A method and system for fast processing test data in long-peak irregular wave tests
Through the splicing, reorganization and spectrum analysis of long-peak irregular wave test data, the problem of low test data processing efficiency is solved, rapid processing and efficient testing are achieved, and costs and rework rates are reduced.
Patent Information
- Application Number
- CN202410248072.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-05
- Publication Date
- 2025-01-21
- Estimated Expiration
- 2044-03-05
AI Technical Summary
The prior art is difficult to quickly process a large amount of test data in long peak irregular wave tests, resulting in low test efficiency, high cost, and inability to achieve real-time quality assessment, resulting in repeated rework of the test.
By acquiring multiple sets of test data, the wave measurement curves are spliced and reorganized to generate spliced and reorganized wave history data, and based on this, the measured spectrum of pool waves is generated. When the measured spectrum is consistent with the preset target parameter spectrum, the test ship model response is spliced and reorganized, and spectrum analysis and time history information statistics are performed.
It achieves rapid processing of test data, improves test efficiency, reduces test costs, enables rapid quality assessment in long-peak irregular wave tests, and reduces repeated test rework.
Smart Images
Figure CN118035498B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a method and system for rapid processing of test data, in particular to a method and system for rapid processing of test data in a long-peak irregular wave test, specifically to a method and system for rapid processing of test data suitable for a hydroelastic ship model in a long-peak irregular wave test. Background Art
[0002] At present, ship design is developing towards larger, lighter and faster directions. At the same time, it will cause the natural vibration frequency of the ship structure to decrease and the wave encounter frequency to increase, making the hydroelastic effect of the ship more prominent, causing the ship to be severely affected by wave-induced vibration loads during navigation.
[0003] The long-peak irregular wave test of the ship model is an important means to simulate the corresponding sea conditions and wave levels of the ship in the real marine environment, and it is also an important means to evaluate the fatigue strength and ultimate strength of the ship structure. For the same long-peak irregular wave test condition, considering the constraints of the test pool site and other conditions, in order to ensure that the test ship model encounters an effective number of long-peak irregular waves during the test, it is generally repeated for multiple voyages until the number of waves encountered by the ship model meets the requirements, forming multiple sets of test data under the same long-peak irregular wave condition.
[0004] For multiple groups of test data under the same long-peak irregular wave condition, the large amount of data will cause great obstacles for technical personnel to process the test data; at the same time, since it is impossible to quickly process the test data, it is impossible to quickly perform real-time quantitative evaluation of the test quality, and even repeat the tests under the long-peak irregular wave condition, resulting in unnecessary waste of manpower and financial resources in test expenses, which is difficult to meet the actual long-peak irregular wave test needs. Summary of the invention
[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method and system for quickly processing test data in a long-peak irregular wave test, which can effectively realize the rapid processing of test data of a ship model in a long-peak irregular wave test, improve test efficiency and reduce test costs.
[0006] According to the technical solution provided by the present invention, a method for quickly processing test data in a long-peak irregular wave test comprises:
[0007] Acquire a test data set under a preset long-peak irregular wave condition, wherein the test data set includes multiple groups of test data, and for any group of test data, includes a wave measurement curve and a test ship model response under the wave measurement curve;
[0008] The wave measurement curves in the test data set are spliced and reorganized to form spliced and reorganized wave time history data, and a measured spectrum of pool waves under the current long-peaked irregular waves is generated based on the spliced and reorganized wave time history data;
[0009] When the measured spectrum of the pool wave generated above is consistent with the target parameter spectrum of the preset long-peak irregular wave, based on the splicing and reorganization method for forming the spliced and reorganized wave time history data, the response data of the same type in the test ship model response are spliced and reorganized respectively to form corresponding spliced and reorganized test ship model responses;
[0010] Spectral analysis and time history information statistics are performed on all spliced and reorganized test ship model responses, so as to obtain the test ship model response spectrum information after spectral analysis, and obtain the test data statistical characteristic information after time history information statistics.
[0011] The wave measurement curve includes wave heights at different times;
[0012] The test ship model response includes hull motion response and section load response.
[0013] When the wave measurement curves are spliced and reassembled, the splicing and reassembly method includes:
[0014] Sort all test data in the test data set by generation time;
[0015] After sorting, for any two adjacent groups of test data, the overlapping states of the first and last segments of the wave measurement curves in the two groups of test data are judged in turn, and the effective time period of the wave measurement curve in each group of test data is determined;
[0016] According to the above sorting order, the wave measurement curves of all valid time periods are spliced and reorganized to form spliced and reorganized wave time history data after splicing and reorganization.
[0017] The spliced and reorganized wave time history data are subjected to fast Fourier transformation to generate the measured spectrum of the pool wave after the transformation;
[0018] The measured shape, measured spectrum value, measured peak frequency and measured significant wave height of the measured spectrum of the pool wave are extracted and compared with the target parameter spectrum, wherein:
[0019] When the measured shape of the measured spectrum of the pool wave basically coincides with the curve trend of the target parameter spectrum, and the differences between the measured spectrum value, measured peak frequency, and measured significant wave height of the measured spectrum of the pool wave and the target spectrum value, target peak frequency, and target significant wave height of the target parameter spectrum are respectively within the preset range, then the measured spectrum of the pool wave is consistent with the target parameter spectrum.
[0020] When the difference between the measured spectrum value of the pool wave spectrum and the target spectrum value of the target parameter spectrum is within the preset range, then:
[0021] The measured spectrum value S(ω) of the pool wave spectrum is greater than 0.15(ω p ), and ΔS(ω) / S(ω) is less than 20%, where (ω p ) is the peak frequency of the measured spectrum of the pool wave, ΔS(ω) is the difference between the measured spectrum value of the measured spectrum of the pool wave and the target spectrum value of the target parameter spectrum;
[0022] When the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum is within the preset range, then:
[0023] Δω p / ω p Less than 5%, where Δω p is the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum;
[0024] When the difference between the measured significant wave height of the pool wave measured spectrum and the target significant wave height of the target parameter spectrum is within the preset range, then:
[0025] ΔH w1 / 3 / H w1 / 3 Less than 5%, among which H w1 / 3 is the target significant wave height, ΔH w1 / 3 It is the difference between the measured significant wave height of the measured spectrum of pool waves and the target significant wave height of the target parameter spectrum.
[0026] When performing spectrum analysis on the response of any spliced and reassembled test ship model, the spectrum analysis method includes:
[0027] The spliced and reassembled test ship model response is subjected to a fast Fourier transform, and after the fast Fourier transform, the frequency distribution of the spectrum and the main frequency corresponding to the high-frequency component are statistically analyzed.
[0028] Based on the frequency distribution obtained by statistics, low-pass filtering, band-pass filtering and / or high-pass filtering are performed on the test ship model response to obtain the time history curve of the low-frequency test ship model response and the time history curve data of the high-frequency test ship model response after filtering;
[0029] The average value is obtained by performing periodic amplitude statistics on the time history data of the low-frequency test ship model response;
[0030] For the nonlinear phenomenon of the test ship model response, the camber amplitude and sagging amplitude of the test ship model response are respectively counted;
[0031] The amplitude of the high-frequency test ship model response is obtained by performing periodic amplitude statistics on the time history data of the high-frequency test ship model response.
[0032] The time history data of the spliced and reassembled test ship model response are statistically analyzed to obtain the corresponding zero-crossing cycle number, peak-to-valley value, average value, significant value, maximum value and variance of the spliced and reassembled test ship model response after periodic amplitude statistics.
[0033] For the section load response of the test ship model in the long-peak irregular wave test, Weibull fitting is performed on the histogram of the peak and valley values of the section load response;
[0034] After the Weibull fitting, the shape parameter ξ and scale parameter α of the Weibull distribution are estimated by the least squares method. For the Weibull fitting, we have:
[0035]
[0036] Among them, f s (s) is the Weibull distribution density function, F s (s) is the Weibull distribution function, and s is the stress range corresponding to the peak and valley values of the section load.
[0037] A system for rapidly processing test data in a long-peak irregular wave test comprises a rapid data processing device, wherein the rapid test data processing method described above is executed based on the rapid data processing device.
[0038] The advantages of the present invention are as follows: for a test data set, the wave measurement curves are spliced and reorganized to form spliced and reorganized wave time history data, and a measured spectrum of water pool waves under the current long-peaked irregular waves is generated based on the spliced and reorganized wave time history data; when the measured spectrum of water pool waves generated above is consistent with the target parameter spectrum of the preset long-peaked irregular waves, based on the splicing and reorganization method for forming the spliced and reorganized wave time history data, the response data of the same type in the test ship model response are spliced and reorganized respectively to form corresponding spliced and reorganized test ship model responses respectively;
[0039] Spectral analysis and time history information statistics are performed on all the spliced and reorganized test ship model responses, so as to obtain the test ship model response spectrum information after spectral analysis, and obtain the test data statistical characteristic information after time history information statistics, so as to efficiently realize the rapid processing of test data of the ship model in the long-peak irregular wave test, meet the test data processing needs under long-peak irregular waves, improve test efficiency and reduce test costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1 The present invention is a flowchart of an embodiment of the method for rapid processing of test data.
[0041] Figure 2 It is a schematic diagram of an embodiment of the wave measurement curve in three groups of test data of the present invention.
[0042] Figure 3 A schematic diagram of an embodiment of the present invention for splicing and reorganizing wave time history data.
[0043] Figure 4 The figure is a schematic diagram of an embodiment of the curve fitting of the sample histogram of the test ship model section load according to the present invention. DETAILED DESCRIPTION
[0044] The present invention will be further described below in conjunction with specific drawings and embodiments.
[0045] In order to effectively realize the rapid processing of test data in the long-peak irregular wave test of the ship model, ensure the quality of the test data, improve the test efficiency, and reduce the test cost, a method for rapidly processing test data in the long-peak irregular wave test is provided. In one embodiment of the present invention, the method for rapidly processing test data includes:
[0046] Acquire a test data set under a preset long-peak irregular wave condition, wherein the test data set includes multiple groups of test data, and for any group of test data, includes a wave measurement curve and a test ship model response under the wave measurement curve;
[0047] The wave measurement curves in the test data set are spliced and reorganized to form spliced and reorganized wave time history data, and a measured spectrum of pool waves under the current long-peaked irregular waves is generated based on the spliced and reorganized wave time history data;
[0048] When the measured spectrum of the pool wave generated above is consistent with the target parameter spectrum of the preset long-peak irregular wave, based on the splicing and reorganization method for forming the spliced and reorganized wave time history data, the response data of the same type in the test ship model response are spliced and reorganized respectively to form corresponding spliced and reorganized test ship model responses;
[0049] Spectral analysis and time history information statistics are performed on all spliced and reorganized test ship model responses, so as to obtain the test ship model response spectrum information after spectral analysis, and obtain the test data statistical characteristic information after time history information statistics.
[0050] According to the specification requirements, the test time of the ship model in long-peaked irregular waves must at least correspond to the 1-hour sailing time of the actual ship in the real ocean environment. However, due to the size limitation of the test pool, the sailing time of the ship model from the front position to the rear position of the test pool cannot meet the specification requirements. Therefore, in order to meet the test requirements, multiple voyages are generally carried out, wherein each voyage is located under the same long-peaked irregular wave condition, that is, each voyage is located under the same long-peaked irregular condition, and each voyage collects test data, and the test data collected by each voyage are independent of each other. The collected test data are independent of each other, specifically referring to the re-collection of test data for each voyage.
[0051] In order to meet the test requirements of multiple voyages, the wave maker in the test pool must also generate long-peaked irregular waves according to the target parameter spectrum and generate waves in sections for each voyage. At the same time, in order to ensure that each long-peaked irregular wave in the wave spectrum can fully act on the test ship model, the waves generated by the wave maker at the initial stage of the next voyage must be completely consistent with the waves generated at the final stage of the test voyage. Specifically, long-peaked irregular waves are used to describe the sea conditions of waves, specifically referring to waves that propagate in a fixed direction and whose crest lines are parallel to each other. They can generally be regarded as the superposition of regular waves with different wave periods and wave heights. The specific situation of long-peaked irregular waves is consistent with the existing ones, so as to meet the required tests on the ship model.
[0052] Figure 1 The flowchart of an embodiment of the method for quickly processing test data of a ship model in a long-peak irregular wave test of the present invention is shown in the figure. In the figure, a test data set is first obtained. Generally, a set of test data is collected and generated for each voyage. Therefore, the number of groups of test data in the test data set is generally consistent with the number of voyages under the long-peak irregular working condition. In specific implementation, any group of test data includes a wave measurement curve and a test ship model response under the wave measurement curve, wherein the wave measurement curve is the wave height at different times in a voyage, and the wave measurement curve can be measured by the commonly used technical means in the technical field; the test ship model response under the wave measurement curve is the test ship model response under the wave height.
[0053] Generally, the test ship model response includes the hull motion response and the section load response; of course, the type of test ship model response can be selected according to the needs, and the test ship model response can be measured by the measurement method commonly used in this technical field. Specifically, the ship model motion response refers to the oscillation and swaying motion of the ship model as a rigid body, which is disturbed in the waves and occurs around its original equilibrium position. The section load response refers to the load induced by ocean waves on the specific section structure of the ship in a wave environment.
[0054] In order to achieve rapid processing of multiple groups of test data, in one embodiment of the present invention, all wave measurement curves are first spliced and reorganized, and the spliced and reorganized wave history data formed by the splicing and reorganization are compared with the target parameter spectrum of the preset long-peaked irregular wave. When the wave measurement curves are spliced and reorganized, the splicing and reorganization method includes:
[0055] Sort all test data in the test data set by generation time;
[0056] After sorting, for any two adjacent groups of test data, the overlapping states of the first and last segments of the wave measurement curves in the two groups of test data are judged in turn, and the effective time period of the wave measurement curve in each group of test data is determined;
[0057] According to the above sorting order, the wave measurement curves of all valid time periods are spliced and reorganized to form spliced and reorganized wave time history data after splicing and reorganization.
[0058] From the above description, it can be seen that although multiple test data are independent of each other, multiple voyages generally occur in chronological order. Therefore, when splicing and reorganizing, all test data need to be sorted by generation time, that is, all wave measurement curves need to be sorted by time. After sorting, the overlapping state of wave height measurement data in two adjacent groups of test data needs to be determined. Figure 2 An embodiment of the wave measurement curve in three sets of test data is shown in the figure, in which the horizontal axis is the time of measurement during the current voyage, and the vertical axis is the wave height in mm.
[0059] Since multiple voyages are carried out in sequence and based on the description of the working mechanism of the wave maker during the above multiple voyages, personnel in this technical field can obtain: there are overlapping waves at the beginning of the long-peak irregular working condition of the next voyage and the end of the long-peak irregular working condition of the previous voyage. Therefore, for two adjacent wave measurement curves, the overlapping state of the tail segments of the two adjacent wave measurement curves can be detected. The overlapping state can be that the adjacent wave measurement curves have the same or similar wave height curves, that is, the wave height curves of multiple periods in the wave measurement curves measured in the adjacent voyage tests under the same long-peak irregular wave condition completely overlap. Figure 2 In the embodiment, the AB segment of the first wave measurement curve overlaps with the AB segment of the second wave measurement curve, and the CD segment of the second wave measurement curve overlaps with the CD segment of the third wave measurement curve. The specific method and process for determining the overlap of the first and last segments of two adjacent wave measurement curves can be selected according to needs.
[0060] After determining the overlapping state of the first and last segments of two adjacent wave measurement curves, the effective time period of each wave measurement curve can be determined. At this time, the wave measurement curves of all effective time periods can be extracted and spliced and reorganized to form spliced and reorganized wave time history data. Figure 2 In the above, all the time of the first wave measurement curve can be considered as the effective time period, and the AB segment of the second wave measurement curve to the CD segment of the second wave measurement curve can be considered as the effective time period of the second wave measurement curve, and so on for other cases. When splicing and reorganizing, the wave measurement curves in the effective time period are directly spliced and connected in the order of time to obtain the spliced and reorganized wave time history data. At this time, by splicing and reorganizing the wave time history data, the wave time history curve under the entire long-peak irregular wave test can be characterized, and the change of wave height over time under the entire long-peak irregular wave test can be characterized. Figure 3 It is shown in Figure 2 An embodiment of forming a wave measurement curve by splicing and reorganizing wave time history data, Figure 3 In the figure, the horizontal axis is the time and the vertical axis is the wave height.
[0061] In one embodiment of the present invention, the spliced and reorganized wave time history data is subjected to a fast Fourier transform to generate a measured spectrum of the pool wave after the transformation;
[0062] The measured shape, measured spectrum value, measured peak frequency and measured significant wave height of the measured spectrum of the pool wave are extracted and compared with the target parameter spectrum, wherein:
[0063] When the measured shape of the measured spectrum of the pool wave basically coincides with the curve trend of the target parameter spectrum, and the differences between the measured spectrum value, measured peak frequency, and measured significant wave height of the measured spectrum of the pool wave and the target spectrum value, target peak frequency, and target significant wave height of the target parameter spectrum are respectively within the preset range, then the measured spectrum of the pool wave is consistent with the target parameter spectrum.
[0064] In specific implementation, the spliced and reorganized wave time history data can be subjected to fast Fourier transform to generate the measured spectrum of the pool wave, that is, the corresponding frequency spectrum information can be obtained. The method and process of fast Fourier transforming the spliced and reorganized wave time history data and generating the measured spectrum of the pool wave can be consistent with the existing methods, and the specific method is to generate the measured spectrum of the pool wave. After the measured spectrum of the pool wave is generated, information extraction can be performed on the measured spectrum of the pool wave to obtain the measured shape, measured spectrum value, measured peak frequency and measured significant wave height of the measured spectrum of the pool wave, wherein,
[0065] The measured spectrum of the pool wave is a curve. The measured shape of the measured spectrum of the pool wave is the shape of the corresponding curve. The measured spectrum value of the pool wave measured spectrum refers to the energy value at the corresponding frequency in the spectrum diagram of the pool frequency measured spectrum. The measured peak frequency of the pool wave measured spectrum refers to the frequency corresponding to the peak of the curve in the spectrum diagram of the pool wave measured spectrum.
[0066] The measured significant wave height of the measured spectrum of pool waves specifically refers to an irregular wave group consisting of n waves, in which the wave heights of these waves are arranged in order from large to small, and the average wave height of the first n / 3 waves is the significant wave height of this irregular wave. In specific implementation, when obtaining the measured significant wave height, it can be directly obtained by statistics based on the spliced and reorganized wave time history data, rather than by statistics on the measured spectrum of pool waves. The number of waves n in the irregular wave group is related to the voyage of long-peaked irregular waves, and can be selected according to needs. The following n all have the same meaning, and can refer to the description here.
[0067] It can be seen from the above description that during the test, it is necessary to pre-set long-peak irregular waves, and based on the preset long-peak irregular waves, the corresponding target parameter spectrum can be obtained, wherein the target parameter spectrum can be directly obtained by referring to the recommendations in the existing relevant specifications, such as the single-parameter wave energy spectrum and dual-parameter wave energy spectrum recommended by the International Ship Test Tank Conference (ITTC). The method of obtaining the target parameter spectrum based on the preset long-peak irregular waves can be selected according to needs, based on which the corresponding target parameter spectrum can be obtained.
[0068] After obtaining the target parameter spectrum, the target parameter spectrum can be subjected to information extraction, thereby extracting the target shape, target spectrum value, target peak frequency and target significant wave height of the target parameter spectrum. Thereafter, the measured shape of the measured pool wave spectrum is compared with the target shape of the target parameter spectrum, and the corresponding differences between the measured spectrum value, measured peak frequency and measured significant wave height of the measured pool wave spectrum and the target spectrum value, target peak frequency and target significant wave height of the target parameter spectrum are calculated respectively, so as to determine whether the measured pool wave spectrum is consistent with the target parameter spectrum.
[0069] In one embodiment of the present invention, when the shape of the measured spectrum of the pool wave basically coincides with the curve trend of the target parameter spectrum, and the measured spectrum value, measured peak frequency, and measured significant wave height of the measured spectrum of the pool wave are respectively within a preset range with the target spectrum value, target peak frequency, and target significant wave height of the target parameter spectrum, then the measured spectrum of the pool wave is consistent with the target parameter spectrum. When the measured spectrum of the pool wave is consistent with the target parameter spectrum, it is considered that the current test data set under the long-peak irregular wave is a test conducted under the preset long-peak irregular wave, that is, the test data set is valid test condition data. In specific implementation, the technical means commonly used in the technical field can be used to determine whether the shape of the measured spectrum of the pool wave basically coincides with the curve trend of the target parameter spectrum, and the specific judgment method can be selected according to needs.
[0070] In one embodiment of the present invention, when the difference between the measured spectrum value of the pool wave measured spectrum and the target spectrum value of the target parameter spectrum is within a preset range, then:
[0071] The measured spectrum value S(ω) of the pool wave spectrum is greater than 0.15(ω p ), and ΔS(ω) / S(ω) is less than 20%, where (ω p ) is the peak frequency of the measured spectrum of the pool wave, ΔS(ω) is the difference between the measured spectrum value of the measured spectrum of the pool wave and the target spectrum value of the target parameter spectrum;
[0072] When the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum is within the preset range, then:
[0073] Δω p / ω p Less than 5%, where Δω p is the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum;
[0074] When the difference between the measured significant wave height of the pool wave measured spectrum and the target significant wave height of the target parameter spectrum is within the preset range, then:
[0075] ΔH w1 / 3 / H w1 / 3 Less than 5%, among which H w1 / 3 is the target significant wave height, ΔH w1 / 3 It is the difference between the measured significant wave height of the measured spectrum of pool waves and the target significant wave height of the target parameter spectrum.
[0076] After obtaining the corresponding spectrum values, peak frequencies and significant wave heights of the measured spectrum of the pool wave and the target parameter spectrum, the above method can be used to determine whether they are within the preset range. For details, please refer to the above description.
[0077] In one embodiment of the present invention, when performing spectrum analysis on the response of any spliced and reassembled test ship model, the spectrum analysis method includes:
[0078] The spliced and reassembled test ship model response is subjected to a fast Fourier transform, and after the fast Fourier transform, the frequency distribution of the spectrum and the main frequency corresponding to the high-frequency component are statistically analyzed.
[0079] It can be seen from the above description that the test ship model response corresponds to the wave measurement curve. Therefore, based on the splicing and reorganization method of forming the spliced and reorganized wave history data, the same type of response data in the test ship model response can be spliced and reorganized to form the corresponding spliced and reorganized test ship model responses; as described above, the effective time period corresponding to the test ship model response is selected and determined according to the effective time period of the wave measurement curve, so as to perform splicing and reorganization. According to the types of the above test ship model responses, after splicing and reorganization, the spliced and reorganized hull motion response and the spliced and reorganized section load response can be obtained respectively.
[0080] The above-mentioned spliced and reassembled hull motion response and spliced and reassembled section load response are respectively subjected to fast Fourier transform. Thereafter, the frequency distribution of the spectrum and the main frequency corresponding to the high-frequency component can be statistically analyzed based on the result of the fast Fourier transform. That is, the fast Fourier transform results corresponding to the spliced and reassembled hull motion response and the spliced and reassembled section load response are respectively subjected to spectrum statistics. In specific implementation, low-frequency components and high-frequency components are obtained according to the spectrum analysis results, wherein the test ship model response caused by the wave frequency is regarded as the low-frequency component, and the subsequent frequency components of the test ship model response (the frequency multiple component of the wave frequency, the first-order frequency component of the hull vibration response, and the second-order frequency component) are the high-frequency components. Specifically, after obtaining the low-frequency component and the high-frequency component, the above-mentioned frequency distribution statistics are completed; according to the high-frequency component, the main frequency corresponding to the high-frequency component can be obtained, such as the above-mentioned frequency multiple component, the first-order frequency component and the second-order frequency component are the main frequencies corresponding to the high-frequency component. During the spectrum analysis, the method and process of obtaining the low-frequency component and the high-frequency component can be consistent with the existing ones, and will not be repeated here.
[0081] In addition, based on the spectrum analysis results, the test ship model response before the fast Fourier transform is processed by low-pass filtering, band-pass filtering and high-pass filtering in sequence. It can be seen from the above description that the test ship model response includes the ship model motion response and the ship model load response. Therefore, the ship model motion response and the ship model load response need to be low-pass filtered, band-pass filtered and high-pass filtered respectively, that is, the spliced and reorganized hull motion response and the spliced and reorganized section load response are respectively subjected to corresponding filtering processing, wherein the filtering methods adopted by the low-pass filtering, band-pass filtering and high-pass filtering can be selected according to needs, so as to adapt to the matching with the above-mentioned spectrum analysis results. For example, according to the low-frequency components and high-frequency components in the above-mentioned spectrum analysis results, the corresponding filtering parameters of the low-pass filtering, band-pass filtering and high-pass filtering can be set. At this time, when filtering is performed based on the set filtering parameters, it can match the spectrum analysis results.
[0082] After the above filtering is performed on the test ship model response, the low-frequency test ship model response and the high-frequency test ship model response can be obtained. At this time, there are low-frequency hull motion response, low-frequency section load response, high-frequency hull motion response and high-frequency section load response.
[0083] The average value of the test ship model response is obtained by performing periodic amplitude statistics on the time history curve data of the low-frequency test ship model response; specifically, the low-frequency test ship model response is the low-frequency hull motion response and the low-frequency section load response mentioned above. The test ship model motion response generally includes the heave, pitch and roll at the center of gravity of the test ship model; the section load response of the test ship model generally includes the bending moment and shear force at the measured section. Therefore, when performing periodic amplitude statistics on the time history curve data of the low-frequency test ship model response, the corresponding average values of heave, pitch, roll, bending moment and shear force can be obtained respectively.
[0084] The amplitude of the high-frequency test ship model response is obtained by performing periodic amplitude statistics on the time history curve data of the high-frequency test ship model response; specifically, the high-frequency test ship model response is the high-frequency hull motion response and high-frequency section load response mentioned above, and the method and process of statistical analysis of the test ship model response can be consistent with the existing ones, that is, the corresponding statistics can be achieved by using the commonly used technical means in this technical field to obtain the required statistical results.
[0085] In addition, for the nonlinear phenomenon of the test ship model response, the mid-arch amplitude and mid-sagging amplitude of the test ship model response are separately counted; generally, nonlinear phenomena may exist in both low-frequency time history data and high-frequency time history data. The nonlinear phenomenon is specifically relative to the linear phenomenon. The mid-sagging amplitude and mid-arch amplitude values of the corresponding measurement parameters (motion response, load response) of the ship in a single cycle wave should be equal, only the positive and negative signs are opposite; and in the vicinity of this wave height, as the wave height increases, its value also increases linearly. In the nonlinear phenomenon, the mid-sagging amplitude and mid-arch amplitude values of the corresponding measurement parameters (motion response, load response) in a single cycle wave should be unequal, and there are differences in the values; and in the vicinity of this wave height, as the wave height increases, its value increases nonlinearly.
[0086] Generally, before conducting a long-peak irregular wave test, numerical calculations are performed using commonly used technical means in this technical field to determine the working conditions of the test ship model in response to nonlinear phenomena, thereby facilitating the statistics of the mid-arch amplitude and mid-sag amplitude under nonlinear phenomena.
[0087] It should be noted that camber and sagging represent two stress states of a ship in waves. Camber refers to the asymmetric longitudinal distribution of gravity and buoyancy of a ship floating on the water. At this time, the wave crest is located in the middle of the ship. When the buoyancy in the middle of the ship is greater than the gravity and the gravity at the bow and stern is greater than the buoyancy, the middle part of the ship is arched, which causes a longitudinal bending state. Sagging is the opposite.
[0088] The camber amplitude and the sagging amplitude represent the amplitude of the corresponding measurement parameters (used to represent the motion response and load response of the ship model) under the wave height and wavelength in the corresponding single cycle wave. In specific implementation, a feasible statistical method is as follows: select the measurement data under several cycles in the wave steady section, and calculate the average value of the peak values under these cycles to obtain the camber amplitude, and calculate the average value of the valley values under these cycles to obtain the sagging amplitude. The specific statistical method and process for obtaining the camber amplitude and the sagging amplitude can be consistent with the existing ones.
[0089] In one embodiment of the present invention, time history information statistics are performed on the spliced and reorganized hull motion response and the spliced and reorganized section load response, so as to obtain the number of zero-crossing cycles, peak and valley values, average values, significant values, maximum values, and variance after statistics.
[0090] In specific implementation, after obtaining the spliced and reassembled hull motion response and the spliced and reassembled section load response, the technical means commonly used in the technical field can be used to perform time history information statistics, such as using the periodic amplitude statistics method for direct statistical analysis. Unlike the above-mentioned periodic amplitude statistics, the time history information statistics here do not require the above-mentioned filtering processing. Specifically, first determine the corresponding zero-crossing period numbers of the spliced and reassembled hull motion response and the spliced and reassembled section load response, such as after obtaining the zero-crossing period number of the spliced and reassembled hull motion response, use the periodic amplitude statistics method to obtain the peak and valley values, average values, significant values, maximum values, and variance of the hull motion response; similarly, after obtaining the zero-crossing period number of the spliced and reassembled section load response, use the periodic amplitude statistics method to obtain the peak and valley values, average values, significant values, maximum values, and variance of the section load.
[0091] The peak-to-valley value specifically refers to the difference between the peak value and the valley value, wherein the peak value specifically refers to the maximum value in the time history response data in one cycle as the peak value; the valley value specifically refers to the minimum value in the time history response data in one cycle as the valley value. For the average value, it specifically refers to the value obtained by summing and averaging the peak and valley values in each cycle for the time history data obtained after splicing and reorganization. Here, the time history data obtained after splicing and reorganization is the above-mentioned spliced and reorganized hull motion response or spliced and reorganized section load response. For the meaningful value of the spliced and reorganized hull motion response: specifically, for the peak and valley values of n cycles arranged from large to small, the average value of the first n / 3 peak and valley values; similarly, the meaningful value of the spliced and reorganized section load response can be obtained.
[0092] The maximum value of the motion response of the spliced and reassembled hull is specifically: for the peak and valley values of n cycles arranged from large to small, the average value of the first n / 10 peak and valley values; similarly, the maximum value of the load response of the spliced and reassembled section can be obtained.
[0093] The variance of the spliced and reassembled hull motion response is specifically: for the peak-to-valley value data of n cycles, the average of the squares of the difference between the peak-to-valley value of each cycle and the average of the peak-to-valley value of the overall data. Similarly, the variance of the spliced and reassembled section load response can be obtained.
[0094] In specific implementation, the above-mentioned test data statistical characteristic information can be obtained through the time history information statistics of the above-mentioned two methods. The situation of the test data statistical characteristic information can refer to the above description.
[0095] In one embodiment of the present invention, for the section load response of the test ship model in the long-peak irregular wave test, in addition to the above-mentioned direct statistics of the time history data and spectrum analysis, Weibull fitting can also be performed on the histogram of the peak and valley values of the section load response;
[0096] After the Weibull fitting, the shape parameter ξ and scale parameter α of the Weibull distribution are estimated by the least squares method. For the Weibull fitting, we have:
[0097]
[0098] Among them, f s (s) is the Weibull distribution density function, F s (s) is the Weibull distribution function, and s is the stress range corresponding to the peak and valley values of the test ship model section load.
[0099] In specific implementation, the shape parameter ξ and scale parameter α can be used to calculate the fatigue strength of the ship and evaluate the safety of the ship during its service life. The stress range s corresponding to the peak-to-valley value of the test ship model section load specifically refers to the difference between the peak value and the valley value of the stress in a single cycle during the action of the alternating stress.
[0100] In one embodiment of the present invention, the cross-sectional load response of the test ship model in the long-peaked irregular waves is analyzed by performing histogram statistics on the peak and valley values, and performing Weibull fitting on the statistical histogram. Figure 4 An embodiment of Weibull fitting of the peak-to-valley value histogram is shown in FIG. 1 , in which the horizontal axis is the profile load in units of N*m. During fitting, the curve represented by the Weibull analytical expression can be used to approximate the discrete data points in the sample histogram so that the deviation between the fitting curve and the discrete data points is minimized, that is, the shape parameter ξ and the scale parameter α can be obtained by least squares estimation.
[0101] In summary, a system for rapidly processing test data in a long-peak irregular wave test is obtained. In one embodiment of the present invention, a rapid data processing device is included, wherein the rapid test data processing method described above is executed based on the rapid data processing device.
[0102] Specifically, the data fast processing device can adopt existing commonly used computer equipment, and the type of data fast processing device can be selected according to needs, so as to meet the requirements of executing the above-mentioned test data fast processing method, which will not be described in detail here. In addition, the execution of the fast processing method and process of the test data can refer to the above description; specifically, through the above-mentioned fast processing method, spectrum analysis and statistical processing of time history information can be realized, which can also meet the test processing requirements under long peak irregular waves.
Claims
1. A method for quickly processing test data in a long-peak irregular wave test, characterized in that: The test data rapid processing method comprises: Acquire a test data set under a preset long-peak irregular wave condition, wherein the test data set includes multiple groups of test data, and for any group of test data, includes a wave measurement curve and a test ship model response under the wave measurement curve; The wave measurement curves in the test data set are spliced and reorganized to form spliced and reorganized wave time history data, and a measured spectrum of pool waves under the current long-peaked irregular waves is generated based on the spliced and reorganized wave time history data; When the measured spectrum of the pool wave generated above is consistent with the target parameter spectrum of the preset long-peak irregular wave, based on the splicing and reorganization method for forming the spliced and reorganized wave time history data, the response data of the same type in the test ship model response are spliced and reorganized respectively to form corresponding spliced and reorganized test ship model responses; Conduct spectrum analysis and time history information statistics on all spliced and reorganized test ship model responses, so as to obtain the test ship model response spectrum information after spectrum analysis, and obtain test data statistical characteristic information after time history information statistics; Under the preset long-peak irregular wave condition, multiple voyages are included, wherein each voyage is under the same long-peak irregular wave condition; in order to ensure that each long-peak irregular wave in the wave spectrum can fully act on the test ship model, the waves generated by the wave maker at the initial stage of the next voyage must be completely consistent with the waves generated at the final stage of the test voyage; When the wave measurement curves are spliced and reassembled, the splicing and reassembly method includes: Sort all test data in the test data set by generation time; After sorting, for any two adjacent groups of test data, the overlapping states of the first and last segments of the wave measurement curves in the two groups of test data are judged in turn, and the effective time period of the wave measurement curve in each group of test data is determined; According to the above sorting order, the wave measurement curves of all valid time periods are spliced and reorganized to form spliced and reorganized wave time history data after splicing and reorganization; When performing spectrum analysis on the response of any spliced and reassembled test ship model, the spectrum analysis method includes: Performing a fast Fourier transform on the spliced and reassembled test ship model response, and statistically analyzing the frequency distribution of the spectrum and the main frequency corresponding to the high-frequency component after the fast Fourier transform; For the section load response of the test ship model in the long-peak irregular wave test, Weibull fitting is performed on the histogram of the peak and valley values of the section load response; After the Weibull fitting, the shape parameter ξ and scale parameter α of the Weibull distribution are estimated by the least squares method. For the Weibull fitting, we have: Among them, f s (s) is the Weibull distribution density function, F s (s) is the Weibull distribution function, and s is the stress range corresponding to the peak and valley values of the section load.
2. The method for rapidly processing test data in a long-peak irregular wave test according to claim 1 is characterized by: The wave measurement curve includes wave heights at different times; The test ship model response includes hull motion response and section load response.
3. The method for rapid processing of test data in a long-peak irregular wave test according to claim 1 is characterized in that: The spliced and reorganized wave time history data are subjected to fast Fourier transformation to generate the measured spectrum of the pool wave after the transformation; The measured shape, measured spectrum value, measured peak frequency and measured significant wave height of the measured spectrum of the pool wave are extracted and compared with the target parameter spectrum, wherein: When the measured shape of the measured spectrum of the pool wave basically coincides with the curve trend of the target parameter spectrum, and the differences between the measured spectrum value, measured peak frequency, and measured significant wave height of the measured spectrum of the pool wave and the target spectrum value, target peak frequency, and target significant wave height of the target parameter spectrum are respectively within the preset range, then the measured spectrum of the pool wave is consistent with the target parameter spectrum.
4. The method for rapid processing of test data in a long-peak irregular wave test according to claim 3 is characterized in that: When the difference between the measured spectrum value of the pool wave spectrum and the target spectrum value of the target parameter spectrum is within the preset range, then: The measured spectrum value S(ω) of the pool wave spectrum is greater than 0.15(ω p ), and |ΔS(ω) / S(ω)| is less than 20%, where (ω p ) is the peak frequency of the measured spectrum of the pool wave, ΔS(ω) is the difference between the measured spectrum value of the measured spectrum of the pool wave and the target spectrum value of the target parameter spectrum; When the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum is within the preset range, then: |Δω p / ω p | is less than 5%, where Δω p is the difference between the measured peak frequency of the pool wave spectrum and the target peak frequency of the target parameter spectrum; When the difference between the measured significant wave height of the pool wave measured spectrum and the target significant wave height of the target parameter spectrum is within the preset range, then: |ΔH w1 / 3 / H w1 / 3 | Less than 5%, of which H w1 / 3 is the target significant wave height, ΔH w1 / 3 It is the difference between the measured significant wave height of the measured spectrum of pool waves and the target significant wave height of the target parameter spectrum.
5. The method for rapid processing of test data in a long-peak irregular wave test according to claim 1 is characterized in that: Based on the frequency distribution obtained by statistics, low-pass filtering, band-pass filtering and / or high-pass filtering are performed on the test ship model response to obtain the time history curve of the low-frequency test ship model response and the time history curve data of the high-frequency test ship model response after filtering; The average value is obtained by performing periodic amplitude statistics on the time history data of the low-frequency test ship model response; For the nonlinear phenomenon of the test ship model response, the camber amplitude and sagging amplitude of the test ship model response are respectively counted; The amplitude of the high-frequency test ship model response is obtained by performing periodic amplitude statistics on the time history data of the high-frequency test ship model response.
6. The method for rapid processing of test data in a long-peak irregular wave test according to claim 1 is characterized in that: The time history data of the spliced and reassembled test ship model response are statistically analyzed to obtain the corresponding zero-crossing cycle number, peak-to-valley value, average value, significant value, maximum value and variance of the spliced and reassembled test ship model response after periodic amplitude statistics.
7. A system for rapidly processing test data in a long-peak irregular wave test, characterized in that: It comprises a data rapid processing device, wherein the test data rapid processing method described in any one of claims 1 to 6 is executed based on the data rapid processing device.