Browsing by Subject "engine"

Now showing 1 - 3 of 3
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    Miniature HCCI Engine Compressor
    (2015-06) Johnson, Dustin
    Hydrocarbon fuels have a much higher specific energy than electric batteries, and are thus a desirable power supply for portable devices. A reliable lightweight long lasting power supply will be an enabler for human assist devices and miniature robotics. A prototype miniature free-piston engine compressor runs for up to 30 s. New pistons and cylinder liners with coatings did not improve reliability when implemented in the engine. The engine stalled because pressure could not be held in the cylinder due to blow-by leakage caused by too large of piston/cylinder clearance. The air compressor part of the device was tested independently. It reached a pressure difference of 450 kPa and produced a maximum 11 W of compressed air power when working against a backpressure of 170 kPa. Two thermodynamic models of the air compressor were created which matched experimental results but did not capture all the details of compressor operation.
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    Particle Emissions from Light Duty Vehicles during Cold-Cold Start and Identified from Ambient Measurements
    (2016-02) Badshah, Huzeifa
    Particle emissions from motor vehicles are an increasing source of atmospheric pollution. Operating conditions that produce significant particle number emissions in light duty vehicles were the focus of this study. Extremely cold conditions cause engines to use significant fuel enrichment during starting and warm-up and thus are prone to high particle emissions. In gasoline direct injection engines, this can lead to even higher soot formation due to liquid fuel impingement on the cold surfaces of the combustion chamber and piston. Humans can be exposed to high particle concentration from cold starting vehicles in parking ramps or any busy traffic areas due to higher vehicle density and poor ventilation. Separating ambient particle emissions according to engine type allows for the identification of vehicles that have the highest tailpipe emissions or contribute to high ambient particle number (PN) concentration. This thesis presents the results of two studies. The first study shows that the average PN emitted during 180 seconds by GDI and PFI vehicles after a cold-cold start were 3.09E+13 and 2.12E+13 particles respectively, based on tailpipe out emissions. The high particle emissions highlight the need for better particle control strategies to reduce particle emissions during engine startup in cold ambient temperatures. Meanwhile, the ambient study conducted at the exit of a parking ramp found that GDI vehicles (only 12% of the vehicle population in this study) contributed to about 50% of the increase of particle concentrations associated with vehicles. Thus, the increasing number of GDI vehicles in the future is expected to lead to an increase in particle concentrations in parking ramps and similar facilities.
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    Real-Time System Identification and Control of Engine System Using Least Squares Learning and Simplex Tessellation
    (2022-12) Tranquillo, Holden
    To aid in engine control for achieving the stable combustion of varying cetane level fuels, a computationally efficient algorithm for the online learning of an engine model based on real-time input and output measurements is developed. Innovations in engine technology has led to the feasibility of robust, multi-fuel engine systems capable of operating on unknown or non-ideal fuel types. To attain such performance, advanced control strategies must be implemented in order to achieve stable engine combustion using such fuels. The method developed in this work, based on piecewise-linear modeling via discrete nodes and recursive linear least squares is first derived for the one-dimensional system of injection timing and combustion phasing. The learning model is then used for adaptive feedforward and feedback control of the SISO system in simulation using a gaussian process model as a virtual engine. The algorithm is then extended to the two-input/two-output system of injection timing and fuel mass and their effect on combustion phasing and indicated mean effective pressure (IMEP). Data generated using computational fluid mechanics is used to supplement experimental data in the development of the 2D model. The theory of barycentric and affine coordinates is introduced and applied to the concept of piecewise planes to approximate nonlinear surfaces. The learning model is utilized in an adaptive MIMO feedforward algorithm to control the engine to a desired combustion phasing and IMEP. Additionally, a decoupled integral feedback control scheme is presented and shown effective in simulation. A generalization of the learning algorithm for higher dimensions is made in order to model higher order systems. Specifically, simplex tessellation and barycentric coordinates as regressor coefficients are shown to generalize node locating and updating in arbitrary dimensions. The generalized learning algorithm is applied to a synthetic three-input data set in order show feasibility of the model for higher order nonlinear systems. The algorithm developed in this work is a unique, generalized, data-driven model capable of the real-time learning and control of multi-dimensional systems. The computational efficiency and generalization of the method allows for the real-time system identification of engine systems that are operating in unknown or untested environments. Existing engine models lack the efficiency to perform at the operating times seen in internal combustion engines. Implemented in a physical engine, the developed algorithm could be used for adaptive modeling of the system when undergoing a fuel or environmental change, which in turn can be used to aid in adaptive control of the engine. In commercial application, the real-time learning model could be used to decrease or eliminate the traditional in-house testing of engines required for lookup table generation, which would in turn decrease the time and cost in getting the engine to final application.