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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
- RESIDENTIAL LOAD DATA COMPRESSION AND LOAD DISAGGREGATION
- Creator
- Xu, Runnan
- Date
- 2021
- Description
-
Non-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into...
Show moreNon-Intrusive Load Monitoring (NILM) for residential applications aims to dis-aggregate the total electricity consumption of a household into the single appliance information. For the customer side, users can change their consumption habit and save more electricity. For the utility, generation scheduling will be more accurate, efficient, and secure. Furthermore, energy management system, demand response and fault diagnosis will benefit from the real time information provided by the NILM. This dissertation first proposes a data compressed method suitable for the NILM data. Then a real time disaggregation based on the Kalman filter is proposed to obtain the appliance state information. A model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method is proposed. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Com-pression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA). An online method based on the transient features of individual appliances and system steady-state characteristics is proposed to estimate the appliances’ working states. It determines the number of states for each appliance via Density-based Spatial Clustering of Applications with Noise (DBSCAN) method and models the transition relationship among different states. The states of working appliances are identified from aggregated power signals by implementing the Kalman filtering method into the Factorial Hidden Markov Model (FHMM) and by the verification of system states which are the combination of working states of individual appliances. The proposed method is event based and the use of transient features extracted from event detection could achieve fast state inference and is suitable for online load disaggregation. The proposed method is tested on high-resolution dataset such as LIFTED and outperforms other related methods, including Segment-wise Integer Quadratic Constraint Programming (SIQCP), Combinatorial Optimization (CO), and the exact FHMM (FHMM_EXACT), in terms of accuracy, f1 score, and computational time.
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- Title
- High-integrity modeling of non-stationary Kalman Filter input error processes and application to aircraft navigation
- Creator
- Gallon, Elisa
- Date
- 2023
- Description
-
Most navigation applications nowadays rely heavily on Global Navigation Satellite Systems (GNSSs) and inertial sensors. Both of these systems...
Show moreMost navigation applications nowadays rely heavily on Global Navigation Satellite Systems (GNSSs) and inertial sensors. Both of these systems are known to be complementary, and as such, their outputs are very often combined in an extended Kalman Filter (KF) to provide a continuous navigation solution, resistant to poor satellite geometry, as well as radio frequency interference. Additionally, recent development in safety critical applications (such as aviation) revealed the performance limitations of current algorithms (Advance Receiver Autonomous Integrity Monitoring - ARAIM) to vertical guidance down to 200 feet above the runway (LPV-200). When nominal constellations are depleted, LPV-200 can only sparsely be achieved. Exploiting satellite motion in ARAIM (for instance using a KF) could help alleviate those limitations, but would require adequate modeling of the errors, including the error's time correlation.Power Spectral Density (PSD) bounding is a methodology that provides high integrity, time correlated error models, but this approach is currently limited to stationary errors (which is rarely the case with real data), and has never been applied to navigation errors. More generally, no high integrity, time correlated error models have ever been derived for navigation errors.As a result, in the first part of this thesis, a methodology for high integrity modeling of time correlated errors is introduced. The PSD bounding methodology is extended to both stationary and non-stationary errors. In the second part of this thesis, these methodologies are applied to the 3 main error sources impacting iono-free GNSS measurements (orbit and clock errors, tropospheric errors and multipath), as well as to inertial errors.The methodology introduced in this dissertation provides high integrity time correlated error models and is applicable to any type of applications where high integrity is required (e.g. Differential GNSS - DGNSS, Aircaft Based Augmentation System - ABAS, Ground Based Augmentation System - GBAS, Space Based Augmentation System - SBAS, etc...). Additionally, the error models derived here are not only limited to high integrity applications, but could also be used in applications were the correlation over time of the errors plays an important role (such as any KF integration).In the last part of this dissertation, we focus on a specific safety critical application: aviation, and in particular ARAIM. The dissertation is concluded with an assessment of the performance improvements provided by recursive ARAIM, using those bounding dynamic error models, with respect to those models, used for baseline snapshot ARAIM. Additionally, a sensitivity analysis is performed on each of the error model parameters to assess which of them impacts the KF performance (i.e. covariance) the most.
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