2. The nearest perspectives of applications of artificial neural networks for diagnostics, modeling, and control in science and industry (methods, results, demonstrations & hands-on) Victor S. Abrukov (2008) e-mail: abrukov@yandex.ru Chuvash State University, Russia Department of Thermo Physics

3. Outline 1. Introduction Ex-1: Deflagration to detonation transition Ex-2: Automatic control system of steam generating unit Ex-3: Wave propagation on a free surface of a fluid Ex-4: Burning Rate vs Temperature profile 2. Perspectives of others (laser scattering technique for remote sensing,, smart sensors for monitoring of industrial equipment (smart watch, soft sensors), etc. 3. Conclusions

4. Introduction - 1 Commercial applications of Data Mining (ANN, etc) in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in last years. There is a significant amount of innovative Data Mining work taking place in the context of scientific and engineering applications but that is not well-represented in the mainstream of Data Mining.

5. Introduction - 2 Scientific Data Mining techniques are being applied to diverse fields such as physics, chemistry, astronomy, engineering, etc. Scientific Data Mining techniques are being applied to diverse fields such as physics, chemistry, astronomy, engineering, etc. Data Mining frequently enhances existing analysis methods based on statistics and exploratory data analysis. Data Mining frequently enhances existing analysis methods based on statistics and exploratory data analysis.

6. The goal The goal The goal of the seminar is: Share experiences Share experiences Learn how ANN can be applied to their data Learn how ANN can be applied to their data Practice in it (You will the first user of Data Fusor in all of the world!) Practice in it (You will the first user of Data Fusor in all of the world!)

7. IntroductionIntroduction (main!) No Science without Data Base and Data Analysis

8. Data Base There are three ways for creation Data Base: Real experiment Numeral experiment Analytical Solution We have obtained DB. What do we have to do further?

9. Data Analysis We have to create a model of DB that help us to use DB (for understanding, diagnostics, testing, control, … pleasures). We have to create a model of DB that help us to use DB (for understanding, diagnostics, testing, control, … pleasures). What is the best way? I think – Artificial Intelligence. Data Fusor is a part of AI tools ANN is a part of Data Fusor

10. EX-1: Modeling of Deflagration-to- Detonation Transition for Pulse Detonation Engine.

11. Ex-1 A deflagration-to-detonation transition under various experiment conditions was studied by Santora R.J. at al (USA). A deflagration-to-detonation transition under various experiment conditions was studied by Santora R.J. at al (USA). The data that were presented by it had many blanks (about 60%). A task of filling them by means of ANN was set. The data that were presented by it had many blanks (about 60%). A task of filling them by means of ANN was set. The results obtained are presented in Table. The results obtained are presented in Table.

12. Ex-1

13. ANN Model of Automatic Control System of Boiler Unit Ex-2: ANN Model of Automatic Control System of Boiler Unit work has been aimed on a development of principles of creation of a new model of automatic control system of a boiler unit in transient (non-linear) regimes (change of load, launch and stop of the boiler aggregate, etc) on a basis of ANN. The work has been aimed on a development of principles of creation of a new model of automatic control system of a boiler unit in transient (non-linear) regimes (change of load, launch and stop of the boiler aggregate, etc) on a basis of ANN.

14. The following problems were set and solved -1 - A complete set of non-linear differential equations for the two-phase flow in cylindrical coordinate system circumscribing processes in a superheater was formulated. It described also a process of injection of water into a superheater during control procedure of vapor temperature in a superheater. - A complete set of non-linear differential equations for the two-phase flow in cylindrical coordinate system circumscribing processes in a superheater was formulated. It described also a process of injection of water into a superheater during control procedure of vapor temperature in a superheater. - The obtained set of equations was converted into a system of finite-difference equations.

15. Equations

16. Equations

17. The following problems were set and solved - 2 Adjustable and controlling parameters were selected by means of experimental data and experts experience. Adjustable and controlling parameters were selected by means of experimental data and experts experience. The rate of a change of vapor temperature on an exit of the boiler aggregate was selected as a controlled parameter. The water mass flow via injector was selected as controlling parameter. The rate of a change of vapor temperature on an exit of the boiler aggregate was selected as a controlled parameter. The water mass flow via injector was selected as controlling parameter. The analytical connection between them was determined by means of the mathematical apparatus of Lee derivatives. The analytical connection between them was determined by means of the mathematical apparatus of Lee derivatives.

18. The analytical connections

19. The following problems were set and solved - 3 The database for training of ANN was created by means of the obtained analytical connection and an optimal architecture of ANN was determined. The database for training of ANN was created by means of the obtained analytical connection and an optimal architecture of ANN was determined. The training of ANN was executed and a model of a control system as well as a skeleton diagram of a position of a gate valve governing a water discharge on injection were created. The training of ANN was executed and a model of a control system as well as a skeleton diagram of a position of a gate valve governing a water discharge on injection were created.

20. Data Base for training Data Base for training

21. The following problems were set and solved - 4 The prototype of an automatic control system of temperature vapor in a superheater of a boiler unit TGME-464 was designed. It consists of the program emulator of ANN established on the personal computer, industrial microcontroller Philips P89LPC935, cable lines and switch gears. The obtained prototype can work both in a mode of advice, and in a mode of automatic control. It can be simply enough integrated in present control systems. The features of ANN technologies of control (property of adaptability to new conditions and ability to self- training) provide simplicity of modernization and escalating of capabilities.

22. Scheme of adaptive control

23. Ex – 3: Wave propagation on a free surface of a fluid The tasks of hard shock about a layer of fluid and of wave propagation on a free surface of a fluid were the problems (at an impulse formulation, the deformation of the bottom of an earthquake occurs for a rather short time, i.e., there is a bottom shock about a fluid) With the help of an available analytical solution the data base consisting of values of dimensionless coordinates, times and the velocity on a free surface of a fluid were obtained.

24. Ex – 3: A part of Data Base The values of coordinates and times were considered the input characteristics, and the values of velocities the output. The values of coordinates and times were considered the input characteristics, and the values of velocities the output. txU 3,520,000050,00011 3,519,500060,00203 3,519,000080,00609 3,518,500120,01119 3,518,000170,01872 3,517,500270,03013 3,517,000410,04731 3,516,500650,07301 3,516,001010,1107 3,515,501570,16531 3,515,00240,24282 3,514,503640,3506 3,514,005460,49698 3,513,508060,69042 3,513,011720,93814 3,512,516751,24358 3,512,023491,60347 3,511,532272,00405 3,511,043332,4185 3,510,556722,80624 3,510,072233,11672 3,59,589323,29733 3,59,107153,30458 3,58,624653,11544 3,58,140672,7343 3,57,65422,19415 3,57,164481,55162 3,56,671130,87784 3,56,174190,24754 3,55,674090,27129 3,55,17160,6273 3,54,667730,79289 3,54,163640,77083 3,53,660360,60398 3,53,158550,359 3,52,658650,08995 3,52,160680,11851 3,51,662730,18944 3,51,16220,20892 3,50,658640,18826 3,50,15290,0484 420,000250,00293 419,500280,01218 419,000380,03098 418,500560,05268 418,000830,08221

25. Ex – 3: Scheme of ANN

26. Ex – 3: results The results of ANN calculations of the velocity values as related to different coordinates and times together with analytical results are presented in Figures.

27. Ex – 3: results (t=2 sec) (coordinate y is U – velocity)

28. Ex – 3: results (t=3,5 sec)

29. Ex – 3: results (t=4 sec)

30. Ex – 4: Burning Rate vs Temperature Profile The “black box” computational model of a flame temperature profile prediction is created. It allows to predict the temperature profiles by means of data about heat of combustion, burning rate, and pressure EVERY TIME WITHOUT NEW MEASUREMENT EVERY TIME

31. EX - 4

32. Ex - 4 The scheme of construction of an ANN PCW model was as follows. The scheme of construction of an ANN PCW model was as follows. We have taken experimental data (from scientific and applied literature) about We have taken experimental data (from scientific and applied literature) about - pressure of burning, P, - burning rate, U, - heat of propellant burning, Q, - as well as temperature profile (values of temperature, T and coordinates, x) for some propellants

33. Ex - 4 Ex - 4

34. Ex – 4 The different sets of values of pressure, burning rate, heat of combustion and coordinates were installed on the input layer of ANN. The corresponding values of temperature were installed on the output layer of ANN. The different sets of values of pressure, burning rate, heat of combustion and coordinates were installed on the input layer of ANN. The corresponding values of temperature were installed on the output layer of ANN.

35. Ex - 4 By means of a training tool named the method of “back propagation of errors”1, we have created the ANN PCW model. By means of a training tool named the method of “back propagation of errors”1, we have created the ANN PCW model. This model is a “black box” type which consists of latent connections between variables. This model is a “black box” type which consists of latent connections between variables. The “black box” obtained we have used in an invented experimental investigation for the prediction of a temperature profile as follows. One of the experimental data that have not been used for training of the ANN we have used for checking of the “black box” obtained. The values of pressure, burning rate, heat of burning and coordinates were installed on the input layer of the “black box”. Then, the correspondence values of temperature T’, were obtained on the exit of the output layer of the “black box”. The “black box” obtained we have used in an invented experimental investigation for the prediction of a temperature profile as follows. One of the experimental data that have not been used for training of the ANN we have used for checking of the “black box” obtained. The values of pressure, burning rate, heat of burning and coordinates were installed on the input layer of the “black box”. Then, the correspondence values of temperature T’, were obtained on the exit of the output layer of the “black box”. The results we have obtained are presented in Table 2. The results we have obtained are presented in Table 2.

36. Ex - 4

37. Ex - 4 An analysis of Table depicts that the “black box” enough correctly “determines” the temperature profile by means of data about burning rate, pressure and heat of burning (or maximum temperature of flame). An analysis of Table depicts that the “black box” enough correctly “determines” the temperature profile by means of data about burning rate, pressure and heat of burning (or maximum temperature of flame). In our opinion, this way of determination the temperature profiles could be considered as perspective unique tool for cases when we have no any experimental results for concrete propellants and would like to have them. In our opinion, this way of determination the temperature profiles could be considered as perspective unique tool for cases when we have no any experimental results for concrete propellants and would like to have them. In order to make it all we need to do is to collect a lot of experimental data published in scientific and technical literature concerning temperature profiles for various propellants upon various condition. In order to make it all we need to do is to collect a lot of experimental data published in scientific and technical literature concerning temperature profiles for various propellants upon various condition. Then we could create the common “black box” model: Burning Rate vs Temperature Profile. Then we could create the common “black box” model: Burning Rate vs Temperature Profile.

38. Perspectives Perspectives

39. Conclusions Practice is the best of all instructors