=Paper=
{{Paper
|id=Vol-2930/paper23
|storemode=property
|title=High-performance Computing for Simulation Testing of Smart Materials for Their Further Employment in Modern Diesel Engine Fuel Supply System
|pdfUrl=https://ceur-ws.org/Vol-2930/paper23.pdf
|volume=Vol-2930
|authors=Vladimir Bogdanov,Sergey Timoshin,Igor Chabunin,Andrey Kovtanyuk,Il’ya Pugachev,Gennadiy Stepanov
}}
==High-performance Computing for Simulation Testing of Smart Materials for Their Further Employment in Modern Diesel Engine Fuel Supply System==
https://ceur-ws.org/Vol-2930/paper23.pdf
High-performance Computing for Simulation Testing of Smart
Materials for Their Further Employment in Modern Diesel
Engine Fuel Supply System
Vladimir V. Bogdanova,e, Sergey V. Timoshina, Igor S. Chabunina, Andrey E. Kovtanyukd,
Il’ya V. Pugachevb and Gennadiy V. Stepanovc
a
Moscow Higher Combined-Arms Command School (MVOKU), Golovacheva st.2, Moscow, 109380, Russia
b
NAMI State Research Centre of the Russian Federation, Avtomotornaya st.2, Moscow, 125438, Russia
c
GNIIChTEOS JSC State Research Center of the Russian Federation, Entuziastov highway 38, Moscow,
105118, Russia
d
Far Eastern Federal University, Far Eastern Center for Research and Education in Mathematics, Ajax Bay
10, Russky Island, Vladivostok, 690922, Russia
e
The State University of Management, Ryazansky Prospekt 99, Moscow, 109542, Russia
Abstract
The article presents the results of the investigation of smart materials (electroactive polymers
(EAPs)) using simulation testing of stiffness properties based on the trained two-layer neural
network. EAPs were modeled as control elements of diesel engine injectors based on certain
criteria. The output data were the quick-action of the nozzle. The final part of the article
presents the main results of the initial stage of the project to introduce smart materials into
fuel supply systems and the prospects for using high-performance computing, modern
software and computer systems for mathematical modeling in solving current scientific and
technical problems of developing and monitoring motor vehicle technical systems.
Keywords 1
Smart materials, magnetoactive elastomers, electroactive polymers, electroactive elastomers,
simulation testing, neural network, fuel supply system, control systems
1. Introducing the problem and setting the task
As it is noted in modern literature [1, 2, 3], one of the ways to minimize harmful emissions of
exhaust gases from internal combustion engines (ICE) in city and road transport is the usage high-
speed sensor devices that can quickly transmit signals for their subsequent processing by a
microcontroller. Of all the types of electroactive polymers (EAP) for electronic control systems (ECS)
of ICE, materials such a class of dielectric elastomers are best suited.
Constantly increasing requirements for energy efficiency and environmental safety of automobile
engines stimulate the development of research into the improvement their workflow, including the
works aimed at improving the performance of fuel supply actuators and injectors. Table 1 provides a
comparative analysis of electronic fuel systems of engines with fuel injection into cylinders. It
contains data on the main characteristics of control of electronic fuel systems in internal combustion
engines and their structural elements.
VI International Conference Information Technologies and High-Performance Computing (ITHPC-2021),
September 14–16, 2021, Khabarovsk, Russia
EMAIL: tchabunin@rambler.ru (Igor S. Chabunin); vvbogd@yandex.ru (Vladimir V. Bogdanov)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
167
Table 1
Comparative analysis of electronic fuel systems of engines with fuel injection into cylinders
Name
Continuous ‘Common rail’ Mechanical Smart
Pneumodriving Hydraulic drive
control battery drive systems systems
Key differences
High Pressure Nozzles
High Pres- Nozzles and
Executing fuel pump and High pressure and high-
sure Fuel high-pressure Injectors
elements medium line pressure
Pump line
pressure line line
Continuous Pulse
Pulse injectors
pressure and injectors
Type of and
Continuous Impulse pulse with Impulsive and
management continuous
other continuous
pressure
parameters pressure
Controlled independently
Pressure - + - + - +
Advance + + + + + +
Type of
- + + - - +
characteristics
Differences - + + + + +
Simplicity of
+ + + - + -
design
Easy to adapt
+ + - - - -
to the engine
Fail-safety - - + - + -
Comparing the set of parameters given in Table 1, it should be noted that the most optimal design
is the one featuring the “Common Rail” battery fuel systems with two types of performing elements,
namely: either a piezo-actuator or an electromagnet. Based on this suggestions, the authors set the
following task: to create a control element for the electronic fuel system of the internal combustion
engine based on the smart material of the electroactive polymer type, which has the same speed as a
piezo-actuator, but possess a more flexible form of the control signal, which helps to avoid problems
with inductance that occur when the nozzle is excited in electromagnetic drives and at a lower cost of
the structural unit as a whole.
At this stage of investigations the problem of choosing a test sample of a certain composition and
properties required for carrying out primary tests of electroactive polymers that has been complicated
by the relative novelty of the material under study, insufficient knowledge of the properties such class
of composites, and the absence of a clear classification (at the level of such regulations as State
Standard or Technical Specifications) the chemical properties or other parameters, in particular
stiffness, prompted the authors to apply the modern methods of simulation, taking into account the
experimental data they previously had, in particular, those for similar smart materials of
magnetoactive elastomers.
The object taken under consideration for implementing smart material is the control element of the
nozzle. Its main functional characteristics are presented in Table 2. It served as the initial data for the
motivated selection of the control element of nozzle.
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Table 2
Initial data for choosing the control element of the nozzle
Ignition feed, [mm3] The main feed, [mm3]
P,
[МPа]
Electromagnet Electropolymer Piezo drive Electromagnet Electropolymer Piezo drive
120 0,49 0,31 0,28 0,72 0,47 0,41
180 0,59 0,3 0,27 0,83 0,4 0,31
220 0,68 0,54 0,47 1,17 0,77 0,63
2. Algorithm and methodology. Key results
During the selection of the most suitable prototype, the following main criteria for choosing the
EAP were established:
- the filling should vary 25-30 [%];
- the size of the polymer should be 1-10 [microns];
- it is advisable to correlate the chemical composition of the matrix with its inherent stiffness
parameter – the elastic modulus E;
- the structure of the test sample must be anisotropic or isotropic. It should also be considered in
the context of the value of the elastic modulus E;
- the operating area of the material must be comparable to the size of the sample itself;
- it is advisable to take into account the speed of mechanical adjustment of the sample structure;
- the value of the voltage applied to the EAP must be 3-5 [ kW · mm-1 ].
The control element, as well as the nozzle, were considered (respectively, simulation conditions
were created) not as separate elements (for example, on a simulation stand), but as assembled with the
engine design. The mounting scheme of the nozzle under study is given in Figure 1.
Figure 1: Fixing the nozzle under study to the engine cylinder head
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The tests conducted were complex in character. When testing samples, they were assigned various
characteristics (from the abovementioned ranges) and the output indexes indicated in the table were
evaluated.
The simulation algorithm included the following sequence of actions:
- filling of test samples were simulated by selecting parameters with a certain chemical
composition and method of filling elements (based on the technological capabilities of the equipment
of JSC «GNIIChTEOS»);
- element thicknesses were selected in a certain range based on the design features of the nozzle
drive shape chosen for the study (0.1 – 0.5 mm);
- based on the obtained maximal forces that occurred at samples, a package with a sizeable number
of EAP elements was selected for further modeling, in which the thickness of an individual element
did not exceed 0.1 – 0.15 mm.
The graph in Figure 2 illustrates the maximum strain occurring at the sample depending on the
height variations. As noted above, cylindrical samples of EAP with the height of 40, 27 and 13 [mm]
and the mass of 94.9, 62.3 and 32.1 [gram] (respectively) were used as prototypes of the executive
nozzle’s design element. The simulation test experiment consisted of two stages. At the first stage, the
sample was modeled under the influence of a constant magnetic field. In the graph below these are
positions 1-69565 (see the bottom line of the graph). At the second stage of the experiment, the
sample was tested without magnetic field influence. At both stages, a high-frequency current supply
(about 10 kHz) was simulated, and the resulting strain transmitted by the samples due to their
deformation were considered as output data. Vertical values of forces (in [N]) are marked on the
graph. As illustrated directly on the graph, the colored lines separate the blocks of the series of
experiments of different types: the yellow line describes the case of induced magnetic field and
without this field; the blue line is for the previously specified height of the sample and the red line is
for the corresponding mass of the sample.
EAP testing
height mass emerging strain magnetic field
Figure 2: Testing of EAP samples for the resulting strain in the sample when a high frequency voltage
is applied
The simulation algorithm was applied in the open software environment Octave, the current
behavior of these samples was studied based on the trained 2-layer neural network using the data were
obtained earlier during experiments with similar EAP samples.
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The algorithm for processing and research included the following sequence of actions:
- processing of a "raw" data array. At this stage, it was necessary to identify similar experimental
curves and remove the data that arose as a result of measurement inaccuracies or noise during signal
processing;
- forming a representative sample of data that overlaps the range under study and the range of
interest to researchers;
- dividing the data into input (height, mass, presence of magnetic field) and output (strain
generated);
- choosing the appropriate neural network. A two-layer neural network (the simplest perceptron)
was used for training due to the small amount of statistical data available to the authors at the
moment. The use of other architectures led to the faster model retraining, which was fraught with
offering results that could be physically unattainable.
The graph given is only a visual illustration of one of the dozens of simulated samples, based on
which the data bank was compiled and optimal EAP options were selected for further investigation. It
should be noted that this method of simulation tests is successfully used in the study of the actuators
of internal combustion engine microcontrollers, but with other materials and with different software
[4, 5]. Moreover, the simulation algorithm may include a sample selection block with pre-selected
optimal characteristics. Depending on the problem formulation and the desired result, it is advisable to
use software for supercomputer clusters based on a super-scalable parallel algorithm for calculating
the properties of EAP, such as the one used by the authors and described in [6-9].
3. Conclusions and recommendations
Summarizing the abovementioned, the main results of the initial stage of the project for applying
smart materials in fuel supply systems can be presented as follows:
- the analysis of the principles of construction and operation of modern fuel control systems for
internal combustion engines has been conducted. The alternatives for improving the quality of
management with the use of electroactive polymers have been identified;
- primary metamodels for EAP simulation testing have been developed in the Octave software. A
comparative analysis of metamodels has been carried out taking the experimental data into account.
The conclusions on the expediency of using EAP samples of a certain structural composition have
been drawn;
- the list of requirements has been formulated and the approach to the EAP design that implements
the fuel injector control concept has been determined;
- the test scenario has been developed for the possibility of carrying out further simulation tests in
order to select an EAP sample of the optimal structure.
For further research, it is advisable to carry out the work on increasing the number of variable
input parameters and accumulating a larger sample of data to use deeper network architectures.
It should also be mentioned that the use of high-performance computing equipment for modeling
the behavior of the above-mentioned smart materials under external influences is due to their ultra-
dispersed structure: a sufficiently large number of superparamagnetic and ferromagnetic particles
combined into a system by means of a long-range dipole-dipole interaction is superimposed with an
action of induced external electromagnetic field. Moreover, the acting elastic forces and external
mechanical influences change the coordinates of the particles in the matrix, with subsequent changes
in the distribution of the internal interaction fields and, therefore, changes in the properties of the
material. The re-counting of the interaction of "all with all" under different external influences, the
calculation of a new structure and the subsequent determination of the integral characteristics inherent
in the modifiable modeled sample for it, is a simple, yet cumbersome calculation task in terms of the
number of mathematical operations, which is not currently solvable based on the computing power
provided by ordinary personal computers. That is why researchers usually limit themselves up to
simplified models, in particular two-dimensional models with a small number of particles, but the
progress made so far in the development of supercomputer methods allows us to solve such a class of
problems. However, it should be borne in mind that such an integrated approach is meaningless
without the mandatory verification of the developed model and comparison of the results of numerical
171
experiments with the results of physical experiments. For verification, the authors have conducted the
studies of the internal structure and properties of smart materials [9], which have been omitted in this
publication of the conference proceedings as going beyond the scope of the topic.
4. Acknowledgements
The reported study was funded by RFBR in the framework of the 19-53-12039 research project.
The studies were carried out using the resources of the Center for Shared Use of Scientific
Equipment "Center for Processing and Storage of Scientific Data of the Far Eastern Branch of the
Russian Academy of Sciences", funded by the Russian Federation represented by the Ministry of
Science and Higher Education of the Russian Federation under project No. 075-15-2021-663.
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10.1088/1757-899X/747/1/012103.
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