the metric invariance of the correlated five factor model across boys and girls. Thus, the present study’s finding supports the internal validity of DSM-IV SAD, SOP, and SP, but raises doubt regarding the internal validity of GAD.^

Biomimetic modeling of the nitrogen-centered radical postulated to occur during the inhibition of ribonucleotide reductases by 2'-azido-2'-deoxynucleotides

Ribonucleotide reductases (RNR) are essential enzymes that catalyze the reduction of ribonucleotides to 2'-deoxyribonucleotides, which is a critical step that produces precursors for DNA replication and repair. The inactivation of RNR, logically, would discontinue producing the precursors of the DNA of viral or cancer cells, which then would consequently end the cycle of DNA replication. Among different compounds that were found to be inhibitors of RNR, 2'-azido-2'-deoxynucleotide diphosphates (N3NDPs) have been investigated in depth as potent inhibitors of RNR. Decades of investigation has suggested that the inactivation of RNR by N3NDPs is a result of the formation of a nitrogen-centered radical (N·) that is covalently attached to the nucleotide at C3' and cysteine molecule C225 [3'-C(R-S-N·-C-OH)]. Biomimetic simulation reactions for the generation of the nitrogen-centered radicals similar to the one observed during the inactivation of the RNR by azionuclotides was investigated. The study included several modes: (i) theoretical calculation that showed the feasibility of the ring closure reaction between thiyl radicals and azido group; (ii) synthesis of the model azido nucleosides with a linker attached to C3' or C5' having a thiol or vicinal dithiol functionality; (iii) generation of the thiyl radical under both physiological and radiolysis conditions whose role is important in the initiation on RNR cascades; and (iv) analysis of the nitrogen-centered radical species formed during interaction between the thiyl radical and azido group by electron paramagnetic resonance spectroscopy (EPR). Characterization of the aminyl radical species formed during one electron attachment to the azido group of 2'-azido-2'-deoxyuridine and its stereospecifically labelled 1'-, 2'-, 3'-, 4'- or 5,6-[2H 2]-analogues was also examined. This dissertation gave insight toward understanding the mechanism of the formation of the nitrogen-centered radical during the inactivation of RNRs by azidonucleotides as well as the mechanism of action of RNRs that might provide key information necessary for the development of the next generation of antiviral and anticancer drugs.

Development of methods for spatial modeling of sediments quality in Lake Okeechobee, Florida

The major objectives of this dissertation were to develop optimal spatial techniques to model the spatial-temporal changes of the lake sediments and their nutrients from 1988 to 2006, and evaluate the impacts of the hurricanes occurred during 1998–2006. Mud zone reduced about 10.5% from 1988 to 1998, and increased about 6.2% from 1998 to 2006. Mud areas, volumes and weight were calculated using validated Kriging models. From 1988 to 1998, mud thicknesses increased up to 26 cm in the central lake area. The mud area and volume decreased about 13.78% and 10.26%, respectively. From 1998 to 2006, mud depths declined by up to 41 cm in the central lake area, mud volume reduced about 27%. Mud weight increased up to 29.32% from 1988 to 1998, but reduced over 20% from 1998 to 2006. The reduction of mud sediments is likely due to re-suspension and redistribution by waves and currents produced by large storm events, particularly Hurricanes Frances and Jeanne in 2004 and Wilma in 2005. Regression, kriging, geographically weighted regression (GWR) and regression-kriging models have been calibrated and validated for the spatial analysis of the sediments TP and TN of the lake. GWR models provide the most accurate predictions for TP and TN based on model performance and error analysis. TP values declined from an average of 651 to 593 mg/kg from 1998 to 2006, especially in the lake’s western and southern regions. From 1988 to 1998, TP declined in the northern and southern areas, and increased in the central-western part of the lake. The TP weights increased about 37.99%–43.68% from 1988 to 1998 and decreased about 29.72%–34.42% from 1998 to 2006. From 1988 to 1998, TN decreased in most areas, especially in the northern and southern lake regions; western littoral zone had the biggest increase, up to 40,000 mg/kg. From 1998 to 2006, TN declined from an average of 9,363 to 8,926 mg/kg, especially in the central and southern regions. The biggest increases occurred in the northern lake and southern edge areas. TN weights increased about 15%–16.2% from 1988 to 1998, and decreased about 7%–11% from 1998 to 2006.

Mastogloia smithii var lacustris Grun.: A Structural Engineer of Calcareous Mats in Karstic Subtropical Wetlands

Mastogloia smithii var. lacustris Grun. is the dominant diatom in periphyton mats of the calcareous, freshwater to brackish wetlands of Caribbean coasts. Despite oligotrophy, frequent desiccation, high irradiance and temperatures, and occasional fire, periphyton communities in these wetlands can produce over 2000 g m-2 of organic biomass, prompting studies that examine stress resistance and maintenance of algal mats under extreme conditions. The diatom flora inhabiting periphyton mats from over 500 sites in the Florida Everglades and similar wetlands in Belize, Jamaica and Mexico was examined, and M. smithii var. lacustris was a persistent component, present in 97% of samples and comprising up to 80% of a diverse diatom assemblage. Valves at various stages of division were observed encased in extracellular polysaccharide that exceeded the cell volume; SEM observations confirm issuance from mantle pores resulting in suspension of the cell in a matrix dominated by cyanobacterial filaments. Using corresponding biophysical data from the collection sites, we define the optima for M. smithii var. lacustris along salinity, pH, phosphorus, and water depth gradients. Experiments revealed a collapse of M. smithii var. lacustris populations in the presence of above-ambient phosphorus concentrations and a rapid resurgence upon reflooding of desiccated mats. This widespread diatom taxon appears to play a critical role similar to that of cyanobacteria in microbial mats, and its disappearance in the presence of enrichment threatens biodiversity and the natural function in these systems that are increasingly influenced by urbanization