Grey-Box Modeling for HCCI Engine Control

2013 ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Engine Control ◽  
Gas Temperature ◽  
Real Time Control ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels

2014 ◽  
Vol 136 (10) ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Gas Temperature ◽  
Real Time Control ◽  
Neural Network Models ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

Integrated HCCI Engine Control Based on a Performance Index

2014 ◽  
Vol 136 (10) ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Performance Index ◽  
Operating Conditions ◽  
Exhaust Aftertreatment ◽  
Engine Control ◽  
New Approach ◽  
Aftertreatment System ◽  
Exhaust Aftertreatment System ◽  
Hcci Engine ◽  
High Efficient ◽  
Hcci Engines

The integrated control of a homogenous charge compression ignition (HCCI) combustion phasing, load, and exhaust aftertreatment system is essential for realizing high-efficient HCCI engines, while maintaining low hydrocarbon (HC) and carbon monoxide (CO) emissions. This paper introduces a new approach for integrated HCCI engine control by defining a novel performance index to characterize different HCCI operating regions. The experimental data from a single cylinder engine at 214 operating conditions is used to determine the performance index for a blended fuel HCCI engine. The new performance index is then used to design an optimum reference trajectory for a multi-input multi-output HCCI controller. The optimum trajectory is designed for control of the combustion phasing and indicated mean effective pressure (IMEP), while meeting catalyst light-off requirements for the exhaust aftertreatment system. The designed controller is tested on a previously validated physical HCCI engine model. The simulation results illustrate the successful application of the new approach for controller design of HCCI engines.

Combustion Trajectory Visualization Model for Study of Conventional and Advanced Direct Injection Combustion Modes

2015 ◽  
Author(s):  
Joshua A. Bittle ◽  
Timothy J. Jacobs
Keyword(s):  
Real Time ◽  
Equivalence Ratio ◽  
Sampling Rate ◽  
Flame Temperature ◽  
Operating Conditions ◽  
Engine Control ◽  
Cylinder Pressure ◽  
Real Time Control ◽  
Time Step ◽  
The Future

Many of the approaches to diagnostics of in-cylinder spatially resolved quantities (such as equivalence ratio, temperature, speciation, etc.) require either optical engines or computational fluid dynamics. These approaches are expensive (time or money) and will likely never be practical for on-board use in the future. The market trend towards real-time control and consumer grade in-cylinder pressure transducers suggest that relatively simple modeling techniques based on cylinder pressure and other standard engine sensors are well situated to be a part of the future engine control schemes. This work expands previous efforts to calculate combustion trajectories (path through equivalence ratio vs. temperature plane) based on cylinder pressure measurements in near real-time. This work incorporates the current state-of-the-art diesel fuel spray mixing models (Kattke and Musculus entrainment waves) and adds features to accounting for changing cylinder pressure, adaptive time step based on sampling rate of cylinder pressure, and optimizing spray axial resolution for reduced calculation time. Based on the predicted local fuel concentration, flame temperature and relating calculated heat release rates to the amount of fuel burned in each portion of the spray the combustion processes can be tracked to give a cumulative history of the ignition, subsequent mixing and heating/cooling that gives a picture of what combustion looks like on the equivalence ratio vs. temperature plane. Various engine operating conditions are explored including conventional diesel operation with and without EGR as well as highly dilute late injection low temperature combustion at different injection pressures. The results obtained in this work give encouragement that this type of approach may enable future engine control using these detailed yet computationally simple approaches.

Integrated HCCI Engine Control Based on a Performance Index

2013 ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Performance Index ◽  
Operating Conditions ◽  
Exhaust Aftertreatment ◽  
Reference Trajectory ◽  
Engine Control ◽  
New Approach ◽  
Aftertreatment System ◽  
Exhaust Aftertreatment System ◽  
Hcci Engine ◽  
Hcci Engines

Integrated control of HCCI combustion phasing, load, and exhaust aftertreatment system is essential for realizing high efficiency HCCI engines, while maintaining low HC and CO emissions. This paper introduces a new approach for integrated HCCI engine control by defining a novel performance index to characterize different HCCI operating regions. The experimental data from a single cylinder engine at 214 operating conditions is used to determine the performance index for a blended fuel HCCI engine. The new performance index is then used to design an optimum reference trajectory for a multi-input multi-output HCCI controller. The optimum trajectory is designed for control of combustion phasing and IMEP, while meeting catalyst light-off requirements for the exhaust aftertreatment system. The designed controller is tested on a previously validated physical HCCI engine model. The simulation results illustrate the successful application of the new approach for controller design of HCCI engines.

Technical and Engineering Decisions That Drive Choices for Predictive Analytics of Plant Data

2011 ◽  
Author(s):  
William Nieman
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
Current Data ◽  
Operating Conditions ◽  
Maintenance Cost ◽  
Real Time Systems ◽  
Efficient Technology ◽  
Time Model ◽  
Trade Offs ◽  
Time Systems

Power generation has the goal of maximizing power output while minimizing operations and maintenance cost. The challenge for plant manager is to move closer to reliability limits while being confident the risks of any decision are understood. To attain their goals and meet this challenge they are coming to realize that they must have frequent, accurate assessment of equipment operating conditions, and a path to continued innovation-. At a typical plant, making this assessment involves the collection and effective analysis of reams of complex, interrelated production system data, including demand requirements, load, ambient temperature, as well as the dependent equipment data. Wind turbine health and performance data is available from periodic and real-time systems. To obtain the timeliest understanding of equipment health for all the key resources in a large plant or fleet, engineers increasingly turn to real-time, model-based solutions. Real-time systems are capable of creating actionable intelligence from large amounts and diverse sources of current data. They can automatically detect problems and provide the basis for diagnosis and prioritization effectively for many problems, and they can make periodic inspection methods much more efficient. Technology exists to facilitate prediction of when assets will fail, allowing engineers to target maintenance costs more effectively. But, it is critical to select the best predictive analytics for your plant. How do you make that choice correctly? Real-time condition monitoring and analysis tools need to be matched to engineering process capability. Tools are employed at the plant in lean, hectic environments; others are deployed from central monitoring centers charged with concentrating scarce resources to efficiently support plants. Applications must be flexible and simple to implement and use. Choices made in selection of new tools can be very important to future success of plant operations. So, these choices require solid understanding of the problems to be solved and the advantages and trade-offs of potential solutions. This choice of the best Predictive Analytic solution will be discussed in terms of key technology elements and key engineering elements.

Transfer Function Modelling of Urban Drainage Systems, and Potential Uses in Real-Time Control Applications

1994 ◽  
Vol 29 (1-2) ◽  
pp. 409-417 ◽  
Author(s):  
Andrea G. Capodaglio
Keyword(s):  
Transfer Function ◽  
Real Time ◽  
Sewage Treatment ◽  
Operating Conditions ◽  
Urban Drainage ◽  
Real Time Control ◽  
Control Applications ◽  
Time Control ◽  
Function Modelling ◽  
Transfer Function Modelling

According to the present state-of-the-art, sewerage systems, sewage treatment plants and their subsequent improvements are often planned and designed as totally separate entities, each subject to a specific set of performance objectives. As a result, sewage treatment efficiency is subject to considerable variability, depending both on general hydrologic conditions in the urban watershed (wet versus dry periods), and on specific “instantaneous” operating conditions. It has been postulated that the integration of urban drainage and wastewater treatment design and operation could allow minimization of the harmful effects of discharges from treatment plants, overflows and surface water runoff. This “ideal condition” can be achieved through the introduction of so-called “real-time control” technology in sewerage collection and treatment operations. To be a feasible goal, this technology poses the demand for more powerful simulation models of either aspect of the system - or, ideally, of a unified sewer-and-treatment plant model - than most of those currently available. This paper examines the requirements of rainfall/runoff transformation and sewer flow models with respect to real-time control applications, and focuses on the methodology of stochastic, transfer function modelling, reporting application examples. Modalities and limitations of the extraction of information from the models thus derived are also analyzed.

A Java software agent framework for hard real-time manufacturing control

2017 ◽  
Vol 65 (11) ◽  
Author(s):  
Sebastian Theiss ◽  
Klaus Kabitzsch
Keyword(s):  
Real Time ◽  
Domain Knowledge ◽  
Real Time Control ◽  
Time Model ◽  
Time Control ◽  
Future Challenges ◽  
Analytical Response ◽  
Communication Flow ◽  
Hard Real Time ◽  
Robust Control Systems

AbstractMultiagent systems (MAS) have widely been recognized as a suitable software engineering approach to design distributed, flexible, and robust control-systems, as are needed to cope with current and future challenges in manufacturing. Yet, applying MAS for real-time control has been subject to several concessions so far. This paper presents a real-time Java multiagent platform, which allows both agent execution and distributed interaction under hard real-time conditions. The paper covers the architecture and implementation of such a platform, the integration of a domain knowledge model into the communication flow, and finally an analytical response time model including interactions, to actually proove the real-time capability of a distributed MAS.

Real-Time Burst Signal Removal Using Multicolor Pyrometry Based Filter for Improved Jet Engine Control

2015 ◽  
Vol 137 (8) ◽  
Author(s):  
Guanghua Wang ◽  
Jordi Estevadeordal ◽  
Nirm Nirmalan ◽  
Sean P. Harper
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Engine Control ◽  
Jet Engines ◽  
Jet Engine ◽  
Root Cause ◽  
Control Functions ◽  
Wide Range ◽  
Voltage Signal ◽  
And Control

Online line-of-sight (LOS) pyrometer is used on certain jet engines for diagnosis and control functions such as hot-blade detection, high-temperature limiting, and condition-based monitoring. Hot particulate bursts generated from jet engine combustor at certain running conditions lead to intermittent high-voltage signal outputs from the LOS pyrometer which is ultimately used by the onboard digital engine controller (DEC). To study the nature of hot particulates and enable LOS pyrometer functioning under burst conditions, a multicolor pyrometry (MCP) system was developed under DARPA funded program and tested on an aircraft jet engine. Soot particles generated as byproduct of combustion under certain conditions was identified as the root cause for the signal burst in a previous study. The apparent emissivity was then used to remove burst signals. In current study, the physics based filter with MCP algorithm using apparent emissivity was further extended to real-time engine control by removing burst signals at real time (1 MHz) and at engine DEC data rate. Simulink models are used to simulate the performances of the filter designs under engine normal and burst conditions. The results are compared with current LOS pyrometer results and show great advantage. The proposed model enables new LOS pyrometer design for improved engine control over wide range of operating conditions.

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