CN112834161B - Wind tunnel measurement data processing method based on feature extraction - Google Patents
Wind tunnel measurement data processing method based on feature extraction Download PDFInfo
- Publication number
- CN112834161B CN112834161B CN202110153238.8A CN202110153238A CN112834161B CN 112834161 B CN112834161 B CN 112834161B CN 202110153238 A CN202110153238 A CN 202110153238A CN 112834161 B CN112834161 B CN 112834161B
- Authority
- CN
- China
- Prior art keywords
- data
- time
- engine
- measurement
- wind tunnel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 51
- 238000000605 extraction Methods 0.000 title claims abstract description 10
- 238000003672 processing method Methods 0.000 title claims abstract description 8
- 238000012360 testing method Methods 0.000 claims abstract description 47
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000012935 Averaging Methods 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 11
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 239000007921 spray Substances 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims description 12
- 230000006641 stabilisation Effects 0.000 claims description 7
- 238000011105 stabilization Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000002485 combustion reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/02—Wind tunnels
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Fluid Mechanics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Aerodynamic Tests, Hydrodynamic Tests, Wind Tunnels, And Water Tanks (AREA)
- Testing Of Engines (AREA)
Abstract
The invention discloses a wind tunnel measurement data processing method based on feature extraction, which relates to the technical field of wind tunnel measurement data processing, and is characterized in that: the method specifically comprises the following steps: s1, defining pressure data of different measuring points of an engine as a function P (x, t); s2, taking wind tunnel spray pipe data as a reference, and extracting characteristics of measurement data by adopting a filtering technology; and S3, data processing, namely performing time shifting, averaging, dimensionless and error analysis processing on the data of the engine measuring points according to the step S2. According to the invention, the time characteristics of the measured data function are extracted by establishing the measured data function conforming to the real physical process of the wind tunnel engine test, the data can be accurately and efficiently processed based on the extracted characteristics, and the errors of the test data obtained by processing can be reduced while the processing efficiency and convenience are improved by performing time shifting, averaging, dimensionless and error analysis processing on the data.
Description
Technical Field
The invention relates to the technical field of wind tunnel measurement data processing, in particular to a wind tunnel measurement data processing method based on feature extraction.
Background
The scramjet engine is used as a propulsion power device, and can economically and reliably realize hypersonic speed flying targets. Currently, under the high Mach number flight condition above Ma8, the main equipment of the scramjet ground test is a wind tunnel. The device can simulate real flight conditions and generate test air flow with high enthalpy, but is limited by the working principle of the device, and the effective time of the test air flow driven by shock waves is only in the order of milliseconds. In such a short time, how to quickly and accurately extract real and effective measurement data from massive raw data obtained by measuring the sensor so as to evaluate the combustion characteristics and the propulsion performance of the engine is an important technical problem to be solved in the field of wind tunnel engine test.
At present, data of measuring points at different positions of an engine are generally simply selected for averaging at the same time in a certain period, physical processes and time delay of airflow flowing through the measuring points are not considered, data stabilization time caused by arrangement of sensors at the measuring points are not considered, and therefore larger errors exist in test data processed by the method, and real engine working characteristics cannot be reflected. In order to reduce errors as much as possible and improve processing efficiency and convenience, the invention aims to provide a wind tunnel measurement data processing method based on feature extraction so as to solve the problems.
Disclosure of Invention
According to the aerodynamic principle of the wind tunnel, under the Lagrange coordinate system, test data (pressure, heat flow, temperature and the like) are established as a binary function of measuring point positions and measuring moments, a real gas flow physical process in an engine test is described by the function, a filtering technology is applied to extract time characteristics of the function, and then data time shifting, averaging, dimensionless and error analysis are carried out based on the extracted characteristics, so that high accuracy and high efficiency processing of mass data can be realized.
The technical aim of the invention is realized by the following technical scheme: the wind tunnel measurement data processing method based on feature extraction specifically comprises the following steps:
s1, defining a data function, and defining pressure data of different measuring points of an engine as a function P (x, t) under a Lagrange coordinate system, wherein x represents the measuring point positions, and if the number of the measuring points is 100, the value range of x is as follows: [1,2 … ] and t represents measurement time, if the measurement time is 1 second, and the measurement frequency is 1MHz, the value range of t is as follows: [1,2, … 106];
s2, extracting data features based on a filtering technology, taking wind tunnel spray pipe data as a reference, extracting features of measurement data by adopting the filtering technology, and then further processing engine measuring point data;
s3, data processing, namely processing the data of the engine measuring points according to the step S2, specifically comprising the following steps:
1) The time shift processing is respectively carried out on the data of each measuring point of the engine, and the calculation formula of the time shift length of each measuring point of the engine is as follows: Δt (X) =t (X, B) -T (0, B), wherein T (0, B) represents a test start time of the wind tunnel nozzle position measurement point, and T (X, B) represents a test start time of the engine measurement point;
2) The measured value of the engine measuring point is subjected to dimensionless treatment by adopting the measured value of the wind tunnel jet pipe, and the calculation formula of the dimensionless treatment is as follows:
3) The pressure data in the effective test time is subjected to averaging treatment, and the calculation formula of the averaging treatment is as follows:
4) The maximum measured value and the minimum measured value of each measuring point of the engine in the effective test time are adopted to calculate an error band, and the calculation formula of the error band is as follows:
according to the processing of the engine measuring point data in the steps 1) to 4), the pressure value of each measuring point of the engine is expressed as:
further, in the process of extracting the features of the measurement data in step S2, the position of the wind tunnel nozzle is defined as a number 0 measurement point, and the data features of the number 0 measurement point are 5 time points as follows: t (0, A) represents the measurement start time, P (0, A) represents the nozzle pressure at the measurement start time, T (0, B) represents the test start time, P (0, B) represents the nozzle pressure at the test start time, T (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, P (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, T (0, D) represents the test effective time end time, P (0, D) represents the nozzle pressure at the test effective time end time, T (0, E) represents the measurement end time, and P (0, E) represents the nozzle pressure at the measurement end time.
In summary, the invention has the following beneficial effects: according to the invention, the time characteristics of the measured data function are extracted by establishing the measured data function conforming to the real physical process of the wind tunnel engine test, the data can be accurately and efficiently processed based on the extracted characteristics, and the errors of the test data obtained by processing can be reduced while the processing efficiency and convenience are improved by performing time shifting, averaging, dimensionless and error analysis processing on the data.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic illustration of a wind tunnel engine test;
FIG. 3 is a schematic view of feature extraction of wind tunnel nozzle measurement data based on filtering technology in an embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to fig. 1-3.
Examples: the wind tunnel measurement data processing method based on feature extraction, as shown in fig. 1, specifically comprises the following steps:
s1, defining a data function, and defining pressure data of different measuring points of an engine as a function P (x, t) under a Lagrange coordinate system, wherein x represents the measuring point positions, and if the number of the measuring points is 100, the value range of x is as follows: [1,2 … ] and t represents measurement time, if the measurement time is 1 second, and the measurement frequency is 1MHz, the value range of t is as follows: [1,2, … ] 106.
S2, extracting data features based on a filtering technology, taking wind tunnel spray pipe data as a reference, extracting features of measurement data by adopting the filtering technology, and then further processing engine measuring point data. In this step, according to the working principle of the wind tunnel, the wind tunnel test airflow can be regarded as a control body integrally flowing into the engine from the wind tunnel jet pipe downstream, the measured data of the jet pipe and the engine have the same time characteristics, and the characteristic extraction schematic diagram of the measured data of the wind tunnel jet pipe based on the filtering technology is shown in fig. 2.
S3, data processing, namely processing the data of the engine measuring points according to the step S2, specifically comprising the following steps:
1) The time shift processing is respectively carried out on the data of each measuring point of the engine, and the calculation formula of the time shift length of each measuring point of the engine is as follows: Δt (X) =t (X, B) -T (0, B), where T (0, B) represents the test start time of the wind tunnel nozzle position measurement point, and T (X, B) represents the test start time of the engine measurement point. In this embodiment, the effective time of the wind tunnel is only in the millisecond level, the test airflow has typical transient operating characteristics, and the airflow parameters measured by the engine measuring points and the measured values of the wind tunnel nozzle have specific time delay, so as to accurately grasp the time correlation, and thus time shift processing is performed on the data of each measuring point of the engine.
2) In order to eliminate errors caused by test airflow parameters among different train numbers, the measured values of the wind tunnel spray pipes are adopted to carry out dimensionless treatment on the measured values of the engine measuring points, and the calculation formula of the dimensionless treatment is as follows:
3) In order to eliminate the influence of combustion oscillation on engine performance evaluation, pressure data in effective test time is subjected to averaging treatment, and a calculation formula of the averaging treatment is as follows:
4) To characterize the data error, the error band is calculated by adopting the maximum measured value and the minimum measured value of each measuring point of the engine in the effective test time, and the calculation formula of the error band is as follows:
according to the processing of the engine measuring point data in the steps 1) to 4), the pressure value of each measuring point of the engine is expressed as:
in the process of extracting the characteristics of the measurement data in the step S2, the position of the wind tunnel nozzle is defined as a number 0 measuring point, and the data characteristics of the number 0 measuring point are 5 time points as follows: t (0, A) represents the measurement start time, P (0, A) represents the nozzle pressure at the measurement start time, T (0, B) represents the test start time, P (0, B) represents the nozzle pressure at the test start time, T (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, P (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, T (0, D) represents the test effective time end time, P (0, D) represents the nozzle pressure at the test effective time end time, T (0, E) represents the measurement end time, and P (0, E) represents the nozzle pressure at the measurement end time. The data of 5 moments A to E are directly read out from the measurement data of the wind tunnel jet pipe. Similarly, each station of the engine has the 5 characteristic data, but the specific times of A-E of each station are different.
In the above embodiment of the present invention, by establishing a measurement data function conforming to a real physical process of a wind tunnel engine test, and then extracting a time feature of the measurement data function by a filtering method, data processing can be accurately and efficiently performed based on the extracted feature, and errors of test data obtained by processing can be reduced while improving processing efficiency and convenience by performing time shift, averaging, dimensionless and error analysis processing on the data.
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and modifications to the present embodiment, which may not creatively contribute to the present invention as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present invention.
Claims (1)
1. The wind tunnel measurement data processing method based on feature extraction is characterized by comprising the following steps of: the method specifically comprises the following steps:
s1, defining a data function, and defining pressure data of different measuring points of an engine as a function P (x, t) under a Lagrange coordinate system, wherein x represents the measuring point positions, and if the number of the measuring points is 100, the value range of x is as follows: [1,2 … ] and t represents measurement time, if the measurement time is 1 second, and the measurement frequency is 1MHz, the value range of t is as follows: [1,2, … 106];
s2, extracting data features based on a filtering technology, taking wind tunnel spray pipe data as a reference, extracting features of measurement data by adopting the filtering technology, and then further processing engine measuring point data;
s3, data processing, namely processing the data of the engine measuring points according to the step S2, specifically comprising the following steps:
1) The time shift processing is respectively carried out on the data of each measuring point of the engine, and the calculation formula of the time shift length of each measuring point of the engine is as follows: Δt (x) =t (x, B) -T (0, B), where T (0, B) represents a test start time of the wind tunnel nozzle position measurement point, and T (x, B) represents a test start time of the engine measurement point;
2) The measured value of the engine measuring point is subjected to dimensionless treatment by adopting the measured value of the wind tunnel jet pipe, and the calculation formula of the dimensionless treatment is as follows:
3) The pressure data in the effective test time is subjected to averaging treatment, and the calculation formula of the averaging treatment is as follows:
4) The maximum measured value and the minimum measured value of each measuring point of the engine in the effective test time are adopted to calculate an error band, and the calculation formula of the error band is as follows:
according to the processing of the engine measuring point data in the steps 1) to 4), the pressure value of each measuring point of the engine is expressed as:
in the process of extracting the characteristics of the measurement data in the step S2, the position of the wind tunnel spray pipe is defined as a No. 0 measuring point, and the data characteristics of the No. 0 measuring point are 5 time points as follows: t (0, A) represents the measurement start time, P (0, A) represents the nozzle pressure at the measurement start time, T (0, B) represents the test start time, P (0, B) represents the nozzle pressure at the test start time, T (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, P (0, C) represents the nozzle pressure at which the test air flow reaches the stabilization time, T (0, D) represents the test effective time end time, P (0, D) represents the nozzle pressure at the test effective time end time, T (0, E) represents the measurement end time, and P (0, E) represents the nozzle pressure at the measurement end time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110153238.8A CN112834161B (en) | 2021-02-04 | 2021-02-04 | Wind tunnel measurement data processing method based on feature extraction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110153238.8A CN112834161B (en) | 2021-02-04 | 2021-02-04 | Wind tunnel measurement data processing method based on feature extraction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112834161A CN112834161A (en) | 2021-05-25 |
| CN112834161B true CN112834161B (en) | 2024-02-23 |
Family
ID=75932024
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110153238.8A Active CN112834161B (en) | 2021-02-04 | 2021-02-04 | Wind tunnel measurement data processing method based on feature extraction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112834161B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114593889B (en) * | 2022-05-10 | 2022-08-09 | 中国空气动力研究与发展中心高速空气动力研究所 | Method for obtaining static pressure matching precision of large-range supersonic jet flow uniform area |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06147075A (en) * | 1992-11-17 | 1994-05-27 | Nippon Carbureter Co Ltd | Engine ignition timing control method |
| FR2755184A1 (en) * | 1996-10-31 | 1998-04-30 | Renault | REGULATION SYSTEM OF THE RICHNESS OF AN INJECTION THERMAL ENGINE WITH ADAPTIVE RICHNESS SETPOINT |
| JP2003042858A (en) * | 2001-07-30 | 2003-02-13 | Natl Space Development Agency Of Japan | Apparatus and method for dynamic calibration of temperature sensor |
| CN102539107A (en) * | 2012-02-17 | 2012-07-04 | 中国空气动力研究与发展中心高速空气动力研究所 | Method for accurately synchronizing test signals of wind tunnel |
| CN107576446A (en) * | 2017-08-02 | 2018-01-12 | 南京航空航天大学 | A kind of active probe device and method of scramjet engine distance piece shock train leading edge |
| CN107679361A (en) * | 2017-09-14 | 2018-02-09 | 内蒙古久和能源装备有限公司 | One kind surveys wind data processing method |
| CN110553813A (en) * | 2019-09-24 | 2019-12-10 | 辽宁工程技术大学 | method for determining local wind resistance test interval of wind window based on PIV (particle image velocimetry) experiment |
| CN110686858A (en) * | 2019-11-07 | 2020-01-14 | 中国空气动力研究与发展中心 | Sound explosion measurement wind tunnel test data processing method |
| CN110849577A (en) * | 2019-11-29 | 2020-02-28 | 中国空气动力研究与发展中心超高速空气动力研究所 | Stress wave balance wind tunnel force measuring method |
| CN111473947A (en) * | 2020-05-07 | 2020-07-31 | 中国科学院力学研究所 | Single-vector dynamic calibration method for pulse wind tunnel force measurement system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10520389B2 (en) * | 2013-11-27 | 2019-12-31 | United States Of America As Represented By The Administrator Of Nasa | Aerodynamic modeling using flight data |
-
2021
- 2021-02-04 CN CN202110153238.8A patent/CN112834161B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06147075A (en) * | 1992-11-17 | 1994-05-27 | Nippon Carbureter Co Ltd | Engine ignition timing control method |
| FR2755184A1 (en) * | 1996-10-31 | 1998-04-30 | Renault | REGULATION SYSTEM OF THE RICHNESS OF AN INJECTION THERMAL ENGINE WITH ADAPTIVE RICHNESS SETPOINT |
| JP2003042858A (en) * | 2001-07-30 | 2003-02-13 | Natl Space Development Agency Of Japan | Apparatus and method for dynamic calibration of temperature sensor |
| CN102539107A (en) * | 2012-02-17 | 2012-07-04 | 中国空气动力研究与发展中心高速空气动力研究所 | Method for accurately synchronizing test signals of wind tunnel |
| CN107576446A (en) * | 2017-08-02 | 2018-01-12 | 南京航空航天大学 | A kind of active probe device and method of scramjet engine distance piece shock train leading edge |
| CN107679361A (en) * | 2017-09-14 | 2018-02-09 | 内蒙古久和能源装备有限公司 | One kind surveys wind data processing method |
| CN110553813A (en) * | 2019-09-24 | 2019-12-10 | 辽宁工程技术大学 | method for determining local wind resistance test interval of wind window based on PIV (particle image velocimetry) experiment |
| CN110686858A (en) * | 2019-11-07 | 2020-01-14 | 中国空气动力研究与发展中心 | Sound explosion measurement wind tunnel test data processing method |
| CN110849577A (en) * | 2019-11-29 | 2020-02-28 | 中国空气动力研究与发展中心超高速空气动力研究所 | Stress wave balance wind tunnel force measuring method |
| CN111473947A (en) * | 2020-05-07 | 2020-07-31 | 中国科学院力学研究所 | Single-vector dynamic calibration method for pulse wind tunnel force measurement system |
Non-Patent Citations (3)
| Title |
|---|
| 周秀银 等.《误差理论与实验数据处理》.北京航空学院出版社,1986,(第1版),第10-80页. * |
| 徐有恒 等.《基础流体实验》.复旦大学出版社,1990,(第1版),第21-38页. * |
| 谌君谋 等.高焓激波风洞试验技术综述.《空气动力学学报》.2018,第36卷(第4期),第543-554页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112834161A (en) | 2021-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111238759B (en) | Icing wind tunnel pressure measurement test method | |
| CN112834161B (en) | Wind tunnel measurement data processing method based on feature extraction | |
| US10180079B2 (en) | Communicating signal between rotating antenna and plurality of stationary antennae based on displacement | |
| EP3584557B1 (en) | System and method for estimating an air mass flow of air flowing in a bypass duct of a gas turbine engine | |
| CN103869383B (en) | Based on air data computer and its implementation of Kalman filtering algorithm | |
| CN110793746B (en) | Wind tunnel test device for measuring thrust of oblique cutting spray pipe of hypersonic vehicle | |
| CN107576446B (en) | Active detection method for front edge of shock wave string of isolation section of scramjet engine | |
| CN105628051A (en) | Embedded atmosphere measuring equipment performance evaluation method | |
| CN106597003B (en) | A method of utilizing shock wave measurement supersonic projectile flight path | |
| Davison et al. | Ice crystal environment-modular axial compressor rig: evaluation of measured water content and melt | |
| CN105628086A (en) | Supersonic speed flight inflow parameter solving method based on conical surface pressure distribution | |
| Dunn et al. | Phase-resolved surface pressure and heat-transfer measurements on the blade of a two-stage turbine | |
| CN202580647U (en) | Online leakage detection and positioning system for heating pipeline | |
| CN105865654B (en) | A kind of choosing method and boiler temp measuring method of sound wave temperature measurement signal | |
| CN108680274A (en) | Air-Cooling Island finned-tube bundle temperature measuring equipment based on acoustic technique and its method | |
| CN114720145A (en) | Low-pressure turbine performance test method with rectifying blades | |
| CN103968907A (en) | Method and device for measuring supercritical and gaseous hydrocarbon fuel dense flow | |
| CN106342303B (en) | A kind of computational methods of afterburning chamber for solid rocket ramjet parameter | |
| CN103399172A (en) | Ultrasonic wind speed measuring method | |
| CN116562194B (en) | Thrust evaluation method and system for ramjet rotary detonation engine | |
| CN114771877B (en) | Optimal interception guidance method considering navigation error | |
| CN218955816U (en) | Engine inlet gas uniform distribution acquisition device | |
| CN113533776B (en) | Device and method for measuring flow velocity of gas in small-aperture pipeline | |
| Zhaoxin et al. | Pitot tube velocity measurement technology for the compressible airflow and its application in wind tunnel | |
| Grzywiński | Research of vibroacoustic disturbances influence on the shock wave detection process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |