Indicated Mean Effective Pressure Estimator Order Determination and Reduction When Using Estimated Engine Statistics

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
J. S. Arbuckle ◽  
J. B. Burl
Keyword(s):  
Estimation Error ◽  
Correlated Data ◽  
Training Data ◽  
Linear Estimator ◽  
Strongly Correlated ◽  
Effective Pressure ◽  
Cylinder Pressure ◽  
Indicated Mean Effective Pressure ◽  
Optimal Linear ◽  
Data Points

The indicated mean effective pressure (IMEP) is typically used as an engine running quality metric. IMEP depends on cylinder pressure, which is costly to measure, therefore it is useful to estimate IMEP from currently measured crankshaft encoder data. In this paper, the difficulties in developing an optimal linear estimator from acceleration computed from crankshaft rotational speed and cylinder pressure data are discussed, and strategies are presented to reduce these difficulties. Estimating IMEP from crankshaft data requires the determination of which data to use in the estimator. Without this step, the estimator can become unnecessarily complex due the inclusion of strongly correlated data points in the estimator. A strategy to determine the angular location of the acceleration points to use is presented and is shown to greatly reduce the estimator complexity without significantly affecting estimation error. Additionally, while increasing the estimator order usually decreases the estimation error, it will be shown that increasing the estimator order can actually increase the estimation error. This effect is due to uncertainties in the gains of the estimator. These uncertainties in the gains can result from using limited training data to estimate the statistics necessary to compute the gains or when dealing with a nonstationary system. A method of reducing the effect of these uncertainties by optimizing the estimator order based on the number of available training data cycles is developed and demonstrated.

An Experimental Investigation of Hydrogen Fumigation in a Small Direct-Injection Diesel Engine During Part-Load Operation

2011 ◽  
Author(s):  
William E. Marin ◽  
Daniel P. Wiese ◽  
Paul A. Erickson
Keyword(s):  
Diesel Engine ◽  
Flow Rate ◽  
Thermal Efficiency ◽  
Direct Injection ◽  
Low Energy ◽  
Injection Timing ◽  
Effective Pressure ◽  
Cylinder Pressure ◽  
Indicated Mean Effective Pressure ◽  
Hydrogen Enrichment

Hydrogen enrichment may offer enhanced performance of internal combustion engines. Hydrogen’s high specific energy, wide flammability limits, and high flame speed are all desirable traits that can potentially enhance combustion. However, hydrogen’s low energy density and its need to be produced from another energy source pose significant challenges for implementation. Hydrogen enrichment involves co-firing of hydrogen and another primary fuel. The hydrogen can be aspirated through the intake manifold via fumigation or injected at the port or cylinder with the primary fuel. The effect of hydrogen fumigation in diesel engines has been studied to some degree but is not fully understood. In this research, a single-cylinder four-stroke direct-injection diesel engine was modified for hydrogen fumigation and was instrumented to monitor combustion related performance parameters. This engine is representative of low-cost systems that are widely used in developing nations for agricultural and other low power applications. A factorial design of experiments was implemented to study the effects and interactions of hydrogen fumigation flow rate, injection timing, and diesel fuel flow rate on part-load engine performance. At relatively low energy fractions, hydrogen was found to have statistically insignificant effects on brake torque and indicated mean effective pressure, leading to modest decreases in brake thermal efficiency. Exhaust gas temperature increased with hydrogen enrichment. The coefficient of variance of indicated mean effective pressure decreased with hydrogen enrichment, and visible changes to the in-cylinder pressure trace were observed, particularly when injection timing was retarded. The results of this investigation show that for this specific configuration, hydrogen enrichment is not beneficial to the combustion process. The marginal improvements in coefficient of variance and changes of in-cylinder pressure cannot justify the decrease in thermal efficiency of the engine.

Properties of Homogeneous Charge Compression Ignition (HCCI) Engine With N-Butane Fuel

2001 ◽  
Author(s):  
Minoru Iida ◽  
Motoaki Hayashi ◽  
David E. Foster ◽  
Jay K. Martin
Keyword(s):  
Fourier Transform ◽  
Homogeneous Charge Compression Ignition ◽  
Maximum Load ◽  
Compression Ignition ◽  
Effective Pressure ◽  
Cylinder Pressure ◽  
Indicated Mean Effective Pressure ◽  
Hydrocarbon Emission ◽  
Infrared Spectrometer ◽  
Homogeneous Charge

Abstract In this paper, some basic properties of homogeneous charge compression ignition operation are investigated. The HCCI operating range for a CFR engine was determined with n-butane as fuel. The minimum and maximum load was determined using criteria of covariance of indicated mean effective pressure and the derivative of in-cylinder pressure respectively. Exhaust emissions, particularly hydrocarbons, were measured using a Fourier transform infrared spectrometer. The concentration of intermediate hydrocarbon species rapidly decreased as the magnitude of the heat release increased. Hydrocarbon emission at the maximum HCCI load mainly consists of the fuel itself, which is probably emitted from colder areas in the combustion chamber. The relation between IMEPCOV and ISFC is discussed.

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

BIAS-VARIANCE CONTROL VIA HARD POINTS SHAVING

2004 ◽  
Vol 18 (05) ◽  
pp. 891-903 ◽  
Author(s):  
STEFANO MERLER ◽  
BRUNO CAPRILE ◽  
CESARE FURLANELLO
Keyword(s):  
Control Strategy ◽  
Noisy Data ◽  
Real Data ◽  
Training Data ◽  
Regularization Technique ◽  
Data Points ◽  
Classification Tasks ◽  
Bias Variance

In this paper, we propose a regularization technique for AdaBoost. The method implements a bias-variance control strategy in order to avoid overfitting in classification tasks on noisy data. The method is based on a notion of easy and hard training patterns as emerging from analysis of the dynamical evolutions of AdaBoost weights. The procedure consists in sorting the training data points by a hardness measure, and in progressively eliminating the hardest, stopping at an automatically selected threshold. Effectiveness of the method is tested and discussed on synthetic as well as real data.

scholarly journals ChemProps: A RESTful API enabled database for composite polymer name standardization

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Bingyin Hu ◽  
Anqi Lin ◽  
L. Catherine Brinson
Keyword(s):  
Data Exchange ◽  
Ground Truth ◽  
Training Data ◽  
Test Accuracy ◽  
Polymer Science ◽  
Data Systems ◽  
Materials Informatics ◽  
Related Data ◽  
Weight Factors ◽  
Data Points

AbstractThe inconsistency of polymer indexing caused by the lack of uniformity in expression of polymer names is a major challenge for widespread use of polymer related data resources and limits broad application of materials informatics for innovation in broad classes of polymer science and polymeric based materials. The current solution of using a variety of different chemical identifiers has proven insufficient to address the challenge and is not intuitive for researchers. This work proposes a multi-algorithm-based mapping methodology entitled ChemProps that is optimized to solve the polymer indexing issue with easy-to-update design both in depth and in width. RESTful API is enabled for lightweight data exchange and easy integration across data systems. A weight factor is assigned to each algorithm to generate scores for candidate chemical names and optimized to maximize the minimum value of the score difference between the ground truth chemical name and the other candidate chemical names. Ten-fold validation is utilized on the 160 training data points to prevent overfitting issues. The obtained set of weight factors achieves a 100% test accuracy on the 54 test data points. The weight factors will evolve as ChemProps grows. With ChemProps, other polymer databases can remove duplicate entries and enable a more accurate “search by SMILES” function by using ChemProps as a common name-to-SMILES translator through API calls. ChemProps is also an excellent tool for auto-populating polymer properties thanks to its easy-to-update design.

Benchmarking and Field-Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization

2021 ◽  
Author(s):  
Faruk Alpak ◽  
Yixuan Wang ◽  
Guohua Gao ◽  
Vivek Jain
Keyword(s):  
Optimization Problems ◽  
Field Testing ◽  
Optimization Method ◽  
Training Data ◽  
Local Optima ◽  
Field Development ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Data Points ◽  
Quasi Newton

Abstract Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir performance optimization problems including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to effectively locate multiple local optima of highly nonlinear optimization problems. However, its performance has neither been validated by realistic applications nor compared to other DFO methods. We have integrated DQN into a versatile field-development optimization platform designed specifically for iterative workflows enabled through distributed-parallel flow simulations. DQN is benchmarked against alternative DFO techniques, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method hybridized with Direct Pattern Search (BFGS-DPS), Mesh Adaptive Direct Search (MADS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization thread are shared with others by updating a unified set of training data points composed of responses (implicit variables) of all successful simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by a linear-interpolation technique using all or a subset of training-data points. The gradient of the objective function is analytically computed using the estimated sensitivities of implicit variables with respect to explicit variables. The Hessian matrix is then updated using the quasi-Newton method. A new search point for each thread is solved from a trust-region subproblem for the next iteration. In contrast, other DFO methods rely on a single-thread optimization paradigm that can only locate a single optimum. To locate multiple optima, one must repeat the same optimization process multiple times starting from different initial guesses for such methods. Moreover, simulation results generated from a single-thread optimization task cannot be shared with other tasks. Benchmarking results are presented for synthetic yet challenging WLO and WCO problems. Finally, DQN method is field-tested on two realistic applications. DQN identifies the global optimum with the least number of simulations and the shortest run time on a synthetic problem with known solution. On other benchmarking problems without a known solution, DQN identified compatible local optima with reasonably smaller numbers of simulations compared to alternative techniques. Field-testing results reinforce the auspicious computational attributes of DQN. Overall, the results indicate that DQN is a novel and effective parallel algorithm for field-scale development optimization problems.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals A Health Indicator for the Online Lifetime Estimation of an Electric Vehicle Power Li-Ion Battery

2020 ◽  
Vol 11 (3) ◽  
pp. 59
Author(s):  
Bin Yu ◽  
Haifeng Qiu ◽  
Liguo Weng ◽  
Kailong Huo ◽  
Shiqi Liu ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Health Management ◽  
Estimation Error ◽  
Health Indicators ◽  
Training Data ◽  
Battery Life ◽  
Li Ion Battery ◽  
Load Curve ◽  
Response Level ◽  
Li Ion

With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness.

Effects of multiple injection strategies on gaseous emissions and particle size distribution in a two-stroke compression-ignition engine operating with the gasoline partially premixed combustion concept

2018 ◽  
Vol 233 (10) ◽  
pp. 2650-2668 ◽  
Author(s):  
Vicente Bermúdez ◽  
Santiago Ruiz ◽  
Ricardo Novella ◽  
Lian Soto
Keyword(s):  
Particulate Matter ◽  
Size Distribution ◽  
Injection Pressure ◽  
Premixed Combustion ◽  
Gaseous Emissions ◽  
Effective Pressure ◽  
Compression Ignition Engine ◽  
Indicated Mean Effective Pressure ◽  
Partially Premixed Combustion ◽  
Partially Premixed

In order to improve performance of internal combustion engines and meet the requirements of the new pollutant emission regulations, advanced combustion strategies have been investigated. The newly designed partially premixed combustion concept has demonstrated its potential for reducing NOx and particulate matter emissions combined with high indicated efficiencies while still retaining proper control over combustion process by using different injection strategies. In this study, parametric variations of injection pressure, second injection and third injection timings were experimentally performed to analyze the effect of the injection strategy over the air/fuel mixture process and its consequent impact on gaseous compound emissions and particulate matter emissions including its size distribution. Tests were carried out on a newly designed two-stroke high-speed direct injection compression-ignition engine operating with the partially premixed combustion concept using 95 research octane number gasoline fuel. A scanning particle sizer was used to measure the particles size distribution and the HORIBA 7100DEGR gas analyzer system to determine gaseous emissions. Three different steady-state operation modes in terms of indicated mean effective pressure and engine speed were investigated: 3.5 bar indicated mean effective pressure and 2000 r/min, 5.5 bar indicated mean effective pressure and 2000 r/min, and 5.5 bar indicated mean effective pressure and 2500 r/min. The experimental results confirm how the use of an adequate injection strategy is indispensable to obtain low exhaust emissions values and a balance between the different pollutants. With the increase in the injection pressure and delay in the second injection, it was possible to obtain a trade-off between NOx and particulate matter emission reduction, while there was an increase in hydrocarbon and carbon monoxide emissions under these conditions. In addition, the experiments showed an increase in particle number emissions and a progressive shift in the particles size distribution toward larger sizes, increasing the accumulation-mode particles and reducing the nucleation-mode particles with the decrease in the injection pressure and delay in the third injection.

scholarly journals Combustion of Hydrogen Enriched Methane and Biogases Containing Hydrogen in a Controlled Auto-Ignition Engine

2018 ◽  
Vol 8 (12) ◽  
pp. 2667
Author(s):  
Antonio Mariani ◽  
Andrea Unich ◽  
Mario Minale
Keyword(s):  
Equivalence Ratio ◽  
Numerical Study ◽  
Chemical Reactivity ◽  
Combustion Process ◽  
Pollutant Emissions ◽  
Ignition Process ◽  
Effective Pressure ◽  
Indicated Mean Effective Pressure ◽  
Auto Ignition ◽  
Combustion Of Hydrogen

The paper describes a numerical study of the combustion of hydrogen enriched methane and biogases containing hydrogen in a Controlled Auto Ignition engine (CAI). A single cylinder CAI engine is modelled with Chemkin to predict engine performance, comparing the fuels in terms of indicated mean effective pressure, engine efficiency, and pollutant emissions. The effects of hydrogen and carbon dioxide on the combustion process are evaluated using the GRI-Mech 3.0 detailed radical chain reactions mechanism. A parametric study, performed by varying the temperature at the start of compression and the equivalence ratio, allows evaluating the temperature requirements for all fuels; moreover, the effect of hydrogen enrichment on the auto-ignition process is investigated. The results show that, at constant initial temperature, hydrogen promotes the ignition, which then occurs earlier, as a consequence of higher chemical reactivity. At a fixed indicated mean effective pressure, hydrogen presence shifts the operating range towards lower initial gas temperature and lower equivalence ratio and reduces NOx emissions. Such reduction, somewhat counter-intuitive if compared with similar studies on spark-ignition engines, is the result of operating the engine at lower initial gas temperatures.

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