scholarly journals Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 844 ◽  
Author(s):  
Seokwon Cho ◽  
Jihwan Park ◽  
Chiheon Song ◽  
Sechul Oh ◽  
Sangyul Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
Combustion Engine ◽  
Cylinder Pressure ◽  
Estimation Model ◽  
Experimental Conditions ◽  
Modeling And Analysis ◽  
Residual Gas Fraction ◽  
Residual Gas ◽  
The Individual

The knock phenomenon is one of the major hindrances for enhancing the thermal efficiency in spark-ignited engines. Due to the stochastic behavior of knocking combustion, analytical cycle studies are required. However, there are many problems to be addressed with regard to the individual cycle analysis of in-cylinder pressure data. This study thus proposes novel, comprehensive and efficient methodologies for evaluating the knocking combustion in the internal combustion engine. The proposed methodologies include a filtering method for the in-cylinder pressure, the determination of the knock onset, and the calculation of the residual gas fraction. Consequently, a smart knock onset model with high accuracy could be developed using a supervised deep learning that was not available in the past. Moreover, an improved zero-dimensional (0D) estimation model for the residual gas fraction was developed to obtain better accuracy for closed system analysis. Finally, based on a cyclic analysis, a knock prediction model is suggested; the model uses 0D ignition delay correlation under various experimental conditions including aggressive cam phase shifting by a dual variable valve timing (VVT) system. Using the proposed analysis method, insight into stochastic knocking combustion can be obtained, and a faster combustion speed can lead to a higher knock intensity in a steady-state operation.

Air Charge and Residual Gas Fraction Estimation for a Spark-Ignition Engine Using In-Cylinder Pressure

2017 ◽  
Author(s):  
Arya Yazdani ◽  
Jeffrey Naber ◽  
Mahdi Shahbakhti ◽  
Paul Dice ◽  
Chris Glugla ◽  
...  
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Cylinder Pressure ◽  
Residual Gas Fraction ◽  
Residual Gas

An On-Line Model for Predicting Residual Gas Fraction by Measuring Intake/Exhaust and Cylinder Pressure in CAI Engine

2008 ◽  
Author(s):  
Seungmok Choi ◽  
Minyoung Ki ◽  
Kyoungdoug Min ◽  
Jinkook Kong ◽  
Kyoungjoon Chang ◽  
...  
Keyword(s):  
Cylinder Pressure ◽  
Line Model ◽  
On Line ◽  
Residual Gas Fraction ◽  
Residual Gas

scholarly journals Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Halit Yaşar ◽  
Gültekin Çağıl ◽  
Orhan Torkul ◽  
Merve Şişci
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
High Performance ◽  
Internal Combustion Engines ◽  
Deep Neural Network ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Cylinder Pressure ◽  
Hcci Engine

AbstractEngine tests are both costly and time consuming in developing a new internal combustion engine. Therefore, it is of great importance to predict engine characteristics with high accuracy using artificial intelligence. Thus, it is possible to reduce engine testing costs and speed up the engine development process. Deep Learning is an effective artificial intelligence method that shows high performance in many research areas through its ability to learn high-level hidden features in data samples. The present paper describes a method to predict the cylinder pressure of a Homogeneous Charge Compression Ignition (HCCI) engine for various excess air coefficients by using Deep Neural Network, which is one of the Deep Learning methods and is based on the Artificial Neural Network (ANN). The Deep Learning results were compared with the ANN and experimental results. The results show that the difference between experimental and the Deep Neural Network (DNN) results were less than 1%. The best results were obtained by Deep Learning method. The cylinder pressure was predicted with a maximum accuracy of 97.83% of the experimental value by using ANN. On the other hand, the accuracy value was increased up to 99.84% using DNN. These results show that the DNN method can be used effectively to predict cylinder pressures of internal combustion engines.

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

Modeling and Analysis of Anti-Icing Power of Aircraft Engine Inlet Based on Symmetric Algorithms

2021 ◽  
Vol 7 (6) ◽  
pp. 6361-6374
Author(s):  
Hui Peng
Keyword(s):  
Flow Field ◽  
Evaluation Method ◽  
Learning Strategy ◽  
Aircraft Engine ◽  
Absolute Error ◽  
Civil Aviation ◽  
Particle Swarm Algorithm ◽  
Intake Port ◽  
Experimental Conditions ◽  
Modeling And Analysis

To evaluate the capability of engine inlet, inlet components and power plant anti ICER under low temperature, this paper introduces the evaluation method of anti icing system for civil aviation engine room, and analyzes the anti icing power of the aircraft intake based on the symmetric algorithm. The realizable k-cube model and wall function method are used to analyze the flow field in the inlet of an aircraft engine. Based on the analysis of the flow field of the intake port of an aircraft engine, the anti ice power of the intake port is calculated according to the heat balance relationship of the intake port surface. The symmetrical particle swarm algorithm is adopted to optimize the calculation process of inlet anti-ice power, and the particle wide area learning strategy is used to promote the calculation of inlet anti-ice power. In this way, the computational complexity is significantly reduce and the accuracy of the power analysis of the inlet anti-ice is enhanced. The simulation results show that the absolute error of the proposed method is less than 1% in 1000 iterations. Through the analysis of the surface temperature changes of the inlet deflector under different experimental conditions, it can be known that the method can effectively analyze the anti-icing power of aircraft engine inlet.

Machine Learning Integration with Combustion Physics to Develop a Composite Predictive Model for Reactivity Controlled Compression Ignition Engine

2021 ◽  
pp. 1-27
Author(s):  
Chinmaya Mishra ◽  
P.M.V. Subbarao
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Alternative Methods ◽  
Compression Ignition ◽  
Combustion Control ◽  
Reactivity Controlled Compression Ignition ◽  
Operating Condition ◽  
Residual Gas Fraction ◽  
Residual Gas ◽  
Engine Operating Condition

Abstract Phasing of combustion metrics close to the optimum values across operation range is necessary to avail benefits of reactivity controlled compression ignition (RCCI) engines. Parameters like start of combustion occurrence crank angle (θsoc), occurrence of burn rate fraction reaching 50% (θ50), mean effective pressure from indicator diagram (IMEP) etc. are described as combustion metrics. These metrics act as markers for macroscopic state of combustion. Control of these metrics in RCCI engine is relatively complex due to the nature of ignition. As direct combustion control is challenging, alternative methods like combustion physics derived models are a subject of research interest. In this work, a composite predictive model was proposed by integrating trained random forest (RF) machine learning and artificial neural networks (ANN) to combustion physics derived modified Livengood-Wu integral, parametrized double-Wiebe function, autoignition front propagation speed based correlations and residual gas fraction model. The RF machine learning established a correlative relationship between physics based model coefficients and engine operating condition. The ANN developed a similar correlation between residual gas fraction parameters and engine operating condition. The composite model was deployed for the predictions of θsoc, θ50 and IMEP as RCCI engine combustion metrics. Experimental validation showed an error standard deviation (θ68.3,err) of 0.67 °CA, 1.19°CA, 0.223 bar and symmetric mean absolute percentage error of 6.92%, 7.87% and 4.01% for the predictions of θsoc, θ50 and IMEP respectively on cycle to cycle basis. Wide range applicability, lesser experiments for model calibration, low computational costs and utility for control applications were the benefits of the proposed predictive model.

Dynamic Validation and Sensitivity Analysis of a NOx Estimation Model Based on In-Cylinder Pressure Measurement

2017 ◽  
Author(s):  
Sergio Mario Camporeale ◽  
Patrizia D. Ciliberti ◽  
Antonio Carlucci ◽  
Daniela Ingrosso
Keyword(s):  
Sensitivity Analysis ◽  
Pressure Measurement ◽  
Cylinder Pressure ◽  
Estimation Model ◽  
Model Based
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