A Machine Learning Based Numerical Approach for Valve Seating Velocity Control in an Electromagnetic Camless System

2021 ◽  
Author(s):  
Srinibas Tripathy ◽  
Mithun Babu M. ◽  
Kanupriya M. ◽  
Mayank Mittal
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Pid Controller ◽  
Engine Performance ◽  
Learning Tool ◽  
Variable Valve Actuation ◽  
Valve Operation ◽  
Variable Valve ◽  
Machine Learning Tool ◽  
Valve Actuation

Abstract Improving internal combustion engine performance is a significant concern over the past few decades for engine researchers and automobile manufacturers. One of the promising methods for improving the engine performance is variable valve actuation system with camless technology. In the camless system, the conventional spring-operated valve actuation mechanism is removed, and an actuator is used to independently control the valve events (lift, timing, and duration). Among different camless systems, electromagnetic variable valve actuation (EMVA) becomes more viable because of its faster valve operation. However, the major challenge is to control the valve seating velocity (velocity at which valve comes to rest during seating on the cylinder head) due to the absence of the cam mechanism. A sophisticated control system must be developed to achieve an acceptable valve seating velocity. In this study, a proportional-integral-derivative (PID) controller was used to control the EMVA system. A machine learning tool, i.e., genetic algorithm, and an iterative method, i.e., Ziegler-Nichols, were used to optimize the PID controller’s gain values. The valve lift profiles obtained using the Ziegler-Nichols method and the genetic algorithm were compared. It was found that the developed algorithm for the EMVA system can achieve faster rise time compared to the experimental results [25] utilized inverse square method. A parametric investigation was performed to verify the robustness of the PID controller with a change in temperature. It is concluded that the temperature rise may increase the resistance and inductance, but the controller with the updated gain values can control the EMVA system without affecting the performance parameter. The simulation was performed for both forward and backward strokes to investigate the valve seating velocity. It was found that the controller can achieve an acceptable valve seating velocity. Hence, the machine learning tool helps in optimizing the PID controller’s gain values to achieve faster valve operation with an acceptable valve seating velocity.

Using Co-Simulation Methods to Establish Variable Valve Actuation Hardware Specifications and Control Strategies

2001 ◽  
Author(s):  
Reinhard Burk ◽  
Frederic Jacquelin ◽  
Russell Wakeman
Keyword(s):  
Internal Combustion Engines ◽  
Production Systems ◽  
Control Strategies ◽  
Engine Performance ◽  
Cost Effective ◽  
Dynamics Simulation ◽  
Variable Valve Actuation ◽  
Variable Valve ◽  
And Control ◽  
Valve Actuation

Abstract With the increasing recognition that variable valve actuation (VVA) in its various forms is a powerful tool for optimizing the performance of internal combustion engines, more and more production systems are being designed and implemented throughout the industry. However, as these control systems become more capable of altering lift, timing, duration, and even the number of valve events, the complexity of designing algorithms and calibrating them becomes enormous. In addition, without prior knowledge of an engine’s response to these algorithms, designing a cost-effective mechanism which provides adequate but not over-reaching capability is difficult. Ricardo has developed methodology for timestep coupled simulations which enables the use of one-dimensional (1-D) gas dynamics simulation of engine performance (WAVE™) coupled to a simulation of the valve actuation mechanism constructed in MATLAB® and AMESim®. This arrangement allows valve motion input to the 1-D code to be controlled either manually or by a VVA controller simulation, allowing such engine parameters as torque, fuel consumption, NVH, and EGR rates to be monitored as a function of valve timing strategy. This method allows the examination of such engine development concerns as tolerances, valve velocities and accelerations, and interactions with other engine controls to be studied without the costs, leadtimes, or hardware reliability problems that are associated with prototyping a VVA system. In addition, the interfacing of the valve control/engine performance simulation combination with the Design of Experiments optimization software iSIGHT allows the control system space to be explored automatically, without the brute force numerical search required to examine all permutations of the control strategies. The output of this procedure is an array of requirements which can be quickly translated into a specification document which will guide hardware and controls design efforts.

scholarly journals Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Scott Broderick ◽  
Ruhil Dongol ◽  
Tianmu Zhang ◽  
Krishna Rajan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Crystal Chemistry ◽  
Persistent Homology ◽  
Hierarchical Classification ◽  
Topological Data Analysis ◽  
Learning Tool ◽  
Coordination Polyhedra ◽  
Machine Learning Tool ◽  
Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

Potential of the Variable Valve Actuation (VVA) Strategy on a Heavy Duty CNG Engine

2014 ◽  
Author(s):  
Mirko Baratta ◽  
Roberto Finesso ◽  
Daniela Misul ◽  
Ezio Spessa ◽  
Yifei Tong ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Operating Conditions ◽  
Intake Valve ◽  
Heavy Duty ◽  
Engine Emissions ◽  
Variable Valve Actuation ◽  
Cng Engine ◽  
Variable Valve ◽  
Working Point ◽  
Valve Actuation

The environmental concerns officially aroused in 1970s made the control of the engine emissions a major issue for the automotive industry. The corresponding reduction in fuel consumption has become a challenge so as to meet the current and future emission legislations. Given the increasing interest retained by the optimal use of a Variable Valve Actuation (VVA) technology, the present paper investigates into the potentials of combining the VVA solution to CNG fuelling. Experiments and simulations were carried out on a heavy duty 6-cylinders CNG engine equipped with a turbocharger displaying a twin-entry waste-gate-controlled turbine. The analysis aimed at exploring the potentials of the Early Intake Valve Closure (EIVC) mode and to identify advanced solutions for the combustion management as well as for the turbo-matching. The engine model was developed within the GT-Power environment and was finely tuned to reproduce the experimental readings under steady state operations. The 0D-1D model was hence run to reproduce the engine operating conditions at different speeds and loads and to highlight the effect of the VVA on the engine performance as well as on the fuel consumption and engine emissions. Pumping losses proved to reduce to a great extent, thus decreasing the brake specific fuel consumption (BSFC) with respect to the throttled engine. The exhaust temperature at the turbine inlet was kept to an almost constant value and minor variations were allowed. This was meant to avoid an excessive worsening in the TWC working conditions, as well as deterioration in the turbocharger performance during load transients. The numerical results also proved that full load torque increases can be achieved by reducing the spark advance so that a higher enthalpy is delivered to the turbocharger. Similar torque levels were also obtained by means of Early Intake Valve Closing strategy. For the latter case, negligible penalties in the fuel consumption were detected. Moreover, for a given combustion phasing, the IVC angle directly controls the mass-flow rate and thus the torque. On the other hand, a slight dependence on the combustion phasing can be detected at part load. Finally, the simulations assessed for almost constant fuel consumption for a wide range of IVC and SA values. Specific attention was also paid to the turbocharger group functioning and to its correct matching to the engine working point. The simulations showed that the working point on the compressor map can be optimized by properly setting the spark advance (SA) as referred to the adopted intake-valve closing angle. It is anyhow worth observing that the engine high loads set a constraint deriving from the need to meet the limits on the peak firing pressure (PFP), thus limiting the possibility to optimize the working point once the turbo-matching is defined.

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