Fast Computation of Combustion Phasing and its Influence on Classifying Random or Deterministic Patterns

2015 ◽  
Author(s):  
Huan Lian ◽  
Jason Martz ◽  
Niket Prakash ◽  
Anna Stefanopoulou
Keyword(s):  
Statistical Technique ◽  
Mean Value ◽  
Operating Conditions ◽  
Permutation Entropy ◽  
Injection Timing ◽  
Si Engine ◽  
Spark Timing ◽  
On Line ◽  
The Mean ◽  
First Time

The classification between a sequence of highly variable combustion events that have an underlying deterministic pattern and a sequence of combustion events with similar level of variability but random characteristics is important for control of combustion phasing. In the case of high cyclic variation (CV) with underlying deterministic patterns, it is possible to apply closed loop combustion control on a cyclic-basis with a fixed mean value, such as injection timing in homogeneous charge compression ignition (HCCI) or spark timing in spark ignition (SI) applications, to contract the CV. In the case of a random distribution, the high CV can be avoided by shifting operating conditions away from the unstable region via advancing or retarding the injection timing or the spark timing in the mean-sense. Therefore, the focus of this paper is on the various methods of computing CA50 for analysing and classifying cycle-to-cycle variability. The assumptions made to establish fast and possibly on-line methods can alter the distribution of the calculated parameters from cycle-to-cycle, possibly leading to incorrect pattern interpretation and improper control action. Finally, we apply a statistical technique named “permutation entropy” for the first time on classifying combustion patterns in HCCI and SI engine for varying operating condition. Then the various fast methods for computing CA50 feed the two statistical methods, permutation and the Shannon entropy, and their differences and similarities are highlighted.

Fast Computation of Combustion Phasing and Its Influence on Classifying Random or Deterministic Patterns

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Huan Lian ◽  
Jason Martz ◽  
Niket Prakash ◽  
Anna Stefanopoulou
Keyword(s):  
Statistical Technique ◽  
Mean Value ◽  
Operating Conditions ◽  
Permutation Entropy ◽  
Injection Timing ◽  
Si Engine ◽  
Spark Timing ◽  
The Mean ◽  
Online Methods ◽  
First Time

The classification between a sequence of highly variable combustion events that have an underlying deterministic pattern and a sequence of combustion events with similar level of variability but random characteristics is important for control of combustion phasing. In the case of high cyclic variation (CV) with underlying deterministic patterns, it is possible to apply closed-loop combustion control on a cyclic-basis with a fixed mean value, such as injection timing in homogeneous charge compression ignition (HCCI) or spark timing in spark ignition (SI) applications, to contract the CV. In the case of a random distribution, the high CV can be avoided by shifting operating conditions away from the unstable region via advancing or retarding the injection timing or the spark timing in the mean-sense. Therefore, the focus of this paper is on the various methods of computing CA50 for analyzing and classifying cycle-to-cycle variability. The assumptions made to establish fast and possibly online methods can alter the distribution of the calculated parameters from cycle-to-cycle, possibly leading to incorrect pattern interpretation and improper control action. Finally, we apply a statistical technique named “permutation entropy” for the first time on classifying combustion patterns in HCCI and SI engine for varying operating conditions. Then, the various fast methods for computing CA50 feed the two statistical methods, permutation and the Shannon entropy, and their differences and similarities are highlighted.

scholarly journals A SLR Pre-Processing Algorithm Based on Satellite Signature Effect

2021 ◽  
Vol 11 (19) ◽  
pp. 8880
Author(s):  
Bowen Guan ◽  
Cunbo Fan ◽  
Ning An ◽  
Ricardo Cesar Podesta ◽  
Dra Ana Pacheco ◽  
...  
Keyword(s):  
Mean Value ◽  
Observation Data ◽  
Short Term ◽  
Error Sources ◽  
Effect Model ◽  
The Mean ◽  
The Stability ◽  
First Time ◽  
Satellite Signature Effect

As one of the major error sources, satellite signature effect should be reduced or even erased from the distribution of the post-fit residuals to improve the ranging precision. A simulation of satellite signature effect removal process for normal point algorithm is conducted based on a revised model of satellite response, which fully considers the structural and distribution characteristics of retroreflectors. In order to eliminate both long-term and short-term satellite signature effect, a clipping method for SLR data processing is proposed by defining the clipping location as 5.6 mm away from the mean value of the long-term fit residuals to select effective returns for normal points. The results indicate that, compared to normal points algorithm, the RMS per NP of LAGEOS-1 observation data processed by the clipping method is reduced from 62.90 ± 9.9 mm to 56.07 ± 4.69 mm, and the stability of RMS is improved 53%. This study improves the satellite signature effect model and simulates the fluctuation of normal points caused by satellite signature effect for the first time. The new method based on the simulation of satellite signature effect has stronger robustness and applicability, which can further minimize the influence of satellite signature effect on the SLR production and significantly improve the data property.

scholarly journals Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 345 ◽  
Author(s):  
Rui-Na Fan ◽  
Fan-Qi Ma ◽  
Quan-Lin Li
Keyword(s):  
Mean Value ◽  
Sustainable Transportation ◽  
Queuing Network ◽  
Value Analysis ◽  
Fleet Size ◽  
Closed Queuing Networks ◽  
Mean Value Analysis ◽  
Bike Sharing ◽  
The Mean ◽  
First Time

The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks.

Numerical Investigation on Mixture Formation in a Turbocharged Port-Injection Natural Gas Engine Using Multiple Cycle Simulation

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Zhenkuo Wu ◽  
Zhiyu Han
Keyword(s):  
Fuel Injection ◽  
Load Condition ◽  
Operating Conditions ◽  
Injection Timing ◽  
Mixture Formation ◽  
Natural Gas Engine ◽  
Cycle Simulation ◽  
Mixture Preparation ◽  
Spark Timing ◽  
Multiple Cycle

In the present study, multidimensional computational fluid dynamics (CFD) simulations were carried out to study mixture formation in a turbocharged port-injection natural gas engine. In order to achieve robust simulation results, multiple cycle simulation was employed to remove the inaccuracies of initial conditions setting. First, the minimal number of simulation cycles required to obtain convergent cycle-to-cycle results was determined. Based on this, the in-cylinder mixture preparation for three typical operating conditions was studied. The effects of fuel injection timing and intake valve open scheme on the mixture formation were evaluated. The results demonstrated that three simulation cycles are needed to achieve convergence of the results for the present study. The analysis of the mixture preparation revealed that only in the initial phase of the intake stroke, there is an obvious difference between the three operating conditions. At the spark timing, for 5500 rpm, full load condition mixture composition throughout the cylinder is flammable, and for 2000 rpm, 2 bar operating condition part of the mixture is lean and nonflammable. The fuel injection timing has an insignificant impact on the mixture flammability at the spark timing. It was observed that the designed nonsynchronous intake valve open scheme has stronger swirl and x-direction tumble motion than the baseline case, leading to better mixture homogeneity and spatial distribution. With an increase in volumetric efficiency, particularly at 2000 rpm, full load condition, by 4.85% compared to the baseline, which is in line with experimental observation.

Survey of cholesterol level of commercial eggs produced on Canadian farms

1991 ◽  
Vol 71 (1) ◽  
pp. 205-209 ◽  
Author(s):  
Siew Lian Chung ◽  
L. K. Ferrier ◽  
E. J. Squires
Keyword(s):  
Dietary Protein ◽  
High Performance ◽  
Cholesterol Content ◽  
Mean Value ◽  
Operating Conditions ◽  
Cholesterol Concentration ◽  
Cholesterol Levels ◽  
Key Words Cholesterol ◽  
The Mean ◽  
Selection Of

A survey of the cholesterol levels of eggs from White Leghorn hens from across Canada was performed. High-performance liquid chromatography was used for cholesterol analysis. The cholesterol concentrations were found to range from 12 to 15 mg g−1 yolk, and the mean value for all eggs was 13.0 mg g−1 or 221 mg egg−1. Large eggs (35 out of 56) contained an average of 214 mg cholesterol egg−1. The effect of farm on cholesterol concentration (mg g−1 yolk) was significant (P < 0.05) and in most cases sample variation (P < 0.05) was found within each farm. Cholesterol content (mg egg−1) correlated positively with hen age, egg weight and yolk weight and negatively with dietary protein and fat. However, correlation of cholesterol concentration (mg g−1 yolk) with the above variables was not significant (P < 0.05). Selection of eggs for lower cholesterol content appears impractical under commercial operating conditions at this time. Key words: Cholesterol, Canadian, chicken egg

scholarly journals Detection of Citrus Bent Leaf Viroid in Citrus Orchards of Sargodha, Pakistan

2021 ◽  
Vol 39 (2) ◽  
pp. 159-163
Author(s):  
Faheema Bakhtawar ◽  
◽  
Yasir Iftikhar ◽  
Muhammad Ahmed Zeshan ◽  
Muhammad Imran Hamid ◽  
...  
Keyword(s):  
Molecular Detection ◽  
Disease Incidence ◽  
Mean Value ◽  
Rt Pcr ◽  
Specific Primers ◽  
Citrus Orchards ◽  
The Mean ◽  
Citrus Varieties ◽  
Near Future ◽  
First Time

A study was conducted to monitor the Citrus bent leaf viroid (CBLVd) in citrus growing areas of district Sargodha, Pakistan during 2017-2018. Collected samples were tested by RT-PCR using specific primers. PCR positive samples were used to confirm the CBLVd incidence and severity on different citrus varieties grown at different regions of Sargodha. Maximum disease incidence was recorded in Kot Momin with the mean value of 24%, with severe symptoms of bark cracking, backward leaf bent and stunting. Minimum disease incidence was recorded in in Sillanwali region with the mean value of 3.33%. The symptoms in Sillanwali were only yellowing and slight leaf bent. Maximum severity was observed in Kot momin (0.60%). Molecular detection of CBLVd by RT-PCR confirmed the diagnosis of the viroid. This survey was carried out for the first time in Sargodha district to monitor the occurrence of citrus bent leaf viroid following the first report of its detection in Pakistan in 2009. Since many declining citrus trees were found negative to CBLVd testing, other causal agents can be involved, and extensive surveys are still required in the near future. Keywords: Citrus, RT-PCR, CBLVd, Disease incidence, viroid, Sargodha, Pakistan

Study on the Mean Value of Particle Concentration Signal in Silicon Power Fluidized Bed

2012 ◽  
Vol 550-553 ◽  
pp. 2936-2940
Author(s):  
Xing Yong Liu ◽  
Hu Yang ◽  
You Cheng Wang ◽  
Zhuo Xu Deng
Keyword(s):  
Neural Network ◽  
Fluidized Bed ◽  
Particle Concentration ◽  
Rbf Neural Network ◽  
Radial Distance ◽  
Mean Value ◽  
Operating Conditions ◽  
Axial Distance ◽  
The Mean ◽  
Concentration Signal

The particle concentration signals of silicon powder in the fluidizing gas i.e. air under different operating conditions were determined. The diameter of silicon particles, operating velocity, radial distance and axial distance are used as input vector; the mean value of particle concentration signal in the silicon power fluidized bed is used as a target vector. The RBF neural network is applied to build the predicted model of the mean value in silicon power fluidized bed. The result shows that the prediction of mean value through the RBF neural network is prior to that by BP neural network, and its error is less than 0.2%.

Numerical and experimental study on knocking combustion in turbocharged direct-injection engines for a wide range of operating conditions

2021 ◽  
pp. 146808742110601
Author(s):  
Magnus Kircher ◽  
Emmeram Meindl ◽  
Christian Hasse
Keyword(s):  
Experimental Data ◽  
Experimental Study ◽  
Numerical Study ◽  
Direct Injection ◽  
Operating Conditions ◽  
Auto Ignition ◽  
Spark Timing ◽  
Crank Angle ◽  
Wide Range ◽  
The Mean

A combined experimental and numerical study is conducted on knocking combustion in turbocharged direct-injection spark-ignition engines. The experimental study is based on parameter variations in the intake-manifold temperature and pressure, as well as the air-fuel equivalence ratio. The transition between knocking and non-knocking operating conditions is studied by conducting a spark timing sweep for each operating parameter. By correlating combustion and global knock quantities, the global knock trends of the mean cycles are identified. Further insight is gained by a detailed analysis based on single cycles. The extensive experimental data is then used as an input to support numerical investigations. Based on 0D knock modeling, the global knock trends are investigated for all operation points. Taking into consideration the influence of nitric oxide on auto-ignition significantly improves the knock model prediction. Additionally, the origin of the observed cyclic variability of knock is investigated. The crank angle at knock onset in 1000 consecutive single cycles is determined using a multi-cycle 0D knock simulation based on detailed single-cycle experimental data. The overall trend is captured well by the simulation, while fluctuations are underpredicted. As one potential reason for the remaining differences of the 0D model predictions local phenomena are investigated. Therefore, 3D CFD simulations of selected operating points are performed to explore local inhomogeneities in the mixture fraction and temperature. The previously developed generalized Knock Integral Method (gKIM), which considers the detailed kinetics and turbulence-chemistry interaction of an ignition progress variable, is improved and applied. The determined influence of spark timing on the mean crank angle at knock onset agrees well with experimental data. In addition, spatially resolved information on the expected position of auto-ignition is analyzed to investigate causes of knocking combustion.

Performance of SI Engine to Improve the Combustion Characteristics by Using Methanol Blended Petrol

2015 ◽  
Vol 813-814 ◽  
pp. 857-861
Author(s):  
A.N. Basavaraju ◽  
Mallikappa ◽  
B. Yogesha
Keyword(s):  
Fuel Injection ◽  
Fuel Efficiency ◽  
Combustion Process ◽  
Injection Pressure ◽  
Operating Conditions ◽  
Injection System ◽  
Optimum Operating ◽  
Injection Timing ◽  
Si Engine ◽  
Single Cylinder

The present energy situation has stimulated active research interest in non-petroleum and non-polluting fuels, particularly for transportation, power generation, and agricultural sectors. This paper describes feasibility of utilization of Spark ignition (SI) engine in single fuel mode and to develop the optimum operating conditions in terms of fuel injection timing and fuel injection pressure. Many modifications were made for the developed direct fuel injection system to improve the performance of the 350 cc four stroke single cylinder petrol engine. The engine is tested to conduct performance, combustion emission characteristics with the aid of carburetor. As single cylinder small engines have low compression ratio (CR), and they run with slightly rich mixture, their power are low and emission values are high. In this study, methanol was used to increase performance and decrease emissions of a single-cylinder engine. Initially, the engine whose CR was 7.5/1 was tested with gasoline and methanol at full load and various speeds. This method is used for increasing the fuel efficiency of a vehicle by adding different percentage of methanol to the petrol and to decrease the pollutants produced during combustion process.

Nerve growth factor concentrations in the synovial fluid from healthy dogs and dogs with secondary osteoarthritis

2011 ◽  
Vol 24 (04) ◽  
pp. 279-284 ◽  
Author(s):  
M. Isola ◽  
V. Ferrari ◽  
F. Stabile ◽  
D. Bernardini ◽  
P. Carnier ◽  
...  
Keyword(s):  
Nerve Growth Factor ◽  
Growth Factor ◽  
Synovial Fluid ◽  
Mean Value ◽  
Nerve Growth ◽  
Significance Level ◽  
Secondary Osteoarthritis ◽  
Healthy Dogs ◽  
The Mean ◽  
First Time

SummaryObjective: To measure the concentrations of nerve growth factor (NGF) in the synovial fluid from normal dogs and dogs with osteoarthritis (OA) secondary to common joint disorders.Methods: Nerve growth factor synovial concentrations were measured by ELISA assay in 50 dogs divided into three groups: 12 healthy, 16 affected by acute lameness within seven days before enrolment, and 22 with chronic lameness persisting by more than one month before enrolment and accompanied by radiological signs of OA. Both acute and chronic lameness were secondary to orthopaedic diseases involving the shoulder, elbow and stifle joints. Nerve growth factor synovial concentrations were compared between means for healthy and acute groups and between the three groups using an F-test. Significance level was set at p ±0.05.Results: Nerve growth factor was detected in all canine synovial fluid samples. However, the mean synovial NGF concentration of healthy dogs (3.65 ± 2.18 pg/ml) was not significantly different from the mean value in dogs with acute lameness (6.45 ± 2.45 pg/ml) (p ± 0.79). Conversely, the mean synovial NGF concentration in dogs with chronic lameness (20.19 ± 17.51 pg/ml) was found to be significantly higher than that found in healthy dogs (p ±0.01).Clinical significance: This study demonstrates for the first time the presence of NGF in canine synovial fluid and its increased concentrations in dogs with chronic lameness compared to healthy dogs and dogs with acute lameness. The association between chronic lameness and raised synovial concentrations may suggest an involvement of NGF in OA inflammation and chronic pain.

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