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Title: Regional climate response to land use and land cover change in contiguous United States
Creator: Nikolić, Jovanka
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Future land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this...
Show moreFuture land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this research aims to quantify the response of several climate variables to the expected LULC change in the CONUS using simulations from a regional climate model. The research is composed of three individual studies. The first study assesses the sensitivity of simulated low-level jet (LLJ) characteristics on changes in LULC pattern. As a prominent weather and climate process responsible for transport of moisture from the Gulf of Mexico northward into central CONUS, LLJ plays an important role in the hydrological cycle and wind energy generation over the Great Plains. Therefore, it is important to quantify the potential changes in jet characteristics, such as jet speed, height and frequency, under the influence of LULC change. The second study investigates the impact of LULC change on frost indices - the dates of last spring frost and first fall frost and the length of frost free seasons. Frost is one of the major factors affecting the growth and development of plants and crop production. Future changes in LULC could make some regions more beneficial, while others more harmful to agricultural practice. Finally, the third study examines the potential impact of the changes in LULC pattern on future wind energy resources. As a zero carbon energy resource, wind energy helps limit greenhouse gasses emissions and mitigate climate change. Knowledge gained on where in the CONUS wind power class would likely to change from unsuitable or marginal to suitable, and vice versa, as a result of LULC change can be useful for future wind farm sitting and for making better informed energy policies.
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Title: Still learning : introducing the learning transfer model, a formal model of transfer
Creator: Olenick, Jeffrey David
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Although training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and...
Show moreAlthough training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and correlates of whether they transfer that material back to their work environment, we know very little about how individuals go about choosing whether to apply their new knowledge to, typically, previously-encountered situations in their work environment and how those decisions unfold over time. Improving our knowledge regarding how individuals transfer learned material will lead to new insights on how to support the transfer of organizationally directed training, or any learning event, back to the work environment. Thus, the present paper introduces a formal model of the transfer process, the Learning Transfer Model (LTM), which proposes a process for how transfer unfolds over time and gives rise to many of the findings we have accumulated in the transfer literature. This is accomplished by reconceptualizing transfer as its own learning process which is affected by the dual nature of human cognitive systems, the learner's social group, and their self-regulatory processes. The LTM was then instantiated in a series of computational models for virtual experimentation. Findings and implications for research and practice are discussed throughout.
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Title: Network-wide traffic state analysis : estimation, characterization, and evaluation
Creator: Saedi Germi, Ramin
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of...
Show moreThe Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of probe trajectories to estimate NFD accounting for limited resources for data collection, network traffic heterogeneity and asymmetry in OD demand in a real-world network. Using a calibrated simulation-based dynamic traffic assignment model of Chicago downtown network, a successful application of the proposed model and solution algorithm to estimate NFD is presented. The proposed model, then, is extended to take into account the stochasticity of day-to-day fluctuations of OD demand in NFD estimation.Three main applications of NFD are also shown in this research: network-wide travel time reliability estimation, network-wide emission estimation, and real-time traffic state estimation for heterogenous networks experiencing inclement weather impact. The main objective of the travel time reliability estimation application is to improve estimation of this network-wide measure of effectiveness using network partitioning. To this end, a heterogeneous large-scale network is partitioned into homogeneous regions (clusters) with well-defined NFDs using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations.This study also proposes a network-level emission modeling framework via integrating NFD properties with an existing microscopic emission model. The NFDs and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogenous vehicle types with different emission generation rates in the network-level model. Non-linear and support vector regression models are developed using simulated trajectory data of thirteen simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from underlying microscopic emission model, regardless of the simulation tool that is used to calibrate the network-level emission model.Finally, the NFD application for real-time traffic state estimation in a network experiencing inclement weather conditions is explored. To this end, the impacts of weather conditions on the NFD and travel time reliability relation are illustrated through a scenario-based analysis using traffic simulation. Then, the real-time traffic state prediction framework in the literature is adjusted to capture weather conditions as a key parameter. The extended Kalman filter algorithm is employed as an estimation engine to predict the real-time traffic state. The results highlight the importance of considering weather conditions in the traffic state prediction model.
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Title: Monte-Carlo simulations of the (d,²He) reaction in inverse kinematics
Creator: Carls, Alexander B.
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Charge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as...
Show moreCharge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as a potential candidate for this probe. This method uses the Active-Target Time Projection Chamber (AT-TPC) to detect the two protons from the unbound 2He system, and the S800 spectrograph to detect the heavy recoil. The feasibility of this method is demonstrated through Monte-Carlo simulations. The ATTPCROOTv2 code is the framework which allows for simulation of reactions within the AT-TPC as well as digitization of the results in the pad planes for realistic simulated data. The analysis performed on this data using the ATTPCROOTv2 code shows the techniques that can be done in experiment to track the scattered protons through the detector using Random Sampling Consensus (RANSAC) algorithms.
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Title: Adaptation to visual perturbations while learning a novel virtual reaching task
Creator: Narayanan, Sachin Devnathan
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn...
Show more"Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn to adapt to perturbations. Methods: 13 college-age participants (females = 7, Mean = 21.74 +/- 2.55) performed upper body movements to control a computer cursor. Visual rotation of the cursor position was introduced as a perturbation for one-half of the practice trials. Movement time and normalized path length were calculated. One way repeated measures ANOVA was performed to analyze significance between the performance at different times of the task. Results: Significant learning seen while learning the initial baseline task (p<0.0001) and a significant drop in performance upon immediate exposure to the perturbation (p =0.005). No significant adaptation over practice with the perturbation (p = 0.103) or significant after-effects on removal of the perturbation (p = 0.383). Conclusions: Results suggests differences in adaptation when the task is novel when compared to other adaptation studies and such novel tasks trigger a different type of learning mechanism when compared to adaptation."--Page ii.
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Title: Agronomic management of corn using seasonal climate predictions, remote sensing and crop simulation models
Creator: Jha, Prakash Kumar
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Management decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always...
Show moreManagement decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always accurate. Remote sensing observations and climate predictions can improve the accuracy in managing time bound climate-sensitive decisions at larger spatiotemporal scale. However, there is also a disconnect between climate forecasts and crop models. The unavailability of downscaling tool that can downscale rainfall and temperature forecasts simultaneously make this task more challenging. To address these knowledge gaps, this dissertation consists of three studies focused on interdisciplinary approaches to agronomic management of corn.In the first study, we calibrated and validated genetic coefficients of CERES-Maize using field data from the Michigan corn performance trials. Multiple methods of estimating genetic coefficients GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), and NMCGA (Noisy Monte Carlo Genetic Algorithm) were evaluated and ensembled to estimate more reliable genetic coefficients. The calibrations were done under irrigated conditions and validation under rainfed conditions. The results suggested that ensembled genetic coefficients performed best among all, with d-index of 0.94 and 0.96 in calibration and validation for anthesis and maturity dates, and yield.In the second study, simulated growth stages from the calibrated crop model were used to develop site-specific crop coefficients (kc) using ensembled ET and reference ET from the nearest weather station. ET from multiple models were ensembled and validated with the measured ET from eddy-covariance flux towers for 2010 - 2017. Results suggest that the ensembled ET performed best among all ET models used, with highest d-index of 0.94. Likewise, the performance of the newly derived kc-curve was compared with FAO-kc curve using a soil water balance model. Then, the derived region-specific Kc-curve was used to design irrigation scheduling and results suggest that it performed better than FAO Kc-curve in minimizing the amount irrigation while maintaining a prescribed allowable water stress.The third study used the calibrated crop model to simulate anthesis using downscaled seasonal climate forecasts. The predicted anthesis and downscaled seasonal climate forecasts were used to develop risk analysis model for ear rot disease management in corn. In this study an innovative downscaling tool, called FResamplerPT, was introduced to downscale rainfall and temperature simultaneously. The results suggest that temperature and relative humidity are better predictors (combined) as compared to temperature and rainfall (combined). With this risk analysis model, growers can evaluate and assess the future climatic conditions in the season before planting the crops. The seasonal climate information with the lead-time of 3 months can help growers to prepare integrated management strategies for ear rot disease management in maize.
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Title: Computational modeling of cardiac mechanics : microstructual modeling & pulmonary arterial hypertension
Creator: Xi, Ce
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Heart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop...
Show moreHeart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop 1) a microstructure-based constitutive model of the heart and 2) patient-specific computational models that would ultimately help medical scientists to diagnose and treat heart diseases.Heart diseases such as heart failure with preserved ejection fraction (HFpEF) are characterized by abnormalities of ventricular function that can be attributed to, changes in geometry, impaired myocyte (LV) relaxation, cardiac fibrosis and myocyte passive stiffening. Understanding how LV filling is affected by each of the many microstructural pathological features in heart diseases is very important and may help in the development of appropriate treatments. To address this need, we have developed and validated a microstructure-based computational model of the myocardium to investigate the role of tissue constituents and their ultrastructure in affecting the heart function. The model predicted that the LV filling function is sensitive to the collagen ultrastructure and the load taken up by the tissue constituents varies depending on the LV transmural location. This finding may have implications in the development of new pharmaceutical treatments targeting individual cardiac tissue constituents to normalize LV filling function in HFpEF.Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by elevated pulmonary artery pressure (PAP) and pulmonary artery vascular resistance, with limited survival rate and can affect patients of all ages. The increased pressure or afterload in the right ventricle (RV) can result in pathological changes in RV mechanics, which are currently not well-understood. To FB01ll this void, we have developed patient-specific computational models to investigate effects of PAH on ventricular mechanics. SpeciFB01cally, we have quantified regional ventricular myoFB01ber stress, myoFB01ber strain, contractility, and passive tissue stiffness in PAH patients, and compare them to those found in age- and gender-matched normal controls. Our results showed that RV longitudinal, circumferential and radial strain were depressed in PAH patients compared with controls; RV passive stiffness increased progressively with the degree of remodeling as indexed by the RV and LV end-diastolic volume ratio (RVEDV/LVEDV); Peak contractility of the RV was found to be strongly correlated, and had an inverse relationship with RVEDV/LVEDV. These results provide the mechanical basis of using RVEDV/LVEDV as a clinical index for delineating disease severity and estimating RVFW contractility in PAH patients.
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Title: Investigating the impact of manmade reservoirs on large-scale hydrology and water resources using high-resolution modeling
Creator: Shin, Sanghoon
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Manmade reservoirs are important components of the terrestrial hydrologic system. Dam installments fragment river systems, and reservoir operations alter flow regimes. The total storage capacity of existing global reservoirs is large enough to hold one sixth of annual continental discharge to global oceans. Due to growing energy demands, hundreds of large dams are being built and planned around the world, especially in the developing countries. Therefore, there is an urgent need to develop a...
Show moreManmade reservoirs are important components of the terrestrial hydrologic system. Dam installments fragment river systems, and reservoir operations alter flow regimes. The total storage capacity of existing global reservoirs is large enough to hold one sixth of annual continental discharge to global oceans. Due to growing energy demands, hundreds of large dams are being built and planned around the world, especially in the developing countries. Therefore, there is an urgent need to develop a better understanding of the impact of the existing and new dams on hydrological, ecological, agricultural, and socio-economic systems. Owing to increasing computational power and needs to understand and simulate processes in small-scale, hydrological models are advancing towards hyper-resolution global hydrological models. One of benefits of the increased spatial resolution is that the dynamics of surface water inundation over natural river-floodplain systems and manmade reservoirs can be explicitly represented; however, existing global models are not capable of simulating the river-floodplain-reservoir inundation dynamics in an integrated manner. This dissertation addresses this important standing issue by developing a high-resolution, continental-scale model to simulate the spatial and temporal dynamics of reservoir storage and release, thus paving pathways toward hyper-resolution surface water modeling in continental- to global-scale hydrological and climate models. The newly developed model is applied to simulate reservoirs within the contiguous United States (CONUS) and the Mekong River Basin (MRB) in Southeast Asia. With respect to the model development, the following advances are made over the previous global reservoir modeling studies: (1) an existing algorithm for reservoir operation is improved by conducting analytical analysis and numerical experiments and by introducing new calibration features for reservoir operation; (2) the spatial extent and its seasonal dynamics of reservoirs are explicitly simulated and reservoirs are treated as an integral part of river-floodplain routing, thus reservoir storage is no longer isolated from river and floodplain storages; and (3) a novel approach for processing and integrating high-resolution digital elevation models (DEMs) in river-floodplain-reservoir routing is introduced. The newly developed reservoir scheme is integrated within the river-floodplain routing scheme of a continental hydrological model, LEAF-Hydro-Flood, which is set for the CONUS, where abundant data are available for model validation. Then, the reservoir scheme is integrated into a global hydrodynamics model, CaMa-Flood, to investigate the historical impact of manmade reservoirs in the MRB that is experiencing an unprecedented boom in hydropower dam construction. Using the new scheme, the role of flood dynamics in modulating the hydrology of the MRB and the potential impact of flow regulation by the dams on the inundation dynamics are investigated. The significance of hydrologic effect of increasing dams is compared with that of climate variability. The fully coupled river-reservoir-floodplain storage simulation approach presented in this dissertation provides an advancement in hydrological modeling in terms of the representation of surface water dynamics, which is indispensable for better attribution of the observed changes in the water cycle, prediction of changes in water resources, and the understanding of the continually changing environmental and ecological systems.
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Title: Experiments and model development of a dual mode, turbulent jet ignition engine
Creator: Tolou, Sedigheh
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can...
Show more"The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can achieve high diesel-like thermal efficiencies at medium to high loads and potentially exceed diesel efficiencies at low-load operating conditions. The DM-TJI strategy extends the mixture flammability limits by igniting lean and/or highly dilute mixtures, leading to low-temperature combustion (LTC) modes in spark ignition (SI) engines. A novel, reduced order, and physics-based model was developed to predict the behavior of a DM-TJI engine with a pre-chamber air valve assembly. The engine model developed was calibrated based on experimental data from a Prototype II DM-TJI engine. This engine was designed, built, and tested at the MSU Energy and Automotive Research Laboratory (EARL). A predictive, generalized model was introduced to obtain a complete engine fuel map for the DM-TJI engine. The engine fuel map was generated in a four-cylinder boosted configuration under highly dilute conditions, up to 40% external exhaust gas recirculation (EGR). A vehicle simulation was then performed to further explore fuel economy gains using the fuel map generated for the DM-TJI engine. The DM-TJI engine was embodied in an industry-based vehicle to examine the behavior of the engine over the U.S. Environmental Protection Agency (EPA) driving schedules. The results obtained from the drive cycle analysis of the DM-TJI engine in an industry-based vehicle were compared to the results of the same vehicle with its original engine. The vehicle equipped with the DM-TJI system was observed to benefit from 103033% improvement in fuel economy and 103031% reduction in CO2 emission over the EPA combined city/high driving schedules. Potential improvements were discussed, as these results of the drive cycle analysis are the first-ever reported results for a DM-TJI engine embodied in an industry-based vehicle. The resulting fuel economy and CO2 emission were used to conduct a cost-benefit analysis of a DM-TJI engine. The cost-benefit analysis followed the economic and key inputs used by the U.S. EPA in a Proposed Determination prepared by that agency. The outcomes of the cost-benefit analysis for the vehicle equipped with the DM-TJI system were reported in comparison with the same vehicle with its base engine. The extra costs of a DM-TJI engine were observed to be compensated over the first three years of the vehicle's life time. The results projected maximum savings of approximately 2400 in 2019 dollars. This includes the lifetime-discounted present value of the net benefits of the DM-TJI technology, compared to the base engine examined. In this dollar saving estimate, the societal effects of CO2 emission were calculated based on values by the interagency working group (IWG) at 3% discount rate."--Pages ii-iii.
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Title: Improving the representation of irrigation and groundwater in global land surface models to advance the understanding of hydrology-human-climate interactions
Creator: Felfelani, Farshid
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Hydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the...
Show moreHydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the variations in the terrestrial hydrology. The representation of irrigation and groundwater-two major hydrologic processes with complex reciprocal interplay-in large-scale hydrological models is rather poorly parameterized and heavily simplified, hindering our ability to realistically simulate groundwater-human-climate interactions. This dissertation advances the physical basis for irrigation and groundwater parameterizations in global land surface models, leveraging the potential of emerging satellite data (i.e., data from GRACE and SMAP satellite missions) toward a more realistic quantification of the impacts of human activities on the hydrological cycle. A comprehensive global analysis is developed to examine the historical spatial patterns and long-term temporal response, i.e., the terrestrial water storage (TWS), of two models to natural and human-induced drivers. Human-induced changes in TWS are then quantified in the highly managed global regions to identify the uncertainties arising from a simplistic representation of irrigation and groundwater. The potential of improving irrigation representation in the Community Land Model version 4.5 (CLM4.5) is then investigated by assimilating the soil moisture data from SMAP satellite mission using 1-D Kalman Filter assimilation approach. The new irrigation scheme is then tested over the heavily irrigated central U.S. Next, the existing groundwater module of CLM5 is broadly evaluated over conterminous U.S. and a new prognostic groundwater module is implemented in CLM5 to account for lateral groundwater flow, pumping, and conjunctive water use for irrigation. In particular, an explicit parameterization for the steady-state well equation is introduced for the first time in large-scale hydrological modeling. Finally, the impacts of climate change on global TWS variabilities and the implications on sea level change are examined for the entire 21st century using multi-model hydrological simulations. The key findings and conclusions from the aforementioned multi-scale analysis and model developments are: (1) in terms of TWS, notable differences exist not only between simulations of hydrological models and GRACE but also among different GRACE products, therefore, TWS variations from a single model cannot be reliably used for global analyses; (2) these differences significantly increase in projections of TWS under climate change, however, models agree in sign of change for most global areas; (3) TWS is expected to decline in many regions in southern hemisphere, but increase in northern high latitudes, projected to accelerate sea level rise by the mid- and late-21st century; (4) constraining the target soil moisture in CLM4.5 using SMAP data assimilation with 1-D Kalman Filter reduces the bias in the simulated irrigation water by up to 60% on average, improving irrigation and soil moisture simulations in CLM4.5; (5) the new groundwater model significantly improves the simulation of groundwater level change and promisingly captures most of the hotspots of groundwater depletion across the U.S. overexploited aquifers; and (6) the simulation with the lateral groundwater flow substantially enhances the TWS trends relative to the default CLM5. These results and findings could provide a basis for improved large-scale irrigation and groundwater modeling and improve our understanding of hydrology-human-climate interactions.
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Title: Sensitivities of simulated fire-induced flows to fire shape and background wind profile using a cloud-resolving model
Creator: Stageberg, Marshall S.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Wildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud...
Show moreWildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud Model 1 (CM1) because it is designed to simulate high resolution, cloud scale processes that are comparable in scale to fire-induced flows. A surface sensible heat flux is added to CM1 to simulate the effect of a fire and the resultant fire-induced circulations and complex flows are examined. Using CM1 allows us to produce simulations with fine spatial and temporal resolution with a detailed representation of the evolution of the fire-atmosphere system. For the purpose of this study, we perform a series of simulations to examine the sensitivity of fire-induced flows to the shape of the simulated fire and to background wind profile. We show how fire shape and the background wind profile affect the intensity and extent of fire-induced perturbations to the lower atmosphere. The results from these numerical simulations, when combined with field observations, help improve our understanding of fire-atmosphere interactions. The results from this study can potentially help fire managers with decision-making when fighting wildland fires.
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Title: Integration of topological data analysis and machine learning for small molecule property predictions
Creator: Wu, Kedi
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Accurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH...
Show moreAccurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants and is different from conventional chemical and physical representations. Based on ESPH and its modified version, element-specific topological descriptors (ESTDs) are constructed. The advantage of ESTDs is that they are systematical, comprehensive, and scalable with respect to molecular size and composition variations, and are readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between different small molecule properties, multi-task frameworks are further employed to simultaneously predict related properties. Deep neural networks, along with ensemble methods such as random forest and gradient boosting trees, are used to develop quantitative predictive models. Physical based molecular descriptors and auxiliary descriptors are also used in addition to ESTDs. As a result, we obtain state-of-the-art results for various benchmark data sets of small molecule properties. We have also developed two online servers for predicting properties of small molecules, TopP-S and TopTox. TopP-S is a software for topological learning predictions of partition coefficient and aqueous solubility, and TopTox is a software for computing element-specific tological descriptors (ESTDs) for toxicity endpoint predictions. They are available at http://weilab.math.msu.edu/TopP-S/ and http://weilab.math.msu.edu/TopTox/, respectively.
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Title: Data-driven and task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
Creator: Ashtawy, Hossam Mohamed Farg
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Molecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation ...
Show moreMolecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation (docking task), binary activity label (screening task), and binding affinity (scoring task) of ligands against a critical protein in the disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for the three tasks to solve three different, but related, prediction problems. The vast majority of these predictive models are knowledge-based, empirical, or force-field scoring functions. The fourth family of SFs that has gained popularity recently and showed potential of improved accuracy is based on machine-learning (ML) approaches. Despite intense efforts in developing conventional and current ML SFs, their limited predictive accuracies in these three tasks have been a major roadblock toward cost-effective drug discovery. Therefore, in this work we present (i) novel task- specific and multi-task SFs employing large ensembles of deep neural networks (NN) and other state-of-the-art ML algorithms in conjunction with (ii) data-driven multi-perspective descriptors (features) for accurate characterization of protein-ligand complexes (PLCs) extracted using our Descriptor Data Bank (DDB) platform.We assess the docking, screening, scoring, and ranking accuracies of the proposed task-specific SFs with DDB descriptors as well as several conventional approaches in the context of the 2007 and 2014 PDBbind benchmark that encompasses a diverse set of high-quality PLCs. Our approaches substantially outperform conventional SFs based on BA and single-perspective descriptors in all tests. In terms of scoring accuracy, we find that the ensemble NN SFs, BsN-Score and BgN-Score, have more than 34% better correlation (0.844 and 0.840 vs. 0.627) between predicted and measured BAs compared to that achieved by X-Score, a top performing conventional SF. We further find that ensemble NN models surpass SFs based on other state-of-the-art ML algorithms. Similar results have been obtained for the ranking task. Within clusters of PLCs with different ligands bound to the same target protein, we find that the best ensemble NN SF is able to rank the ligands correctly 64.6% of the time compared to 57.8% obtained by X-Score. A substantial improvement in the docking task has also been achieved by our proposed docking-specific SFs. We find that the docking NN SF, BsN-Dock, has a success rate of 95% in identifying poses that are within 2 Å RMSD from the native poses of 65 different protein families. This is in comparison to a success rate of only 82% achieved by the best conventional SF, ChemPLP, employed in the commercial docking software GOLD. As for the ability to distinguish active molecules from inactives, our screening-specific SFs showed excellent improvements over the conventional approaches. The proposed SF BsN-Screen achieved a screening enrichment factor of 33.90 as opposed to 19.54 obtained from the best conventional SF, GlideScore, employed in the docking software Glide. For all tasks, we observed that the proposed task-specific SFs benefit more than their conventional counterparts from increases in the number of descriptors and training PLCs. They also perform better on novel proteins that they were never trained on before. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from three tasks to simultaneously predict binding poses, affinities, and activity labels. MT-Net is composed of shared hidden layers for the three tasks to learn common features, task-specific hidden layers for higher feature representation, and three outputs for the three tasks. We show that the performance of MT-Net is superior to conventional SFs and competitive with other ML approaches. Based on current results and potential improvements, we believe our proposed ideas will have a transformative impact on the accuracy and outcomes of molecular docking and virtual screening.
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Title: Computational chemistry : investigations of protein-protein interactions and post-translational modifications to peptides
Creator: Jones, Michael R. (Graduate of Michigan State University)
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Computational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit...
Show moreComputational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit β (IKKβ) or NF-κB Inducing Kinase (NIK), initiates this cascade of events. Understanding the structure-property relationships associated with IKKβ and NIK is essential for the development of prevention strategies. Although the signaling pathways are known, how the molecular mechanisms respond to changes in the intracellular microenvironment (i.e., pH, ionic strength, temperature) remains elusive. In this dissertation, computer simulation and modeling techniques were used investigate two protein kinases complexed with either small molecule activators or inhibitors in the active, inactive, and mutant states to correlate structure-property and structure-function relationships as a function of intracellular ionic strength. Additionally, radical-induced protein fragmentation pathways, as a result of reactions with reactive oxygen species, were investigated to yield insight into the thermodynamic preference of the fragmentation mechanisms. Analyses of the relationship between structure-activity and conformational-activity indicate that the protein-protein interactions and the binding of small molecules are sensitive to changes in the ionic strength and that there are several factors that influence the selectivity of peptide backbone cleavage. As there are many computational approaches for predicting physical and chemical properties, several methods were considered for the predictions of protein-protein dissociation, protein backbone fragmentation, and partition coefficients of drug-like molecules.
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Title: Design and simulation of single-crystal diamond diodes for high voltage, high power and high temperature applications
Creator: Suwanmonkha, Nutthamon
Date: 2016
Collection: Electronic Theses & Dissertations
Description: ABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these...
Show moreABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these properties are crucial for a semiconductor that is used to make electronic devices that can operate at high power levels, high voltage and high temperature.Two-dimensional semiconductor device simulation software such as Medici assists engineers to design device structures that allow the performance requirements of device applications to be met. Most physical material parameters of the well-known semiconductors are already compiled and embedded in Medici. However, diamond is not one of them. Material parameters of diamond, which include the models for incomplete ionization, temperature-and-impurity-dependent mobility, and impact ionization, are not readily available in software such as Medici. Models and data for diamond semiconductor material have been developed for Medici in the work based on results measured in the research literature and in the experimental work at Michigan State University. After equipping Medici with diamond material parameters, simulations of various diamond diodes including Schottky, PN-junction and merged Schottky/PN-junction diode structures are reported. Diodes are simulated versus changes in doping concentration, drift layer thickness and operating temperature. In particular, the diode performance metrics studied include the breakdown voltage, turn-on voltage, and specific on-resistance. The goal is to find the designs which yield low power loss and provide high voltage blocking capability. Simulation results are presented that provide insight for the design of diamond diodes using the various diode structures. Results are also reported on the use of field plate structures in the simulations to control the electric field and increase the breakdown voltage.
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Title: Unconstrained 3D face reconstruction from photo collections
Creator: Roth, Joseph (Software engineer)
Date: 2016
Collection: Electronic Theses & Dissertations
Description: This thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input...
Show moreThis thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input is a long-standing computer vision problem. Traditional photometric stereo-based reconstruction techniques work on aligned 2D images and produce a 2.5D depth map reconstruction. We extend face reconstruction to work with a true 3D model, allowing us to enjoy the benefits of using images from all poses, up to and including profiles. To use a 3D model, we propose a novel normal field-based Laplace editing technique which allows us to deform a triangulated mesh to match the observed surface normals. Unlike prior work that require large photo collections, we formulate an approach to adapt to photo collections with few images of potentially poor quality. We achieve this through incorporating prior knowledge about face shape by fitting a 3D Morphable Model to form a personalized template before using a novel analysis-by-synthesis photometric stereo formulation to complete the fine face details. A structural similarity-based quality measure allows evaluation in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on Internet, synthetic, and personal photo collections.
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Title: Modeling and simulation of strongly coupled plasmas
Creator: Chowdhury, Rahnuma Rifat
Date: 2016
Collection: Electronic Theses & Dissertations
Description: The objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the...
Show moreThe objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the impact of statistical fluctuations in strongly coupled plasmas that leads to non-Markovian effects. Proper modeling of such systems through consideration of Lévy flight processes gives rise to fractional derivatives in time that result in an incorporation of time history in the model. A Lévy flight is a random walk in which the steps are defined in terms of the step-lengths, which have a certain probability distribution, with the directions of the steps being isotropic and random. Lévy processes in the plasma give rise to fluctuations in medium through which the electromagnetic waves are propagating. Averaging over the Lévy processes will allow us to relate to other important parameters in the plasma.
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Title: Advances in metal ion modeling
Creator: Li, Pengfei (Chemist)
Date: 2016
Collection: Electronic Theses & Dissertations
Description: Metal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte...
Show moreMetal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte Carlo orMolecular Dynamics simulation protocol. However, there are various metal ions and it isstill challenging to model them using available force field models. Generally there areseveral models available for modeling metal ions using the force field approach such asthe nonbonded model, the bonded model, the cationic dummy atom model, the combinedmodel, and the polarizable models. Our work concentrated on the nonbonded and bondedmodels, which are widely used nowadays. Firstly, we focused on filling in the blanks ofthis field. We proposed a noble gas curve, which was used to describe the relationshipbetween the van der Waals radius and well depth parameters in the 12-6 Lennard-Jonespotential. By using the noble gas curve and multiple target values (the hydration freeenergy, ion-oxygen distance, coordination number values), we have consistentlyparameterized the 12-6 Lennard-Jones nonbonded model for 63 different ions (including11 monovalent cations, 4 monovalent anions, 24 divalent cations, 18 trivalent cations,and 6 tetravalent cations) combined with three widely used water models (TIP3P, SPC/E, and TIP4PEW). Secondly, we found there is limited accuracy of the 12-6 model, whichmakes it hard to simulate different properties simultaneously for ions with formal chargeequal or larger than +2. By considering the physical origins of the 12-6 model, weproposed a new nonbonded model, named the 12-6-4 LJ-type nonbonded model. Wehave systematically parameterized the 12-6-4 model for 55 different ions (including 11monovalent cations, 4 monovalent anions, 16 divalent cations, 18 trivalent cations, and 6tetravalent cations) in the three water models. It was shown that the 12-6-4 model couldreproduce several properties at the same time, showing remarkable improvement over the12-6 model. Meanwhile, through the usage of a proposed combining rule, the 12-6-4model showed excellent transferability to mixed systems. Thirdly, we have developed theMCPB.py program to facilitate building of the bonded model for metal ion containingsystems, which can largely reduce human efforts. Finally, an application case of ametallochaperone - CusF was shown, and based on the simulations we hypothesized anion transfer mechanism.
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Title: Reliability improvement of DFIG-based wind energy conversion systems by real time control
Creator: Elhmoud, Lina Adnan Abdullah
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Reliability is the probability that a system or component will satisfactorily perform its intended function under given operating conditions. The average time of satisfactory operation of a system is called the mean time between failures (MTBF) and. the higher value of MTBF indicates higher reliability and vice versa. Nowadays, reliability is of greater concern than in the past especially for offshore wind turbines since the access to these installations in case of failures is both costly and...
Show moreReliability is the probability that a system or component will satisfactorily perform its intended function under given operating conditions. The average time of satisfactory operation of a system is called the mean time between failures (MTBF) and. the higher value of MTBF indicates higher reliability and vice versa. Nowadays, reliability is of greater concern than in the past especially for offshore wind turbines since the access to these installations in case of failures is both costly and difficult. Power semiconductor devices are often ranked as the most vulnerable components from reliability perspective in a power conversion system. The lifetime prediction of power modules based on mission profile is an important issue. Furthermore, lifetime modeling of future large wind turbines is needed in order to make reliability predictions in the early design phase. By conducting reliability prediction in the design phase a manufacture can ensure that the new wind turbines will operate within designed reliability metrics such as lifetime.This work presents reliability analysis of power electronic converters for wind energy conversion systems (WECS) based on semiconductor power losses. A real time control scheme is proposed to maximize the system's lifetime and the accumulated energy produced over the lifetime. It has been verified through the reliability model that a low-pass-filter-based control can effectively increase the MTBF and lifetime of the power modules. The fundamental cause to achieve higher MTBF lies in the reduction of the number of thermal cycles.The key element in a power conversion system is the power semiconductor device, which operates as a power switch. The improvement in power semiconductor devices is the critical driving force behind the improved performance, efficiency, reduced size and weight of power conversion systems. As the power density and switching frequency increase, thermal analysis of power electronic system becomes imperative. The analysis provides information on semiconductor device rating, reliability, and lifetime calculation. The power throughput of the state-of-the-art WECS that is equipped with maximum power point control algorithms is subjected to wind speed fluctuations, which may cause significant thermal cycling of the IGBT in power converter and in turn lead to reduction in lifetime. To address this reliability issue, a real-time control scheme based on the reliability model of the system is proposed. In this work a doubly fed induction generator is utilized as a demonstration system to prove the effectiveness of the proposed method. Average model of three-phase converter has been adopted for thermal modeling and lifetime estimation. A low-pass-filter based control law is utilized to modify the power command from conventional WECS control output. The resultant reliability performance of the system has been significantly improved as evidenced by the simulation results.
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Title: Predictive control of a hybrid powertrain
Creator: Yang, Jie
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Powertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This...
Show morePowertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This dissertation focuses on driving torque prediction, battery boundary management, and fuel economy optimization.For a hybrid powertrain, when the desired torque (driver torque demand) is outside of battery operational limits, the internal combustion (IC) engine needs to be turned on to deliver additional power (torque) to the powertrain. But the slow response of the IC engine, compared with electric motors (EMs), prevents it from providing power (torque) immediately. As a result, before the engine power is ready, the battery has to be over-discharged to provide the desired powertrain power (torque). This dissertation presents an adaptive recursive prediction algorithm to predict the future desired torque based on past and current vehicle pedal positions. The recursive nature of the prediction algorithm reduces the computational load significantly and makes it feasible for real-time implementation. Two weighting coefficients are introduced to make it possible to rely more on the data newly sampled and avoid numerical singularity. This improves the prediction accuracy greatly, and also the prediction algorithm is able to adapt to different driver behaviors and driving conditions.Based on the online-predicted desired torque and its error variance, a stochastic predictive boundary management strategy is proposed in this dissertation. The smallest upper bound of future desired torque for a given confidence level is obtained based on the predicted desired torque and prediction error variance and it is used to determine if the engine needs to be proactively turned on. That is, the engine can be ready to provide power for the “future” when the actual power (torque) demand exceeds the battery output limits. Correspondingly, the battery over-discharging duration can be reduced greatly, leading to extended battery life and improved HEV performance.To optimize powertrain fuel economy, a model predictive control (MPC) strategy is developed based on the linear quadratic tracking (LQT) approach. The finite horizon LQT control is based on the discrete-time system model obtained by linearizing the nonlinear HEV and only the first step of the solution is applied for current control. This process is repeated for each control step. The effectiveness of the supervisory control strategy is studied and validated in simulations under typical driving cycles based on a forward power split HEV model. The developed MPC-LQT control scheme tracks the predicted desired torque trajectory over the prediction horizon, minimizes the powertrain fuel consumption, maintains the battery state of charge at the desired level, and operates the battery within its designed boundary.
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