SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.

Parametric combustion modeling for ethanol-gasoline fuelled spark ignition engines

Ethanol-gasoline fuel blends are increasingly being used in spark ignition (SI) engines due to continued growth in renewable fuels as part of a growing renewable portfolio standard (RPS). This leads to the need for a simple and accurate ethanol-gasoline blends combustion model that is applicable to one-dimensional engine simulation. A parametric combustion model has been developed, integrated into an engine simulation tool, and validated using SI engine experimental data. The parametric combustion model was built inside a user compound in GT-Power. In this model, selected burn durations were computed using correlations as functions of physically based non-dimensional groups that have been developed using the experimental engine database over a wide range of ethanol-gasoline blends, engine geometries, and operating conditions. A coefficient of variance (COV) of gross indicated mean effective pressure (IMEP) correlation was also added to the parametric combustion model. This correlation enables the cycle combustion variation modeling as a function of engine geometry and operating conditions. The computed burn durations were then used to fit single and double Wiebe functions. The single-Wiebe parametric combustion compound used the least squares method to compute the single-Wiebe parameters, while the double-Wiebe parametric combustion compound used an analytical solution to compute the double-Wiebe parameters. These compounds were then integrated into the engine model in GT-Power through the multi-Wiebe combustion template in which the values of Wiebe parameters (single-Wiebe or double-Wiebe) were sensed via RLT-dependence. The parametric combustion models were validated by overlaying the simulated pressure trace from GT-Power on to experimentally measured pressure traces. A thermodynamic engine model was also developed to study the effect of fuel blends, engine geometries and operating conditions on both the burn durations and COV of gross IMEP simulation results.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

Analysis of in-cylinder pressure transducer data quality utilizing a SIDI turbocharged engine

In-cylinder pressure transducers have been used for decades to record combustion pressure inside a running engine. However, due to the extreme operating environment, transducer design and installation must be considered in order to minimize measurement error. One such error is caused by thermal shock, where the pressure transducer experiences a high heat flux that can distort the pressure transducer diaphragm and also change the crystal sensitivity. This research focused on investigating the effects of thermal shock on in-cylinder pressure transducer data quality using a 2.0L, four-cylinder, spark-ignited, direct-injected, turbo-charged GM engine. Cylinder four was modified with five ports to accommodate pressure transducers of different manufacturers. They included an AVL GH14D, an AVL GH15D, a Kistler 6125C, and a Kistler 6054AR. The GH14D, GH15D, and 6054AR were M5 size transducers. The 6125C was a larger, 6.2mm transducer. Note that both of the AVL pressure transducers utilized a PH03 flame arrestor. Sweeps of ignition timing (spark sweep), engine speed, and engine load were performed to study the effects of thermal shock on each pressure transducer. The project consisted of two distinct phases which included experimental engine testing as well as simulation using a commercially available software package. A comparison was performed to characterize the quality of the data between the actual cylinder pressure and the simulated results. This comparison was valuable because the simulation results did not include thermal shock effects. All three sets of tests showed the peak cylinder pressure was basically unaffected by thermal shock. Comparison of the experimental data with the simulated results showed very good correlation. The spark sweep was performed at 1300 RPM and 3.3 bar NMEP and showed that the differences between the simulated results (no thermal shock) and the experimental data for the indicated mean effective pressure (IMEP) and the pumping mean effective pressure (PMEP) were significantly less than the published accuracies. All transducers had an IMEP percent difference less than 0.038% and less than 0.32% for PMEP. Kistler and AVL publish that the accuracy of their pressure transducers are within plus or minus 1% for the IMEP (AVL 2011; Kistler 2011). In addition, the difference in average exhaust absolute pressure between the simulated results and experimental data was the greatest for the two Kistler pressure transducers. The location and lack of flame arrestor are believed to be the cause of the increased error. For the engine speed sweep, the torque output was held constant at 203 Nm (150 ft-lbf) from 1500 to 4000 RPM. The difference in IMEP was less than 0.01% and the PMEP was less than 1%, except for the AVL GH14D which was 5% and the AVL GH15DK which was 2.25%. A noticeable error in PMEP appeared as the load increased during the engine speed sweeps, as expected. The load sweep was conducted at 2000 RPM over a range of NMEP from 1.1 to 14 bar. The difference in IMEP values were less 0.08% while the PMEP values were below 1% except for the AVL GH14D which was 1.8% and the AVL GH15DK which was at 1.25%. In-cylinder pressure transducer data quality was effectively analyzed using a combination of experimental data and simulation results. Several criteria can be used to investigate the impact of thermal shock on data quality as well as determine the best location and thermal protection for various transducers.

STUDY OF SPARK IGNITION ENGINE COMBUSTION MODEL FOR THE ANALYSIS OF CYCLIC VARIATION AND COMBUSTION STABILITY AT LEAN OPERATING CONDITIONS

A fundamental combustion model for spark-ignition engine is studied in this report. The model is implemented in SIMULINK to simulate engine outputs (mass fraction burn and in-cylinder pressure) under various engine operation conditions. The combustion model includes a turbulent propagation and eddy burning processes based on literature [1]. The turbulence propagation and eddy burning processes are simulated by zero-dimensional method and the flame is assumed as sphere. To predict pressure, temperature and other in-cylinder variables, a two-zone thermodynamic model is used. The predicted results of this model match well with the engine test data under various engine speeds, loads, spark ignition timings and air fuel mass ratios. The developed model is used to study cyclic variation and combustion stability at lean (or diluted) combustion conditions. Several variation sources are introduced into the combustion model to simulate engine performance observed in experimental data. The relations between combustion stability and the introduced variation amount are analyzed at various lean combustion levels.

Big Manistee River Tributaries as Potential Arctic Grayling Habitat

The Big Manistee River was one of the most well known Michigan rivers to historically support a population of Arctic grayling (Thymallus arctics). Overfishing, competition with introduced fish, and habitat loss due to logging are believed to have caused their decline and ultimate extirpation from the Big Manistee River around 1900 and from the State of Michigan by 1936. Grayling are a species of great cultural importance to Little River Band of Ottawa Indian tribal heritage and although past attempts to reintroduce Arctic grayling have been unsuccessful, a continued interest in their return led to the assessment of environmental conditions of tributaries within a 21 kilometer section of the Big Manistee River to determine if suitable habitat exists. Although data describing historical conditions in the Big Manistee River is limited, we reviewed the literature to determine abiotic conditions prior to Arctic grayling disappearance and the habitat conditions in rivers in western and northwestern North America where they currently exist. We assessed abiotic habitat metrics from 23 sites distributed across 8 tributaries within the Manistee River watershed. Data collected included basic water parameters, streambed substrate composition, channel profile and areal measurements of channel geomorphic unit, and stream velocity and discharge measurements. These environmental condition values were compared to literature values, habitat suitability thresholds, and current conditions of rivers with Arctic grayling populations to assess the feasibility of the abiotic habitat in Big Manistee River tributaries to support Arctic grayling. Although the historic grayling habitat in the region was disturbed during the era of major logging around the turn of the 20th century, our results indicate that some important abiotic conditions within Big Manistee River tributaries are within the range of conditions that support current and past populations of Arctic grayling. Seven tributaries contained between 20-30% pools by area, used by grayling for refuge. All but two tributaries were composed primarily of pebbles, with the remaining two dominated by fine substrates (sand, silt, clay). Basic water parameters and channel depth were within the ranges of those found for populations of Arctic grayling persisting in Montana, Alaska, and Canada for all tributaries. Based on the metrics analyzed in this study, suitable abiotic grayling habitat does exist in Big Manistee River tributaries.