Browsing by Author "Young, Peter, committee member"

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Open Access
A computational and experimental study on combustion processes in natural gas/diesel dual fuel engines
(Colorado State University. Libraries, 2015) Hockett, Andrew, author; Marchese, Anthony J., advisor; Hampson, Greg, committee member; Olsen, Daniel B., committee member; Gao, Xinfeng, committee member; Young, Peter, committee member
Natural gas/diesel dual fuel engines offer a path towards meeting current and future emissions standards with lower fuel cost. However, numerous technical challenges remain that require a greater understanding of the in-cylinder combustion physics. For example, due to the high compression ratio of diesel engines, substitution of natural gas for diesel fuel at high load is often limited by engine knock and pre-ignition. Additionally, increasing the natural gas percentage in a dual fuel engine often results in decreasing maximum load. These problems limit the substitution percentage of natural gas in high compression ratio diesel engines and therefore reduce the fuel cost savings. Furthermore, when operating at part load dual fuel engines can suffer from excessive emissions of unburned natural gas. Computational fluid dynamics (CFD) is a multi-dimensional modeling tool that can provide new information about the in-cylinder combustion processes causing these issues. In this work a multi-dimensional CFD model has been developed for dual fuel natural gas/diesel combustion and validated across a wide range of engine loads, natural gas substitution percentages, and natural gas compositions. The model utilizes reduced chemical kinetics and a RANS based turbulence model. A new reduced chemical kinetic mechanism consisting of 141 species and 709 reactions was generated from multiple detailed mechanisms, and has been validated against ignition delay, laminar flame speed, diesel spray experiments, and dual fuel engine experiments using two different natural gas compositions. Engine experiments were conducted using a GM 1.9 liter turbocharged 4-cylinder common rail diesel engine, which was modified to accommodate port injection of natural gas and propane. A combination of experiments and simulations were used to explore the performance limitations of the light duty dual fuel engine including natural gas substitution percentage limits due to fast combustion or engine knock, pre-ignition, emissions, and maximum load. In particular, comparisons between detailed computations and experimental engine data resulted in an explanation of combustion phenomena leading to engine knock in dual fuel engines. In addition to conventional dual fuel operation, a low temperature combustion strategy known as reactivity controlled compression ignition (RCCI) was explored using experiments and computations. RCCI uses early diesel injection to create a reactivity gradient leading to staged auto-ignition from the highest reactivity region to the lowest. Natural gas/diesel RCCI has proven to yield high efficiency and low emissions at moderate load, but has not been realized at the high loads possible in conventional diesel engines. Previous attempts to model natural gas/diesel RCCI using a RANS based turbulence model and a single component diesel fuel surrogate have shown much larger combustion rates than seen in experimental heat release rate profiles, because the reactivity gradient of real diesel fuel is not well captured. To obtain better agreement with experiments, a reduced dual fuel mechanism was constructed using a two component diesel surrogate. A sensitivity study was then performed on various model parameters resulting in improved agreement with experimental pressure and heat release rate.
Open Access
A domain-protocol mapping based middleware for distributed application development
(Colorado State University. Libraries, 2014) Mandalaparty, Sai Pradeep, author; France, Robert, advisor; Rajopadhye, Sanjay, committee member; Young, Peter, committee member
Distributed systems such as Internet of Things, Sensor Networks and Networked Control Systems are being used in various application domains, including industrial, environmental, medical and energy management domains. A distributed application in these domains may need to access data from different devices, where they may all be of the same type or a combination of different types. In addition, these devices may communicate through standardized protocols or proprietary interfaces. The development of such a distributed application may also require a team of developers with expertise in different disciplines. Therefore, the application development that involves heterogeneous devices and multidisciplinary teams can be made more effective by introducing an interface layer that shields developers from aspects of software and hardware heterogeneity. This work proposes a 'domain-protocol mapping' technique that is implemented as a middleware framework. The proposed mapping method maps the application data schema represented as object-oriented domain object to the appropriate communication protocol packet data and also updates the domain object from the response packet data. The middleware provides APIs for the domain experts to read the data from the device or to write the data to the device. The marshalling and unmarshalling process of the domain objects are hidden from the domain expert who may or may not be a software engineer. The use of the developed middleware is illustrated in two case-studies, one involving a simulation of distributed network controls for power system and the other involving integration of different types of power meters in power monitoring application.
Open Access
A modeling toolkit for comparing AC vs. DC electrical distribution efficiency in buildings
(Colorado State University. Libraries, 2021) Othee, Avpreet, author; Cale, James, advisor; Young, Peter, committee member; Herber, Daniel, committee member; Jia, Gaofeng, committee member
An increasing proportion of electrical devices in residential and commercial buildings operate from direct current (DC) power sources. In addition, distributed power generation systems such as solar photovoltaic (PV) and energy storage natively produce DC power. However, traditional power distribution is based on an alternating current (AC) model. Performing the necessary conversions between AC and DC power to make DC devices compatible with AC distribution results in energy losses. For these reasons, DC distribution may offer energy efficiency advantages in comparison to AC distribution. However, reasonably fast computation and comparison of electrical efficiencies of AC-only, DC-only, and hybrid AC/DC distributions systems is challenging because DC devices are typically (nonlinear) power-electronic converters that produce harmonic content. While detailed time-domain modeling can be used to simulate these harmonics, it is not computationally efficient or practical for many building designers. To address this need, this research describes a toolkit for computation of harmonic spectra and energy efficiency in mixed AC and DC electrical distribution systems, using a Harmonic Power Flow (HPF) methodology. The toolkit includes a library of two-port linear and nonlinear device models which can be used to construct and simulate an electrical distribution system. This dissertation includes a description of the mathematical theory and framework underlying the toolkit, development and fitting of linear and nonlinear device models, software implementation in Modelica, verification of the toolkit with laboratory measurements, and discussion of ongoing and future work to employ the toolkit to a variety of building designs.
Open Access
Advanced control techniques and sensors for gas engines with NSCR
(Colorado State University. Libraries, 2012) Gattoni, John, author; Olsen, Daniel, advisor; Marchese, Anthony, committee member; Young, Peter, committee member
High exhaust emissions reduction efficiency from an Internal Combustion Engine (ICE) utilizing a Non Selective Catalyst Reduction (NSCR) catalyst system requires complex fuel control strategies. The allowable equivalence ratio operating range is very narrow where NSCR systems achieve simultaneous reduction of Carbon Monoxide (CO), Nitrogen Oxides (NOx), Total Hydrocarbons (THC), Volatile Organic Compounds (VOC's), and formaldehyde (CH2O). This range is difficult to maintain as transients are introduced into the system. Current fuel control technologies utilizing lambda sensor feedback are reported to be unable to sustain these demands for extended operation periods. Lambda sensor accuracy is the critical issue with current fuel controllers. The goal of this project was to develop a minimization control algorithm utilizing a Continental NOx sensor installed downstream of the NSCR catalyst system for feedback air/fuel ratio control. When the engine is operated under lean conditions, NOx is produced in the engine out exhaust emissions and the NOx sensor responds accordingly. When the engine is operated under rich burn conditions, the NSCR catalyst system produces Ammonia (NH3). NOx sensors have a cross sensitivity to NH3 and will respond as though it has been exposed to NOx. This behavior provides a unique control strategy that allows lambda sensor calibration to be ignored. Testing was performed on a Cummins-Onan Generator Set, model GGHD 60HZ, capable of a power output of 100kW at standard ambient air conditions. The engine was reconfigured to operate utilizing an electronic gas carburetor (EGC2) with lambda sensor feedback, manufactured by Continental Controls Corporation (CCC) and a high reduction efficiency NSCR catalyst system manufactured by DCL International. A Data Acquisition (DAQ) system manufactured by National Instruments (NI) acquired the NOx sensor output. The control algorithm was programmed utilizing a LabVIEW interface and a feed forward command was executed through the NI DAQ system to the CCC EGC2 where the fuel trim adjustment was physically made. Exhaust gas species measurements were acquired via a Rosemount 5-gas analyzer and a Nicolet 6700 FTIR. Fuel composition was acquired utilizing a Varian CP-4900 Micro GC and Air Fuel Ratio (AFR) was obtained with an ECM AFRecorder 4800R. Results utilizing NOx sensor feedback control revealed that under steady state operating conditions, improvements in emissions reduction efficiency of CO, NOx, and THC were significant. The system was also evaluated during load and fuel composition transients.
Open Access
Application of distributed DC/DC electronics in photovoltaic systems
(Colorado State University. Libraries, 2017) Kabala, Michael, author; Collins, George, advisor; Sakurai, Hiroshi, committee member; Siegel, H. J., committee member; Young, Peter, committee member
In a typical residential, commercial or utility grade photovoltaic (PV) system, PV modules are connected in series and in parallel to form an array that is connected to a standard DC/AC inverter, which is then connected directly to the grid. This type of standard installation; however, does very little to maximize the energy output of the solar array if certain conditions exist. These conditions could include age, temperature, irradiance and other factors that can cause mismatch between PV modules in an array that severely cripple the output power of the system. Since PV modules are typically connected in series to form a string, the output of the entire string is limited by the efficiency of the weakest module. With PV module efficiencies already relatively low, it is critical to extract the maximum power out of each module in order to make solar energy an economically viable competitor to oil and gas. Module level DC/DC electronics with maximum power point (MPP) tracking solves this issue by decoupling each module from the string in order for the module to operate independently of the geometry and complexity of the surrounding system. This allows each PV module to work at its maximum power point by transferring the maximum power the module is able to deliver directly to the load by either boosting (stepping up) the voltage or bucking (stepping down) the voltage. The goal of this thesis is to discuss the development of a per-module DC/DC converter in order to maximize the energy output of a PV module and reduce the overall cost of the system by increasing the energy harvest.
Open Access
Applications of simulation in the evaluation of SCADA and ICS security
(Colorado State University. Libraries, 2020) Reutimann, Brandt R., author; Ray, Indrakshi, advisor; Gersch, Joseph, advisor; Young, Peter, committee member
Power grids, gas pipelines, and manufacturing centers provide an interesting challenge for cybersecurity research. Known as supervisory control and data acquisition systems (SCADA), they can be very large in scale and consist of hundreds to thousands of physical controllers. These controllers can operate simple feedback loops or manage critical safety systems. Following from this, cyber-attacks on these controllers can be extremely dangerous and can threaten the distribution of electricity or the transmission of natural gas that powers electrical plants. Since SCADA systems operate such critical infrastructure, it's important that they are safe from cyber-attacks. However, studying cyber-attacks on live systems is nearly impossible because of the proprietary nature of the systems, and because a test gone wrong can cause substantial irreversible damage. As a result, this thesis focuses on an approach to studying SCADA systems using simulation. The work of this thesis describes considerations for developing accurate and useful simulations as well as concerns for cyber vulnerabilities in industrial control environments. We describe a rough architecture for how SCADA simulators can be designed as well as dive into the design of the SCADA simulator built for research at Colorado State University. Finally, we explore the impact of falsified sensor readings (measurement attacks) on the safety of the natural gas pipeline using simulation. Our results show that a successful measurement attack on a gas system requires a sophisticated plan of attack as well as the ability to sustain such an attack for a long period of time. The results of this work show that a gas system reacts slower than would be expected of a typical electrical system.
Open Access
Autonomous UAV control and testing methods utilizing partially observable Markov decision processes
(Colorado State University. Libraries, 2018) Eaton, Christopher M., author; Chong, Edwin K. P., advisor; Maciejewski, Anthony A., advisor; Bradley, Thomas, committee member; Young, Peter, committee member
The explosion of Unmanned Aerial Vehicles (UAVs) and the rapid development of algorithms to support autonomous flight operations of UAVs has resulted in a diverse and complex set of requirements and capabilities. This dissertation provides an approach to effectively manage these autonomous UAVs, effectively and efficiently command these vehicles through their mission, and to verify and validate that the system meets requirements. A high level system architecture is proposed for implementation on any UAV. A Partially Observable Markov Decision Process algorithm for tracking moving targets is developed for fixed field of view sensors while providing an approach for more fuel efficient operations. Finally, an approach for testing autonomous algorithms and systems is proposed to enable efficient and effective test and evaluation to support verification and validation of autonomous system requirements.
Open Access
Cloud Computing cost and energy optimization through Federated Cloud SoS
(Colorado State University. Libraries, 2017) Biran, Yahav, author; Collins, George J., advisor; Pasricha, Sudeep, advisor; Young, Peter, committee member; Borky, John M., committee member; Zimmerle, Daniel J., committee member
The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks.
Open Access
Customer and system impacts of grid support functions for voltage management strategies
(Colorado State University. Libraries, 2020) Giraldez Miner, Julieta, author; Suryanarayanan, Siddharth, advisor; Atadero, Rebecca, committee member; Yang, Liuqing, committee member; Young, Peter, committee member; Zimmerle, Daniel, committee member
This document describes modeling techniques and methods to study the impacts to the utility and to the customer of using DERs such as advanced inverters to provide voltage support in order to maintain voltage within the recommended voltage limits. For this, a method for accurately representing secondary circuits in distribution feeders is proposed and quasi-static-time series (QSTS) simulation techniques are used to study the impact of advance inverter functions to the utility for managing voltage and to the customer in terms of possible generation curtailment. This dissertation looks at factors in medium and low-voltage circuit topology that drive customer voltages with DERs, and investigates where along the distribution feeder are voltage based advance inverter grid support function most effective. The described modeling techniques and methods have informed policy and regulatory type decisions such as updating DER interconnection tariffs and standards.
Open Access
Dedicated exhaust gas recirculation applied to a rich burn industrial natural gas engine
(Colorado State University. Libraries, 2020) Van Roekel, Chris, author; Olsen, Daniel B., advisor; Jathar, Shantanu, committee member; Marchese, Anthony, committee member; Young, Peter, committee member
Rich burn natural gas engines provide power for industrial applications such as gas compression. In this application where exhaust oxides of nitrogen (NOx) requirements can be critical, rich burn engines offer best in class aftertreatment emission reduction and operating cost capabilities by using a non-selective catalyst reduction (NSCR) or three-way catalyst system. However, due to high combustion temperatures associated with near stoichiometric air-fuel ratio (AFR) operation, rich burn engines are limited in brake mean effective pressure (BMEP) by combustion temperature. Consumers in the gas compression application are left to choose between engines that are capable of meeting even the most stringent emission requirements (rich burn) and engines with high BMEP rating (lean burn). Charge dilution by way of excess air (lean burn) or exhaust gas recirulation (EGR) is a common method used to lower combustion temperature with the purpose of limiting the production of engine out NOx. Conventional configurations of EGR consist of high pressure loop (HPL) and low pressure loop (LPL), each of which rely on components exposed to relatively high temperatures to control the impact that EGR has on combustion. Dedicated EGR is a novel variant of conventional EGR configurations which allows for the impact that EGR has on combustion to be controlled by components exposed to ambient temperature natural gas while also lowering rich burn combustion temperatures. Due to the lack of published research on dedicated EGR applied to industrial natural gas engines and consumer driven need for technologies to increase rich burn industrial natural gas engine BMEP this work represents an initial investigation into challenges associated with and capabilities of dedicated EGR. A Chemkin chemical kinetics model using the SI Engine Zonal, Flame Speed Calculator, and Equilibrium models was developed to quantify dedicated cylinder exhaust composition, laminar flame speed, and equilibrium combustion composition, respectively. The Aramco 2.0 mechanism was used for natural gas kinetics and was modified to include Zel'dovich mechanism for NOx formation. Engine experiments were conducted using a Caterpillar G3304 rich burn natural gas engine modified to operate with and without dedicated EGR. Initial tests that included power sweeps at fixed dedicated cylinder AFR revealed that operating conditions appropriate for dedicated EGR gasoline engines were not suitable for dedicated EGR natural gas engines. A response surface method (RSM) optimization was performed to find improved operating conditions at part load, 3.4 bar BMEP. Results showed that advanced spark timing and slightly rich dedicated cylinder AFR were optimal to achieve decreased coefficient of variance of indicated mean effective pressure (COV IMEP) and balanced cylinder IMEP output. In order to assess how operating with dedicated EGR would affect the performance of a NSCR system at 6.7 bar BMEP and fixed operating conditions engine AFR was swept between rich and lean conditions to quantify catalyst reduction efficiency and find the emissions compliance window. Without intentional AFR dithering the emissions compliance window was increased significantly. Finally, using best operating conditions from the RSM optimization and engine AFR sweep tests engine BMEP was increased beyond the 6.7 bar rating to find the possible increase in power density resulting from dedicated EGR.
Open Access
Design and construction of electric motor dynamometer and grid attached storage laboratory
(Colorado State University. Libraries, 2011) Lutz, Markus, author; Bradley, Thomas, advisor; Zimmerle, Daniel, committee member; Young, Peter, committee member
The purpose of this thesis is to describe the design and development of a laboratory facility to both educate students on electric vehicle components as well as allow researchers to gain experimental results of grid-attached-storage testing. With the anticipated roll out of millions of electric vehicles, manufacturers of such vehicles need educated hires with field experience. Through instruction with this lab, Colorado State University plans to be a major resource in equipping the future electric vehicle work force with necessary training and hands-on experience using real world, full-scale, automotive grade electric vehicle components. The lab also supports research into grid-attached-storage. This thesis explains the design objectives, challenges, selections, construction and initial testing of the lab, and also provides context for the types of education and research which can be performed utilizing the laboratory.
Open Access
Design and control of kinematically redundant robots for maximizing failure-tolerant workspaces
(Colorado State University. Libraries, 2021) Bader, Ashraf M., author; Maciejewski, Anthony A., advisor; Oprea, Iuliana, committee member; Pezeshki, Ali, committee member; Young, Peter, committee member
Kinematically redundant robots have extra degrees of freedom so that they can tolerate a joint failure and still complete an assigned task. Previous work has defined the "failure-tolerant workspace" as the workspace that is guaranteed to be reachable both before and after an arbitrary locked-joint failure. One mechanism for maximizing this workspace is to employ optimal artificial joint limits prior to a failure. This dissertation presents two techniques for determining these optimal artificial joint limits. The first technique is based on the gradient ascent method. The proposed technique is able to deal with the discontinuities of the gradient that are due to changes in the boundaries of the failure tolerant workspace. This technique is illustrated using two examples of three degree-of-freedom planar serial robots. The first example is an equal link length robot where the optimal artificial joint limits are computed exactly. In the second example, both the link lengths and artificial joint limits are determined, resulting in a robot design that has more than twice the failure-tolerant area of previously published locally optimal designs. The second technique presented in this dissertation is a novel hybrid technique for estimating the failure-tolerant workspace size for robots of arbitrary kinematic structure and any number of degrees of freedom performing tasks in a 6D workspace. The method presented combines an algorithm for computing self-motion manifold ranges to estimate workspace envelopes and Monte-Carlo integration to estimate orientation volumes to create a computationally efficient algorithm. This algorithm is then combined with the coordinate ascent optimization technique to determine optimal artificial joint limits that maximize the size of the failure-tolerant workspace of a given robot. This approach is illustrated on multiple examples of robots that perform tasks in 3D planar and 6D spatial workspaces.
Open Access
Design considerations for an engine-integrated reciprocating natural gas compressor
(Colorado State University. Libraries, 2014) Malakoutirad, Mohammad, author; Bradley, Thomas H., advisor; Young, Peter, committee member; Olsen, Daniel, committee member
This thesis presents the development of an engine retrofit concept to turn a ICE vehicle's engine into a compressor for convenient natural gas refueling, as opposed to building a smaller secondary standalone unit. More specifically, this project seeks to outfit an internal combustion engine (ICE) to serve the dual purposes of providing vehicle propulsion and compression for natural gas refueling with minimal hardware substitution. The principal objective of this thesis is to describe and analyze the dynamic and thermal design considerations for an automotive engine-integrated reciprocating natural gas (NG) compressor. The purpose of this compressor is to pressurize storage tanks in NG vehicles from a low-pressure NG source by using one of the cylinders in an engine as the compressor. The engine-integrated compressor is developed by making minor changes to a 5.9 liter displacement diesel-cycle automotive engine. In this design, a small tank and its requisite valving are added to the engine as an intermediate storage tank to enable a single compressor cylinder to perform two-stage compression. The resulting pressure in the compressor cylinder and storage tank is 25 MPa, equivalent to the storage and delivery pressure of conventional compressed NG delivery systems. The dynamic simulation results show that the high cylinder pressures required for the compression process create reaction torques on the crankshaft, but do not generate abnormal rotational speed oscillations. The thermal simulation results show that the temperature of the storage tank and engine increases over the safety temperature of the NG unless an active thermal management system is developed to cool the NG before it is admitted to the storage tanks. Results are then translated into vehicle-level operating costs and petroleum consumption for a dual-fuel NG-diesel vehicle.
Open Access
Design tradeoffs of a reciprocating auxiliary power unit
(Colorado State University. Libraries, 2013) Renquist, Jacob Vinod, author; Bradley, Thomas H., advisor; Olsen, Daniel, committee member; Young, Peter, committee member
This thesis presents a comparison of reciprocating auxiliary power units to conventional, gas turbine auxiliary power units. A metric of interest is created to represent the specific auxiliary power system weight including the prime mover, generator, gearbox, and fuel consumed. The metric of interest is used to compare the different auxiliary power unit technologies by incorporating detailed engine simulations, auxiliary power unit system weight modeling, and flight path-realized fuel consumption modeling. Results show that reciprocating auxiliary power units can be competitive with gas turbines in near-term, more-electric aircraft applications.
Open Access
Development of predictive energy management strategies for hybrid electric vehicles
(Colorado State University. Libraries, 2017) Baker, David, author; Bradley, Thomas H., advisor; Petro, John, committee member; Young, Peter, committee member
Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods.
Open Access
Effect of phase change material on dynamic thermal management performance for power electronics packages
(Colorado State University. Libraries, 2021) Hollis, Justin Ralph, author; Bandhauer, Todd M., advisor; Marchese, Anthony, committee member; Young, Peter, committee member
High temperature silicon carbide (SiC) die are the most critical and expensive component in electric vehicle (EV) power electronic packages and require both active and passive methods to dissipate heat during transient operation. The use of phase change materials (PCMs) to control the peak junction temperature of the SiC die and to buffer the temperature fluctuations in the package during simulated operation is modeled here. The latent heat storage potential of multiple PCM and PCM composites are explored in both single-sided and dual-sided package configurations. The results of this study show that the addition of phase change material (PCM) into two different styles of power electronics (PE) packages is an effective method for controlling the transient junction temperatures experienced during two different drive cycles. The addition of PCM in a single-sided package also serves to decrease temperature fluctuations experienced by the package as a whole and may be used to reduce the necessary number of SiC die required to divide the heat load, lowering the overall material cost and volume of the package by over 50%. PCM in a single-sided package may be nearly as effective as the double-sided cooling approach of a dual-sided package in the reduction of both peak junction temperature of SiC as well as controlling temperature variations between package layers.
Open Access
Empirical evaluation of a dimension-reduction method for time-series prediction
(Colorado State University. Libraries, 2020) Ghorbani, Mahsa, author; Chong, Edwin K. P., advisor; Pezeshki, Ali, committee member; Young, Peter, committee member; Bradley, Thomas, committee member
Stock price prediction is one of the most challenging problems in finance. The multivariate conditional mean is a point estimator to minimize the mean square error of prediction giver past data. However, the calculation of the condition mean and covariance involves the numerical inverse of a typically ill-conditioned matrix, leading to numerical issues. To overcome this problem, we develop a method based on filtering the data using principle components. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. Our method bears some similarities with subspace filtering methods. Projecting the noisy observation onto a principle subspace leads to significantly better numerical conditioning. Our method accounts for time-varying covariance information. We first introduce our method for predicting future price values over a short period of time using just historical price values. The literature provides strong evidence that stock price values can be predicted from past price data. Different economic variables have also been used in the literature to estimate stock-price values with high accuracy. To accommodate using historical data for such economic variables, we build on our method to include multiple predictors. We use multichannel cross-correlation coefficient as a measure for selecting the most correlated set of variables for each stock. Then we apply our filtering operation based on the local covariance of the data. Our method is easily implemented and can be configured to include an arbitrary number of predictors, subject to computational constraints. Time-series prediction can be posed as a matrix completion problem. Matrix completion is an important problem in many fields and has been receiving considerable attention in recent years. Different approaches and algorithms have been proposed to solve this problem. We investigate the effectiveness of an iterative rank minimizing matrix completion algorithm for predicting financial time series. As a key performance to compare different schemes, we use computational complexity, which focuses on the computational burden of these schemes. We compare the prediction results from the iterative matrix completion method to our method in terms of asymptotic and empirical computational complexity. Both methods show similar performance for forecasting future stock price values in terms of different performance metrics, but our proposed method has lower computational complexity.
Open Access
Enabling predictive energy management in vehicles
(Colorado State University. Libraries, 2018) Asher, Zachary D., author; Bradley, Thomas H., advisor; Chong, Edwin, committee member; Young, Peter, committee member; Zhao, Jianguo, committee member
Widespread automobile usage provides economic and societal benefits but combustion engine powered automobiles have significant economic, environmental, and human health costs. Recent research has shown that these costs can be reduced by increasing fuel economy through optimal energy management. A globally optimal energy management strategy requires perfect prediction of an entire drive cycle but can improve fuel economy by up to 30\%. This dissertation focuses on bridging the gap between this important research finding and implementation of predictive energy management in modern vehicles. A primary research focus is to investigate the tradeoffs between information sensing, computation power requirements for prediction, and prediction effort when implementing predictive energy management in vehicles. These tradeoffs are specifically addressed by first exploring the resulting fuel economy from different types of prediction errors, then investigating the level of prediction fidelity, scope, and real-time computation that is required to realize a fuel economy improvement, and lastly investigating a large computational effort scenario using only modern technology to make predictions. All of these studies are implemented in simulation using high fidelity and physically validated vehicle models. Results show that fuel economy improvements using predictive optimal energy management are feasible despite prediction errors, in a low computational cost scenario, and with only modern technology to make predictions. It is anticipated that these research findings can inform new control strategies to improve vehicle fuel economy and alleviate the economic, environmental, and human health costs for the modern vehicle fleet.
Open Access
Energy management of a university campus utilizing short-term load forecasting with an artificial neural network
(Colorado State University. Libraries, 2012) Palchak, David, author; Bradley, Thomas, advisor; Suryanarayanan, Siddharth, advisor; Zimmerle, Daniel, committee member; Young, Peter, committee member
Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.
Open Access
Exploration based design methodology using the theory of constraints in extending plastics manufacturing for novel high performing fabrics
(Colorado State University. Libraries, 2022) Shekoni, Aderemi, author; Troxell, Wade, advisor; Simske, Steve, committee member; Young, Peter, committee member; Prieto, Amy, committee member
The world of textiles is comprised of several materials. From the conventional, such as cotton and silk, to the contemporary, such as polyester and nylon, textiles have changed over time. Nonwovens, a category of material frequently referred to as the "third-generation" of textiles, have emerged as one of the most exciting breakthroughs in the textile industry during the past few years. Nonwovens, which are frequently confused with fibers, yarns, and fabrics, have evolved as a new category of versatile material with medicinal and industrial applications. An issue associated with the use of lightweight nonwovens is their single-use, in which a fabric weight category can be employed for only one product. The number of products per weight class that can be utilized in businesses that utilize the materials is limited. Therefore, companies utilizing these textiles in their operations must engage with plastic producers to plan, implement, and develop a single weight class for a single product. This procedure is time-consuming and generates plastic waste because of unfinished fabrics. By creating a multipurpose nonwoven fabric, organizations will be able to improve their operations by saving time and energy, improving profits, decreasing plastic waste, and enabling process innovation. To use a fabric with the same weight and similar physical properties in a different product, a different fabric is manufactured for that process, despite the similarity in weight and physical properties between the fabric used in the previous process and the fabric needed for the new process. Due to this limitation, the concept of redesigning nonwoven materials for different applications was conceived. Air Permeability, a barrier to airflow, is a significant component in the inability to support numerous uses. When a fabric's desired attribute is not satisfied, the fabric's air permeability can be optimized by utilizing a variety of process approaches to attain the appropriate performance qualities. This permits the use of a single fabric in a variety of items. Due to the fabric's weight and volume, the usage of nonwoven in aviation and public works has expanded drastically. Thermal insulation is one of the most prevalent applications of nonwoven materials in the aviation industry. Nonwoven fabrics are also utilized as dynamic biofilters for filtration in public works, with an aerobic layer that aids in the recovery of alkalinity in the filtration systems used in these facilities. The two significant outcomes of this research are (1) Improvement of the airflow barrier, also known as air permeability (AP), which enables the use of a single weight class to make several goods as opposed to a single weight class for a single product, and the addition of a thermal barrier to the fabric. Permeability enhancements in nonwovens enhance the fabric's sound absorption, filtration, and heat absorption. (2) The capacity to recycle undesired nonwoven fabrics following production, as opposed to disposing of the plastic components in landfills. Nonwovens are semi-crystalline polypropylene plastics that are not easily biodegradable due to the strong chemical bond between the polypropylene polymers. Because polypropylenes, which are plastics, are not biodegradable, unused nonwoven fabrics are landfilled. It was through the process of prototyping that a subsystem alteration was made that enabled the development of nonwoven fabric with better air permeability. Design as Exploration concepts are used to accomplish this. Reicofil I, II, III, and IV are the four nonwoven production systems used in this research to develop the novel fabric. In addition, this study has handled another issue by reusing and recycling unwanted fabrics to reduce the amount of plastic waste in landfills. An extrusion method that recycles rejected and waste fabrics were the result of these approaches. The innovative method used in developing the new nonwoven fabric is being explored for use in the production of plastic films to improve the quality of goods made with polyethylene plastic polymers.