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Title: Economic and Computational Methods for the Control of Uncertain Systems
Creator: Zhang, Jin
Date: 2019
Description: The Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional...
Show moreThe Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional optimization and control hierarchy. This dissertation investigates the computational procedures used to obtain a global solution to the ELOC problem. The proposed method employs the Generalized Benders Decomposition (GBD) algorithm. Compared to the previous branch and bound approach, the application of GBD to the ELOC problem will greatly improve computational performance. A technological benefit of decomposing the problem into steady-state and dynamic parts is the ability to utilize nonlinear steady-state models, since the relaxed master problem is free of SDP type constraints and can be solved using any global nonlinear programming algorithm.In order to address the issue of model/plant mismatch, the dissertation will also investigate how to handle box-type uncertainties in ELOC. We consider two methods, a robust formulation for when the uncertainty is completely unknown and a Linear Parameter Varying formulation for when uncertainty can be measured in real time. In both cases, the infinite number of conditions that need to be satisfied are reduced to a finite set of constraints. The resulting problem formulations have a similar structure to the ELOC and can be solved globally by employing the generalized Benders decomposition.Despite a high-quality control law, the ultimate performance of closed-loop systems will be dictated by the quality and limitation of hardware element. Thus, hardware selection is also investigated in the dissertation. The cost-optimal hardware selection problem has been shown to be of the Mixed Integer Convex Programming (MICP) class. While such a formulation provides a route to global optimality, use of the branch and bound search procedure has limited application to fairly small systems. In this dissertation, we illustrate that a simple reformulation of the MICP and subsequent application of the GBD algorithm will result in massive reductions in computational effort.Finally, the problems of value-optimal sensor network design (SND) for steady-state and closed-loop systems are investigated. The value-optimal SND problem has been shown to be of the nonconvex mixed integer programming class. In the dissertation, it is demonstrated after transforming into an equivalent reformation, the application of GBD algorithm will significantly reduce the computational effort.
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Title: Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems
Creator: Sui, Wenbo
Date: 2018
Description: High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control...
Show moreHigh combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NOx) emissions of vehicles.There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions.
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Title: COMPUTATIONAL MODELLING OF FALLING FILM FLOW AND HEAT TRANSFER OVER HORIZONTAL TUBES
Creator: Karmakar, Avijit
Date: 2021
Description: In this study, numerical simulations based on the volume of fluid (VOF) method are conducted to investigate the hydrodynamic behavior,...
Show moreIn this study, numerical simulations based on the volume of fluid (VOF) method are conducted to investigate the hydrodynamic behavior, sensible heat transfer behavior, and tube surface wettability effects for a falling film over heated horizontal tubes encountered in falling film heat exchangers. The Reynolds number ranges from 15 - 210, covering the droplet, jet (inline and staggered), and sheet flow modes. To consider evaporation under liquid film waviness and gas (vapor and air) flow effects, a simplified case was studied for the wavy liquid film over a heated vertical surface with the surrounding gas flowing in either co-current or counter-current direction. The OpenFOAM CFD solver has been used to conduct the numerical simulations.For hydrodynamics, the liquid film thickness and interface velocity variation for all the flow modes are presented. In droplet mode, the movement of the liquid waves formed by the drop impact causes an over 350% change in film thickness. A dimple around the jet impingement region in the steady inline jet mode is formed with a relative change in film thickness by 40%. The base of the impinging jets possess ripples of wavelengths 0.3-1.0 times the capillary length. For the steady staggered jet mode, the neighboring jets interact to develop crest and stable segments with film thickness ratio of 1.7. Finally, for the sheet mode, interfacial waves are seen to travel along the tube periphery with amplitudes of about 20% of the nominal film thickness. A set of correlations have been presented to predict film thickness and interfacial velocity with RMSE = 0.2 for 80% of the data.The local Nusselt number (Nu) distribution depends on the flow features in each mode. In the droplet mode, the Nu value varies significantly as the droplet impinges and the remnant liquid-bridge retracts (peak instantaneous Nu = 6), followed by wave propagation with peak Nu = 0.25. For the jet modes, the local maximum in Nu occurs off-center to the impingement location with peak Nu = 3.1 for the inline jet mode and Nu = 2.7 for the staggered jet mode, while for other locations, Nu varies as inversely proportional to film thickness. Substantial variations in the Nu value are also recorded in the middle of the two impinging jets with Nu = 0.95 in the inline jet mode, and Nu = 0.60 in the crest region of the staggered jet mode. In the sheet mode, the Nu varies with the thickness of the traversing liquid waves. Lower Nu values were recorded beneath the crest location of the liquid waves, which increases (1.4 - 11.6%) abruptly at the advancing fronts of the waves. The temperature distribution in the liquid film in each of the modes was examined to evaluate the mechanism of heat transfer process. This study also compares the Nu distribution with the available analytical heat transfer models.The tube surface wettability results present the liquid film thickness, the wetted areas, and the Nusselt number (Nu) over the tube surface. The resistance imposed by the increasing contact angles inhibits the extent of the liquid spreading over the tube surface, and this, in turn, influences film thickness and wetted areas. A significant decrement in the heat transfer rate from the tube surfaces was observed as the equilibrium contact angle increased from 2 to 175 degrees. The local distributions of the Nu over the tube surface are strongly influenced by the flow recirculation in the liquid bulk.Finally, for wavy film evaporation under gas flow effects, the results show a 15% and 16% enhancement in time-averaged Sherwood number (Sh) due to film waviness (sinusoidal and solitary) with gas flow rate, Qg = +50 and Qg = -50, respectively. This enhancement in the Sh for both the waves further increases by 11% with Qg =+800 and by 196% with Qg = -800. Closer examination of the mass transfer process over a wave demonstrates that with Qg = +50, the concentration of the gas side streamlines at the trough locations of the wave leads to higher values of Sh than the rest of the locations. However, with Qg = +800, although the overall Sh increases, vortices appear at the wave trough locations, leading to decreased local Sh values than the surrounding locations.
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Title: MODELING AND CONTROL OF A GASOLINE-FUELED COMPRESSION IGNITION ENGINE
Creator: Pamminger, Michael
Date: 2021
Description: This work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio...
Show moreThis work investigates a novel combustion concept, Gasoline Compression Ignition, that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel fuel. Gasoline Compression Ignition was experimentally tested on a 12.4L truck engine and the acquired data were leveraged to develop a physics-based 0-dimensional combustion model for an engine operating with a low-reactivity fuel. The proposed 0-dimensional combustion model was developed to account for the different stages in combustion caused by the fuel stratification of various injection events and fuel mass fractions. As the ignition delay model is an integral part of the entire combustion process and significantly affects the predictionaccuracy, special attention was paid to local phenomena influencing ignition delay. A 1-dimensional spray model by Musculus and Kattke was employed in conjunction with a Lagrangian tracking approach in order to estimate the local fuel-air ratio within the spray tip, as a proxy for reactivity. The local fuel-air ratio, in-cylinder temperature and pressure were used in an integral fashion to estimate the ignition delay. Heat release rates were modeled by using first-order non-linear differential equations. Model prediction errors in combustion phasing of less than 1 crank angle degree across most conditions were achieved. Modeling results of other combustion metrics such as combustion duration and indicated mean effective pressure are also suitably accurate. Also, the model has been shown to be capable of estimating the ringing intensity for most conditions. While the performance of the proposed model was very satisfactory, the high computational time made it unsuitable for simulations. The high computational cost was mostly caused by the 1-dimensional spray model which described the fuelstratifcation in the spray tip as a function of crank angle for multiple injection events. Insights obtained from the 1-dimensional spray model were leveraged and applied to a 0-dimensional model to reduce the computation time. With the reduced order model, the simulation time decreased by three orders of magnitude for an entire engine cycle over the combustion model with the 1-dimensional spray model. Capturing only the basic features of the spray propagation did not show a substantial increase in prediction error compared to the initially proposed model. In order for this model to reflect a virtual engine, the influence of changes in actuator settings on intake manifold dynamics was modeled with first-order transfer functions. The intake manifold dynamics in turn influence intake valve closure conditions and further the entire combustion process. The proposed model provides information about in-cylinder metrics such as combustion phasing and indicated mean effective pressure. By taking into account the losses due to gas-exchange and friction, the brake mean effective pressure was modeled. The model was also augmented to capture cycle-to-cycle variations, thereby ensuring a faithful representation of real engine behavior. The Gasoline Compression Ignition combustion model, the intake dynamics and gas-exchange and friction model as well as the cycle-to-cycle variations model were combined to create a full engine model. This Gasoline Compression Ignition engine model was used as the plant in a control system and implemented in Matlab/Simulink.The Gasoline Compression Ignition engine model was then leveraged to investigate control actions and engine behavior with and without limiting in-cylinder peak pressure as well as combustion noise. Controlling combustion noise is of particular interest for injection strategies where fuel introduction happens early in the cycle. State estimation was performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The Gasoline Compression Ignition engine model was also utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations. Disturbance rejection was implemented to reduce state fluctuations and control efforts when high cycle-to-cycle variations are present. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.
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Title: ELECTROCHEMICAL BEHAVIOR OF ADDITIVELY MANUFACTURED NON-SPHERICAL TI-6AL-4V POWDER IN 3.5 WT. % NACL SOLUTION
Creator: Bagi, Sourabh Dilip
Date: 2021
Description: In laser powder bed fusion (LPBF), also known as selective laser melting (SLM), the feedstock powder and processing parameters affect the...
Show moreIn laser powder bed fusion (LPBF), also known as selective laser melting (SLM), the feedstock powder and processing parameters affect the properties of additively manufactured parts. Limited research has been conducted on non-spherical Ti6Al4V feedstock powder prepared by Hydride-Dehydride process. Significant progress in metal powder additive manufacturing (AM) requires the inter-linking of multiple variables, which includes starting materials, process settings, and post-treatment to achieve desired resultant properties. Owing to the rapid emergence of metal 3D-printing, process-property relationships, and appropriate post-treatment conditions have not been as extensively characterized as for conventional materials, thus requiring significant attention. Over the years, spherical powders were used in powder bed AM machines and there have been various concerns related to powder as well as processing parameters leading to defects formation, poor part quality, and unsatisfactory performance. It is critical to keep the cost of manufacturing low for large-scale production which results in significant interest in low-cost powder, making it vital to understand the effect of microstructural defects on corrosion behavior. Recently, economical powder attracted attention in AM, thus, making it is necessary to understand the role of possible microstructural defects on corrosion behavior. In powder bed additive manufacturing, feedstock and processing affect final microstructure and properties of the 3D printed parts. While numerous studies have evaluated 3D-printing of spherical powder, very limited research has examined the processing of the non-spherical feedstock. In this research, parts are manufactured by SLM of hydride-dehydride (HDH) Ti6Al4V powder. heat treatment and hot isostatic pressing are applied on SLM parts. The microstructures, potentiodynamic curves, and electrochemical impedance spectroscopy are characterized for SLM processed, heat treated, and hot isostatically pressed HDH Ti6Al4V specimens. Results indicate although the as-built specimen has anisotropic microstructure (i.e., lamellar α + acicular α’ + β phases), the heat treatment and hot isostatic pressing result in homogenized grain structures and enhanced corrosion behavior. Results indicate that type of constituent phase, grain size, and morphology directly determine corrosion resistance. This research is beneficial for the manufacturing of low-cost titanium alloys. In the current research, we evaluate non-spherical powder processing by hydride-dehydride (HDH) method and selective laser melted in powder bed AM machine followed by heat treatment and hot isostatic pressing to alter microstructure and electrochemical behavior. If successful, the usage of non-spherical morphology in conjunction with the newer powder dispensing method of double smoothing will enable remarkable improvements in the quality and performance of additively manufactured products. This method will also cut down costs associated with a greener powder production method and enhance the fabrication rate. It is a well-established fact that corrosion behavior is drastically affected by heterogeneous microstructure and defects. Thus, it is paramount to conduct a systematic study on the role of processing parameters and post process heat treatment, which can enhance our understanding of possible defect formation in micro and macro scale and their impact on electrochemical behavior.
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Title: Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
Creator: Bhattacharyya, Viranjan
Date: 2021
Description: This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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Title: Modeling and Control Methods for Boundary Constrained Soft Robots
Creator: Zhou, Qiyuan
Date: 2021
Description: Soft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply...
Show moreSoft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply much force to an environment due to the limitations of the flexible materials from which they are made of. To help overcome this limitation, a new architecture named the Jamming and Morphing Enabled Bot Array (JAMoEBA) system was conceived. This system consists of a flexible outer membrane which encloses an interior composed of a granular medium. Active sub-units along the flexible outer membrane allow for actuation and locomotion of the system. The granular material coupled with the flexible outer membrane allows the robot to maintain the characteristics typically associated with soft robots (continuum, compliant, configurable). At the same time, the granular material is also able to undergo a solid phase transition with the application of pressure to the flexible outer membrane and allow the system to behave more like a rigid robot if needed. This allows for the robot system to exploit the desirable characteristics of both soft and rigid robots in its tasks.The purpose of this thesis is to offer a discussion and demonstration of various simulation methods for the physically accurate modeling of the JAMoEBA constrained boundary robotic system and to show some of the control methods which have been investigated within the selected modeling framework. Simulation methods based on Lennard-Jones (L-J) potentials, non-smooth contact dynamics (NSCD), as well as the discrete element methods based on complementarity (DEM-C) and penalty (DEM-P) conditions as implemented in the open source physics library Project Chrono are considered. Comparisons are made in the areas of physical accuracy, computational efficiency, and feature availability in the consideration of the best simulation method for the JAMoEBA system. Investigations of control strategies such as leader-follower and heuristics based approaches are carried out using the selected simulation method. Finally, a framework for self contained localization which relies on measurements from onboard sensors and linear Kalman filtering is tested within the simulation framework, and the effectiveness of approximating the shape of the JAMoEBA system using elliptical Fourier descriptors is shown.The main contributions made in this thesis are in the areas of suitable modeling methods, controls strategies, and localization techniques for the novel boundary constrained JAMoEBA soft robot architecture. The work done serves as a solid foundation for the future study of this novel soft robotic architecture due to the demonstration of successful methods for modeling, control, and localization of the system. The work presented is not meant to be a comprehensive or deep dive into any one specific area, but rather a jumping off point for future areas of research.
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Title: DUST MITIGATION OF MICRO-STRUCTURED (GECKO-LIKE) ADHESIVES
Creator: Alizadehyazdi, Vahid
Date: 2019
Description: Controllable adhesives (i.e. those capable of being turned on and off) are used in a wide range of applications including robotic grippers and...
Show moreControllable adhesives (i.e. those capable of being turned on and off) are used in a wide range of applications including robotic grippers and climbing robots. Electromagnets, suction, and microspines have been used to meet this demand, but are typically limited to a specific substrate roughness or material. Microstructured (gecko-like) adhesives on the other hand, offer the potential to be the most universal among controllable adhesives since they can work on a wide variety of surfaces. The development of microstructured (gecko-like) adhesives has focused almost solely on their adhesive strength. However, for practical applications, especially in real-world environments, the adhesive's long-term performance is arguably equally important. One impediment to long-term viability is the adhesive's susceptibility to contamination, which decreases adhesion significantly. To have practical microstructure adhesives in real-world environments, the detrimental effect of dust and other contaminants should be dealt with. The first general approach involves removing adhered dust particles. The second approach is to create adhesives that minimize dust adsorption such that extensive cleaning is not necessary or they can be removed easily. Regarding the first approach, this research describes the use of electrostatic forces and ultrasonic vibration to repel dust particles. Results are non-destructive, non-contact cleaning methods that can be used in conjunction with other cleaning techniques, many of which rely on physical contact between the fibrillar adhesive and substrate. Electrostatic cleaning results show that a two-phase square wave with the lowest practically feasible frequency has the best cleaning results. Combining electrostatic and ultrasonic cleaning results in far higher efficiency than when using electrostatic repulsion or ultrasonic alone. Moreover, I showed that the piezoelectric element in the ultrasonic cleaning method can also be used as a releasing mechanism to turn the adhesive off and as a force/contact sensor. Regarding the second approach, I experimentally explored the effect of the modulus of elasticity, work of separation, and work of adhesion (adhesion energy) on the shear stress and particle detachment capabilities of microstructured adhesives. Particle removal is evaluated using both non-contact cleaning methods (centripetal force and electrostatic particle repulsion) and a dry contact cleaning method (load-drag-unload test). Results show that for a material with a high work of separation, high elastic modulus, and low work of adhesion, it is possible to create a microstructured adhesive with both high shear stress strength and low adhesion to dust particles. Results also show that, for dry contact cleaning, shear stress recovery mostly stems from particle rolling and not particle sliding. Moreover, shear test results show that augmenting the microstructured adhesive with electrostatic adhesion can reduce the negative effects on adhesion of a high elastic modulus materials' conformability to a substrate by providing a preload to the microstructured elements. Finally, I applied mentioned dust mitigation methods on two different gecko-like adhesives grippers. The first design was used to pick up flat objects, while the second one is designed to grip curved objects of different shapes and sizes. Since the second gripper is flexible and piezoelectric is stiff (it can only be applied to rigid backings), only electrostatic dust mitigation is applicable.
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Title: Modeling and Optimization of Power Plant Cooling Tower Systems Using Physics-Based and Neural-Network-Based Models
Creator: Salomon, Basile Clément Paul
Date: 2023
Description: Condensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed...
Show moreCondensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed-water into the boiler in a closed-loop system. These condensers typically use cooling water drawn from a water body (lake, river etc) to condense the steam. Cooling towers are used to lower the temperature of the warm water exiting the condenser. Since the steam condensation temperature plays an important role in the power plant efficiency, cool- ing tower performance which is limited by the wet-bulb temperature of the ambient air has been extensively studied. This work investigates the modeling of an enhanced cooling tower technology using a new pre-cooling and dehumidifying system (PDHS). This new system, based on a reversed Brayton cycle, is made out of a compressor, an air-cooled heat exchanger (HX), a heat and mass exchanger (HMX) and an expander. The goal of this PDHS concept is to pre-cool the air entering the cooling tower in order to improve its performance. In this work, a systems model has been developed. Thermodynamic models have been used for the compressor, the air-cooled heat exchanger and the expander. For the remaining components, i.e. the heat and mass exchanger, the cooling tower and the condenser, physics-based models have been developed and tested. Once tested and validated, each model can be integrated into the integrated PDHS-cooling tower-condenser system. Two different configurations of the PDHS have been considered in this thesis. In the open water loop configuration, the water in the HMX is obtained from the municipal water supply (or an alternate water source) and is released back to the source after exiting the HMX. In the closed water loop configuration, the water used to cool down the air in the HMX is being recirculated and cooled in the power plant cooling tower. The physics-based model of the PDHS developed in this work has been validated using results from an empirical model of the PDHS by GTI Energy. This first case study also shows how the PDHS can be used to save water in the cooling tower (CT). Indeed, when using the PDHS, a 37% reduction in the cooling tower evaporation rate can be observed when comparing to the baseline. This decrease in the CT evaporation rate is the main source of make-up water savings. Moreover, the water harvested by condensation in the PDHS can be redirected towards the CT, bringing another source of water savings. These two combined lead to an overall 46% decrease of the make-up water usage in the cooling tower. Another case study has been conducted on a 500 MW condenser unit. It shows that, under summer ambient conditions i.e. Ta,db = 35°C and φ = 47%, the PDHS can help the condenser restore its designed cooling load of 453 MW. Finally, using the physics-based model to create a dataset, an artificial neural network model of the PDHS has been developed to constitute a black box for the PDHS that would be able to predict with sufficient accuracy the condenser and HMX loads, the air conditions at the inlet of the CT and water temperature at both ends of the condenser and CT given the ambient air condition, the compressor pressure ratio and the water split between the condenser and the heat and mass exchanger.
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Title: Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator: Riley, Christopher W.
Date: 2023
Description: The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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Title: Deep Learning and Model Predictive Methods for the Control of Fuel-Flexible Compression Ignition Engines
Creator: Peng, Qian
Date: 2022
Description: Compression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However,...
Show moreCompression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However, diesel engines can cause concerning environmental pollution because of their high nitrogen oxide (NOx) and soot emissions. In addition to meeting the stringent emission regulations, the demand to reduce greenhouse gas emissions has become urgent due to the more frequent destructive catastrophes caused by global warming in recent decades. In an effort to reduce emissions and improve fuel economy, many techniques have been developed and investigated by researchers. Air handling systems like exhaust gas recirculation and variable geometry turbochargers are the most widely used techniques on the market for modern diesel engines. Meanwhile, the concept of low temperature combustion is widely investigated by researchers. Low temperature combustion can increase the portion of pre-mixed fuel-air combustion to reduce the peak in-cylinder temperature so that the formation of NOx can be suppressed. Furthermore, the combustion characteristics and performance of bio-derived fuel blends are also studied to reduce overall greenhouse gas emissions through the reduced usage of fossil fuels. All the above mentioned systems are complicated because they involve not only chemical reactions but also complex fluid motion and mixing processes. As such, the control of these systems is always challenging and limits their commercial application. Currentlymost control methods are feed-forward control based on load condition and engine speed due to the simplicity in real-time application. With the development of faster control unit and deep learning techniques, the application of more complex control algorithms is possible to further improve the emissions and fuel economy. This work focuses on improvements to the control of engine air handling systems and combustion processes that leverage alternative fuels.Complex air handling systems, featuring technologies such as exhaust gas recirculation (EGR) and variable geometry turbochargers (VGTs), are commonly used in modern diesel engines to meet stringent emissions and fuel economy requirements. The control of diesel air handling systems with EGR and VGTs is challenging because of their nonlinearity and coupled dynamics. In this thesis, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are applied to control the low pressure (LP) EGR valve position and VGT vane position simultaneously on a light-duty multi-cylinder diesel engine. In addition, experimental examination of a low temperature combustion based on gasoline compression ignition as well as its control has also been studied in this work. This type of combustion has been explored on traditional diesel engines in order to meet increasingly stringent emission regulations without sacrificing efficiency. In this study, a six-cylinder heavy-duty diesel engine was operated in a mixing controlled gasoline compression ignition mode to investigatethe influence of fuels and injection strategies on the combustion characteristics, emissions, and thermal efficiencies. Fuels, including ethanol (E), isobutanol (IB), and diisobutylene (DIB), were blended with a gasoline fuel to form E10, E30, IB30, and DIB30 based on volumetric fraction. These four blends along with gasoline formed the five test fuels. With these fuels, three injections strategies were investigated, including late pilot injection, early pilot injection, and port fuel injection/direct injection. The impact of moderate exhaust gas recirculation on nitrogen oxides and soot emissions was examined to determine the most promising fuel/injection strategy for emissions reduction. In addition, first and second law analyses were performed to provide insights into the efficiency, loss, and exergy destruction of the various gasoline fuel blends at low and medium load conditions. Overall, the emission output, thermal efficiency, and combustion performances of the five fuels were found to be similar and their differences are modest under most test conditions.While experimental work showed that low temperature combustion with alternative fuels could be effective, control is still challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends. A modification was made to the functional ignition delay model to cover the significantly different ignition delay behavior between conventional and oxygenated fuel blends. The parameters in the model were calibrated with only gasoline data at a load of 14 bar brake mean effective pressure. The results showed that this physics-based model can be applied to the other four fuel blends at three differentpilot injection strategies without recalibration. In order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control(MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional-integral (PI) control as the baseline. The simulation results show that MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.
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Title: Self-Reconfigurable Soft Robots Based on Boundary-Constrained Granular Swarms
Creator: Karimi, Mohammad Amin
Date: 2022
Description: Unlike conventional robots, which consist of rigid bodies and linkages, soft robots are composed of compliant and flexible components and...
Show moreUnlike conventional robots, which consist of rigid bodies and linkages, soft robots are composed of compliant and flexible components and actuators. This distinction enables adaptive behaviors in response to unpredictable environments, like manipulating objects with a variety of shapes. As such, soft robots afford greater potential over traditional robots for safe human interaction.Despite these advantages, there remain obstacles due to the challenges in modeling, controlling, and fabricating soft materials. For example, soft robots that rely on thermal or electrical actuation are typically slow to respond and unable to apply large forces as compared to traditional robots. Pneumatically actuated soft robots, while more responsive and capable of applying larger forces, generally need to be tethered to external control mechanisms, which becomes limiting in tasks that require lightweight, autonomous functionality.In contrast, this thesis describes a new type of robot that exhibits those same characteristics, but achieves them via a boundary-constrained swarm.The robotic structure consists of passive granular material surrounded by an active membrane that is composed of a swarm of interconnected robotic sub-units. The internal components are important for overall function, but their relative configuration is not. This allows for an effectively random, unstructured placement of the internal components, which in turn creates excellent morphability. Collectively, the subunits determine the overall shape of the robot and enable locomotion through interaction with external surfaces.The constrained swarm embodies the continuum, compliant, and configurable properties found in soft robots, but in this state the robot is limited in its ability to manipulate objects due to the relatively low force it can apply to external objects.To address this issue, the unique ability to execute a jamming phase transition is added to the robot. Importantly, jamming is controlled by the degree by which the passive particles are spatially confined by the membrane, and this in turn is controlled by the active sub-unit robots using different jamming mechanisms. The robot exploits its ability to transition between soft (unjammed) and rigid (jammed) states to induce fluid-like flexibility or solid-like rigidity in response to objects and features in the environment.In order to investigate this design concept, I have studied different prototype designs for the robot that varied in terms of the locomotion and jamming mechanisms. I also present a simulation framework in which I model the design and study the scalability of this class of robots. The simulation framework uses the Project Chrono platform, which is a multi-body dynamics library that allows for physics-driven collision and contact modeling.
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Title: Predictive Energy Management of Connected Hybrid Electric Vehicles in the Presence of Uncertainty
Creator: Sotoudeh, Seyedeh Mahsa
Date: 2022
Description: Energy efficiency improvements brought by electrification of the powertrain in Hybrid Electric Vehicles (HEVs) highly depend on their...
Show moreEnergy efficiency improvements brought by electrification of the powertrain in Hybrid Electric Vehicles (HEVs) highly depend on their powertrain Energy Management Strategy (EMS) that determines optimal power allocation between powertrain components.Eco-driving based EMS seeks further energy efficiency improvements through optimizing vehicle's driving cycle (velocity and hence torque demand), in addition to the powertrain's EMS. A novel hierarchical EMS is developed in this thesis for connected human-driven HEVs and then extended to automated HEVs that effectively addresses some of the major challenges of the energy management problem. At its high-level, a computationally-tractable Pseudospectral Optimal Controller (PSOC) with discounted cost is employed to approximately solve the powertrain's energy management problem over driving cycle previews of the entire trip. The high-level's approximate solution is then used as a reference by the low-level tube-based Model Predictive Controller (MPC) that solves the problem over higher-quality, short-horizon driving cycles in a real-time applicable fashion. For human-driven HEVs, a Long Short-Term Memory (LSTM) neural network predicts the human driver's velocity profile over low-level's short horizons. A velocity optimizer is added to the low-level for automated HEVs that optimizes the vehicle's driving cycle by effectively utilizing regenerative braking capability of the HEV. At the low-level, the tube-based MPC controller solves the powertrain's energy management problem over either predicted (human-driven HEV) or optimized (automated HEV) driving cycles by accounting for driving cycle's uncertainty, due to uncertain future information, and hence ensures robust constraints satisfaction. A novel cost-to-go approximation method is developed that uses the optimal costate trajectories obtained from the high-level PSOC controller to generate terminal costs for the low-level controller. This improves suboptimality of the short-horizon solutions and ensures charge balance constraint satisfaction at the end of the trip without having to impose conservative constraints. A novel learning-based framework is also proposed to jointly optimize the automated HEV's driving cycle and its powertrain's power split. A Deep Neural Network (DNN)-based MPC controller is developed for the low-level that jointly optimizes the HEV's driving cycle and powertrain energy management in a real-time applicable manner. To ensure constraints satisfaction, a novel Quadratic Programming (QP)-based projection of the DNN-based approximate control laws is proposed that can be efficiently solved in real-time. Simulation results over standard and real-world driving cycles demonstrate efficacy of the proposed control frameworks in terms of suboptimality (fuel efficiency) improvement, potential real-time applicability, and constraints (especially charge balance constraint) satisfaction in the presence of driving cycle uncertainty.
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Title: Data-Driven Methods for Soft Robot Control and Turbulent Flow Models
Creator: Lopez, Esteban Fernando
Date: 2022
Description: The world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies,...
Show moreThe world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies, we are now able to collect more data than ever before. This has dawned a new age of data-driven methods which can describe systems and behaviors with increasing accuracy. Whereas before we relied on the expertise of a few professionals with domain-specific knowledge developed over years of rigorous study, we are now able to rely on collected data to reveal patterns, develop novel ideas, and offer solutions to the world’s engineering problems. No domain is safe. Within the engineering realm, data-driven methods have seen vast usage in the areas of control and system identification. In this thesis we explore two areas of data-driven methods, namely reinforcement learning and data-driven causality. Reinforcement learning is a method by which an agent learns to increase its selection of ideal actions and behaviors which result in an increasing reward. This method was applied to a soft-robotic concept called the JAMoEBA to solve various tasks of interest in the robotics community, specifically tunnel navigation, obstacle field navigation, and object manipulation. A validation study was conducted to show the complications that arise when applying reinforcement learning to such a complex system. Nevertheless, it was shown that reinforcement learning is capable of solving three key tasks (static tunnel navigation, obstacle field navigation, and object manipulation) using specific simulation and learning hyperparameters. Data-driven causality encompasses a range of metrics and methods which attempt to uncover causal relationships between variables in a system. Several information theoretic causal metrics were developed and applied to nine mode turbulent flow data set which represents the Moehlis model. It was shown that careful consideration into the method used was required to identify significant causal relationships. Causal relationships were shown to converge over several hundred realizations of the turbulent model. Furthermore, these results match the expected causal relationships given known information of self-sustaining processes in turbulence, validating the method’s ability to identify causal relationships in turbulence.
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Title: Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Creator: Di Russo, Miriam
Date: 2023
Description: For conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their...
Show moreFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits.
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Title: Improving Localization Safety for Landmark-Based LiDAR Localization System
Creator: Chen, Yihe
Date: 2024
Description: Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety.
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Title: Predictive energy efficient control framework for connected and automated vehicles in heterogeneous traffic environments
Creator: Vellamattathil Baby, Tinu
Date: 2023
Description: Within the automotive industry, there is a significant emphasis on enhancing fuel efficiency and mobility, and reducing emissions. In this...
Show moreWithin the automotive industry, there is a significant emphasis on enhancing fuel efficiency and mobility, and reducing emissions. In this context, connected and automated vehicles (CAVs) represent a significant advancement, as they can optimize their acceleration pattern to improve their fuel efficiency. However, when CAVs coexist with human-driven vehicles (HDVs) on the road, suboptimal conditions arise, which adversely affect the performance of CAVs. This research analyzes the automation capabilities of production vehicles to identify scenarios where their performance is suboptimal, and proposes a merge-aware modification of adaptive cruise control (ACC) method for highway merging situations. The proposed algorithm addresses the issue of sudden gap and velocity changes in relation to the preceding vehicle, thereby reducing substantial braking during merging events, resulting in improved energy efficiency. This research also presents a data-driven model for predicting the velocity and position of the preceding vehicle, as well as a robust model predictive control (MPC) strategy that optimizes fuel consumption while considering prediction inaccuracies. Another focus of this research is a novel suggestion-based control framework in interactive mixed traffic environments leveraging the emerging connectivity between vehicles and with infrastructure. It is based on MPC to optimize the fuel efficiency of CAVs in heterogeneous or mixed traffic environments (i.e., including both CAVs and HDVs). In this suggestion-based control framework, the CAVs are considered to provide non-binding velocity and lane change suggestions to the HDVs to follow to improve the fuel efficiency of both the CAVs and the HDVs. To achieve this, the host CAV must devise its own fuel-efficient control solution and determine the recommendations to convey to its preceding HDV. It is assumed that the CAVs can communicate with the HDVs via Vehicle to Vehicle (V2V) communication, while the Signal Phase and Timing (SPaT) information is accessed via Vehicle-to- Infrastructure (V2I) communication. These velocity suggestions remain constant for a predefined period, allowing the driver to adjust their speed accordingly. It is also considered that the suggestions are non binding, i.e., a driver can choose not to follow the suggested velocity. For this control framework to function, we present a velocity prediction model based on experimental data that captures the response of a HDV to different suggested velocities, and a robust approach to ensure collision avoidance. The velocity prediction’s accuracy is also validated with the experimental data (on a table-top drive simulator), and the results are presented. In cases of low CAV penetration, a CAV needs to provide suggestions to multiple surrounding HDVs and incorporating the suggestions to all the HDVs as decision variables to the optimal control problem can be computationally expensive. Hence, a suggestion-based hierarchical energy efficient control framework is also proposed in which a CAV takes into account the interactive nature of the environment by jointly planning its own trajectory and evaluating the suggestions to the surrounding HDVs. Joint planning requires solving the problem in joint state- and action-space, and this research develops a Monte Carlo Tree Search (MCTS)-based trajectory planning approach for the CAV. Since the joint action- and state-space grows exponentially with the number of agents and can be computationally expensive, an adaptive action-space is proposed through pruning the action-space of each agent so that the actions resulting in unsafe trajectories are eliminated. The trajectory planning approach is followed by a low-level model predictive control (MPC)-based motion controller, which aims at tracking the reference trajectory in an optimal fashion. Simulation studies demonstrate the proposed control strategy’s efficacy compared to existing baseline methods.
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Title: Effect of Stress Triaxiality and Lode Angle on Ductile Fracture
Creator: Nia, Mahan
Date: 2023
Description: Although many ductile damage accumulation studies have been done in recent years, there is still insufficient research towards the development...
Show moreAlthough many ductile damage accumulation studies have been done in recent years, there is still insufficient research towards the development of ductile fracture models, mainly due to the difficulty of performing experiments under different states of multiaxial stress. The goals of this Ph.D. research are to (i) produce much-needed experimental data, (ii) investigate the performance of existing models against these data, and (iii) develop a new predictive ductile fracture model validated by experiments. The new model seeks to predict the fracture strain as a function of the stress triaxiality and normalized Lode angle. One of the prominent works in this area was done by Bai and Wierzbicki in 2008 by testing 2024-T351 aluminum alloy. They proposed an asymmetric 3D empirical fracture model with six model parameters. Thus, the Bai method was investigated alongside a new model for predicting ductile fracture. For that purpose, 2139-T8 aluminum alloy was chosen for our experimental program to evaluate these models better, and the data extracted from Bai's work was also used as an additional data set. An extensive experimental program was considered to create different stress states in the material, including tensile tests (with round smooth and four round notched and plate specimens), torsion, compression (with four smooth and two notched specimens), and shear-compression experiments (two different sizes). The specimens were longitudinally machined from a block of 2139-T8 aluminum alloy. The combined effects of two variables, stress triaxiality and normalized Lode angle, define a 3D fracture envelope for fracture strain. A parallel FE simulation (fine-tuned by the experimental results) has been performed for each experiment to evaluate the evolution of stress triaxiality and Lode angle in the gauge section of the specimens with complicated geometries. Finally, these results were used in developing two predictive fracture models. The first model is based on the Bai-Wierzbicki form of fracture. The second one is a new model that has been presented in this research. This new model is a modification of the Johnson-Cook fracture model and considers the simultaneous effects of Lode angle and stress triaxiality in fracture. The original Johnson-Cook fracture model (1984) does not consider the Lode angle effect. In the end, errors in the proposed approach to modeling ductile fracture have been compared to errors from Bai's work, resulting in the conclusions and recommendations for future studies.
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