Development of a physically-based method for delineation of hydrologically homogeneous regions and flood quantile estimation in ungauged basins via the index flood method

Regional flood frequency techniques are commonly used to estimate flood quantiles when flood data is unavailable or the record length at an individual gauging station is insufficient for reliable analyses. These methods compensate for limited or unavailable data by pooling data from nearby gauged sites. This requires the delineation of hydrologically homogeneous regions in which the flood regime is sufficiently similar to allow the spatial transfer of information. It is generally accepted that hydrologic similarity results from similar physiographic characteristics, and thus these characteristics can be used to delineate regions and classify ungauged sites. However, as currently practiced, the delineation is highly subjective and dependent on the similarity measures and classification techniques employed. A standardized procedure for delineation of hydrologically homogeneous regions is presented herein. Key aspects are a new statistical metric to identify physically discordant sites, and the identification of an appropriate set of physically based measures of extreme hydrological similarity. A combination of multivariate statistical techniques applied to multiple flood statistics and basin characteristics for gauging stations in the Southeastern U.S. revealed that basin slope, elevation, and soil drainage largely determine the extreme hydrological behavior of a watershed. Use of these characteristics as similarity measures in the standardized approach for region delineation yields regions which are more homogeneous and more efficient for quantile estimation at ungauged sites than those delineated using alternative physically-based procedures typically employed in practice. The proposed methods and key physical characteristics are also shown to be efficient for region delineation and quantile development in alternative areas composed of watersheds with statistically different physical composition. In addition, the use of aggregated values of key watershed characteristics was found to be sufficient for the regionalization of flood data; the added time and computational effort required to derive spatially distributed watershed variables does not increase the accuracy of quantile estimators for ungauged sites. This dissertation also presents a methodology by which flood quantile estimates in Haiti can be derived using relationships developed for data rich regions of the U.S. As currently practiced, regional flood frequency techniques can only be applied within the predefined area used for model development. However, results presented herein demonstrate that the regional flood distribution can successfully be extrapolated to areas of similar physical composition located beyond the extent of that used for model development provided differences in precipitation are accounted for and the site in question can be appropriately classified within a delineated region.

Computational simulations of latent heat thermal energy storage systems - with innovative and first-principles based simulation for the underlying unsteady melting (and solidification) processes

This thesis develops an effective modeling and simulation procedure for a specific thermal energy storage system commonly used and recommended for various applications (such as an auxiliary energy storage system for solar heating based Rankine cycle power plant). This thermal energy storage system transfers heat from a hot fluid (termed as heat transfer fluid - HTF) flowing in a tube to the surrounding phase change material (PCM). Through unsteady melting or freezing process, the PCM absorbs or releases thermal energy in the form of latent heat. Both scientific and engineering information is obtained by the proposed first-principle based modeling and simulation procedure. On the scientific side, the approach accurately tracks the moving melt-front (modeled as a sharp liquid-solid interface) and provides all necessary information about the time-varying heat-flow rates, temperature profiles, stored thermal energy, etc. On the engineering side, the proposed approach is unique in its ability to accurately solve – both individually and collectively – all the conjugate unsteady heat transfer problems for each of the components of the thermal storage system. This yields critical system level information on the various time-varying effectiveness and efficiency parameters for the thermal storage system.

Assessing Decision Making Strategies In Construction Management By Using A Schedule-Based Simulation Framework

During the project, managers encounter numerous contingencies and are faced with the challenging task of making decisions that will effectively keep the project on track. This task is very challenging because construction projects are non-prototypical and the processes are irreversible. Therefore, it is critical to apply a methodological approach to develop a few alternative management decision strategies during the planning phase, which can be deployed to manage alternative scenarios resulting from expected and unexpected disruptions in the as-planned schedule. Such a methodology should have the following features but are missing in the existing research: (1) looking at the effects of local decisions on the global project outcomes, (2) studying how a schedule responds to decisions and disruptive events because the risk in a schedule is a function of the decisions made, (3) establishing a method to assess and improve the management decision strategies, and (4) developing project specific decision strategies because each construction project is unique and the lessons from a particular project cannot be easily applied to projects that have different contexts. The objective of this dissertation is to develop a schedule-based simulation framework to design, assess, and improve sequences of decisions for the execution stage. The contribution of this research is the introduction of applying decision strategies to manage a project and the establishment of iterative methodology to continuously assess and improve decision strategies and schedules. The project managers or schedulers can implement the methodology to develop and identify schedules accompanied by suitable decision strategies to manage a project at the planning stage. The developed methodology also lays the foundation for an algorithm towards continuously automatically generating satisfactory schedule and strategies through the construction life of a project. Different from studying isolated daily decisions, the proposed framework introduces the notion of {em decision strategies} to manage construction process. A decision strategy is a sequence of interdependent decisions determined by resource allocation policies such as labor, material, equipment, and space policies. The schedule-based simulation framework consists of two parts, experiment design and result assessment. The core of the experiment design is the establishment of an iterative method to test and improve decision strategies and schedules, which is based on the introduction of decision strategies and the development of a schedule-based simulation testbed. The simulation testbed used is Interactive Construction Decision Making Aid (ICDMA). ICDMA has an emulator to duplicate the construction process that has been previously developed and a random event generator that allows the decision-maker to respond to disruptions in the emulation. It is used to study how the schedule responds to these disruptions and the corresponding decisions made over the duration of the project while accounting for cascading impacts and dependencies between activities. The dissertation is organized into two parts. The first part presents the existing research, identifies the departure points of this work, and develops a schedule-based simulation framework to design, assess, and improve decision strategies. In the second part, the proposed schedule-based simulation framework is applied to investigate specific research problems.

Parametric combustion modeling for ethanol-gasoline fuelled spark ignition engines

Ethanol-gasoline fuel blends are increasingly being used in spark ignition (SI) engines due to continued growth in renewable fuels as part of a growing renewable portfolio standard (RPS). This leads to the need for a simple and accurate ethanol-gasoline blends combustion model that is applicable to one-dimensional engine simulation. A parametric combustion model has been developed, integrated into an engine simulation tool, and validated using SI engine experimental data. The parametric combustion model was built inside a user compound in GT-Power. In this model, selected burn durations were computed using correlations as functions of physically based non-dimensional groups that have been developed using the experimental engine database over a wide range of ethanol-gasoline blends, engine geometries, and operating conditions. A coefficient of variance (COV) of gross indicated mean effective pressure (IMEP) correlation was also added to the parametric combustion model. This correlation enables the cycle combustion variation modeling as a function of engine geometry and operating conditions. The computed burn durations were then used to fit single and double Wiebe functions. The single-Wiebe parametric combustion compound used the least squares method to compute the single-Wiebe parameters, while the double-Wiebe parametric combustion compound used an analytical solution to compute the double-Wiebe parameters. These compounds were then integrated into the engine model in GT-Power through the multi-Wiebe combustion template in which the values of Wiebe parameters (single-Wiebe or double-Wiebe) were sensed via RLT-dependence. The parametric combustion models were validated by overlaying the simulated pressure trace from GT-Power on to experimentally measured pressure traces. A thermodynamic engine model was also developed to study the effect of fuel blends, engine geometries and operating conditions on both the burn durations and COV of gross IMEP simulation results.

Analysis of transport properties of mechanically alloyed [Pb1-xSnxTe]

The work described in this thesis had two objectives. The first objective was to develop a physically based computational model that could be used to predict the electronic conductivity, Seebeck coefficient, and thermal conductivity of Pb1-xSnxTe alloys over the 400 K to 700 K temperature as a function of Sn content and doping level. The second objective was to determine how the secondary phase inclusions observed in Pb1-xSnxTe alloys made by consolidating mechanically alloyed elemental powders impact the ability of the material to harvest waste heat and generate electricity in the 400 K to 700 K temperature range. The motivation for this work was that though the promise of this alloy as an unusually efficient thermoelectric power generator material in the 400 K to 700 K range had been demonstrated in the literature, methods to reproducibly control and subsequently optimize the materials thermoelectric figure of merit remain elusive. Mechanical alloying, though not typically used to fabricate these alloys, is a potential method for cost-effectively engineering these properties. Given that there are deviations from crystalline perfection in mechanically alloyed material such as secondary phase inclusions, the question arises as to whether these defects are detrimental to thermoelectric function or alternatively, whether they enhance thermoelectric function of the alloy. The hypothesis formed at the onset of this work was that the small secondary phase SnO2 inclusions observed to be present in the mechanically alloyed Pb1-xSnxTe would increase the thermoelectric figure of merit of the material over the temperature range of interest. It was proposed that the increase in the figure of merit would arise because the inclusions in the material would not reduce the electrical conductivity to as great an extent as the thermal conductivity. If this were to be true, then the experimentally measured electronic conductivity in mechanically alloyed Pb1-xSnxTe alloys that have these inclusions would not be less than that expected in alloys without these inclusions while the portion of the thermal conductivity that is not due to charge carriers (the lattice thermal conductivity) would be less than what would be expected from alloys that do not have these inclusions. Furthermore, it would be possible to approximate the observed changes in the electrical and thermal transport properties using existing physical models for the scattering of electrons and phonons by small inclusions. The approach taken to investigate this hypothesis was to first experimentally characterize the mobile carrier concentration at room temperature along with the extent and type of secondary phase inclusions present in a series of three mechanically alloyed Pb1-xSnxTe alloys with different Sn content. Second, the physically based computational model was developed. This model was used to determine what the electronic conductivity, Seebeck coefficient, total thermal conductivity, and the portion of the thermal conductivity not due to mobile charge carriers would be in these particular Pb1-xSnxTe alloys if there were to be no secondary phase inclusions. Third, the electronic conductivity, Seebeck coefficient and total thermal conductivity was experimentally measured for these three alloys with inclusions present at elevated temperatures. The model predictions for electrical conductivity and Seebeck coefficient were directly compared to the experimental elevated temperature electrical transport measurements. The computational model was then used to extract the lattice thermal conductivity from the experimentally measured total thermal conductivity. This lattice thermal conductivity was then compared to what would be expected from the alloys in the absence of secondary phase inclusions. Secondary phase inclusions were determined by X-ray diffraction analysis to be present in all three alloys to a varying extent. The inclusions were found not to significantly degrade electrical conductivity at temperatures above ~ 400 K in these alloys, though they do dramatically impact electronic mobility at room temperature. It is shown that, at temperatures above ~ 400 K, electrons are scattered predominantly by optical and acoustical phonons rather than by an alloy scattering mechanism or the inclusions. The experimental electrical conductivity and Seebeck coefficient data at elevated temperatures were found to be within ~ 10 % of what would be expected for material without inclusions. The inclusions were not found to reduce the lattice thermal conductivity at elevated temperatures. The experimentally measured thermal conductivity data was found to be consistent with the lattice thermal conductivity that would arise due to two scattering processes: Phonon phonon scattering (Umklapp scattering) and the scattering of phonons by the disorder induced by the formation of a PbTe-SnTe solid solution (alloy scattering). As opposed to the case in electrical transport, the alloy scattering mechanism in thermal transport is shown to be a significant contributor to the total thermal resistance. An estimation of the extent to which the mean free time between phonon scattering events would be reduced due to the presence of the inclusions is consistent with the above analysis of the experimental data. The first important result of this work was the development of an experimentally validated, physically based computational model that can be used to predict the electronic conductivity, Seebeck coefficient, and thermal conductivity of Pb1-xSnxTe alloys over the 400 K to 700 K temperature as a function of Sn content and doping level. This model will be critical in future work as a tool to first determine what the highest thermoelectric figure of merit one can expect from this alloy system at a given temperature and, second, as a tool to determine the optimum Sn content and doping level to achieve this figure of merit. The second important result of this work is the determination that the secondary phase inclusions that were observed to be present in the Pb1-xSnxTe made by mechanical alloying do not keep the material from having the same electrical and thermal transport that would be expected from “perfect" single crystal material at elevated temperatures. The analytical approach described in this work will be critical in future investigations to predict how changing the size, type, and volume fraction of secondary phase inclusions can be used to impact thermal and electrical transport in this materials system.

SYSTEM DYNAMICS MODELING AS A QUANTITATIVE-QUALITATIVE FRAMEWORK FOR SUSTAINABLE WATER RESOURCES MANAGEMENT: INSIGHTS FOR WATER QUALITY POLICY IN THE GREAT LAKES REGION

Early water resources modeling efforts were aimed mostly at representing hydrologic processes, but the need for interdisciplinary studies has led to increasing complexity and integration of environmental, social, and economic functions. The gradual shift from merely employing engineering-based simulation models to applying more holistic frameworks is an indicator of promising changes in the traditional paradigm for the application of water resources models, supporting more sustainable management decisions. This dissertation contributes to application of a quantitative-qualitative framework for sustainable water resources management using system dynamics simulation, as well as environmental systems analysis techniques to provide insights for water quality management in the Great Lakes basin. The traditional linear thinking paradigm lacks the mental and organizational framework for sustainable development trajectories, and may lead to quick-fix solutions that fail to address key drivers of water resources problems. To facilitate holistic analysis of water resources systems, systems thinking seeks to understand interactions among the subsystems. System dynamics provides a suitable framework for operationalizing systems thinking and its application to water resources problems by offering useful qualitative tools such as causal loop diagrams (CLD), stock-and-flow diagrams (SFD), and system archetypes. The approach provides a high-level quantitative-qualitative modeling framework for "big-picture" understanding of water resources systems, stakeholder participation, policy analysis, and strategic decision making. While quantitative modeling using extensive computer simulations and optimization is still very important and needed for policy screening, qualitative system dynamics models can improve understanding of general trends and the root causes of problems, and thus promote sustainable water resources decision making. Within the system dynamics framework, a growth and underinvestment (G&U) system archetype governing Lake Allegan's eutrophication problem was hypothesized to explain the system's problematic behavior and identify policy leverage points for mitigation. A system dynamics simulation model was developed to characterize the lake's recovery from its hypereutrophic state and assess a number of proposed total maximum daily load (TMDL) reduction policies, including phosphorus load reductions from point sources (PS) and non-point sources (NPS). It was shown that, for a TMDL plan to be effective, it should be considered a component of a continuous sustainability process, which considers the functionality of dynamic feedback relationships between socio-economic growth, land use change, and environmental conditions. Furthermore, a high-level simulation-optimization framework was developed to guide watershed scale BMP implementation in the Kalamazoo watershed. Agricultural BMPs should be given priority in the watershed in order to facilitate cost-efficient attainment of the Lake Allegan's TP concentration target. However, without adequate support policies, agricultural BMP implementation may adversely affect the agricultural producers. Results from a case study of the Maumee River basin show that coordinated BMP implementation across upstream and downstream watersheds can significantly improve cost efficiency of TP load abatement.