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Welcome to our new site - IOSO
Technology Center
NEW
OPTIMIZATION TECHNOLOGY
FOR COMPLEX TECHNICAL SYSTEMS EFFICIENCY INCREASING
Our technology is designed for multidimensional (up to 100 and more
variables) optimization of practical problems with continuous, breaking
nondifferentiative and stochastic goal functions.
- What`s
New
- Features of Application:
- Mathematical Statement and Basic
Algorithm of Problem Solving.
- Stability and Main Limitations of IOSO
Applicability
- IOSO and Other Optimization Methods
Combination Possibilities.
- Program code realization.
- The Level of Method Approbation.
- Test functions
- Practical
Tasks
- Bibliography
- Contact Us
At the moment the raise of complex technical systems effectiveness (such
as gas-turbine engines, flying vehicles etc.) is reached by introduction of the
new manufacturing technologies, by application of the new conceptual solutions
etc. However, for majority of engineering systems it is possible to achieve an
essential raise of their effectiveness at the expense of multiparameter (50 and
more variables) and multicriteria optimization.
For
this purpose the unique technology for optimization investigations has been
developed. The technology is oriented to researches, analysis, search of ways
to increase the efficiency and development forecasting of complex technical
systems by means of:
- optimum designing of the
separate elements and system as a whole;
- optimum matching of the
elements, included in the systems;
- determination of the
optimum control laws
during their designing, operational development and modernization. The
technology is based on a new method of indirect optimization on the basis of
self-organizing - IOSO, which has been developed by Proff. I.N.Egorov (Moscow,
Russia).
The
distinctive features of the technology are:
- - multicriteria
optimization possibility for the problems of large dimensionality (up
to 200 and more variables), that allows to reach the object's
efficiency increasing, which is up to 2...7 times higher than under
traditional optimization methods use (see fig.1).
- - low costs for search
of the optimum decision (reduction of a time required for search of
the decision up to 20...150 times depending on complexity and dimensionality
of a problem (fig. 2);
- multicriteria
optimization for the problems in stochastic statement (up to 100
variables), with a complex topology of the goal functions and with a
plenty of the constraints (the analogues of the decision of similar problems
are unknown);- - decision opportunity
for the optimization problems of various classes, including stochastic,
multiextreme and non-differentiated.
A distinctive feature of the given technology is its high effectiveness
in search for an optimum solution when investigating into technical systems,
modeled at high levels of complexity and hierarchy including the last
achievements in mathematical modeling (2-D and 3-D problems). Now, the
technology is being successfully implemented for the following problems
solution:
- obtaining the most
effective engineering solutions, at the expense of multidimensional
optimization of system model (fig.1) and assurance of extremeness one or
several efficiency criteria for the technical system and its units in a
wide range of operational modes;
- definition optimal
control law of complex technical system for different operational modes;
- definition of the optimum
design solutions regarding to the complex criterion "efficiency probability",
"efficiency - cost" and other;
- requirements validation
to the production technology level of the technical system's separate
units;
- definition of the set of
optimum engineering solutions, directed on efficiency increase of elements
and of a technical system as a whole;
- comparative analysis of
alternative optimum versions and substantiation the final choice of
technical solution;
- definition of the
probability of the design indices realization for the given production
technology level.
Features of Application
• Mathematical Statement and
Basic Algorithm of Problem Solving.
• Stability and Main Limitations
of IOSO Applicability
• IOSO and Other Optimization
Methods Combination Possibilities.
• Program code realization.
• The Level of Method
Approbation.
For info about EXAMPLES OF OPTIMIZATION
PROBLEMS click here.
In an
engine-building the given technology was used for the solution of the following
practical problems (for the "Aviadvigatel", Inc. (Russia);
"Lyulka Saturn", Inc.(Russia); SSTC "NK-Engines" (Russia);
SNECMA (France), "AutoVAZ"(Russia)):
1. Determination of the
perfection level and analysis of the possibilities to improve
the characteristics of subsonic and supersonic variable
and non-variable axial flow compressors on the basis of
the 2-D axisymmetrical mathematical model (number of variables - up to
99; number of constraints - up to 220; number of
optimization criteria - up to 4):
-
optimum profiling of blade rows and of a passage layout;
-
formulating of optimum laws to control compressor guide vanes and impellers;
-
simultaneous optimization of geometrical parameters and control laws of
variable compressors;
- optimization
of a compressor gasdynamic layout with consideration for the accuracy in blade
row manufacturing;
-
determination of a probability to realize the design parameters of a compressor
for the available level of production technology;
-
determination of requirements to the accuracy in blade row manufacturing on the
basis of a preset probability to realize a project;
-
optimization of compressor row geometrical parameters against the criteria of
an engine efficiency;
-
determination of optimum design parameters of a compressor which ensure high
values of engine efficiency criteria over a wide range of
operating modes (multicriterion setting of a task).
2. Optimum control of aircraft gas-turbine engines for
unsteady operational modes (number of
variables - up to 216; number of constraints - up to 26).
-
determination of optimum laws to control GTE variable components (nozzle,
mixer, guide vanes etc.) for various operating modes;
-
optimization of control effects upon GTE under severe external non-stationary
flow disturbances;
-
search for disturbance immune optimum control laws with consideration for the
stochastness of external effects and technological dispersion
of automatic control unit (ACU) characteristics;
-
determination of reasonable structure of control elements;
-
definition and development of the requirements to an accuracy in realization of
laws to control GTE flow path elements.
3. Matching of flying vehicle and gas-turbine engine,
and definition its optimal control laws (number of
variables - up to 70; number of constraints - up to 8; number of optimization
criteria - up to 2):
-
determination of optimum parameters of an operational process in an engine with
a HIDEC-type control of the flow path elements which ensure
high indices of flying vehicle efficiency over a wide range of flight altitudes
and speeds;
-
determination of optimum laws to control GTE flow path elements (mixer,
differential control of compressor rows etc.) as included in
a flying vehicle system for various flight modes over a total range of
altitudes and speeds;
-
assessment of the effectiveness of a HIDEC-type control of the engine flow path
elements as included in a flying vehicle, and determination
of a reasonable structure of the components to be controlled;
-
optimization of the constructive parameters and control laws for the VSTOL
engine for vertical and short takeoff and landing.
4. Reduction of the negative influence of the
compressor flow path erosion on the engine's
characteristics.
5. Optimum calibration of the microprocessor control
systems of a automobile engine directly on a test stand
for ensuring of a minimum of the fuel consumption at a given emission level.
6. Definition of the parameters and structure of a
covers of the engine units for ensuring the given
optical, thermal and strength characteristics (number of variables - up to
20,
number of constraints - up to 50).
The technology is invariant as to the objects to be investigated and can
be easily adapted to using mathematical models of different complexity level
applied by design companies when solving a large series of practical problems
in various fields of science and engineering (mechanical engineering, medicine,
chemistry etc).
The more
detailed technology description and the examples of its usage for the aviation
engines are cited in the following papers:
1. Egorov
I.N. et al. "Procedures of Indirect Statistical Optimization on the Basis
of Self-Organization and Their Use in Aircraft GTE
Optimization Problems", VINITI (2622-B89), 1989.
2.
Egorov I.N., Fomin V.N., Talyzina V.S. "Numerical Method of Optimization
of a Multistage Axial Compressor"
The 1-st ISAIF, Beijing, 1990.
3.
Beknev V.S., Egorov I.N., Talyzina V.S. Multicriteial Design Optimization of
the Multistage Axial Flow Compressor. 5-th ASME,
"COGEN-TURBO-V" Budapest, 1991. To
get this article click here.
4.
Egorov I.N. "Optimization of a Multistage Axial Compressor in a
Gas-Turbine Engine System". ASME, 92-GT-424, 1991.
5.
Egorov I.N., "Optimization of a Multistage Axial Compressor. Stochastic
Approach", ASME , 92-GT-163, 1992.
To get this article click here.
6.
Egorov I.N., Kretinin G.V., "Multicriterion Stochastic Optimization of
Axial Compressor". ASME, COGEN-TURBO VI, Houston,
USA, 1992.
7.
Egorov I.N. "Deterministic and Stochastic Optimization of Variable Axial
Compressor". ASME, 93-GT-397, 1993.
To get this article click here.
8.
Egorov I.N.,Kretinin G.V. "Optimization of Gas Turbine Engine Elements by
Probability Criteria". ASME, 93-GT-191. 1993.
9.
Egorov I.N., Kretinin G.V. "Optimum Control of Variable Components in
Aircraft Gas Turbine Engines under Non-stationary Flow
Disturbances at the Inlet". ASME, 94-GT-268. 1994.
10. Egorov I.N. Report At The Simposium on
Exhibithion "Engine-96", 3 June, 1996 (Moscow, Russia).
11.
Egorov I.N., Kretinin G.V., "Search for Compromise Solution of the
Multistage Axial Compressor's Stochastic
Optimization." The 3-rd ISAIF, Beijing, 1996.
12.
Egorov I.N., Kretinin G.V., Talyzina V.S., et al. "Multistage Axial Flow
Compressor's Development on the Basis of
the Numerical Optimization Methods"
The 3-rd ISAIF, Beijing, 1996.
13.
Egorov I.N., Kretinin G.V., Leshchenko I.A. "Multicriteria Optimization of
Time Control Laws of Short Take-Off and
Vertical Landing Aircraft." ASME TURBO EXPO'97-Land, Sea & Air,
Orlando, 1997, 97-GT-263.
In case
of your interest we are ready to present you the more complete information and
to decide your optimization problem as the demonstration of our possibilities.
We
invite to cooperation.
Please give us your Comments or Suggestings.
"Techno-Pulsar", Copyright ©,
1997.
Last Modified: 8 Dec. 1998